The Genetics of Atrial Septal Defect and Patent Foramen Ovale

The Genetics of Atrial Septal Defect

and Patent Foramen Ovale

EDWIN PHILIP ENFIELD KIRK

A thesis submitted in fulfilmentof the requirements for the degree of

Doctor of Philosophy

School of Women’s and Children’s Health University of New South Wales

December, 2007

ORIGINALITY STATEMENT I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.

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DEDICATION

To my dear wife, Sue.

With gratitude, and with all my love.

Abstract

Congenital heart disease is the most common form of birth defect, affecting

approximately 1% of liveborn babies. Secundum atrial septal defect (ASD) is

the second most common form of congenital heart disease (CHD). Most cases

have no known cause. Chromosomal, syndromal and teratogenic causes

account for a minority of cases. The hypothesis that mutations in the ASD

genes NKX2-5 and GATA4 may cause apparently sporadic ASD was tested by

sequencing them in unrelated probands with ASD. In this study, 1/102

individuals with ASD had an NKX2-5 mutation, and 1/129 had a deletion of the

GATA4 gene.

The cardiac transcription factor TBX20 interacts with other ASD genes but had

not previously been associated with human disease. Of 352 individuals with

CHD, including 175 with ASD, 2 individuals, each with a family history of CHD,

had pathogenic mutations in TBX20. Phenotypes included ASD, VSD, valvular

abnormalities and dilated cardiomyopathy.

These studies of NKX2-5, GATA4 and TBX20 indicate that dominant ASD

genes account for a small minority of cases of ASD, and emphasize the

considerable genetic heterogeneity in dominant ASD (also caused by mutations

in MYH6 and ACTC). A new syndrome of dominant ASD and the Marcus Gunn

jaw winking phenomenon is reported. Linkage to known loci was excluded,

extending this heterogeneity, but a whole genome scan did not identify a

candidate locus for this disorder.

Previous studies of inbred laboratory mice showed an association between

patent foramen ovale (PFO) and measures of atrial septal morphology,

particularly septum primum length (“flap valve length” or FVL). In humans, PFO

is associated with cryptogenic stroke and migraine, and is regarded as being in

a pathological contiuum with ASD. Twelve inbred strains, including

129T2/SvEms and QSi5, were studied, with generation of [129T2/SvEms x

ii

iii

QSi5] F1, F2 and F14 mice. Studies of atrial morphology in 3017 mice

confirmed the relationship between FVL and PFO but revealed considerable

complexity. An F2 mapping study identified 7 significant and 6 suggestive

quantitative trait loci (QTL), affecting FVL and two other traits, foramen ovale

width (FOW) and crescent width (CRW). Binary analysis of PFO supported four

of these.

iv

Acknowledgements During the course of an 8 year candidature, I have become indebted to a great number

of people for help of many kinds. Above all, I am grateful to my wife, Susan O’Regan,

for her love, support, encouragement and forbearance. My children, Seamus, Yasmin

and Finn are starting to understand what I’m up to and have been cheering me on in

recent months. I followed my father into medicine and he has been an inspiration to

me. My mother’s love and encouragement have been very important to me. In addition,

she has given me help with graphics (a talent I managed not to inherit from her).

Academically, my greatest debts are, of course, to my supervisor, Prof Richard Harvey

and co-supervisor, Dr Michael Buckley, for their patient guidance and friendship over

the years. I’ve learned a great deal from both of them, and in particular I think I’m

starting to get the hang of Richard’s lessons on structure (of manuscripts, as well as

hearts). Prof Richard Henry was initially my supervisor. Over time the research moved

in a different direction, but he continued to provide support and encouragement.

Many members of Richard Harvey’s lab have helped me and I am grateful to all of

them. I want especially to thank Dr Christine Biben for teaching me to dissect mouse

hearts, and for providing ongoing advice and occasional second opinions; Dr David

Elliott for advice and guidance with molecular methods; and Dr Changbaig Hyun for his

contributions to the QTL study and the studies of GATA4 and TBX20. Others at the

Victor Chang Cardiac Research Institute who have helped include Leticia Castro,

Louise Lynagh, Haley Crotty, Milena Furtado, and Drs Mauro Costa, Robyn Otway,

Thomas Yeoh, Guanglan Guo, Owen Prall, Orit Wolstein, Daniel Schaft, Mark

Solloway, Aaron Schindeler, Suchitra Chander, Fiona Stennard, and Donna Lai have

all helped me in various ways and have made the lab an enjoyable place to work.

A/Prof Diane Fatkin has been generous with her time and advice.

At Sydney University, Prof Chris Moran has been a major guiding force, generous with

his time and experience. A better collaborator could not be found. Dr Ian Martin taught

me everything I have learned about care and breeding of laboratory mice, including

how to dissect them. He also bred the F1 and F2 mice (Chapters 5 and 6), a large

undertaking. Dr Peter Thomson has been unstinting in giving of his time and expertise

in matters mathematical. All of the animal house staff, but especially Matt Jones and

Mamdouh Nessiem, have been unfailingly helpful, often going well beyond the scope of

v

their duties to make sure that things ran well. Noelia Lopez ordered and bred the

Hapmap mice, and did a number of dissections as well as co-measuring a subset of

these mice. Kim Dilati provided assistance in the last few weeks of the advanced

intercross line breeding and dissection, when things were very busy. At Sydney

Children’s Hospital, members of the Department of Medical Genetics have been

unfailingly supportive and helpful. Dr Fiona McKenzie worked as a part time research

assistant during the first year of the project, and I doubt it would have got off the ground

at all without her kickstarting things. Dr Owen Jones was invaluable in recruitment of

children with ASD. At the South Eastern Area Laboratory Service, Peter Taylor, George

Eliakis and Glenda Mullan have been especially helpful, but many other members of

staff have given advice or help over the years. At the Children’s Hospital at Westmead,

A/Prof David Winlaw and members of his lab have contributed greatly to the human

studies.

Prof Ian Glass first identified the ASD + Marcus Gunn family and contributed greatly to

recruitment of family members; he continues to advise and encourage on that project.

A/Prof Jenny Donald provided guidance on mapping, and Dr Kyall Zenger taught me to

drive LINKAGE, as well as doing a good deal of analysis towards the project himself.

Dr Carol Cheung did a similar QTL study and taught me to use Mapmaker/QTL, along

with a great deal of other advice and help. Many cardiologists have contributed their

time and expertise, especially Dr Rob Justo, Dr Michael Tsicalis (and his wonderful

secretary, Dianne Reddell) and Prof Michael Feneley. My thanks to them. Every co-

author on the papers arising from this project has helped me, and my thanks are due to

all. There have also been many others, such as the private laboratory services who

never failed to help with specimen collection and shipping, whom I’m unable to name

but would like to acknowledge. Likewise, I am immensely grateful to but unable to

name the many people who volunteered as research subjects, particularly those whose

families I studied and into whose homes and workplaces I was invited. If there are

others I should have named but have omitted, my apologies as well as thanks to them.

Lastly, I am deeply indebted to the bodies which funded this research. The National

Heart Foundation of Australia awarded me a scholarship as well as providing research

grant support. Goldman Sachs Australia, the Sydney Children’s Hospital Foundation,

the National Institutes of Health in the United States, the RT Hall Trust and the Royal

College of Pathologists of Australasia all provided research grant support; I am very

grateful to them all.

Publications arising from this work

1. Elliott DA, Kirk EP, Yeoh T, Chandar S, McKenzie F, Taylor P, Grossfeld P,

Fatkin D, Jones O, Hayes P, Feneley M, Harvey RP. Cardiac homeobox gene

NKX2-5 mutations and congenital heart disease - associations with atrial septal

defect and hypoplastic left heart syndrome. Journal of the American College of

Cardiology 2003;41(11):2072-2076

2. Kirk EP, Hyun C, Thomson PC, Lai D, Castro ML, Biben C, Buckley MF,

Martin ICA, Moran C, Harvey RP. Quantitative Trait Loci Modifying Cardiac

Atrial Septal Morphology and Risk of Patent Foramen Ovale in the Mouse.

Circulation Research 2006;98:651-658

3. Kirk EP*, Sunde M*, Costa MW, Rankin SA, Wolstein O, Castro ML, Butler

TL, Hyun C, Guo G, Otway R, Mackay JP, Waddell LB, Cole AD, Hayward C,

Keogh A, Macdonald P, Griffiths L, Fatkin D, Sholler GF, Zorn AM, Feneley MP,

Winlaw DS, Harvey RP. Mutations in cardiac T-box factor gene TBX20 are

associated with diverse cardiac pathologies, including defects of septation and

valvulogenesis and cardiomyopathy. American Journal of Human Genetics

2007;81:280-291

*shared first authorship

Published conference proceedings 1. Harvey RP, Lai D, Elliott D, Biben C, Solloway M, Prall O, Stennard F,

Schindeler A, Groves N, Lavulo L, Hyun C, Yeoh T, Costa M, Furtado M. and

Kirk E. Homeodomain Factor Nkx2-5 in Heart Development and Disease. Cold

Spring Harbor Symposium on Quantitative Biology. Volume 67, Cold Spring

Harbor Laboratory Press, Cold Spring Harbor 2002.

vi

Table of Contents

Dedication…………………………………………………………………………..iAbstract……………………………………………………………………………..iiAcknowledgements………………………………………………………………iv Publications arising from this work...…………………………………………vi Table of Contents…………………………………………………………………vii List of figures……………………………………………………………………...xvi List of tables……………………………………………………………………….xviii Abbreviations used……………………………………………………………….xxi

1. Literature review

1.1 Overview ...................................................................................................... 11.2 Genes and human disease ......................................................................... 21.3 Mapping Mendelian disorders ................................................................... 4

1.3.1 Principles of Mendelian inheritance ........................................................ 4

1.3.1.1 Dominance and recessiveness ........................................................ 5

1.3.2 Meiotic recombination ............................................................................. 8

1.3.3 Maps of genetic variation ........................................................................ 9

1.3.4 Mapping Mendelian disorders ................................................................ 9

1.4 Quantitative Genetics ............................................................................... 111.4.1 Quantitative trait loci ............................................................................. 11

1.4.1.1 The liability model for binary traits ................................................. 12

1.4.2 Mapping QTL ........................................................................................ 13

1.4.2.1 Experimental designs for QTL mapping ......................................... 15

1.4.2.2 Significance thresholds .................................................................. 15

1.4.2.3 Selective genotyping ...................................................................... 15

1.4.2.4 Software packages for QTL mapping ............................................ 16

1.4.2.5 The mouse as a model organism ................................................... 17

1.4.3 Identifying the underlying genetic basis of QTL .................................... 17

1.4.4 The mouse Hapmap project: application to QTL mapping .................... 19

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viii

1.5 The heart .................................................................................................... 201.5.1 Normal cardiac anatomy ....................................................................... 20

1.5.2 Heart development ............................................................................... 21

1.5.3 The interatrial septum ........................................................................... 23

1.5.4 Regulation of cardiac development by transcription factors ................. 26

1.6 Congenital heart disease .......................................................................... 281.6.1 Types of CHD ....................................................................................... 29

1.6.2 Causes of CHD .................................................................................... 29

1.6.3 Patent foramen ovale ........................................................................... 31

1.6.3.1 PFO and stroke .............................................................................. 32

1.6.3.2 PFO and migraine .......................................................................... 33

1.6.3.3 Other pathological consequences of PFO ..................................... 34

1.6.3.4 Genetics of PFO ............................................................................ 34

1.6.4 Atrial septal defect ................................................................................ 35

1.6.4.1 Secundum ASD ............................................................................. 36

1.6.4.2 Ostium primum ASD ...................................................................... 36

1.6.4.3 Sinus venosus ASD ....................................................................... 36

1.6.4.4 Coronary sinus ASD ...................................................................... 36

1.6.4.5 Pathology associated with ASD ..................................................... 36

1.6.5 Relationship between PFO and ASD ................................................... 37

1.7 Causes of ASD .......................................................................................... 391.7.1 Syndromes associated with CHD ......................................................... 39

1.7.1.1 Holt-Oram syndrome ...................................................................... 40

1.7.1.2 Chromosomal disorders, particularly 8p deletions ......................... 41

1.7.2 Non-syndromal Mendelian ASD ........................................................... 44

1.7.3 Multifactorial/polygenic causation of ASD ............................................. 49

1.7.3.1 Excess of females affected by ASD ............................................... 51

1.7.3.2 QTL for CHD .................................................................................. 51

1.7.4 Environmental factors ........................................................................... 51

1.7.4.1 Major teratogens ............................................................................ 52

1.7.4.2 Other environmental factors ........................................................... 52

1.8 Project outline ........................................................................................... 61

2. Materials and Methods

2.1 Mouse experiments .................................................................................. 622.1.1 Ethics committee approval ................................................................... 62

2.1.2 Animal resources .................................................................................. 62

2.1.3 Breeding protocols ............................................................................... 63

2.1.3.1 F2 mice .......................................................................................... 63

2.1.3.2 Advanced intercross line ................................................................ 63

2.1.4 Mouse phenotyping .............................................................................. 66

2.1.4.1 Initial dissection ............................................................................. 66

2.1.4.2 Fine dissection ............................................................................... 67

2.1.4.3 Identification of patent foramen ovale ............................................ 73

2.1.4.4 Measurements of atrial septal anatomy ......................................... 73

2.1.4.5 Blinding .......................................................................................... 73

2.1.5 Strain selection for F2 and AIL studies ................................................. 74

2.2 Human subjects ........................................................................................ 752.2.1 Ethics committee approval ................................................................... 75

2.2.2 Ascertainment of subjects .................................................................... 75

2.2.2.1 Children ......................................................................................... 75

2.2.2.2 Adults ............................................................................................. 75

2.2.2.3 Numbers of subjects studied for mutations in NKX2-5 & GATA4 ... 76

2.2.2.4 Follow-up of family members…………………………………………76

2.2.3 History .................................................................................................. 76

2.2.4 Examination .......................................................................................... 77

2.2.5 Investigations ....................................................................................... 78

2.3 Molecular genetics methods.................................................................... 782.3.1 Extraction of DNA from human blood and mouse spleens ................... 78

2.3.1.1 DNA extraction from mouse spleens .............................................. 78

2.3.1.2 DNA extraction from blood ............................................................. 80

2.3.4 Polymerase chain reaction (PCR) and sequencing of NKX2-5 and

GATA4........................................................................................................... 82

2.3.4.1 PCR of NKX2-5 .............................................................................. 82

2.3.4.2 PCR of Exon 5 of GATA4 .............................................................. 84

2.3.5 Sequence analysis ............................................................................... 86ix

2.4 Microsatellite analysis .............................................................................. 882.5 Marker selection ........................................................................................ 88

2.5.1 Human Markers .................................................................................... 88

2.5.2 Mouse Markers ..................................................................................... 88

2.6 Laboratory methods used at AGRF ......................................................... 882.7 Error checking ........................................................................................... 89

2.7.1 Error Checking of Human Data ............................................................ 89

2.7.2 Error Checking of Mouse Data ............................................................. 89

2.8 Statistical methods ................................................................................... 902.8.1 Basic statistical analyses ...................................................................... 90

2.9 Linkage analysis ....................................................................................... 912.9.1 Linkage analysis for autosomal dominant trait ...................................... 91

2.9.2 QTL analysis ........................................................................................ 91

2.9.2.1 Selective Genotyping ..................................................................... 91

2.9.2.2 Linkage analyses ........................................................................... 91

2.9.2.3 Binary trait analysis ........................................................................ 92

3. The role of mutations in the cardiac transcription factors NKX2-5, GATA4 and TBX20 in causing CHD and cardiomyopathy 3.1 Introduction ............................................................................................... 943.2 Mutations in NKX2-5 cause autosomal dominant CHD and AV conduction block ............................................................................................ 94

3.2.1 Subjects screened for NKX2-5 mutations ........................................... 104

3.2.2 Results ............................................................................................... 106

3.2.2.1 Family 1024: T178M .................................................................... 106

3.2.2.2 Family AF1: E21Q ....................................................................... 108

3.2.3 Role of NKX2-5 mutations in nonsyndromal ASD .............................. 109

3.2.4 Implications for asymptomatic mutation-positive individuals ............... 110

3.3 The role of mutations in GATA4 in ASD and PFO ................................ 1113.3.1 Subjects screened for GATA4 mutations ............................................ 115

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3.3.2 Results of sequencing and cytogenetic analysis ................................ 115

3.3.2.1 Family 1012 – GATA4 variants A411V and S377G ..................... 115

3.3.2.2 Family z10 – GATA4 variant D425N ............................................ 118

3.3.2.3 Family 1006 – 8p23 deletion ........................................................ 118

3.3.3 The common variant S377G – possible role in PFO with stroke ........ 120

3.3.4 Role of GATA4 mutations in nonsyndromal ASD ............................... 124

3.4 Mutations in TBX20 are associated with diverse cardiac pathologies, including abnormal septation and valvulogenesis, andcardiomyopathy ............................................................................................ 125

3.4.1 Subjects screened for TBX20 mutations ........................................... 126

3.4.2 TBX20 mutations ................................................................................ 128

3.4.2.1 Family 9001: TBX20 mutation 152M ............................................ 129

3.4.2.2 Family z103: TBX20 mutation Q195X.......................................... 129

3.4.2.3 Family WM1: TBX20 polymorphism T209I ................................... 130

3.4.3 Functional and other studies of the TBX20 mutations ........................ 130

3.4.3.1 Transcriptional assays of Tbx20 function ..................................... 131

3.4.3.2 Xenopus embryo gastrulation assay ............................................ 131

3.4.3.3 Protein modelling ......................................................................... 131

3.4.4 Significance of mutations in TBX20 .................................................... 133

3.5 Conclusions: the role of mutations in NKX2-5, GATA4 and TBX20 inhuman disease .............................................................................................. 135

4. Atrial septal defect and Marcus Gunn phenomenon: further evidence for clinical and genetic heterogeneity in autosomal dominant atrial septal defect

4.1 Introduction ............................................................................................. 1374.2 Marcus Gunn phenomenon .................................................................... 1374.3 Phenotypes of affected family members .............................................. 1394.4 Cytogenetics ........................................................................................... 140

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4.5 Sequencing of cardiac genes ................................................................ 1404.6 Mapping results ...................................................................................... 142

4.6.1 Chromosome 1………………………………………………………….…142

4.6.2 Chromosome 5…………………………………………………………….143

4.6.3 Chromosome 6…………………………………………………………….144

4.6.4 Chromosome 7…………………………………………………………….144

4.6.5 Chromosome 8…………………………………………………………….144

4.6.6 Chromosome 12…………………………………………………………...144

4.6.7 Chromosome 14…………………………………………………………...144

4.6.8 Chromosome 15…………………………………………………………...145

4.7 Discussion ............................................................................................... 1454.7.1 Linkage results ................................................................................... 145

4.7.2 ASD and MGP .................................................................................... 146

4.7.3 Clefting ............................................................................................... 146

4.7.4 Future studies ..................................................................................... 147

5. Cardiac atrial septal morphology and risk of patent foramen ovale in inbred laboratory mice

5.1 Introduction ............................................................................................. 1485.2 The relationship between atrial septal morphology and PFO: previous work ............................................................................................................... 1495.3 Selection and breeding of mice for study ............................................. 1505.4 Analysis of data from QSi5, 129T2/SvEms, and the [QSi5 x 129T2/SvEms] F1, F2 and F14 mice ............................................................. 152

5.4.1 Descriptive statistics ........................................................................... 152

5.4.2 Relationships between the continuous traits ...................................... 155

5.4.3 Analysis of variance for factors affecting FVL, FOW and CRW in F2

mice ............................................................................................................. 157

5.4.4 Relationship between PFO and the continuous variables…………….159

5.4.4.1 Relationship between FVL and PFO ............................................ 160

xii

5.4.4.2 Relationship between FOW and PFO .......................................... 163

5.4.4.3 Relationship between CRW and PFO .......................................... 165

5.4.5 Biological significance of the relationships between FVL, FOW and

CRW ............................................................................................................ 168

6. Quantitative trait loci modifying cardiac atrial septal morphology and risk of patent foramen ovale in inbred laboratory mice

6.1 Introduction ............................................................................................. 1696.2 Study design ........................................................................................... 1716.3 Selection of mice for genotyping .......................................................... 1716.4 Markers used ........................................................................................... 1726.5 Linkage results ........................................................................................ 1726.6 Chromosomes with noteworthy findings .............................................. 184

6.6.1 MMU1 ................................................................................................. 187

6.6.2 MMU2 ................................................................................................. 187

6.6.3 MMU3 ................................................................................................. 187

6.6.4 MMU4 ................................................................................................. 187

6.6.5 MMU6 ................................................................................................. 188

6.6.6 MMU7 ................................................................................................. 188

6.6.7 MMU8 ................................................................................................. 188

6.6.8 MMU9 ................................................................................................. 188

6.6.9 MMU10 ............................................................................................... 188

6.6.10 MMU13 ............................................................................................. 188

6.6.11 MMU15 ............................................................................................. 188

6.6.12 MMU18 ............................................................................................. 188

6.6.13 MMU19 ............................................................................................. 189

6.7 Discussion ............................................................................................... 1896.7.1 Cryptic QTL ........................................................................................ 190

6.7.2 Binary trait analysis ............................................................................ 190

xiii 6.7.3 Genetic relationship between FVL, FOW and CRW ........................... 191

6.7.4 Contribution of the identified QTL to the phenotypes under study ...... 191

6.7.5 Candidate genes ................................................................................ 192

6.7.6 Future studies……………………………………………………………...193

7. Comparisons of atrial septal anatomy in 12 strains of inbred laboratory mice reveal unexpected complexity

7.1 Introduction ............................................................................................. 1947.2 History of the inbred laboratory mouse ................................................ 1957.3 Application of mouse haplotype data to mapping ............................... 1967.4 Number of mice to phenotype ............................................................... 1977.5 Analyses of Hapmap strains .................................................................. 1987.6 Training of a second observer ............................................................... 1987.7 Assessments of the reliability of measurement of atrial septalanatomy ......................................................................................................... 1987.8 Descriptive statistics for the Hapmap strains (including 129T2/SvEms and QSi5) ....................................................................................................... 2007.9 ASD in DBA/1J mice ............................................................................... 2037.10 Relationships between PFO and other traits in the 12 strains of inbred mice ................................................................................................................ 205 7.11 Comparison with the study by Biben and colleagues ....................... 210 7.12 Conclusions .......................................................................................... 211

8. Conclusions and Future Directions

8.1 Genetic heterogeneity and its clinical implications ............................. 2128.2 Cardiac phenotypes other than CHD..................................................... 213

8.2.1 AV conduction abnormalities and ASD ............................................... 213

8.2.2 Cardiomyopathy in association with mutations in TBX20

and NKX2-5 ................................................................................................. 214

xiv 8.3 The role of NKX2-5, GATA4 and TBX20 in multifactorial ASD ............ 214

8.4 Future studies of dominant ASD genes in unselected subjects ......... 2158.5 Mapping genes affecting prevalence of PFO in inbredlaboratory mice ............................................................................................. 2168.6 Significance of findings .......................................................................... 219

REFERENCES…………………………………………………………………..221

APPENDIX 1: ASD and Marcus Gunn Phenomenon - Linkage Results…245

APPENDIX 2: Microsatellite markers used for QTL mapping……………..262

APPENDIX 3: Candidate genes within QTL ................................................ 265

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List of figures

Page Figure 1.1: Dominance 8

Figure 1.2: The adult mammalian heart 22

Figure 1.3: Normal cardiac development 24

Figure 1.4: Relative arrangement of septum primum and

septum secundum 26

Figure 2.1: Cartoon illustrating the breeding scheme used 64

Figure 2.2: Dissection of mouse hearts

2.2A: Initial dissection 68

2.2B: Opening the auricle 69

2.2C: Laying open the atrium 70

2.2D: Final appearance of the heart 71

Figure 2.3: Detail of atrial septum 72

Figure 3.1: Families with ASD and NKX2-5 sequence changes 107

Figure 3.2: Families with GATA4 variants and 8p23 deletion 117

Figure 3.3: Families with TBX20 mutations 128

Figure 3.4: Transcription studies and Xenopus gastrulation

assay 132

Figure 4.1: Family with ASD and MGP 141

Figure 4.2: Multipoint mapping of chromosome 5 143

Figure 5.1: Scatterplot of FVL vs heart weight in F2 mice 157

Figure 5.2: Histogram of FVL in F2 mice with and without PFO 162

Figure 5.3: Histogram of FVL in F14 mice with and without PFO 162

Figure 5.4: Histogram of FOW in F2 mice with and without PFO 164

Figure 5.5: Histogram of FOW in F14 mice with and without PFO 164

Figure 5.6: Histogram of CRW in F2 mice with and without PFO 166

xvi Figure 5.7: Histogram of CRW in F14 mice with and without PFO 167

xvii

Page Figure 6.1: MMU1 173

Figure 6.2: MMU2 174









Figure 6.11: MMU1 1 178

Figure 6.12: MMU1 2 179

Figure 6.13: MMU1 3 179

Figure 6.14: MMU1 4 180

Figure 6.15: MMU1 5 180

Figure 6.16: MMU1 6 181

Figure 6.17: MMU1 7 181

Figure 6.18: MMU1 8 182

Figure 6.19: MMU1 9 182

Figure 6.20: MMUX (female mice) 183

Figure 6.21: MMUX (male mice) 183

Figure 7.1: ASD in a DBA/1J mouse 204

Figure 7.2: Scatterplot of %PFO vs FVL 207

Figure 7.3: Scatterplot of %PFO vs FOW 207

Figure 7.4: Scatterplot of %PFO vs CRW 208

Figure 7.5: Scatterplot of Weight vs Heart Weight 208

Figure 8.1: Mapping results for chromosome 1 218

List of tables

Page Table 1.1: Percentage of CHD accounted for by the most

common lesions 30

Table 1.2: Reports of dominant ASD prior to the first

identification of causative mutations 47

Table 1.3: Environmental exposures and other factors

significantly associated with risk of ASD 54

Table 1.4: Environmental exposures and other factors with

no significant association with risk of ASD 57

Table 2.1: Breeding scheme for AIL 65

Table 2.2: Primers used for PCR and sequencing 86

Table 3.1: Mutations in NKX2-5 97

Table 3.2: Patient characteristics (NKX2-5) 105

Table 3.3: Mutations in GATA4 112

Table 3.4: S377G allele distribution in indigenous human

populations 121

Table 3.5: S377G in Caucasian subjects 122

Table 3.6: Characteristics of subjects sequenced for TBX20

mutations 127

Table 5.1: Characteristics of parental strains, F1

and F2 mice 154

Table 5.2: Basic statistical data and correlations for F2 mice 155

Table 5.3: Basic statistical data and correlations for F14 mice 156

Table 5.4: Comparison between data for 129T2/SvEms mice

with and without PFO 159

Table 5.5: Analysis of variance for FVL in F2 mice 160

Table 5.6: Analysis of variance for FVL in F14 mice 161

xviii

xix

Page Table 5.7: Analysis of variance for FOW in F2 mice 163

Table 5.8: Analysis of variance for FOW in F14 mice 163

Table 5.9: Analysis of variance for CRW in F2 mice 165

Table 5.10: Analysis of variance for CRW in F14 mice 166

Table 6.1a: Loci with LOD score >2.8 for FVL 184

Table 6.1b: Loci with LOD score >2.8 for FOW 185

Table 6.1c: Loci with LOD score >2.8 for CRW 186

Table 6.2: LOD scores at loci of orthologues of reported

human ASD genes 189

Table 7.1: Measures of inter-rater reliability 199

Table 7.2: Descriptive statistics for 12 strains of inbred

laboratory mice 201

Table 7.3: Correlation between mean values for %PFO, FVL,

FOW, CRW, weight and heart weight 206

Table 7.4: Results of ANOVA for FVL, FOW and CRW – single

analyses with PFO as the model 209

Table 7.5: Combined mean values for 12 mouse strains with

and without PFO 209

Table A1.1a: Chromosome 1 245

Table A1.1b: Chromosome 1 – additional markers 246

Table A1.2: Chromosome 2 247







xx

Page Table A1.9: Chromosome 9 253

Table A1.10 Chromosome 10 254













Table A2.1 List of markers with map location 262

Table A3.1: Genes within QTL affecting FVL 265

Table A3.2: Genes within QTL affecting FOW 268

Table A3.3: Genes within QTL affecting CRW 270

Abbreviations used

AF Atrial fibrillation

AGRF Australian Genome Research Facility

AIL Advanced intercross line

ANOVA Analysis of variance

ASD Atrial septal defect

AS Aortic stenosis

ASA Atrial septal aneurysm

AV Atrioventricular

AVCD Atrioventricular canal defects

AVN Atrioventricular node

BAV Bicuspid aortic valve

Coarct Coarctation of the aorta

CHD Congenital heart disease

CI Confidence interval

cM centiMorgans

CMP Cardiomyopathy

CRW Crescent width

DC Direct current

DCM Dilated cardiomyopathy

DNA Deoxyribonucleic acid

d-TGA d-Transposition of the great arteries

ECG Electrocardiogram

FDR False discovery rate

FISH Fluorescent in-situ hybridization

FOW Foramen ovale width

FVL Flap valve length

GLM General linear model

HLHS Hypoplastic left heart syndrome

HOS Holt-Oram syndrome

IM Interval mapping

LA Left atrium

xxi

xxii

LOD Logarithm of odds

LSVC Left superior vena cava

LV Left ventricle

MGP Marcus Gunn phenomenon

MR Mitral regurgitation

MS Mitral stenosis

MV Mitral valve

MVP Mitral valve prolapse

OR Odds ratio

PA Pulmonary atresia

PS Pulmonary stenosis

PCR Polymerase chain reaction

PDA Patent ductus arteriosus

PFO Patent foramen ovale

PS Pulmonary stenosis

PTA Persistent truncus arteriosus

QTL Quantitative trait locus

RA Right atrium

RFLP Restriction fragment length polymorphism

RNA Ribonucleic acid

RV Right ventricle

SD Standard deviation

SHF Second heart field

SMM Single marker mapping

SNP Single nucleotide polymorphism

TA Tricuspid atresia

TAPVR Total anomalous pulmonary venous return

TOF Tetralogy of Fallot

TR Tricuspid regurgitation

VSD Ventricular septal defect

VE Variance due to environmental effects

VF Ventricular fibrillation

VG Variance due to genetic effects

xxiii

VT Total variance

WPW Wolff-Parkinson-White syndrome

� Recombination fraction

1. Literature review

1.1 Overview Congenital heart disease (CHD) is the most common form of birth defect, with

estimates of birth incidence in liveborn children ranging from 0.4-1.0% (Bower

and Ramsay, 1994; Grech and Gatt, 1999; Ferencz et al., 1985; Gillum, 1994).

A comprehensive review of epidemiological studies of CHD by Hoffman and

Kaplan (Hoffman and Kaplan, 2002) yielded a combined incidence of 6/1000

live births, rising to 75/1000 live births if trivial lesions, such as tiny muscular

ventricular septal defects (VSDs) present at birth but closing thereafter are

included. Although some cardiac malformations are relatively benign, the overall

morbidity and mortality associated with CHD are enormous. In the 10 year

period 1979-1988, there were 46,450 deaths attributed to CHD in the United

States of America, of which 26,319 occurred in the first year of life (Gillum,

1994). In Australia, 15% of neonatal deaths and 11% of post-neonatal childhood

deaths are attributable to CHD (Bower and Ramsay, 1994).

Given this, it is perhaps surprising how little is known of the causes of CHD.

There are many genetic syndromes associated with CHD, and there has been

considerable success in elucidating the causes of these. However, 75% of CHD

is non-syndromic, in the sense that there are no evident associated features

(Bower and Ramsay, 1994), and even among the “syndromic” category, not all

cases have a known cause. Genetic factors make an important contribution to

non-syndromic CHD, but specific mutations have so far been identified in only a

small minority of cases (Elliott et al., 2003; McElhinney et al., 2003). Teratogens

such as alcohol, although important because of the potential for prevention,

account for a small minority of cases (Tikkanen and Heinonen, 1992). This

applies even where the relative risk associated with an exposure is

comparatively high, such as the risk of CHD associated with maternal diabetes.

For this exposure, Pradat (Pradat, 1992) found a relative risk for all CHD of

2.67 (95% CI 1.43-4.99), and an even higher risk for septal defects (ASD and

VSD combined) for which the relative risk was 6.2 (95% CI 1.97-19.5).

1

2

Nonetheless, in this study only 1.2% of CHD was attributable to maternal

diabetes (specific figures not available for the subgroup of septal defects).

The parents of a child born with CHD naturally want to know why their child has

this problem. Based on current knowledge, as sketched above, the clinician

treating the child will usually be unable to answer this question in more than

general terms. This is the underlying motivation behind the work reported here:

the drive to understand the causes of CHD in more detail. The focus is

necessarily narrow – an attempt to delineate the genetic contribution to defects

of atrial septal morphogenesis, specifically secundum atrial septal defect (ASD)

and patent foramen ovale (PFO). However, it is anticipated that the lessons

learned from study of these disorders will have wider relevance to other forms

of CHD.

To place these genetic studies in context, this chapter starts with a discussion

of the contribution of genes to human disease, and ways of unravelling it,

including mapping techniques for Mendelian and quantitative traits. Next, there

is a description of cardiac development and particularly the role of the

transcription factors NKX2-5, GATA4 and TBX20 in early cardiac development.

Finally the nature, epidemiology and causation of CHD are reviewed, with a

focus on ASD and PFO.

1.2 Genes and human disease At the time of writing, the Entrez Genome Project web page listed 413

eukaryotic genome sequencing projects, of which 25 were complete, 162 at the

assembly stage and 226 in progress

(http://www.ncbi.nlm.nih.gov/genomes/leuks.cgi, accessed on 23rd August,

2007). In addition, there are nearly fourteen hundred prokaryote genome

projects at various stages. The human genome project published its draft

sequence of the human genome as long ago as 2001 (Lander et al., 2001).

From this vantage point, with vast and ever-increasing repositories of genomic

information readily accessible to us, it is easy to forget that it is only a little over

http://www.ncbi.nlm.nih.gov/genomes/leuks.cgi

3

a century ago that the first paper correctly identifying a genetic mechanism for a

human disease was published. This was Garrod’s seminal paper on

alkaptonuria (Garrod, 1902), in which he recognised that alkaptonuria is

inherited in an autosomal recessive fashion. This in turn occurred only 37 years

after Mendel’s publication of the principles of what is now known as Mendelian

inheritance. Prior to these advances, while it was undoubtedly recognised that

some traits and diseases were hereditary, there was no accurate understanding

of the mechanisms underlying this.

It is now clear that there is a genetic contribution to many, and perhaps most,

forms of human disease. Over 1000 genes for rare disorders which conform to

Mendelian inheritance have now been identified. However, even taken together,

these account for only a small proportion of human disease (Altshuler et al.,

2005) – perhaps 1-2% in all (Rimoin et al., 2002). More importantly, most

common disease has an identifiable genetic contribution – from ischaemic heart

disease (Shiffman et al., 2005) to most forms of cancer (Knoepfler, 2007). In the

case of cancer the genetic contribution is not necessarily hereditary – in most

instances it consists of acquired, somatic mutation rather than inherited

germline mutation. Even conditions which have a readily identifiable external

cause, such as trauma and infectious diseases, can be shown to have a genetic

contribution. Impulsive behaviour and risk-taking, which increase the risk of

trauma, are contributed to by genetic factors (Kreek et al., 2005). Host factors

which are genetically determined affect the response to infectious disease,

influencing likelihood of clinically recognised infection and severity (Casanova

and Abel, 2007).

The reverse is also true. Disorders which have been thought of as purely

genetic in origin are influenced by environment. Children with cystic fibrosis, a

classic example of an autosomal recessive disorder, have lung disease the

severity of which is determined in part by which pathogens they happen to

encounter (Jones et al., 2004). The phenotype in phenylketonuria can be

greatly modified by provision of a modified diet low in phenylalanine (Scriver

and Waters, 1999). There are numerous other examples, but more importantly,

4

it is undoubtedly the case that less obvious, currently unrecognised,

environmental influences contribute to the phenotype of most (perhaps all)

genetic disease. Chance also represents an intrinsically unobservable, but

important, component of this environmental contribution. The regulation of gene

expression (and by extension, development) is an inherently stochastic process

(Fiering et al., 2000; Kaern et al., 2005).

Human disease, then, can be seen as the outcome of a complex interplay

between genes and environmental factors. Understanding the causation of

many forms of human disease – including CHD – thus requires investigation of

both genetic and environmental contributions. Environmental influences can be

studied by epidemiological means, or in some instances by the use of animal

models, although this can only ever provide indirect evidence for the role of a

particular environmental exposure in human disease. Epidemiological studies of

ASD and PFO are reviewed in section 1.7.4 of this chapter. In the remainder of

this section, methods of mapping traits inherited in a Mendelian fashion and

quantitative trait mapping will be reviewed.

1.3 Mapping Mendelian disorders Mapping of Mendelian disorders relies on Mendel’s principles of inheritance,

modified by the increased likelihood of co-segregation of alleles which are in

physical proximity, and on the effects of meiotic recombination. It is greatly

facilitated by the availability of maps of human genetic variation.

1.3.1 Principles of Mendelian inheritance Mendel derived four main principles from his work on the garden pea,

summarised by Cook et al (Cook J et al., 2002) as follows:

1. Genes come in pairs (Mendel termed them factors), one inherited from each

parent.

2. Individual genes can have different alleles, some of which (dominant traits)

exert their effects over others (recessive traits) – the principle of dominance.

5

In Mendel’s own words “those characters which are transmitted entire, or

almost unchanged in the hybridisation, and therefore in themselves constitute

the characters of the hybrid, are termed the dominant, and those which become

latent in the process recessive”

3. At meiosis alleles segregate from each other with each gamete receiving only

one allele – the principle of segregation, or Mendel’s first law.

4. The segregation of different pairs of alleles is independent – the principle of

assortment, or Mendel’s second law.

Some modification to these principles has been required. X-linked inheritance,

in which females have a pair of alleles but males only a single copy

(hemizygosity), was not discussed by Mendel. Nonetheless X-linked disorders

are considered Mendelian because the principles of X-linked inheritance are

essentially a special case of the principles described by Mendel.

Mendel’s second law is true except for alleles which are located close together

on the same chromosome, which do not segregate independently. Modification

of Mendel’s principles by the addition of this fact formed the basis on which the

mapping studies of Morgan, and indeed all subsequent genetic mapping

studies, including those reported here, are based.

1.3.1.1 Dominance and recessiveness The term “dominance” is used somewhat differently in Mendelian and

quantitative genetics, and somewhat differently again in reference to disease

states. For convenience, all three uses of the term will be discussed at this

point. Elsewhere in this thesis, the terms are used in the sense relevant to the

topic under discussion.

In both Mendelian and quantitative genetics, dominance is a term which

describes the relationship between two alleles. In Mendelian genetics, an allele

(A) is dominant to another (B) if the phenotype in heterozygous organisms is the

6

same as the phenotype in those homozygous for the A allele (Cook J et al.,

2002). The allele B is recessive to the allele A. Incomplete dominance, also

called semidominance, occurs if the phenotype in the heterozygote is

intermediate between that seen with the two homozygous states.

In speaking of diseases with Mendelian inheritance, however, dominant

inheritance refers to any disorder in which heterozygosity for a mutated allele is

sufficient to cause a pathological state.

There are some disorders in which it has been demonstrated that autosomal

dominant human diseases conform to the more rigorous definition of

dominance. For example, homozygosity for the triplet repeat expansion

responsible for Huntington disease produces a phenotype indistinguishable

from that seen in heterozygotes (Wexler et al., 1987). Similarly, a woman

homozygous for a BRCA1 mutation had breast cancer at the age of 32,

consistent with the phenotype seen in heterozygotes (Boyd et al., 1995). The

age of onset of her cancer would not have been unusually early for a woman

with a heterozygous mutation, and indeed a woman in the same family,

presumably heterozygous for the same mutation, had breast cancer aged 22

(Boyd et al., 1995).

However, there are numerous examples of autosomal dominant disorders which

in classical Mendelian terminology would be referred to as semidominant. For

example, heterozygosity for mutations in KCNQ1 causes long QT syndrome,

which is described as an autosomal dominant condition. Homozygosity causes

the Jervell and Lange-Nielsen syndrome with severe long QT syndrome and

sensorineural deafness (Splawski et al., 1997). Similarly, heterozygous

mutations in CDMP1 cause minor skeletal anomalies including brachydactyly

type C (Polinkovsky et al., 1997) and a phenotype resembling brachydactyly

type A1 (Thomas et al., 1997); however, homozygous mutations cause a severe

bone dysplasia, Grebe type chondrodysplasia (Thomas et al., 1997).

7

In practice, the effect of homozygosity for alleles associated with autosomal

dominant disorders is generally not known. It is likely that many, even most,

disorders described as having autosomal dominant inheritance are like these

examples, and in a strict sense should be termed semidominant. Homozygosity

for “dominant” mutations has been reported on numerous occasions, often as a

result of consanguinity or assortative mating (eg in achondrplasia). This often

results in a more severe phenotype than in the heterozyote (Zlotogora, 1997).

The mechanism by which the mutation produces a phenotype is likely to

influence this. It is highly likely, for example, that if haploinsuffiency is sufficient

to produce a phenotype, complete loss of gene function will produce a severe

phenotype. The effects of homozygosity for mutations which act in a different

fashion (gain of function, abnormal participation in homodimer function and so

on) are more difficult to predict but might reasonably be expected to be more

severe than heterozygosity in many cases.

In summary, there is overlap between the use of the term “dominant” and

“recessive” in classical Mendelian genetics and in the terminology of human

diseases with Mendelian inheritance. However, the terminology is more loosely

applied in relation to human diseases.

In quantitative genetics, the use of the term dominance is closely related to its

strict Mendelian meaning. Here, dominance refers to the heterozygote effect of

one allele relative to another (Fig 1.1, below). Suppose that homozygosity for

allele 1 results in a phenotypic value of +1 for a given trait, and homozygosity

for allele 2 results in a value of –1. If the heterozygote has a phenotypic value of

0, there is no dominance effect. Heterozygote values between 0 and 1 reflect

dominance of allele 1 over allele 2, values between –1 and 0 reflect partial

dominance of allele 2 over allele 1 and values of 1 or –1 represent complete

dominance of allele 1 or 2, respectively. Overdominance refers to the situation

in which the heterozygote has a more extreme phenotype than either

homozygous state (>1 or < -1). Note that this refers to a theoretical situation in

which the effects of a single pair of alleles on a quantitative trait can be

separated out from all other effects and measured.

No dominance

A1A1A2A2 A1A2

8

Partial dominance -1 0 1

A1A1A2A2 A1A2

-1 1Complete dominanceA1A2

A2A2 A1A1

-1 1

Overdominance A1A2A1A1A2A2

1-1

Fig 1.1: Dominance (see text for description). Adapted from Fig. 2.1 in

Introduction to Quantitative Genetics (Falconer DS and Mackay TF, 1996))

1.3.2 Meiotic recombination During meiosis, homologous chromosomes pair and exchange segments by

recombination. The effect of this is that the copy of each chromosome present

in the gamete is not identical to either of the parental chromosomes, but rather

is a mosaic of segments derived from each of them (Anderson NH, 2002). The

effect of this process occurring over successive generations is illustrated in

figure 2.1. The relevance of this process to genetic mapping is that

recombination events, although more likely to occur in some parts of a

chromosome than others, are widely distributed along the chromosomal length

and vary considerably from meiosis to meiosis. This means that with an

increasing number of meioses within a pedigree, there is an increasing

probability that the region containing a mutated gene will become separated

from genetic markers which were close to it on the chromosome of the first

family member to have carried the mutation. In turn, this allows the progressive

narrowing down of the region likely to contain the mutated gene.

9

1.3.3 Maps of genetic variation In order to track transmission of chromosomal segments through a pedigree, a

map of genetic variation is required. To be useful for genetic linkage studies, an

ideal map would contain markers which are densely spaced and highly

polymorphic – and thus likely to be informative within a family. Such maps have

been progressively developed, starting with restriction fragment length

polymorphisms (RFLPs) (Botstein et al., 1980), progressing to short tandem

repeats (also known as microsatellites) (Weber and May, 1989) and finally to

single nucleotide polymorphisms (SNPs) (The International Hapmap

Consortium, 2003; Altshuler et al., 2005). The availability of densely-spaced

marker maps is obviously also important to mapping for complex diseases.

1.3.4 Mapping Mendelian disorders Mapping of Mendelian disorders depends on identifying one or more alleles

which segregate with the phenotype in accordance with the principles of

Mendelian inheritance (Anderson NH, 2002; Ott and Hoh, 2000). Generally the

mode of inheritance will be known, but there will be no prior information

regarding the likely chromosomal localisation of the gene of interest. Exceptions

to this include X-linked disorders, and the rare circumstance of identification of

one or more affected individuals with apparently balanced chromosomal

translocations segregating with the phenotype (in which case the breakpoints

represent candidate loci). If no localising information is available, a screen of

the entire genome (less the sex chromosomes if X and Y-linkage can be

excluded on the basis of pedigree analysis) will be required. Polymorphic loci

spaced as closely as possible across the genome are genotyped in all available

family members. With the availability of extremely dense genetic maps, cost has

become the main limiting factor restricting marker density used in such a

screen.

While in principle it should be possible to identify regions of genetic linkage

simply by inspecting haplotypes to identify markers which segregate with

disease state, in practice this is not usually a straightforward undertaking (Ott

and Hoh, 2000). The large amount of data produced during a mapping exercise,

10

the problem of incomplete penetrance, and the fact that in the real world there

are often missing individuals or other barriers to such an approach, necessitate

the use of computer programs in data analysis in most instances. These

calculate the likelihood that the disease locus is present on a marker map. The

likelihood ratio is the ratio between the probability that the hypothesis that there

is linkage (�<0.5), LHA, and the null hypothesis of no linkage (�=0.5), LH0. �

(theta) is the recombination fraction, which is 0.5 when there is no linkage (i.e.

there is a 50% chance that two unlinked alleles will be transmitted together).

This is expressed as a logarithm of odds (LOD) score, which is the log10 of LHA/

LH0 (Ott and Hoh, 2000; Nyholt, 2002). Lander and Kruglyak calculated that in

a whole genome scan in humans, a LOD score of 1.9 would be expected to

occur by chance once per whole genome scan, and a LOD score of 3.3 would

be expected to occur by chance once per 20 whole genome scans (Lander and

Kruglyak, 1995). These were proposed as cutoffs for reporting suggestive and

significant linkage results, respectively. Thresholds were also calculated for

quantitative trait locus (QTL) mapping using various study designs.

Map distances are measured in centiMorgans (cM), named for the great fly

geneticist Thomas Hunt Morgan. The cM is equivalent to the recombination

fraction expressed as a percentage. A recombination fraction of 0.5 represents

a map distance of 50cM, a recombination fraction of 0.01represents a map

distance of 1cM and so on.

Examples of programs used in mapping of Mendelian traits (or potentially

Mendelian traits, in the case of nonparametric programs) include those used for

parametric analyses such as the LINKAGE package (Lathrop et al., 1984) and

VITESSE (O'Connell and Weeks, 1995) and nonparametric programs such as

GENEHUNTER (Kruglyak et al., 1996). Parametric programs specify a genetic

model (eg autosomal dominant inheritance) and other parameters such as

penetrance, whereas nonparametric methods are “model-free”. Parametric

methods are powerful when there is good information available about the mode

of inheritance and other relevant parameters. However, for more complex

11

situations or where there is limited information available about the disorder,

nonparametric approaches are superior (O'Connell and Weeks, 1995; Nyholt,

2002).

1.4 Quantitative GeneticsThe application of Mendelian genetics to human disease virtually always

involves discrete phenotypes –a patient either has or does not have cystic

fibrosis. Gradations of severity (variable expressivity) and the phenomenon of

incomplete penetrance, in which an individual has a genotype which is

associated with disease in others but is not affected by the disorder in question,

do not negate this observation. However, the great majority of variation within a

population involves traits which are continuously variable rather than discrete in

nature. These are quantitative traits. Examples include body weight, blood

pressure and levels of blood lipids and homocysteine. Of themselves, none of

these necessarily represents a pathological state, even at the extremes of their

distribution in the population (arguably malignant hypertension represents an

exception to this). However, these examples were chosen for discussion

because all of them represent risk factors for atherogenic cardiovascular

disease (Fruchart et al., 2004). Many, perhaps most, such traits are under

genetic control to some degree. Quantitative genetics deals with efforts to

identify the underlying genetic variants which are responsible for variation in

quantitative traits.

1.4.1 Quantitative trait loci A QTL is a chromosomal segment which contains one or more genetic

elements which affect a quantitative trait (Falconer DS and Mackay TF, 1996).

The use of the term “genetic elements” here is deliberate, as it is not certain that

all QTL have their effect due to variation in the coding region of a gene, or even

in the promotor region or other regulatory elements of a gene. Given the

emerging understanding of the regulatory function of RNAs not directly

associated with genes, or with regulatory effects beyond their immediate

chromosomal environment (Mattick, 2007), it is possible that variations in some

of these may affect quantitative traits and thus be responsible for the effects

12

observed due to a QTL. Any given QTL may, on closer dissection, resolve into

multiple loci (Flint et al., 2005), each of smaller effect than the original QTL. This

means that elucidating the underlying basis of a QTL can be a daunting task

(see section 1.4.3 below). Despite its difficulty, this is an important endeavour,

given the major contribution of polygenic inheritance to human disease. QTL

mapping is also important in agriculture, with the use of marker assisted

selection to improve conventional breeding schemes for the modification of

economically important species (Ribaut and Ragot, 2007).

The infinitesimal model, developed by RA Fisher in 1918 and still influential now

(Barton and Keightley, 2002), considered polygenic disorders as being

determined by a very large number of loci each of very small effect (Fisher RA,

1918). In practice, however, QTL of large effect are frequently detected,

sometimes accounting for up to 50% of observed variation in the trait under

study (Flint et al., 2005). Although most QTL effect sizes are substantially

smaller than this, QTL of moderate effect size (>10%) are fairly common.There

are a number of possible explanations for this, including overestimation of effect

size. This is particularly likely if the sample size is smaller than about 500

(Barton and Keightley, 2002). Having said that, the majority of detected QTL are

of relatively small size (~5%) and it is likely that for most traits there are indeed

many further QTL of very small effect.

1.4.1.1 The liability model for binary traits The focus of this thesis is CHD, and as CHD is a binary trait (either present or

absent in an individual) it is worth explicitly discussing the relationship between

binary traits and QTL. While it is undoubtedly true that as such, CHD is

intrinsically less informative and more difficult to analyze from a quantitative

genetic perspective than a continuously distributed trait, QTL mapping remains

a potentially important tool in understanding the genetic basis of CHD. Falconer

(Falconer, 1965) developed the liability model for binary traits with multifactorial

inheritance. This model assumes an underlying continuously distributed but

unobservable scale of liability, with a threshold above which the observable

binary trait is expressed. If it is possible to identify a quantitative phenotype

13

which confers a risk of an individual developing the binary trait, the quantitative

phenotype can act as a proxy for the binary trait in mapping experiments. QTL

mapping for traits such as blood pressure represents such an undertaking, with

the binary trait of interest being the occurrence or non-occurrence of a

cardiovascular event (myocardial infarction or stroke, for example).

1.4.2 Mapping QTL The principles on which methods for mapping QTLs are based are similar to

those for mapping of Mendelian traits (Falconer DS and Mackay TF, 1996).

Considering a single locus, there is evidence for a QTL at that locus if there is a

statistically significant difference between individuals with different genotypes at

that locus (Mackay TF, 2001). As for mapping of Mendelian traits, this implies

the need for an informative genetic map. It is necessary for the trait of interest to

be under significant genetic control. Although it is possible to study QTL directly

in humans, animal models have considerable advantages. It is possible to set

up crosses which allow phenotyping and genotyping of very large numbers of

individuals. Moreover, inbred laboratory animals are essentially homozygous at

every locus (Moore and Nagle, 2000). This means that in a breeding experiment

based on a cross between two strains, identification of a marker which is

polymorphic between the strains means that it will be informative in all offspring

derived from the cross. F1 animals will be heterozygous at every locus, and

subsequent generations will segregate the two parental alleles in predictable

ratios depending on the nature of the second (and subsequent, where relevant)

cross. Heterozygotes provide information regarding dominance effects but not

regarding the location of the QTL.

Formal description of the fundamentals of QTL mapping in an F2 population of

mice (as described in chapter 6) is relatively simple (Mackay TF, 2001).

Consider a hypothetical marker locus, M, and a QTL, Q, each with two alleles

(M1, M2, Q1, Q2), with the recombination fraction between M and Q being �. The

QTL has additive (a) and dominance (d) effects. In the F2 population, the

difference in the quantitative trait between homozygotes for each allele of the

QTL will be a(1-2�). The difference between the average mean phenotype of

14

the homozgotes and the mean phenotype of F1 (heterozygous) animals is d(1-

2�)2. If there is no linkage between Q and M, �=0.5 (see 1.3.4) and therefore

a(1-2x0.5) = 0; likewise d(1-2x0.5)=0. Since the total variance in the trait, VT, is

the sum of variance due to environmental effects (VE) and genetic effects (VG),

and VG in turn is the sum of the additive and dominance effects, it follows that

(assuming shared environment, which would be the expectation in experimental

conditions) at �=0.5 there will be no difference between the phenotypes of

M1M1 and M2M2 mice.

The closer the marker locus is to the QTL, the smaller the value of � and the

larger the difference in trait phenotype between the two homozygous classes.

Within a chromosomal region, the marker which is associated with the greatest

difference in mean values between homozygotes for the two alleles is closest to

the QTL. From this, it would intuitively follow that denser spacing of markers will

lead to more accurate localization of QTL. This is only true up to a certain point.

Exact figures will depend on the effect size, but for a QTL of moderate effect

(allele substitution effect 0.25) spacing of markers at 10cM has almost the same

power to detect a QTL as an infinitely dense map (Darvasi et al., 1993), and

spacing at 20cM and even 50cM does not substantially reduce power. For a

study size of 500 animals, power of detecting a QTL on a 100cM chromosome

is 0.64, 0.58 and 0.47 for a QTL located halfway between the interval midpoint

and the nearest marker, with map densities of 10cM, 20cM and 50cM

respectively. Increasing the size of the experiment does make a difference; the

equivalent figures for an experiment with 1000 animals are 0.91, 0.90 and 0.81

respectively (Darvasi et al., 1993). Increasing experiment size also has an

important effect on the capacity of the experiment to resolve the location of the

QTL. Resolving power (defined by Darvasi and Soller as “the 95% confidence

interval for the QTL map location, that would be obtained when scoring an

infinite number of markers”) is inversely proportional to the sample size and to

the proportion of variance explained by the QTL (Darvasi and Soller, 1997).

15

1.4.2.1 Experimental designs for QTL mapping The description above is of an intercross experiment. An alternate strategy is a

backcross, where the F1 (heterozygous) mice are crossed with one or both of

the parental strains. The choice of study design will depend on the phenotype

and mode of inheritance of the QTL. While an intercross design is more

powerful than a backcross design in many circumstances and is most

commonly used, backcross is superior in some situations - for example, if one

of the strains is zero for a fully recessive phenotype (Moore and Nagle, 2000).

This is a relatively uncommon scenario, however, and most commonly there is

no information available about the nature of the QTL prior to commencing the

experiment.

1.4.2.2 Significance thresholds Lander and Kruglyak calculated significance thresholds for backcross and

intercross studies in mouse or rat (Lander and Kruglyak, 1995), based on

results which would be expected to occur by chance once per genome scan

(“suggestive”) or once per 20 genome scans (“significant”). For the intercross

design used in this study (Chapter 6) the LOD score thresholds are 2.8 for

suggestive linkage and 4.3 for significant linkage. The reason such calculations

are required is to compensate for the effects of analysis of large numbers of

markers. The figures derived by Lander and Kruglyak are conservative

thresholds (Moore and Nagle, 2000), reducing the risk of type I statistical error

(i.e. false positive), although this is at the expense of increasing the risk of type

II error (i.e. false negative). An alternate approach is permutation testing, in

which the experimental data are repeatedly randomized and the randomized

figures analyzed to obtain experiment-specific levels of significance.

1.4.2.3 Selective genotyping As discussed above, larger numbers of animals lead to increased power to

detect QTL and increased ability to resolve the location of QTL. However,

increasing the number of animals in an experiment inevitably increases the

associated cost of genotyping, which is usually much more expensive than

measuring the phentoype under study. Lander and Botstein (Lander and

16

Botstein, 1989) introduced the principle of selective genotyping, in which only

individuals with extreme phenotypes are genotyped. The logic behind this is that

such individuals contain most of the genetic information.

Specifically, for a normally distributed trait, progeny with phenotypes more than

1 standard deviation (SD) from the mean comprise about 33% of the total

population but contribute about 81% of the total linkage information. Growing a

population only 25% larger and genotyping these extremes of the distribution

would provide the same amount of linkage information but require genotyping

only 40% as many individuals (Lander and Botstein, 1989). Progeny with

offspring 2 SD from the mean comprise about 5% of the population but

contribute about 28% of the total linkage information, and so on. However,

extension of selective genotyping beyond this level (and perhaps even to this

level) is not recommended, because of the risk that some of the more extreme

phenotypes may be artefactual (eg measurement error). Moreover, the relative

cost of phenotyping goes up as the percentage of animals to be genotyped

goes down (Lander and Botstein, 1989). The extent to which this is a problem

depends on the phenotype of interest and the cost of breeding and maintaining

the mice until they are old enough to be phenotyped.

1.4.2.4 Software packages for QTL mapping There are numerous software packages available for QTL mapping. A

comprehensive list is available at http://linkage.rockefeller.edu/soft/ . Among the

most commonly used are the MAPMAKER/EXP and MAPMAKER/QTL package

(Lander ES et al., 1987) and Mapmanager QT (Manly and Olson, 1999).

MAPMAKER/QTL performs interval mapping (IM) based on the maximum-

likelihood theorem. This uses data from multiple marker loci simultaneously to

estimate both the position and effects of QTL. A particular strength of this

package is its ability to handle missing genotype data – the program can “fill in”

a missing datum and still use the phenotype information from that individual.

This makes it particularly useful for data analysis in conjunction with selective

genotyping (Moore and Nagle, 2000).

http://linkage.rockefeller.edu/soft/

17

Mapmanager QT uses multiple regression analysis, which is less

computationally demanding but almost as powerful as maximum-likelihood

analysis (Moore and Nagle, 2000); it is also more user-friendly than the

MAPMAKER/QTL. Unfortunately, however, it is much less tolerant of missing

data, and in the context of selective genotyping overestimates effect sizes

because it is unable to take the bulk of the phenotype data (that which is not in

the genotyped extremes) into account.

1.4.2.5 The mouse as a model organism Despite the obvious differences between humans and laboratory animals such

as mice, genetic studies in animals have contributed importantly to our

understanding of human genetics (Moore and Nagle, 2000), aided by the high

homology between the mouse and human genomes. Approximately 80% of

mouse genes have a single identifiable orthologue in the human genome; only

about 1% of mouse genes have no detectable orthologue in the human genome

(the reverse is also true) (Waterston et al., 2002). The mouse also has the

advantages of being easy to keep in a laboratory setting, having a short

generation time (approximately 9 weeks) and, for many strains, fecundity which

is high enough to make colony maintenance and breeding experiments

straightforward.

1.4.3 Identifying the underlying genetic basis of QTL By 2005, over 2000 QTL had been mapped in mice (Flint et al., 2005).

However, the genetic basis of at most 21 of these had been identified,

depending on the standard used. Two reviews at the time identified 21

quantitative trait genes in total, but agreed on only 4 genes in mice and 5 in rats

(Flint et al., 2005). Clearly, progressing from identifying a QTL to identifying its

underlying genetic basis is an extremely difficult task.

The confidence intervals for most QTL at the time of identification are very wide,

and contain large numbers of genes and even larger amounts of non-coding

DNA which may be of functional importance. It follows that the first step in

18

identifying the genetic basis of a QTL is to narrow down the region of interest. A

number of techniques have been developed or proposed to achieve this.

Congenic mouse strains have the QTL interval transferred from one of the

strains under study into the other, by repeated backcrossing (Moore and Nagle,

2000). This process can be accelerated by genotyping mice at each generation

to follow the region of interest (Markel et al., 1997), obviating the need for

phenotyping of mice at each generation to aid selection of mice to breed. Once

the region of interest has been bred from strain A into strain B, replacing the

original locus for that strain, further breeding between the congenic and strain B

can be done, looking for recombination events within the region of interest.

When these occur, mice homozygous for the alternate segregants can be

phenotyped to determine which side of the recombination contains the QTL.

An alternate approach is to watch for recombination events occurring during the

backcrossing process. Any mouse with a recombination event becomes the

founder of a new congenic line, so that a series of subinterval-specific congenic

strains are developed and can be phenotyped to determine the localization of

the QTL. In theory, analysis of ~1000 mice could reduce a QTL to ~1cM

(Darvasi, 1997).

Advanced intercross lines, a method proposed by Darvasi and Soller (Darvasi

and Soller, 1995) have the advantage of allowing fine mapping of multiple QTL

simultaneously. This is discussed in more detail in chapters 2 and 5. Briefly, the

pair of strains in which a QTL has been identified are crossed to produce an F1

population. These are then intercrossed and offspring of the F2 generation are

crossed in turn, continuing for a number of generations but avoiding inbreeding.

With each generation there are additional recombination events (Figure 2.1).

Phenotyping and genotyping are performed only at the last generation. Analysis

is then done along the same lines as the original QTL analysis.

Once a QTL has been narrowed down to as small an interval as possible, the

next, and possibly most difficult, step is to pinpoint the genetic basis of the QTL.

19

Identification and study of candidate genes is a common approach at this point

(Moore and Nagle, 2000). Problems with this include the likelihood that there

will be a large number of candidate genes even in the smallest region to which

a QTL is resolvable (Flint et al., 2005), and the existence of large numbers of

SNPs, meaning that any gene which is studied is likely to contain variants which

could potentially influence gene expression, and hence underlie the QTL.

Candidate variants can be used in association studies, but may prove to be

closely linked to the genetic variant which truly underlies the QTL. Resolving

this is likely to be very difficult. Expression studies, particularly using microarray,

may provide evidence supporting gene candidacy (Guo and Lange, 2000), but

such evidence is likely to fall short of proof. Targeted knock-in and knock-out

mice potentially can provide strong evidence for a gene’s role in a QTL – if the

phenotype under study is substantially altered in the knock-in or knock-out mice

(Flaherty et al., 2005). The resources involved in creating such mice are still

very considerable, however, forming a barrier to such studies.

At present, QTL mapping techniques are well-established. Experimental

designs are tried and tested, dense marker maps exist and software for data

analysis is readily available. However, the transition from identification of a QTL

to elucidation of its underlying genetic basis remains extremely difficult.

1.4.4 The mouse Hapmap project: application to QTL mapping The findings of the mouse Hapmap project (Frazer KA et al., 2007), including

the identification of over 8 million SNPs densely distributed across the mouse

genome, open up a new approach to QTL mapping (Flaherty et al., 2005;

Frazer et al., 2004). The mouse Hapmap project initially studied 11 classical

laboratory mouse strains and four wild-derived strains. The haplotype map

developed had over 40,000 segments each with an average of three ancestral

haplotypes, reflecting origin from one of the wild mouse substrains – M.m.

musculus, M.m.castaneus, M.m domesticus and the hybrid M.m. molossinus. In

the classical strains these subspecies contributed 68%, 6%, 3% and 10%

respectively, with the remainder being of unknown origin (Frazer KA et al.,

2007). Knowledge of the structure and distribution of these ancestral segments

20

allows the performance of association studies with considerable power to map

QTL (Frazer et al., 2004). In the next phase of this project, a total of 38

“classical” inbred strains and 11 wild-derived strains have been genotyped with

a SNP microarray covering 138,793 SNPs (data available at

http://www.broad.mit.edu/mouse/hapmap/ ). The existence of large amounts of

data regarding quantitative traits in the common laboratory strains means that

mapping for many such traits will be possible without the need to perform

additional experiments. For less well-studied phenotypes, like those described

here, however, additional phenotyping will be required to make use of this

resource.

1.5 The heart The heart’s role in mammalian physiology is a central one – without constant

distribution of oxygenated blood to the body’s tissues, oxidative metabolism

rapidly ceases, leading to death of the organism within minutes. This

fundamental role has meant that the heart’s structure and development have

been highly conserved during evolution, and in particular mammalian hearts,

from mouse to whale, share very similar anatomy. It is remarkable that, despite

the critical importance of the heart, severely disordered cardiac development,

leading to a markedly structurally and functionally abnormal heart, may still be

compatible with survival to and beyond birth. Lesser degrees of cardiac

maldevelopment may be asymptomatic, or may become symptomatic only well

into adult life.

1.5.1 Normal cardiac anatomy The adult mammalian heart is shown in Figure 1.2. Functionally, the heart can

be viewed as a pair of two-chambered muscular pumps, fused anatomically and

controlled by an electrical conducting system. The normal arrangement consists

of a right-sided circulation which receives deoxygenated blood from the

systemic veins, and distributes it to the lungs via the pulmonary arteries; and a

left sided circulation which receives oxygenated blood from the lungs and

distributes it to the body via the aorta. The atria are collecting chambers,

separated from the ventricles by valves (tricuspid on the right and mitral on the

http://www.broad.mit.edu/mouse/hapmap/

21

left) which prevent regurgitation of blood during ventricular contraction. The

tricuspid and mitral valves are supported by fibrous chords, the chordae

tendinae. The ventricles are pumping chambers and are separated from the

great arteries by valves (pulmonary on the right and aortic on the left) which

prevent regurgitation of blood after ventricular contraction. Cardiac muscle

receives its own blood supply from the coronary arteries, which originate from

the proximal aorta. Cardiac contraction is regulated by an electrical conduction

system. Specialized cells are organized into nodes and tracts. The sinoatrial

node, located at the junction between the right atrium and the superior vena

cava, initiates the beat. The electrical impulse is then propagated throughout

the atria, and to the atrioventricular (AV) node and thence to the ventricles.

From the AV node, the impulse passes down the bundle of His and its branches

to the apex of the ventricles, and then along the Purkinje fibres to the remainder

of the ventricles.

1.5.2 Heart development The heart becomes the first functional organ in the developing embryo

(Buckingham et al., 2005). Cardiac development is illustrated in Fig 1.3. The

first identifiable cardiac tissue (expressing myocardial markers) forms as

bilateral groups of cells in the lateral mesoderm of the early embryo, having

originated as undifferentiated mesodermal cells which migrate from the anterior

region of the primitive streak (Buckingham et al., 2005). These cells are linked

across the anterior-ventral midline, to form the so-called cardiac crescent, which

in turn folds to form the linear heart tube. This is a transient structure composed

of an inner endothelial tube surrounded by a myocardial layer (Harvey, 2002).

At this point, cells from an addiional cardiac progenitor field called the second

heart field (SHF) migrate from dorsal mesocardium, pericardium and branchial-

arch mesoderm into the heart, and contribute to its structural development at

both poles(Moorman et al., 2003). These contribute to the outflow tract, right

ventricle and much of the atria (Buckingham et al., 2005).

Figure 1.2. The adult mammalian heart The structure of the adult human heart,

whole (panel a) and in section (panel b). The right atrium (RA) receives venous blood

from the body, and passes it through the tricuspid valve to the right ventricle (RV). The

RV pumps the blood via the pulmonary artery to the lungs. Oxygenated blood from the

lungs is returned to the left atrium (LA) via the pulmonary veins, and passes through

the mitral valve to the to the left ventricle (LV). The tricuspid and mitral valves are

supported by the chordae tendinae. The left ventricle pumps blood through the aortic

valve to the systemic circulation. The coronary circulation branches off from the

proximal aorta. The heart beat is regulated by specialised electrical conducting cells

which are organised into clusters (nodes) or tracts; it is initiated at the sino-atrial node,

propogates through the atria and to the atrioventricular node (AVN). After a delay it is

then passed via the bundle of His and its bundle branches to the apex of the ventricles,

and then to the rest of the ventricles via the Purkinje fibres. Ca = caudal (inferior), Cr =

cranial (superior); L = left, R = right. Adapted by permission from Macmillan Publishers

Ltd [NATURE REVIEWS GENETICS] (Harvey, 2002), copyright 2002 (license no

1838550679750)

22

23

The heart tube during its progressive formation undergoes a process of looping,

with the development of a rightward spiral form, leading to the formation of

distinct anatomical features. The caudal part of the heart tube moves dorsally

and anteriorly to form the future atria (Prall et al., 2002). The ventricles become

distinct at this stage and balloon outwards. The spatial relationship between the

developing chambers at this point approximates their final alignment. The

precursors of the atrioventricular valves first appear as endocardial cushions at

this stage, forming at the level of the atrioventricular canal from cells from the

endocardial layer of the heart. Endocardial cushions giving rise to the aortic and

pulmonary valves also form in the outflow tract at this stage. Specification of

distinct myogenic tissue types is also a feature of the looping stage of heart

development. Of particular importance is the emergence of “working

myocardium” along the outer curvature of the looping heart tube (Christoffels et

al., 2000) – this becomes the contractile tissue of the heart chambers. Non-

chamber myocardium gives rise to elements of the conduction system and

fibrous tissue.

The looping phase of cardiac development is followed by a remodelling phase,

during which septation of the heart chambers becomes complete, with the

formation of distinct right and left atria and ventricles.The inflow and outflow

tracts assume their final positions at this stage.

1.5.3 The interatrial septum The interatrial septum is a wall of tissue which, in postnatal life, separates the

left and right atrium, preventing the flow of blood from left to right. During normal

heart development in mammals, the interatrial septum acts as a valved

communication between the atrial chambers, allowing a right-to-left atrial blood

shunt that helps to bypass circulation to the lungs, which are nonfunctional until

after birth. Two distinct septal walls, the septum primum and septum secundum,

contribute to its final structure (Webb et al., 1998). Each maintains a natural but

offset opening between the atrial chambers, creating a one-way flap valve

(Figure 1.4). Expansion of the lungs at birth is accompanied by an increase in

24

Figure 1.3 Normal cardiac development This figure illustrates the main stages in

early heart development in mammals and other amniotes, with staging in days of

embryonic development (E) based on mouse development. The whole embryo or

25

isolated heart is shown at left. At right, a representative section (transverse in panels band d, longitudinal in panels f and h) illustrates the main internal features. All views are

ventral. Myocardium and its progenitors are depicted in red. The cardiac progenitors

are first recognisable as a crescent-shaped epithelium (the cardiac crescent) at the

cranial and cranio-lateral parts of the embryo (panels a and b). Next, heart progenitors

move ventrally to form the linear heart tube (panels c and d). The inflow region of the

linear heart tube is located caudally and its outflow cranially. The linear heart tube

undergoes a complex process called cardiac looping, in which the tubular heart

becomes spiral with its outer surface moving rightwards (panels e and f). Endocardial

cushions (EC), precursors to the tricuspid and mitral valves, are forming in the

atrioventricular (AV) canal. The trabeculae (T) also form at this stage. Panels g and hdepict the remodelling phase of heart development, during which septation of the

cardiac chambers is completed, and distinct right and left ventricles (LV and RV) and

atria (LA and RA) become evident. Further spiralling of the heart tube results in the

outflow region becoming wedged between the developing ventricles on the ventral side

(panel g) and the inflow region spans the ventricles dorsally (panel h). The chambers

and vessels have now reached the same alignment as in the adult heart. The muscular

inter-atrial and inter-ventricular septae fuse with the non-muscular AV septum, which is

derived from the endocarial cushions. Ca = caudal, Cr = cranial, L = left, R = right.

Adapted by permission from Macmillan Publishers Ltd [NATURE REVIEWS

GENETICS] (Harvey, 2002), copyright 2002 (license no 1838550679750)

Figure 1.4 Relative arrangement of septum primum and septum secundum Cartoon of the interatrial septum in the developing mammalian heart, showing the

relative arrangements of the septum primum (orange) and septum secundum (purple).

The red arrow indicates direction of blood flow in prenatal life. Adapted with permission

from (Biben et al., 2000) Lippincott Williams and Wilkins, copyright 2000

left atrial pressure, which forces the septum primum against the septum

secundum. In humans, the two septa seal permanently by adhesion in the first

year of life in ~75% of individuals. However, the valve remains unsealed to

varying degrees in ~25% of the adult population, a condition termed patent

foramen ovale (PFO) (Hagen et al., 1984).

1.5.4 Regulation of cardiac development by transcription factors Cardiac development, as outlined above, is an extremely complex process and

occurs under the control of an interactive cascade of genetic regulators

(Harvey, 2002; Olson, 2006; Dunwoodie, 2007). A core set of evolutionarily

conserved transcription factors (NK2, MEF2, GATA, Tbx and Hand) control

cardiac cell fates, the expression of contractile protein-encoding genes and 26

27

cardiac morphogenesis (Olson, 2006). In turn these transcription factors

regulate one another, and many other transcription factors are involved. Of

these, MEF2 is the key myogenic transcription factor, involved in the

differentiation of all types of myocyte. In turn it is under regulation by NK2

homeobox genes, particularly tinman in Drosophila and its orthologues in

mammals. The homeodomain factor NKX2-5 is a key transcription factor in

cardiac development (Dunwoodie, 2007). It is expressed in cardiac progenitor

cells of both the first and second heart fields. Expression continues in the

primary heart tube and in the looping heart, in the outflow tract, ventricles,

common atrium and the proximal horns of the sinus venosus. Expression

continues in muscular layers of the heart throughout the remainder of

embryogenesis and into postnatal and adult life (Prall et al., 2002) The absence

of Nkx2-5 is catastrophic to heart development in the mouse embryo, resulting

in complete failure of cardiac morphogenesis, chamber formation and outflow

tract development.

NKX2-5 acts as part of a pathway in which it physically interacts with a set of

other transcription factors to activate target genes. For example, the zinc finger

transcription factor GATA4 (one of a group of genes named because their

protein products bind to the nucleotide sequence GATA) physically interacts

with NKX2-5. When co-expressed, their effect on the transcription of some

cardiac genes is synergistically augmented (Prall et al., 2002). GATA4 protein is

regulated by other co-transcription factors including the Friend of GATA (Fog)

proteins. Gata4 null mouse embryos have severely disrupted cardiac

development, with failure to form the primitive heart tube among other severe

developmental abnormalities (Molkentin et al., 1997).

The T-box genes are a group of transcription factors which share a highly

conserved 180-amino acid DNA binding domain called the T-box (Stennard and

Harvey, 2005). Of the seven or more T-box genes expressed in the developing

human heart, TBX1, TBX5 and TBX20 (chapter 3) have been implicated in

human congenital heart disease. TBX1 is important in the secondary heart field

and subsequently the outflow tract, consistent with its role as the major

28

determinant of the cardiac phenotype in velocardiofacial syndrome

(characterized by conotruncal malformations) (Yagi et al., 2003).

There is evidence that TBX5 functions as part of the NKX2-5 pathway (Prall et

al., 2002; Dunwoodie, 2007). Mouse Tbx5 associates directly with Nkx2-5 and

Gata4, synergistically stimulating chamber-specific genes in later stages of

cardiac development. Tbx5 is specifically expressed in the first heart field, at the

cardiac crescent stage and later in the primary heart tube. Tbx5 null mouse

embryos are severely dysmorphic and fail to undergo cardiac looping.

Interestingly, mice heterozygous for a Tbx5 null allele have similar cardiac

abnormalities to those seen in Holt-Oram syndrome, with septal defects and AV

conduction block (Stennard and Harvey, 2005; Prall et al., 2002).

Mouse Tbx20 is expressed in the cardiac crescent, and in some cells of the

secondary heart field. In the heart tube, it is expressed in myocardium and in

endothelial cells associated with the endocardial cushions; this latter expression

persists with further development, as myocardial expression weakens. Tbx20

interacts directly with Tbx5, Nkx2-5, and Gata4 (Stennard and Harvey, 2005).

Tbx20 null mouse embryos have hypoplastic, unlooped hearts. Expression of

Tbx20 is required for normal levels of Nkx2-5 expression (Dunwoodie, 2007).

1.6 Congenital heart disease The complexity of the developmental process briefly outlined above provides

many opportunities for maldevelopment of the heart, although it is still not

possible to ascribe a patho-developmental mechanism to all types of CHD with

confidence (Anderson et al., 1999). While some malformations are lethal in

utero, a wide variety of malformations are seen in liveborn infants, occurring

singly or in combination. The reported incidence of CHD varies from 0.4-1.0%

(Hoffman and Kaplan, 2002), with most studies at the higher end of that range.

A trend towards higher incidence in more recent studies appears to be due to

improved detection of minor lesions of no clinical importance, particularly small

VSDs with advances in cardiac imaging, especially 2-D echocardiography

(Wilson et al., 1993; Srephensen et al., 2004).

29

1.6.1 Types of CHD Various classification systems have been devised for CHD. Classification by

severity (Hoffman and Kaplan, 2002), by embryological origin (Marino and

Digilio, 2000), by presumed aetiology (Boughman et al., 1987), by associated

features (Bower and Ramsay, 1994; Stoll et al., 1989) and by anatomical

divisions such as sequential segmental analysis (Craatz et al., 2002) have been

proposed. While all may have merits, the proliferation of classification systems

can make comparisons between studies of the epidemiology of CHD

challenging. Depending on the classification system used, various

malformations may be grouped in different ways, making it hard to separate out

data relevant to particular lesions. Regardless of the classification system used,

however, the relative frequencies of the more common malformations are

generally consistent between studies, where data for individual lesions is

provided. The proportion of CHD accounted for by VSD has increased over

time, due to increased detection of small VSDs, as discussed above. Table 1.1

shows the percentages of common malformations reported in three studies from

different continents (Australia, Europe and North America) as an illustration of

this point, along with the data pooled from a large numbers of studies by

Hoffman and Kaplan (Hoffman and Kaplan, 2002).

1.6.2 Causes of CHD The majority of cases of CHD do not have a single identifiable cause. Among

identifiable causes, chromosomal abnormalities are the most common,

accounting for 8.5% of cases in an Australian series (Bower and Ramsay,

1994). Genetic syndromes, mainly inherited in a Mendelian fashion, account for

a further 1.2%. The Australian data are comparable to those obtained in other

populations. There have been recent epidemiological surveys from Malta

(Grech and Gatt, 1999), Iceland (Srephensen et al., 2004), Italy (Calzolari et al.,

2003) and a combination of California, France and Sweden (Harris et al., 2003).

In these studies, the percentage of cases attributable to chromosomal

abnormalities ranged from 4.9%-18%; 2%-4.5% of subjects had identifiable

syndromes and 6%-12% had associated extra-cardiac abnormalities.

30

Table 1.1: Percentage of CHD accounted for by the most common lesions

Bower and

Ramsay, 1994

Samanek,

1994

Boughman et

al., 1987

Hoffman and

Kaplan, 2002a

n 1337 4409 1055 pooled data

VSD 42.6 41.3 25.6 37.2

ASD 7.2 12.0 9.7 9.8

PS 7.3 6.4 7.3 7.6

PDA 6.8 5.8 2.6 8.3

Coarct 5.8 5.6 6.9 4.3

TOF 3.5 2.8 7.5 4.4

d-TGA 5.8 5.1 4.6 3.3

AS 3.3 6.2 3.1 4.2

AVCD 1.6 2.9 8.5 3.6

HLHS 2.8 3.2 4.2 2.8

PA 1.8 1.9 1.6 1.4

TA 1.9 0.6 1.6 0.8

TAPVR 1.0 0.8 1.6 1.0

Other 8.6 17.4 15.0 11.3

All figures other than the first row are percentages.

a. – this study pooled results from 62 separate studies. Percentages listed

here are based on the mean values derived.

VSD, ventricular septal defect; ASD, atrial septal defect; PS, pulmonary

stenosis; PDA, patent ductus arteriosus; Coarct, coarctation of the aorta; TOF,

tetralogy of Fallot; d-TGA, d-transposition of the great arteries; AS, aortic

stenosis; AVCD, atrioventricular canal defects; HLHS, hypoplastic left heart

syndrome; PA, pulmonary atresia; TA, tricuspid atresia; TAPVR, total

anomalous pulmonary venous return

31

Non-syndromal familial forms of CHD account for an as yet undetermined

percentage. Multifactorial inheritance, with a strong genetic contribution,

probably accounts for most of the remainder – i.e. the great majority of all CHD.

The exact model which best describes the relative contribution of genes and

environment is uncertain. The classical multifactorial model, with numerous

genes each contributing a small amount to variation in the population, has been

challenged (Burn J and Goodship J, 2002) and it is possible that some forms of

CHD are better explained by oligogenic or other models.

Although there is clearly an important environmental contribution to the

causation of most CHD, it is usually not possible to identify a specific

environmental factor. Having said that, a small minority of cases are caused by

identifiable teratogens such as maternal exposure to alcohol and medications,

and the teratogenic effect of maternal diabetes. While it is possible to use

statistical analyses to determine the proportion of cases attributable to relatively

common in utero exposures such as maternal diabetes, in most instances it is

not possible to say for certain which infants have malformations which would

not have been present but for that teratogenic effect. Thus, for approximately

90% of children affected by CHD, no specific cause can be found.

1.6.3 Patent foramen ovale As described above (1.5.3), passage of blood from right to left atrium via the

foramen ovale is a normal feature of circulation before birth. Following birth,

persistent patency of the foramen ovale is very common, as noted above (found

in ~25% of adults) (Hagen et al., 1984) and has generally been considered

benign. Haemodynamically, PFO is usually of no consequence. Although there

is a structural passage between the atria, the pressure differential between the

left (higher pressure) and right sides produces a functional seal by pressing the

septum primum against the septum secundum, preventing shunting of important

volumes of blood in either direction. The small size of most PFOs (mean width

5mm (Hagen et al., 1984)) also reduces the likelihood of significant shunting of

blood.

32

1.6.3.1 PFO and stroke However, in recent years it has become apparent that PFO is not always

harmless. PFO has been shown to be a risk factor for ischaemic stroke without

an identifiable cause, known as “cryptogenic stroke”, and also for migraine.

Case-control studies show that, particularly among younger stroke patients,

there is a significantly higher incidence of PFO than among controls. In stroke

patients aged under 40 years, Webster and colleagues found PFO in 50% of

cases, but only 15% of controls (Webster et al., 1988). In patients younger than

55, Lechat and colleagues found PFO in 40% of cases and 10% of controls

(Lechat et al., 1988). The association is not seen in older patients (Jones et al.,

1994) but this may be due to the fact that other causes of ischaemic stroke

become very much more common with increasing age (particularly in individuals

>60), potentially masking the effect of PFO in older individuals.

The mechanism by which PFO confers an increased risk of stroke in younger

individuals is thought to be predominantly by producing a vulnerability to

“paradoxical embolism” in which an embolus (usually thrombus detached from a

deep vein thrombosis) crosses from the right side of the circulation to the left

(McGaw and Harper, 2001). This may be more likely to occur at times when the

systemic venous pressure is increased, for example if the patient is lifting a

heavy object or straining at stool.

The observation that PFOs in individuals with cryptogenic stroke are both larger

and are associated with a greater degree of right to left shunting of blood than

those seen in individuals with known causes of ischaemic stroke (Homma et al.,

1994) appears to support this proposition. However, although paradoxical

embolism has been directly observed during echocardiography in at least one

instance (Maier et al., 2007), there is little direct evidence to support this model

(Kizer and Devereux, 2005). There is seldom a history connecting the stroke

with an episode or condition which would be expected to raise right sided

pressure, and a source of emboli is seldom found.

33

It is thus possible that other factors are responsible for the observed association

between PFO and stroke. In situ thrombosis and atrial tachyarrhythmias have

been considered. Large PFO are often associated with other structural

anomalies such as aneurysm of the septum primum (McGaw and Harper, 2001)

and persistence of embryonic features of right atrial morphology (Hagen et al.,

1984), which could promote in situ formation of thrombus. However, mural

thrombi and atrial tachyarrhythmias are not commonly found in association with

cryptogenic stroke (Kizer and Devereux, 2005). There are likely to be cell

adhesion factors which are involved in physical closure of the foramen ovale

following birth. Hypothetically, these could be involved pathologically later in life,

in the development of small thrombi in the systemic circulation which then

embolize and cause stroke. Abnormalities of such factors could then predispose

to PFO and stroke without the requirement for paradoxical embolism to occur.

There is no direct evidence to support this concept either, however.

1.6.3.2 PFO and migraine Migraine is a common, recurrent form of headache with distinctive

characteristics. The headache is throbbing, unilateral, often severe, is

accompanied by nausea, vomiting or sensitivity to sound and light. The

headache typically lasts from 4-72 hours (Post et al., 2007). About one third of

patients with migraine experience aura, which is a period of focal neurological

symptoms usually occurring within the hour preceding the onset of headache

(Henry et al., 1992). The pathogenesis of migraine is not well understood but

changes in cerebral bloodflow have been implicated (Henry et al., 1992). The

observation that PFO was associated with stroke, combined with the previous

finding that migraine is also a risk factor for stroke, led to the hypothesis that

PFO may be associated with migraine.

A number of studies have been performed which appear to confirm that such a

relationship exists. Firstly, retrospective studies of the prevalence and severity

of migraine in people who have had PFO closure show a decreased prevalence

of migraine (particularly migraine with aura) after closure (Wilmshurst et al.,

2000; Reisman et al., 2005; Post et al., 2004). In a recent prospective study of

34

patients undergoing PFO closure (Anzola et al., 2006), 36% of patients with

migraine had resolution of their migraine symptoms 1 year after closure. Among

subjects with migraine with aura, only 7/33 still had aura symptoms 1 year after

closure of PFO, compared with 21/21 controls with migraine and PFO who did

not have closure.

Again, the mechanism for this relationship is not certain, although the passage

of microemboli from the systemic venous to arterial circulation across the atrial

septum has been suggested (Post et al., 2007) – a form in miniature of

paradoxical embolism.

1.6.3.3 Other pathological consequences of PFO In addition to stroke and migraine, PFO has been implicated in the

pathophysiology of decompression illness in divers (Wilmshurst et al., 2001)

and in hypoxaemia in individuals with obstructive sleep apnoea (Shanoudy et

al., 1998).

1.6.3.4 Genetics of PFO There has been relatively little study of the underlying causes of PFO. Research

to date has focused entirely on the genetics of PFO, at the level of establishing

that there is an increased risk of PFO in relatives of individuals with PFO and/or

ASD. Arquizan et al (Arquizan et al., 2001) studied sibs of patients with

ischaemic stroke, comparing sibs of those with PFO with sibs of those without

PFO. 61.5% of sibs of patients with PFO also had PFO, compared with 30.6%

of sibs of those without PFO. Interestingly, concordance for this trait was higher

among female than male sibs. Although ASD shows a female preponderance

(see 1.7.3.1), PFO seems to be evenly distributed between the sexes (Hagen et

al., 1984). The authors comment that the relatively small numbers in each group

once the figures are broken down by sex make a type I statistical error possible.

Wilmshurst and colleagues performed echocardiography on 71 relatives of 20

probands with either small ASD or large PFO with shunt (Wilmshurst et al.,

2004). Of the 71 individuals, 60.6% had ASD or PFO. There were no controls

35

but this is well above the population prevalence of PFO. However, the data may

have been skewed to some extent by the identification of several families in

which atrial shunting appeared to be segregating in an autosomal dominant

fashion, including one family with 21 affected members. This family was not

included in table 1.2 (below), because the authors do not differentiate between

ASD and PFO.

Rodriguez et al studied differences in PFO, ASA and right atrial anatomy in

patients with ischaemic stroke (Rodriguez et al., 2003) from white, black and

Hispanic backgrounds. They also assessed subjects for the presence of Chiari’s

network, a congenital remnant of the right valve of the sinus venosus, the

presence of which is associated with a high incidence of PFO and ASA

(Schneider et al., 1995). The presence of Chiari’s network is not a major

contributor to PFO, however, as it is present in only about 2% of adults

(Schneider et al., 1995). Rodriguez et al found a similar incidence of PFO, ASA

and Chiari’s network across the different racial groups. However, whites and

Hispanics were more likely to have a large PFO and the degree of shunt was

greater than in blacks. The significance of this finding is uncertain, particularly

as it may relate to ASD. In the Baltimore-Washington infant study, nonwhites

were found to be more likely to have ASD than whites, although the difference

was small (OR 1.5, 95% CI 1.1-2.0).

1.6.4 Atrial septal defect Notwithstanding the potential pathological consequences of PFO discussed

above, in most individuals PFO is associated with a functionally competent atrial

septum with minimal if any blood flow between the atria. An ASD is present

when there is a frank hole in the atrial septum. There are four anatomical types

of ASD; secundum ASD, ostium primum ASD, sinus venosus ASD and

coronary sinus defect, and these are described below. Confluent ASDs, large

holes caused by a combination of two types of lesion, can generally be

classified as secundum ASD; and common atrium is a form of primum ASD

(Kouchoukos NT et al., 2003). By far the most common type is secundumASD, which, with PFO, is the main focus of this thesis. Unless otherwise

36

specified (eg “primum ASD”), from this point onwards “ASD” refers to secundum

ASD.

1.6.4.1 Secundum ASD Secundum ASD forms in the region of the foramen ovale and is in essence a

failure of the flap valve (septum primum) to cover the opening of the foramen

ovale. This may be because the foramen ovale is too large, because the

septum primum is too short, because the septum primum forms abnormally and

is fenestrated, or from a combination of these abnormalities.

1.6.4.2 Ostium primum ASDOstium primum ASD is a form of endocardial cushion defect, in which the

septum primum does not fuse with the endocardial cushions, leaving a patent

foramen primum. There is usually an associated cleft in the anterior cusp of the

mitral valve.

1.6.4.3 Sinus venosus ASD Sinus venosus ASDs are located high in the septum, just below the orifice of the

superior vena cava, and are usually associated with anomalous pulmonary

venous return. This is a very rare type of ASD.

1.6.4.4 Coronary sinus ASD Coronary sinus ASDs are part of the “unroofed coronary sinus syndrome”

(Kouchoukos NT et al., 2003), in which the coronary sinus lacks a partition to

separate it from the left atrium. As a result, the ostium of the coronary sinus

forms a hole in the atrial septum.

1.6.4.5 Pathology associated with ASD Regardless of type, the main pathological consequence of ASD is the presence

of an intracardiac shunt (Krasuski, 2007). In postnatal life, the pressure in the

left atrium is greater than that in the right atrium, and an ASD permits left to

right shunting of blood. This results in volume overload of the right side of the

heart, as well as an increase in the pressure in the right atrium. Very large holes

37

may cause important symptoms in infancy or early childhood, with failure to

thrive, frequent respiratory infections and even heart failure, requiring early

surgical closure (Lammers et al., 2005). Smaller lesions, however, may take

many years to produce symptoms. If not detected on routine examination in

childhood, presentation well into adulthood is not uncommon. Presenting

symptoms of fatigue and breathlessness result from progressive pulmonary

hypertension, secondary to volume overload (Krasuski, 2007). Progressive right

atrial enlargement and thickening of the atrial wall is usually seen, in the

presence of a normal sized left atrium (Kouchoukos NT et al., 2003). This may

lead to atrial fibrillation. If untreated, pulmonary hypertension can result in

Eisenmenger syndrome, in which right sided pressure exceeds that on the left

and the shunt is reversed, causing systemic hypoxaemia (Krasuski, 2007).

Cryptogenic stroke has also been associated with ASD (Bartz et al., 2006),

although ASD is a much less common finding than PFO in patients with stroke.

This is not surprising given the very high prevalence of PFO in the population.

1.6.5 Relationship between PFO and ASD Clinically, the smallest ASDs can be viewed as large PFOs with an incompetent

flap valve, and indeed ASD and PFO are viewed as existing in a continuum by

cardiac surgeons and cardiologists (Kouchoukos NT et al., 2003) (personal

communication, Prof Michael Feneley, 2006). This clinical observation suggests

an aetiological relationship between ASD and PFO, which is supported by

studies in mice and humans. Wilmshurst and colleagues (Wilmshurst et al.,

2004) studied family members of patients with left to right shunting on

echocardiography. The probands had been investigated for a variety of reasons

including decompression illness, ischaemic stroke, haemodynamic features of

ASD and migraine with aura. Of the 71 family members who had contrast

echocardiography, 61% had either ASD or PFO. There were no controls but this

incidence is well above the population prevalence of PFO. Although most of

these lesions appear to have been PFOs, and the pedigrees presented do not

distinguish between ASD and PFO, the authors state that family members with

both lesions were identified. Evidence is presented that inter-atrial shunt

38

segregated as an autosomal dominant trait in at least 6/20 families. This study

supports the concept that there is a genetic link between ASD and PFO in

humans.

In mice, the main evidence that ASD and PFO have a shared aetiology comes

from the work of Biben and colleagues (Biben et al., 2000), which forms the

basis for most of the mouse work reported in this thesis. Biben and colleagues

studied the effects of heterozygous Nkx2-5 mutations on the murine atrial

septum. NKX2-5 mutations in humans had been shown to cause a variety of

forms of congenital heart disease, but particularly ASD. NKX2-5, and its murine

orthologue Nkx2-5, are homeodomain-containing transcription factors important

in early cardiac development in man and mouse, respectively (see above).

In Nkx2-5+/- mice, Biben et al found an increased incidence of patent formane

ovale, atrial septal aneurysm, and decreased length of the septum primum flap

valve compared with wild-type mice. These effects varied between strains and

were particularly striking in mice from the strain 129T2/SvEms, in which

heterozygosity for the Nkx2-5 null allele resulted in severe PFO bordering on

ASD in 6/35 mice (17%). In total, 5/425 Nkx2-5+/- had ASD – much lower than in

humans heterozygous for NKX2-5 mutations (see below) but considerably

higher than in wild-type mice, in which ASDs are generally very rare (see

chapter 5). Importantly, in every strain assessed, heterozygosity for the Nkx2-5

null allele was associated with a markedly increased incidence of PFO

compared with wild-type mice – 78% vs 26% in B6 mice, 94% vs 74% in

129T2/SvEms, 36% vs 6% in a Swiss x B6 cross, and 62% vs 2.6% in an FVB x

B6 cross. Thus, a mutation in Nkx2-5 led to measurable changes in atrial septal

morphology, and an increased incidence of both PFO and ASD.

In summary, ASD and PFO are common and closely related conditions. While

the role of PFO in human pathology makes it worthy of study in its own right, the

apparent aetiological link between the two lesions suggests that PFO in the

mouse may represent a valid model of ASD in the human. Insights gained from

39

the study of murine PFO may then be of importance in understanding ASD and,

in turn, other forms of CHD.

1.7 Causes of ASD In this section the known causes of ASD will be discussed.

1.7.1 Syndromes associated with CHD Numerous malformation syndromes have been described in which CHD occurs.

This can be one of the main defining features of a syndrome (as in Holt-Oram

syndrome (Basson et al., 1997), velocardiofacial syndrome (Fokstuen et al.,

1998) and Noonan syndrome (Tartaglia et al., 2002). Alternately, it may be an

association of variable importance in a syndrome defined primarily by non-

cardiac pathology – as in Smith-Magenis syndrome (Sweeney et al., 1999) and

Rubinstein-Taybi syndrome (Stevens and Bhakta, 1995), in both of which about

a third of affected individuals have CHD; or cerebrocostomandibular syndrome,

in which CHD is a rare but apparently real association (Plotz et al., 1996; Kirk

and Ades, 1998).

It is difficult to be accurate about how many syndromes are associated with

CHD. The London Dysmorphology database (London Medical Databases Ltd,

Bushey UK 2004) lists 873 syndromes in which CHD is a feature. The POSSUM

dysmorphology database (Murdoch Children’s Research Institute, Melbourne,

2005) lists 933 syndromes with abnormalities of structure or function of the

heart as a feature, although 151 of these are chromosomal anomalies, and

some refer to cardiomyopathy rather than structural lesions. A search using the

term “congenital heart disease” in the online database OMIM (Online Mendelian

Inheritance in Man, http://www.ncbi.nlm.nih.gov/sites/entrez?db=OMIM ;

accessed 7 June 2007) yields 387 results, although this is likely to be an

incomplete set because of the way OMIM is written – for example, an entry

which referred to “heart defects” but not to “congenital heart disease” would not

be captured by this search. In the appendix to their majestic review of the

genetics of CHD, Burn and Goodship (Burn J and Goodship J, 2002) list 317

syndromes associated with cardiac malformation, including many for which the

http://www.ncbi.nlm.nih.gov/sites/entrez?db=OMIM

40

evidence that CHD is a feature of the disorder is limited to a single case report,

and also including a number of teratogenic syndromes such as fetal alcohol

syndrome. Of the Mendelian and chromosomal causes of CHD, although some

may be allelic disorders, the majority will be caused by distinct genetic

abnormalities, and in many instances mutations in more than one gene can lead

to a single syndrome. Thus, there are a very large number of separate routes

which lead to the common endpoint of CHD.

Almost every known mechanism of inheritance has been associated with CHD,

and ASD in particular, including all forms of Mendelian inheritance,

multifactorial/polygenic inheritance and chromosomal aneuploidies including

small deletions and duplications (Burn J and Goodship J, 2002). Even

mitochondrial inheritance has been suggested (Sherman et al., 1985), although

the evidence for this is limited, and it is difficult to see how abnormalities in the

function of the respiratory chain (the sole function of the mitochondrial genome

(DiMauro and Schon, 2003)) would lead to isolated CHD.

1.7.1.1 Holt-Oram syndrome Among syndromes with CHD as a feature, Holt-Oram syndrome (HOS)

deserves special mention here, for three reasons. Firstly, the great majority of

affected individuals have CHD, and particularly ASD, which affected most

individuals in the first reported family (Holt and Oram, 1960) and has been the

most commonly reported lesion in subsequent studies, affecting 60% of people

with HOS (Sletten and Pierpont, 1996). HOS is thus the syndrome most

strongly associated with ASD in the minds of clinical geneticists.

Secondly, HOS can be viewed as one form of autosomal dominant ASD with

conduction abnormalities, which is discussed in section 1.4.1.3 below. The non-

cardiac manifestations in HOS are skeletal abnormalities of the upper limbs,

which range from severe reduction anomalies in approximately 5% of affected

individuals through to very mild anomalies such as clinodactyly and sloping

shoulders (Newbury-Ecob et al., 1996). Although penetrance for limb anomalies

is very high in HOS, the high frequency of mild anomalies reported by Newbury-

41

Ecob and colleagues implies that HOS should always be considered in the

differential diagnosis of autosomal dominant CHD, particularly where ASD is the

main lesion observed in a family.

Thirdly, a proportion of patients with HOS can be shown to have mutations in

TBX5 (Basson et al., 1997; Li et al., 1997). As discussed in section 1.5.3, TBX5

interacts with NKX2-5 and GATA4, mutations in which cause autosomal

dominant ASD as well as other forms of CHD. It also interacts with TBX20,

mutations in which are shown here (see chapter 3) also to cause CHD,

particularly ASD. Although TBX5 is the main gene associated with HOS, in most

studies fewer than half of all patients with HOS have identifiable TBX5

mutations (Basson et al., 1997; Li et al., 1997; Borozdin W et al., 2006),

although when strict criteria for diagnosis (personal an/or family history of

abnormalities of cardiac septation and/or conduction with preaxial radial ray

deformity) are applied, the detection rate goes up to 74%(McDermott et al.,

2005). This is still low enough to suggest the possibility of genetic

heterogeneity; it is clear that such heterogeneity exists among the heart-hand

syndromes in general (Basson et al., 1995), but it may also be the case for HOS

in particular. Mutations in the gene SALL4 have been reported in a small

number of individuals with features of HOS (Brassington et al., 2003), although

no further cases have been reported, and there is clinical overlap between the

Duane-radial ray syndrome (caused by SALL4 mutations) and HOS.

1.7.1.2 Chromosomal disorders, particularly 8p deletions Numerous chromosomal abnormalities have been reported in association with

ASD, ranging from very common abnormalities such as trisomy 21 (Vida et al.,

2005) and 22q deletions associated with velocardiofacial syndrome (Fokstuen

et al., 1998) through to unique chromosomal rearrangements reported only in

single individuals. With the exception of balanced chromosomal

rearrangements, which cause phenotypic effects either by direct disruption of

genes at the breakpoint or by regional genomic effects of the translocation

(Lettice et al., 2002), the common theme in chromosomal disorders is

abnormalities of gene copy number. Trisomies, duplications, tetrasomies and so

42

on increase the copy number of genes within the affected chromosome or

chromosomal segment from two to three or more (in the case of autosomal

genes and X-linked genes in females) or from one to two or more (in the case of

X-linked genes in males). Deletions reduce the copy number of genes from two

to one (in the case of autosomal genes and X-linked genes in females) or from

one to none, in the case of X-linked genes in males.

There are undoubtedly many genes for which copy number is of no

consequence. For example, the great majority of autosomal recessive disorders

produce no discernable phenotype in heterozygotes, even if the mutation in

question is functionally a null mutation. Deletion of one copy of CFTR (for

example) would not of itself be expected to produce a phenotype. Similarly, for

many genes an increase in copy number would not be expected to produce a

phenotype. However, there are many autosomal dominant disorders for which

haploinsufficiency appears to be the main reason for the phenotypic effect of

mutations. Examples include TBX5 in HOS (Borozdin W et al., 2006), SHOX in

Leri-Weill syndrome (Rappold et al., 2002) and SOX9 in campomelic dysplasia

(Wunderle et al., 1998); numerous others are known. Similarly, it is clear that

increased gene copy number, as in trisomy 21, must be the reason for most, if

not all, of the pathological effects associated with chromosomal abnormalities

which result in a gain of chromosomal material.

Study of patients with chromosomal abnormalities and congenital heart disease

therefore holds out the prospect of identifying genes important in cardiac

development and relevant to CHD in general. In order for study of a

chromosomal abnormality to lead to identification of such a gene, the

chromosomal anomaly must either involve a very small region, containing few

genes, or must be sufficiently common that it is possible – by study of large

numbers of affected patients – to identify a relatively small critical region which

must be involved in order to produce a cardiac phenotype. Most chromosomal

abnormalities are detected because they are large enough to be visible

cytogenetically, and therefore are likely to contain large numbers of genes. This

43

means that in practice only relatively common chromosomal abnormalities are

likely to yield useful information in the study of CHD.

Examples of such lesions include deletions of 1p36, 22q11 and 8p23. Deletions

of 1p36 have been increasingly commonly recognised and are associated with

CHD, which however only infrequently includes ASD (2/30 cases in one series)

(Heilstedt et al., 2003). Deletions of 22q11 are very common and have been

intensively studied. Recently, evidence has been presented that

haploinsufficiency for TBX1 is responsible for most of the cardiac phenotype

associated with deletions of 22q11 (Yagi et al., 2003). Although ASD occurs in

patients with 22q11 deletions, conotruncal lesions such as tetralogy of Fallot are

more characteristic of this deletion, with only 4% of patients having ASD

(Fokstuen et al., 1998).

Deletions of 8p23 are of perhaps the greatest interest in the context of the

current study, because of the location of GATA4 within this region. CHD is a

common feature of patients with deletions of this cytogenetic band. Both

primum and secundum ASD have been reported in multiple patients with 8p23

deletions, although a variety of other cardiac lesions including pulmonary valve

stenosis, double outlet right ventricle, aortic valve stenosis and ventricular

septal defect have also been reported (Devriendt et al., 1999; Giglio et al.,

2000).

Devriendt et al defined a critical region for cardiac malformations in individuals

with 8p deletions and suggested GATA4 as a candidate gene which may be

responsible for the CHD in affected subjects (Devriendt et al., 1999). Pehlivan

and colleagues used fluorescent in-situ hybridization (FISH) to show that 4

patients with deletion of 8p23.1 and CHD were haploinsufficient for GATA4; a

fifth patient with a normal heart had two copies of GATA4 (Pehlivan et al.,

1999). Contradicting these findings, Giglio et al presented evidence that not all

subjects with 8p deletions and CHD were deleted for GATA4. Two subjects in

their study with CHD were FISH positive for a GATA4 probe, and they defined a

critical region between markers WI-8327 and D8S1825 which excluded GATA4

44

(Giglio et al., 2000). From this it appeared that GATA4 may not play a role in

causation of CHD in 8p23 deletions. However, in 2003, Garg et al reported

GATA4 mutations in two families with congenital heart disease, predominantly

ASD but also AVSD and various valvular abnormalities (Garg et al., 2003). It is

thus clear that GATA4 haploinsufficiency does contribute to CHD in at least

some indviduals with 8p23 deletions, although it is likely that at least one other

gene in the region also contributes to the cardiac phenotype, given the findings

of Giglio et al.

1.7.2 Non-syndromal Mendelian ASD Prior to the first identification of mutations associated with ASD, there were at

least 15 separate reports of nonsyndromal autosomal dominant ASD (Table

1.2), with 12 of the reported families having ASD and disorders of cardiac

conduction. There have been no reports of X-linked nonsyndromal ASD, and no

definite reports of autosomal recessive ASD, although it would be difficult to

confidently state that recurrence in a sibship was due to recessive inheritance

rather than multifactorial causation (see 1.4.1.4). It is possible that the family

reported by Libshitz and Barth (Libshitz and Barth, 1974), in which 4 sibs had

ASD and a fifth died at two years of suspected CHD, had an autosomal

recessive form of ASD. Dominant inheritance in this family could not be

excluded as assessment of the parents was described only as “routine cardiac

workup” and the study antedates 2-D routine echocardiography. Even if both

parents did have normal hearts, one of them may have been nonpenetrant for

cardiac disease. Thus, dominant inheritance cannot be excluded in this family.

Despite the large number of affected sibs, multifactorial causation is also

possible.

Autosomal recessive inheritance has been reported for at least one other form

of nonsyndromic CHD, persistent truncus arteriosus (PTA). In a large

consanguineous Kuwaiti family, a mutation in NKX2-6 segregated with PTA,

with affected individuals being homozygous (Heathcote et al., 2005). Indirect

evidence for autosomal recessive forms of ASD comes from studies of

consanguinity and CHD. In a case-control study of neonates with CHD, Khalid

45

et al found that parental consanguinity was a risk factor for CHD (Khalid et al.,

2006). Overall, first cousin marriages were associated with an adjusted odds

ratio (OR) for CHD of 1.8 (95% CI 1.1-3.1). In particular, consanguinity was

associated with an increased risk of ASD (first cousin marriages, p = 0.002,

more distant relatives, p=0.044). Similar studies comparing the rate of

consanguinity in cases to population data rather than directly to controls had

similar findings to those of Khalid et al, including an effect specifically on ASD

(Becker et al., 2001; Nabulsi et al., 2003). These data could be consistent with

recessively acting alleles contributing to multifactorial causation, but it is also

possible that there are indeed rare autosomal recessive forms of ASD.

In considering the reports of autosomal dominant ASD, some consistent themes

emerge. Firstly, there are substantially more reports of ASD with conduction

disorder than of ASD without conduction disorder. This is somewhat skewed by

the fact that four of the families were ascertained as part of a study of relatives

of probands with ASD + conduction disorder (Emanuel et al., 1975) and by the

inclusion of small families with ASD + conduction disorder but exclusion of small

families with ASD alone. It is also possible that families with conduction disorder

are more likely to be published, particularly as there is a striking tendency to

progression of the conduction disease with age, and there are numerous

instances of sudden unexpected death in family members. Nonetheless, it does

appear likely that dominant ASD with conduction abnormalities is more common

than dominant ASD without conduction abnormalities.

While atrial septal defect is the predominant cardiac lesion in all of the reported

families summarised in Table 1.2, the majority of the families include at least

one affected individual with other forms of CHD in addition to or instead of ASD.

There are a wide variety of lesions, but valvular abnormalities, particularly

affecting the mitral valve, are common, as are VSDs. On the whole, penetrance

for some form of cardiac abnormality is high, and in families with ASD with

conduction abnormalities, there are often individuals with structurally normal

hearts but with abnormal cardiac conduction. Conduction abnormalities range

from mild prolongation of the PR interval to complete heart block and in many

46

families there is progression with age. Older individuals often report syncopal

episodes and sudden death in adulthood is not uncommon.

The family reported by Mégarbané et al (Megarbane et al., 1999) is also worthy

of mention here. Although reported as a syndromal form of ASD, 8/15 affected

individuals had no noncardiac features. In many of the individuals who did have

noncardiac abnormalities these were realtively mild (pectus excavatum and

hypertelorism). Two subjects had cleft lip or cleft lip and palate; there were no

other malformations of note. Cardiac malformations other than ASD included

PS, MS, Ebstein anomaly and AS. One subject had Wolff-Parkinson-White

syndrome, which was also observed in two members of the family reported by

Zuckerman et al and Lynch et al (Zuckerman et al., 1962; Lynch et al., 1978).

Seven had right bundle branch block but there were no other conduction

abnormalities and this family is probably best classified in the group of ASD

without conduction abnormalities.

Subsequent to the reports summarised in Table 1.2, mutations in six genes

have been associated with autosomal dominant ASD. These are TBX5 (Basson

et al., 1997), NKX2-5 (Schott et al., 1998) (both associated with conduction

abnormalities), GATA4 (Garg et al., 2003), MYH6 (Ching et al., 2005), ACTC

(Matsson H et al., 2005) and TBX20 (Kirk et al., 2007) (this study; chapter 3).

Mutations in the latter four genes are not associated with conduction

abnormalities. In addition, there has been one report of a family linked to

chromosome 5p for which no causative mutation has yet been identified

(Benson et al., 1998). Mohl and Mayr reported linkage to the HLA complex on

chromosome 6p in three families, two quite small (Mohl and Mayr, 1977). The

paper is very brief (only a page in length) and contains no pedigrees. It is

difficult to be confident about the reliability of these findings, which have never

been replicated.

47

Table 1.2: Reports of dominant ASD prior to the first identification of causative mutations

Reference(s) Conductionabnormalities(numberaffected)

Number of individuals with CHD

NumberwithASD

Othercardiacabnormalities

Number of nonpenetrantindividuals

(Zetterqvist P, 1960) (Johansson BW and Sievers J, 1967) (Zetterqvist P et al., 1971)1

No 20+2 20+ - -3

(Zuckerman et al., 1962) (Lynch et al., 1978)1

No 15 11 WPW, VSD, PDA, MS, Complex

14

(Williamson EM, 1969)

No 11 5 CMP, VSD,MS, Coarct

-

(Benson et al., 1998) 5,6

No 12 9 ASA 1

(Benson et al., 1998)

No 11 6 PDA,Bicuspid AV, AS

-

(Weil and Allenstein, 1961)

Yes (3) 5 5 VSD, PS -

(Kahler et al., 1966) (Maron et al., 1978)1

Yes (10) 10 7 - -7

(AmarasinghamR and Fleming, 1967)

Yes (3) 3 3 - -

(Bizarro et al., 1970)

Yes (11) 16 16 VSD, PS, MS, AF

-8

(Bjornstad P, 1974)

Yes (10) 10 109 AF, MS, -

(Emanuel et al., 1975)10

Yes (3) 4 4 MR -

(Emanuel et al., 1975)

Yes (2) 3 3 Cleft MV -


Yes (3)11 7 3 MV abn -


Yes (2)12 1 1 - -

48

Reference(s) Conductionabnormalities(numberaffected)

Number of individuals with CHD

NumberwithASD

Othercardiacabnormalities

Number of nonpenetrantindividuals

(Pease et al., 1976)

Yes (11)13 15 8 Coarct, TOF, TOF+PA,PTA, VSD, AS

4+14

(Schaede and Ramacher,1977)

Yes (3) 5 5 - -

(Bosi et al., 1992)

Yes (3) 3 3 - -15

WPW – Wolff-Parkinson-White syndrome; VSD – ventricular septal defect; PDA –

patent ductus arteriosus; AS – aortic stenosis; bicuspid AV – bicuspid aortic valve; MS

– mitral stenosis; Complex – complex CHD, lethal in infancy; CMP – cardiomyopathy;

Coarct – aortic coarctation; ASA – atrial septal aneurysm; AF - atrial fibrillation (only

noted if affecting multiple family members); MR – mitral regurgitation; cleft MV –cleft

mitral valve; MV abn – cusps thin with nodular edges and elongated chordae; no

evidence of rheumatic heart disease (one individual); TOF – tetralogy of Fallot;

TOF+PA – tetralogy of Fallot with pulmonary atresia; PTA – persistent truncus

arteriosus; AS – aortic stenosis

Only reports of families with >4 affected individuals or with associated A-V conduction

block are included. There are numerous reports of families with smaller numbers of

affected individuals without conduction block, which are hard to distinguish from familial

clustering due to multifactorial causation and therefore are not included here. The

presence of right bundle branch block is not regarded as sufficient for families to be

classified as having conduction abnormalities.

1. Multiple reports of the same family

2. 12 confirmed by cardiac catheterization; 8 based on clinical assessment; others in

earlier generations suspected to have been affected based on history

3. No definite nonpenetrance but diagnosis uncertain in deceased members of earlier

generations

4. In addition, 3 obligate mutation carriers were identified as probably affected on

history

5. Two families in one report. A third family reported in this paper was subsequently

shown to be segregating an NKX2-5 mutation(Schott et al., 1998) and is discussed in

the introduction to Chapter 3.

6. This family linked to 5p

49

7. No unaffected individuals but 3/10 had conduction abnormalities with structurally

normal hearts

8. One individual with clinical diagnosis only. Two obligate heterozygotes not examined

and no history available (deceased).

9. 4/10 diagnosis on history only (deceased individuals)

10. Study of relatives of 10 probands with ASD + conduction delay; 4 families with >1

affected individual identified, listed separately here

11. 4/7 affected family members deceased, exact nature of CHD and presence of

conduction abnormalities uncertain

12. Father with ASD + conduction abnormality; son with conduction abnormality but

structurally normal heart

13. 2/11 had structurally normal heart

14. In one branch of the family, a grandmother (daughter of a deceased obligate

heterozygote) had a normal heart, as did three of her children each of whom had one

affected child. It seems very unlikely that three of 11 children in the last generation

would have had CHD by chance (i.e. phenocopy) implying nonpenetrance in 4

members of this branch of the family. In other branches of the family, there were

individuals who may have been nonpenetrant. One deceased obligate heterozgote had

no history of CHD. In another branch of the family, an affected individual had an

unaffected son and grandson but affected great-grandson. In another branch of the

family, an unaffected son of an obligate heterozygote had an unaffected son who had

an affected daughter. This case is more likely tohave been a phenocopy because there

were 24 individuals in that branch of the family of whom only one was affected.

15. One individual with structurally normal heart but with conduction delay

1.7.3 Multifactorial/polygenic causation of ASD Most common human diseases can be viewed as having multifactorial

causation, with contributions from both genes and environment. In general, the

genetic component involves contributions from multiple genes (usually modelled

as having additive effects), termed “polygenic inheritance”. Since polygenic

disorders (like all genetic disease) will inevitably be influenced by the

environment to some degree, and multifactorial causation implies a contribution

from multiple genes, the terms are effectively interchangeable. For continuous

traits, such as blood pressure or height, it is relatively straightforward to posit

the interaction of a number of genes with environmental factors, some of which

50

(eg salt intake for blood pressure and nutrition in childhood) can be readily

identified.

The proposition that most CHD is multifactorial in origin was put by Nora in

1968 (Nora, 1968) and was widely accepted for some time thereafter. In favour

of the concept were the recurrence risks for CHD – generally on the order of 2-

5%, similar to the square root of the incidence for individual lesions, as

predicted for multifactorial causation. Studies of heritability suggested

heritability of ~0.6 for CHD in general (Williamson EM, 1969; Burn J and

Goodship J, 2002). Other evidence such the work on PDA by Zetterqvist

(Zetterqvist P, 1972); discussed by Burn and Goodship (Burn J and Goodship J,

2002), supported multifactorial causation. PDA showed the characteristics

expected of a disorder with multifactorial causation; the recurrence risks were

as above, the risks to sibs and offspring were about equal, the risk to more

distant relatives declines rapidly, and the risk increases when there are multiple

affected family members.

More recent evidence, however, has called this model into question. In

particular, studies of the offspring of adults with CHD suggest a higher risk to

offspring of affected individuals than to sibs, with a higher risk to offspring if the

affected parent is the mother (Buskens et al., 1995; Burn et al., 1998; Romano-

Zelekha et al., 2001). Even taking into account the fact that a proportion of

families have CHD due to single gene disorders (as discussed in 1.4.2 above),

which makes interpretation of studies of the relatives of affected individuals

more difficult, it seems likely that at least some forms of CHD will prove to be

best explained by means other than the standard multifactorial model. In

particular, hypoplastic left heart syndrome (Boughman et al., 1987) and AVSD

(Burn et al., 1998) have been proposed to fit single gene models, and tetralogy

of Fallot an oligogenic model (Burn et al., 1998).

Notwithstanding these doubts about precise mechanisms, the debate is about

how genes and environment interact to produce CHD, rather than whether they

do.

51

1.7.3.1 Excess of females affected by ASD Since sex is genetically determined, it is relevant to comment at this point that

there is an apparent excess of females affected by ASD. Not all studies confirm

this finding, with the reported ratio of boys: girls ranging from 1.5:1 to 1:3

(Samanek, 1994), but larger studies generally do show an excess, with typically

about 55-60% of affected individuals being female (Rothman and Fyler, 1976;

Ferencz C et al., 1997).

1.7.3.2 QTL for CHD A Medline search combining the terms Quantitative Trait Loci/ and Heart/

(performed on 29th September, 2007) gave 22 results, none of which referred

to CHD; replacing Heart/ with Heart Defects, Congenital/ yielded only one

result, which did not in fact refer to a QTL study, and replacing Heart/ with

Cardiovascular Diseases/ yielded 48 results, none of which was relevant to

CHD. However, the published report of the findings described in chapter 6 (Kirk

et al., 2006) was not identified by either of these search strategies, suggesting

that it is possible that there have been other such studies which are not readily

identifiable using Medline. Although there have been numerous QTL studies

relevant to cardiovascular disease (Yagil and Yagil, 2006), the focus of such

research has been phenotypes relevant to atherogenic disease, not CHD. It

seems likely that the study reported here represents the first effort to identify

QTL potentially relevant to congenital heart disease.

1.7.4 Environmental factors There have been numerous studies of environmental contributors to CHD.

There are several clearly teratogenic influences which are capable of producing

CHD, probably with a relatively small influence from genetic effects. On the

other hand, there are numerous factors which have been identified as making

relatively small contributions in epidemiologic studies. The focus here will be on

environmental contributors to the causation of ASD rather than on CHD in

general.

52

1.7.4.1 Major teratogensThese can be divided into infections, the effects of maternal exposure to

medications and other substances, and the effects of maternal illness such as

diabetes. Fetal rubella infection, particularly in the first trimester, is strongly

associated with CHD (up to 48% affected (Overall, 1972)), with ASD being one

of the more common lesions. Maternal exposure to medications is not

particularly strongly associated with ASD, although ASD has been reported as

part of retinoic acid embryopathy (Lammer et al., 1985) and the fetal valproate

syndrome (Clayton-Smith and Donnai, 1995).

Maternal consumption of large amounts of ethanol during pregnancy may cause

the fetal alcohol syndrome (FAS). About a third of children with the full-blown

syndrome have CHD, typically ASD or VSD (Sandor et al., 1981). However, it is

possible that exposure to smaller amounts of ethanol in the first trimester may

produce ASD without other features of FAS (see section 1.7.4.2, below). It is

likely that genetic factors contribute to individual susceptibility to the effects of

ethanol on the fetal heart, as for other features of the syndrome (Gemma et al.,

2007).

Similarly, maternal diabetes can produce a multi-system embryopathy which

can include CHD. Although ASD is not commonly reported as part of this

syndrome (Ferencz et al., 1990), it is mentioned here because some of the

epidemiological surveys discussed in 1.7.4.2 support a contribution from

maternal diabetes to the causation of some cases of ASD.

1.7.4.2 Other environmental factors The distinction made here between “major teratogens” and “other environmental

factors” is arguably somewhat artificial, since an environmental influence which

causes a malformation is by definition a teratogen. The intention is to

distinguish between exposures which are clearly the major cause of ASD in at

least some individuals, and those for which there is statistical evidence of an

association but which are likely to be but one of multiple factors contributing to

ASD. Moreover, some of the factors discussed here, such as birth order, are not

53

likely to be teratogenic even under the strictest interpretation of the term. It is

possible that birth order is a marker for advancing maternal and paternal age,

with associated increase in risk of chromosomal abnormalities or new dominant

mutations. Alternately, large family size may reflect low socio-economic status

which could increase the risk of ASD on a purely environmental basis. Clearly, it

is hard in some cases to separate apparent environmental influences from

genetic effects.

Papers summarised in Tables 1.3 and 1.4 are restricted to those for which

separate data for ASD are given. Studies of paternal and maternal age effects

are included here, because such studies are done in conjunction with studies of

environmental influences. Two studies in particular, those of Tikkanen and

colleagues in Finland (Tikkanen and Heinonen, 1992; Tikkanen and Heinonen,

1991; Tikkanen J and Heinonen OP, 1990) and the Baltimore-Washington

Infant Study (Boughman et al., 1987; Ferencz et al., 1985; Ferencz C et al.,

1997) provide the bulk of the data discussed here.

54

Table 1.3: Environmental exposures and other factors significantly associated with risk of ASD

Exposure Reference Country OR (95% CI)

p value (if OR notreported)

Negativestudiesof same exposure

OR(95% CI)

Birth order (first born as risk factor)

(Rothmanand Fyler, 1976)

USA N/A 0.021

Birth order (>1st born as risk factor)

(Zhan et al., 1991)

China 2.139(1.109-4.126)2

-

Birth order (fourth or subsequentchild vs first born)

(Ferencz C et al., 1997)

USA 1.7 (1.1-2.5)3

-

Maternal fever >380C

(TikkanenandHeinonen,1991)

Finland N/A <0.01 (Stoll et al., 1989)

0.76(0.18-1.29)

Maternalalcoholconsumptionin first trimester

(Tikkanen J andHeinonenOP, 1990; TikkanenandHeinonen,1992)4

Finland 2.0 (1.1-3.6)5

- (FerenczC et al., 1997)

1.2(0.9-1.6)

Maternalexposure to chemicals at work in first trimester

(Tikkanen J andHeinonenOP, 1990; TikkanenandHeinonen,1992)4

Finland 1.9 (1.1-3.4)

- (Stoll et al., 1989)

1.29(0.67-2.52)

Paternalexposure to paint stripping


USA 1.6 (1.0-2.6)

Paternalexposure to “miscellaneoussolvents”


USA 1.7 (1.1-2.7)

55

Exposure Reference Country OR (95% CI)

p value (if OR notreported)

Negativestudiesof same exposure

OR(95% CI)

Paternal age6,father’s age 45-49

(Olshan et al., 1994)

USA 2.7 (1.3-5.8)

- (FerenczC et al., 1997)7

1.0(0.8-1.4)

(Zhan etal.,1991)8

0.898(0.78-1.034)

(Stoll etal.,1989)9

0.61(0.38-1.06)

Maternaleducation (< high school)


USA 1.4(1.0-2.0)

Gestationaldiabetes


USA 2.4 (1.4-4.3)10

(Stoll etal., 1989)

1.64(0.38-7.09)

Urinary tract infection in first trimester


USA 1.7(2.2-2.5)

Bleedingduringpregnancy


USA 1.5 (1.0-2.2)

Corticosteroidsin first trimester


USA 5.1 (2.1-12.7)

Paternalcocaineconsumption


USA 2.3 (1.3-4.2)

Paternalsmoking >20 cigarettes/day


USA 1.7 (1.1-2.7)11

Paternaloccupationalexposure to extremely cold temperatures12


USA 8.7 (2.6-28.4)

OR – odds ratio; CI – confidence interval

1. Significantly higher risk for first born (p = 0.02) compared with observed distributions

for other cardiac disorders, but no clear pattern in subsequent children i.e. no

significant fall in risk for third vs second or fourth vs third child.

No OR provided. The same study found an increase in risk with young maternal age

(<20 years) which was not independent from the birth order risk. Note that Ferencz et

56

al(Ferencz C et al., 1997) found no significant association with young or old maternal

age.

2. OR for birth order 2 or higher, compared with birth order 1.

3. No significant association with birth order otherwise. However, note the positive

association with advanced maternal age from the same study, consistent with this

finding

4. Reported twice from essentially the same dataset

5. Significant association was for the group “at least a single drink in first trimester”,

56% of 50 cases vs 39%of 756 controls. No significant association for regular alcohol

consumption (every week) or for “at least 2-3 drinks per occasion” but the numbers

were much smaller in each of these groups. Figure from Ferencz et al is for “any

amount consumed in first trimester”.

6. Paternal age 45-49 compared with paternal age 25-29. No other age groups had

significant associations and there was no clear trend to lower risk with lower paternal

age and higher risk with higher paternal age. Thus, the status of this association is

doubtful.

7. Paternal age >29 compared with paternal age 20-29. Younger paternal age (<20)

also not significantly associated.

8. Paternal age <24 years compared with 24 years+

9. Comparison not explicitly described. Mean paternal age 29.5 vs controls 29.2.

10. “Overt” diabetes was not significantly associated with ASD, OR 2.5 (95% CI 0.7-

8.5), although the numbers were very small for this exposure (only 3 cases) and it

seems likely that a larger study might reveal an increased risk.

11. No association with paternal smoking of 20 or fewer cigarettes/day

12. 4/187 cases, 9/3572 controls

57

Table 1.4: Environmental exposures and other factors with no significant association with risk of ASD

Exposure Reference Country OR (95% CI) p value (if OR not reported)

Maternal age (Zhan et al., 1991)

China 1.034 (0.932-1.15)1

-

(Stoll et al., 1989)

France 1.01 (0.59-1.72)2 -

Workplacetemperature during first trimester (mother)


Finland N/A >0.05

Frequency of maternal sauna bathing during first trimester


Finland N/A >0.05

Maternal epilepsy (Stoll et al., 1989)

France 1.01 (0.99-1.03) -

Maternal X-rays in first trimester


France 0.82 (0.11-1.35) -

(Ferencz Cet al., 1997)

USA 1.2 (0.4-3.9)3

Maternalhypertension in first trimester


France 0.43 (0.16-1.13) -

Maternal “flu” in first trimester


France 0.73 (0.26-2.03) -


USA 0.8 (0.4-1.5)

Maternalmedication in first trimester


France 0.83 (0.24-1.34) -


Finland 1.0 (0.4-2.2) (all medications)

0.3 (0-2.4) (salicylic acid)

(Boneva et al., 1999)3

USA 0.61 (0.15-2.41) -

58

Exposure Reference Country OR (95% CI) p value (if OR not reported)

Maternal smoking in first trimester


France 0.71 (0.42-1.19) -


Finland 0.7 (0.3-1.6) (1-14

cigarettes/day);0.8 (0.1-5.7) (15-

29cigarettes/day)

-


USA 1.5 (0.9-2.1) (1-10

cigarettes/day),1.1(0.7-1.7) (11-

20cigarettes/day),

1.6 (0.9-2.8)(>20 cigarettes/day)

Exposure to passive smoking in first trimester


Finland 1.0 (0.5-1.9) (exposure at

home),0.5 (0.2-1.5) (exposure at

work)Maternal coffee consumption


Finland 0.7 (0.4-1.5)

Maternal use of deodorants


Finland 1.2 (0.7-2.2)

Maternal work attendance in first trimester


Finland 1.2 (0.6-2.4)

Maternal regular exposure to organic solvents at work in first trimester


Finland 2.6 (0.7-9.1)4

Maternal regular exposure to dyes, lacquers or paints at work in first trimester


Finland 2.2 (0.3-1.8)4

59

OR- odds ratio; CI – confidence interval; N/A – not available

1. Maternal age 29 years+ compared with <29 years

2. Anti-nausea medication only

3. Occupational exposure

4. Note significant effect from “exposure to chemicals at work” from same study

As can be seen from these tables, the literature is contradictory at times and

some of the reported significant associations are difficult to interpret or

biologically implausible. A brief discussion of each of these follows.

The data on birth order and parental age are contradictory, with two studies

suggesting later birth order as a risk factor for ASD (Zhan et al., 1991; Ferencz

C et al., 1997) and one suggesting that the first born is at higher risk (Rothman

and Fyler, 1976). Zhan et al find that advanced maternal age is associated with

increased risk, whereas Rothman et al find that lower maternal age is

associated with increaed risk. In a fourth study (Olshan et al., 1994), higher

paternal age is associated with increased risk, but the results are significant

only for a single age range and need to be treated with caution. Nonetheless,

overall it seems likely that late birth order and advancing parental age may be a

minor risk factor for ASD. The mechanism for this is uncertain, as discussed

above.

Tikkanen and Heinonen (Tikkanen and Heinonen, 1991) exhaustively

investigated the possibility that maternal hyperthermia in the first trimester

may contribute to causing CHD. CHD was divided into 5 categories, of which

ASD was one. They studied maternal fever >380C, workplace temperature,

suana bathing (which is very popular in Finland), month of birth (because of the

marked seasonal temperature variations in Finland) and a history of upper

respiratory tract infection and acetylsalicylic acid use as a marker of febrile

illness. The only significant association for ASD was with maternal fever. There

were relatively few positive associations among the other groups of

malformations. Even assuming this is a genuine association, it is not clear

60

whether the putative teratogenic effect relates to the fever per se or to

teratogenic effects of the pathogen responsible for the fever. Two other studies

did not find a relationship between viral upper respiratory tract infections in the

first trimester and ASD (Stoll et al., 1989; Ferencz C et al., 1997), and Stoll et al

also studied fever directly and found no association.

Given the teratogenic effect of ethanol in the fetal alcohol syndrome and of

diabetes in diabetic embryopathy (discussed in 1.4.4.1) it is not surprising that

positive associations with ASD have been reported (Tikkanen J and Heinonen

OP, 1990; Tikkanen and Heinonen, 1992; Ferencz C et al., 1997), although not

all studies confirm a role for ethanol (Ferencz C et al., 1997) or for diabetes

(Stoll et al., 1989). Urinary tract infection and maternal corticosteroid exposure were also identified as risk factors in one study (Ferencz C et al.,

1997). On the other hand, maternal smoking, including passive smoking, has

been extensively studied and no evidence has been found of any link with ASD

(Stoll et al., 1989; Tikkanen and Heinonen, 1992; Ferencz C et al., 1997)

The role of occupational exposures, if any, remains uncertain. Maternal

exposure to chemicals at work was implicated in one study (Tikkanen and

Heinonen, 1992) but not confirmed in another (Stoll et al., 1989). Given the

diversity of chemicals used in workplaces it seems unlikely that a broad

grouping of all chemical exposures would provide meaningful results. When

more specific exposures (organic solvents and dyes, lacquers and paints) were

studied, no association was found (Tikkanen and Heinonen, 1992), but the

numbers exposed were small and it is possible that a larger study would identify

a real causative role for such exposures.

Surprisingly, paternal exposures have also been identified as risk factors.

These include paternal exposure to paint stripping, “miscellaneous solvents”,

and extremely cold temperatures (Ferencz C et al., 1997). Paternal smokingand cocaine use are also significantly associated with ASD (presumably not

usually in an occupational setting) (Ferencz C et al., 1997). Again, it is difficult

to draw a biologically plausible link between such exposures and ASD.

61

Mutagenesis affecting sperm is implausible as a mechanism. It is possible that

these associations represent markers for other factors, such as socioeconomic

status, although there is little direct evidence for that as a significant contributor

(other than an association with low maternal education levels (Ferencz C et al.,

1997).

1.8 Project outline The aim of this project was to add to our understanding of the genetics of CHD,

with a focus on ASD and PFO. Complementary strategies, involving mouse and

man, were used.

In human subjects, two separate studies were undertaken, both focusing on

Mendelian forms of CHD. In the first, subjects with CHD including ASD,PFO

and a variety of other lesions were screened for mutations in the cardiac

transcription factors NKX2-5, GATA4 and TBX20. This work is described in

chapter 3. In the second study, a large family with a previously undescribed

autosomal dominant ASD syndrome was investigated, with clinical evaluation

and an attempt to map the disorder. This is described in chapter 4.

The major mouse study, reported in chapters 5 and 6 involved a QTL study,

using an F2 intercross design. The parental strains were 129T2/SvEms and

QSi5. In addition, an Advanced Intercross Line using the same parental strains

has been bred to completion and ~1000 mice from the final generation (F14)

phenotyped. Analysis of phenotype data from the parental strains and from F1,

F2 and F14 mice is presented in chapter 5. Chapter 6 describes the results of

the QTL study. After this study was completed, 10 additional mouse strains

were phenotyped as part of work towards an analysis based on the recently

published mouse Hapmap data. Phenotype data from these mice are reported

in Chapter 7. Overall conclusions and future directions are discussed in chapter

8.

2. Materials and Methods

2.1 Mouse experiments 2.1.1 Ethics committee approval Animal experiments were performed under Animal Care and Research Ethics

approvals N02/2-2001/1/3336, N02/2-2001/2/3336 and N02/2-2001/3/3336 (for

all studies other than the AIL) and N00/4-2003/1/3745, N00/4-2003/2/3745 and

N00/4-2003/3/3745 (for the AIL) from the University of Sydney.

2.1.2 Animal resources Parental inbred mice were obtained from the Centre for Advanced Technologies

in Animal Genetics and Reproduction, University of Sydney (QSi5), and from

the Garvan Institute (129T2/SvEms). Mice were kept in a rodent facility at the

University of Sydney in a purpose-built air-conditioned room with a 12 hour

light/dark cycle and ad libitum access to food and water until dissection at 6-8

weeks of age. The aim was to dissect at 6 weeks and efforts were made to

keep the age of dissection as close to that as possible. In practice, breeding

production varied from week to week. This resulted in a variable number of mice

reaching the age of 6 weeks in any given week. It was not always possible to

dissect all the available mice in a particular week, and if this happened the

excess mice were kept until they could be dissected.

For the F2 study, a total of 1437 mice (680 female, 757 male) were dissected

on 63 sampling days over a 9 month period. Mean age at dissection was 46.3

days (SD 3.46, range 39-60). Breeding of the AIL took three years and three

months, including the period during which the dissections were done. A total of

1003 AIL F14 mice were dissected on 34 sampling days over a 6 month period,

but data for 27 of these was lost as the result of a motor vehicle accident. Of the

remaining 976 F14 mice, 480 were male and 496 female. Mean age at

dissection was 44.0 days (SD 2.6, range 40-55).

In addition, 75 129T2/SvEms mice and 137 QSi5 mice were dissected, although

data are analyzed in chapter 5 for only 66 of the QSi5 mice. This is because

62

63

only minimal information was recorded about the first 71, which were dissected

as part of learning the dissection technique. Subsequently, 85 F1 mice were

also dissected. An additional 280 mice from the HapMap strains were

dissected. Of these, Ms Noelia Lopez did part or all of the dissections for 232.

Of these, in turn, 147 were co-measured by EK and Ms Lopez (results recorded

by each without knowledge of the other’s measurement), and 28 were

dissected and measured by Ms Lopez and not co-measured by EK. The total

number of mice dissected during the course of this project was 3017.

2.1.3 Breeding protocols 2.1.3.1 F2 mice The initial matings for the F2 mice were between 129T2/SvEms sires and QSi5

dams. The resulting F1 mice were then crossed to produce F2 mice. As the

parental strains were inbred mice, and hence essentially homozygous at every

locus, F1 mice were heterozygous at every locus. As discussed in Chapter 1,

meiotic recombination results in F2 mice inheriting variable contributions from

each of the parental strains. On average, at any given locus, the expected ratio

of alleles in F2 mice should be aa:2ab:bb where a represents the parental allele

from one strain and b the parental allele from the other. The cartoon (Fig 2.1)

illustrates breeding and transmission of alleles for F2 and onwards, as for the

AIL pedigree (see below).

2.1.3.2 Advanced intercross line Initial breeding for the AIL proceeded exactly as for the original F2 resource.

However, breeding then continued for a further 12 generations, and the F14

mice were dissected. Sufficient F2 mice were bred to stock 48 cages, each

containing one male and one female mouse. The offspring of these mice were

the F3 mice, and so on. For the F3 x F3 and subsequent matings, avoiding

inbreeding was essential, to reduce the risk of genetic drift causing loss of

genetic information within the AIL. To avoid brother/sister matings, a system of

cascading matings was used. Wherever possible, a female mouse born in one

cage would be mated with a male mouse from the next cage, and so on, as

illustrated in table 2.1.

.

Figure 2.1 Cartoon illustrating the breeding scheme used. 129T2/SvEms mice are

white-bellied agouti chinchilla in colour(Eppig et al., 2005), and are represented here as

dark grey; QSi5 mice are albino(Holt et al., 2004); the F1 mice were chinchilla and are

represented as light grey; and the F2 mice were white, chinchilla or agouti in a ratio of

1:2:1. The parental 129T2/SvEms chromosomes are shown in grey and the parental

QSi5 chromosomes are shown in white; recombinant chromosomes in F2 and

subsequent generations have a mixture of 129T2/SvEms and QSi5 parental

chromosomal material.

64

65

Table 2.1: Breeding scheme for AIL

Box 1 Box 2 Box 3 Box 4 Box5

Offspring ofpreviousmatingpresent in box

M1 F1 M2 F2 M3 F3 M4 F4 M5 F5

Mice used for next mating

M1 FR M2 F1 M3 F2 M4 F3 M5 F4

M1 = male born in box 1, M2 = male born in box 2, F1 = female born in box 1,

and so on. FR = random female (see below)

In practice, it took several rounds of breeding to produce sufficient F2 mice to

stock all 48 boxes. This meant that the boxes with higher numbers produced

litters later. Additionally, there was considerable variation in inter-litter times.

This meant that it was not possible in any generation to mate the male from box

1 with a female from box 48, although on several occasions females from box

48 were used in other matings. A selection of male and female mice from each

generation were kept as reserves – usually 3 boxes each of males and females,

with the mice in each box selected from boxes 1-16, 17-32, or 33-48. If a mouse

needed to be replaced (eg because it had died before reproducing) a

replacement would be selected at random, from one of the two reserve boxes

chosen from the range which did not include that mouse’s original box. This

avoided inbreeding without the need to keep reserve mice from every litter. On

some occasions it was possible to use a female from box 48 as a “random”

mouse. Most generations required 0 or 1 such substitutions, with a maximum of

3 substitutions being recorded in any one generation. It was also necessary to

use random females to mate with the males from box 1, for every generation

after F2. Rarely (once every 3-4 generations), it was also necessary to use

random mice if a pair of mice failed to produce offspring for a prolonged period.

No specific time limit was set for a pair of mice to produce offspring. However,

66

there was a policy of not setting up matings for one generation until all matings

for the previous generation had been set up (meaning that there were only ever

a maximum of two generations present in the colony at any one time) and this

influenced the timing of such decisions.

Despite the variability in inter-litter time for individual boxes, the intergeneration

time averaged 11 weeks over the course of the breeding programme, only two

weeks more than the expected minimum inter-litter time of 9 weeks. Fairly

frequently, there were delays of several weeks between one box being ready to

mate and the next box in the series being ready. As weaned litters were not

separated by sex, due to a lack of space, this created the risk that females

would already be pregnant by their littermates at the time of mating. This would

be undesirable as it would lead to inbreeding and loss of genetic information

from the colony. Careful attention was therefore paid to the time between

mating and birth of the first litter of pups in a box; any litters born before 21 days

post-mating were culled.

2.1.4 Mouse phenotyping 2.1.4.1 Initial dissection Mice were killed by asphyxiation in carbon dioxide. Within 15 minutes of death,

the thoracic organs were removed as follows. A nick was made in the

abdominal skin and the skin was firmly distracted rostrally and caudally,

exposing the thorax and abdomen. The xiphisternum was grasped with toothed

forceps and elevated. Using fine scissors, an incision was made below the

xiphisternum and this was extended laterally on each side in an arc through the

rib cage (convex laterally), leaving the sternum and the portions of the ribs

attached to it anchored proximally and by the diaphragm, but otherwise

unattached. The diaphragm was divided and the flap containing sternum and

attached rib ends reflected upwards and caudally. The heart and lungs were

gently lifted to expose the great vessels and oesophagus. These were then

firmly grasped with non-toothed forceps and cut distally to the forceps. Firm

upwards traction on the great vessels and oesophagus allowed rapid dissection

of the structures passing through the thoracic inlet, with removal en bloc of the

67

heart, lungs, thymus and associated mediastinal structures and tissues

(oesophagus, trachea, fat, mediastinal lymph nodes etc). These were then

placed without further dissection into a 1.5ml Eppendorf tube, containing

approximately 0.5 ml of phosphate buffered saline (PBS), for storage and

transport until fine dissection could be done.

The abdominal cavity was opened with a transverse incision. The spleen was

exposed by blunt dissection, removed and snap-frozen in liquid nitrogen. For

the AIL mice, a tail biopsy was also taken and snap-frozen.

2.1.4.2 Fine dissection Fine dissection and measurement were done on the same day as the initial

dissection, generally within 6 hours. A Leica MZ8 dissecting microscope was

used. The dissections and determination of PFO status were done under low

magnification, with the measurements done under higher magnification using an

eyepiece graticule. The thoracic organs were placed in a dish containing PBS

and the lungs, trachea and bronchi, oesophagus, thymus, great vessels and

mediastinal fat were removed. The heart was then weighed. The left atrium was

then opened as illustrated in Fig 2.2.

A

68

Fig 2.2: Dissection of mouse hearts Fig 2.2A: Initial dissection. The heart following removal of other organs, most

mediastinal fat and distal great vessels. The auricle of the left atrium (marked

“auricle”), the remainder of the aorta (marked “Ao”) and the stumps of the

pulmonary veins (marked “PV”) are indicated by arrows.

Auricle

Ao

PV

B

Figure 2.2B: Opening the auricle An incision has been made across the

auricle of the left atrium, removing about half of the auricle and opening the

atrium. The arrow indicates the cut edge of the auricle.

69

C

Figure 2.2C: Laying open the atrium. Fine dissecting scissors are inserted

through the opening created by the previous step. The lower blade is kept in as

superficial a position as possible to avoid damage to the atrial septum. A cut is

made to open the atrium. This is extended through the proximal pulmonary

veins for maximum exposure of the septum. Throughout the dissection the

heart is held in position by a needle (indicated by arrow) inserted through the

apex of the heart into the foam backing material.

70

D

Figure 2.2D: Final appearance of the heart. The heart is shown following the

incision shown in 2.2C. Light traction with fine forceps is now sufficient to

expose the left side of the atrial septum. Note that the right auricle (marked with

an arrow) is intact. This makes it possible to pressurize the right atrium to

produce a flow of blood across a patent foramen ovale, if present.

71

Fig 2.3: Detail of atrial septum. Atrial septal detail as seen from the left

aspect after dissection as described above (A). The annulus of the mitral valve

is toward the lower left of this panel. B, Same as in A, with septal landmarks

and quantitative septal measurement used in this study identified. Note that the

foramen ovale in A appears indistinct because it is covered by the membranous

atrial septum primum. Crescent corresponds to the leftward edge of the atrial

septum primum, forming a prominent ridge.

72

73

2.1.4.3 Identification of patent foramen ovale As described in Fig. 2.2, the integrity of the right atrium was protected during

dissection so that it was possible to pressurize the atrium and use the remaining

blood contained in it to determine whether a PFO was present. If present, blood

would pass across the septum, emerging to the left of the crescent with the

heart in the orientation illustrated above. The amount of blood passing across

the septum varied considerably, from free passage of large quantities of blood

without pressurization of the right atrium down to tiny quantities of blood, visible

only after careful and repeated pressurization of the right atrium. On rare

occasions, Orange G dye was injected into the left superior vena cava under

pressure, to supplement the use of the atrial blood as described above. This

was usually done when repeated pressurization of the atrium had exhausted the

available blood in the atrium and there was still a question as to whether a PFO

was present.

2.1.4.4 Measurements of atrial septal anatomy Measurements were done using an eyepiece graticule. Care was taken to

orientate the atrial septum perpendicularly to the observer’s line of sight, to

avoid parallax error. It was necessary to use dissecting forceps to hold the cut

edges of the atrium apart in order to expose the atrial septum. As the septum is

a highly elastic structure, measurements could be altered considerably by the

amount of force used to expose the septum. An excess of stretch would result

in falsely high measurements. Considerable attention was therefore directed

towards maintaining a consistent amount of stretch, with the aim being to use

the minimum amount of force necessary to fully expose the area of interest.

2.1.4.5 Blinding On the first day of dissections of the parental strains (129T2/SvEms and Qsi5)

an attempt was made to do dissections blinded to the strain of the mice. All

hearts for the day were placed in a plastic bag and each tube containing a heart

was removed with its number concealed. The heart was dissected and only

once all measurements were recorded was the number checked. However,

blinding to strain proved impossible. The two strains had such different atrial

74

septal wall morphology that the strain of the mouse being dissected was

instantly obvious once the atrium was opened. Attempts at blinding were

therefore discontinued for this pair of strains. For the Hapmap strains,

measurements by EK were generally done blinded to strain. Since there was no

prior information about the phenotypes for each strain, observer-introduced bias

is unlikely.

For dissections of the F2 and AIL mice, the genotypes of the mice were

unknown at the time of dissection, so blinding was not a consideration.

2.1.5 Strain selection for F2 and AIL studies As described in section 1.6.5, previous work by Christine Biben and colleagues

(Biben et al., 2000) had established that different strains of inbred laboratory

mice have varying cardiac septal anatomy, with a particularly close relationship

between mean flap valve length (FVL) and incidence of PFO (Biben et al.,

2000) (this study is discussed in detail in section 5.2). This measure formed the

basis for strain selection for this study. Strains evaluated by Biben et al included

FVB/N, 129T2/SvEms, C57Bl6, and Swiss QS (the latter is not an inbred strain),

as well as crosses of several of these strains and mice heterozygous for an

Nkx2-5 null mutation. Of the wild-type strains and crosses, the shortest flap

valve lengths were seen in 129T2/SvEms and the longest in FVB/N.

In addition to these strains, for this study a further inbred strain, QSi5, was

evaluated. This strain was developed at the University of Sydney with selection

for high fecundity and short inter-litter interval (Holt et al., 2004). The large

numbers of mice needed for this study made this superior reproductive

performance desirable. QSi5 proved to have the longest flap valve length of any

strain assessed, with FVL of 1.13mm (SD 0.11). Although FVB/N had a lower

incidence of PFO (FVB/N: 0 of 51, 0%, as opposed to QSi5: 3 of 66, 4.5%) the

combination of the long FVL and reproductive characteristics led to the choice

of QSi5 as one of the parental strains for the study. The other strain used,

129T2/SvEms, had the highest incidence of PFO (21 of 31, 75%) and shortest

FVL (0.6mm, SD 0.11). Although data on the characteristics of 129T2/SvEms

75

were already available, a further 75 129T2/SvEms mice were dissected at the

beginning of the study, to confirm the characteristics of the strain, and to

provide baseline measures for FOW and CRW, measurements which are

slightly different from those used in the study of Biben et al (Biben et al., 2000).

2.2 Human subjects2.2.1 Ethics committee approval Human experiments were conducted under Human Research Ethics Committee

approval from the South East Health Research Ethics Committee – Eastern

Division (approval no 99/261), the St Vincent’s Hospital Research Ethics

Committee (approval no H01/076) and the Children’s Hospital at Westmead

Research Ethics Committee (approval no 2003/049).

2.2.2 Ascertainment of subjects 2.2.2.1 Children Children were recruited from Sydney Children’s Hospital (SCH) and the

Children’s Hospital at Westmead (CHW). SCH subjects were recruited

retrospectively by searching the records of the cardiology department for

individuals with a diagnosis of ASD, and excluding those who had other

significant cardiac pathology. There was no selection on the basis of family

history, extracardiac pathology or the presence of abnormalities of cardiac

conduction. CHW patients were recruited prospectively by a cardiac surgeon

(Dr David Winlaw) by approaching patients with congenital heart disease (CHD)

during outpatient clinics. Again, there was no selection of subjects other than on

the basis of confirmed CHD.

2.2.2.2 Adults Adult subjects were recruited prospectively from St Vincent’s Hospital and St

Vincent’s Private Hospital. Most were recruited at the time that they were having

trans-oesophageal echocardiography (TOE). A number of different cardiologists

were involved, but Prof Michael Feneley was the main driving force behind this

recruitment effort.

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2.2.2.3 Numbers of subjects studied for mutations in NKX2-5 and GATA4

In this study, a total of 146 individuals were screened for mutations in NKX2-5.

Subjects were unselected for familial disease and the proposal was to test the

significance of NKX2-5 mutations in common CHD and its prevalence among

familial cases, focusing on ASD and PFO. Recruitment at this stage of the study

came from St Vincent’s Hospital and Sydney Children’s Hospital. Additionally,

following the identification of one subject with HLHS and a mutation in NKX2-5,

a group of 18 children with HLHS recruited via the Children’s Hospital-San

Diego and US HLHS support group was included. Subjects with PFO were

recruited at the time of investigation for cryptogenic stroke, and thus represent a

group likely to have relatively severe forms of PFO.

A total of 129 subjects with ASD, 109 with other types of CHD, 59 with PFO

ascertained during investigation of cryptogenic stroke, and 29 with PFO

ascertained during investigation for reasons other than stroke had GATA4

sequencing performed, including the exons and intron/exon boundaries.

2.2.2.4 Follow-up of family members Whenever a possible or definite mutation was identified in a family, as many

first degree relatives as possible were recruited, and depending on the results

of testing of those individuals, more distant relatives were also sometimes

recruited.

2.2.3 History For all children recruited at Sydney Children’s Hospital, and for all subjects in

whom a possible or definite mutation was identified, plus the members of their

families, a detailed clinical history was taken. This included details of cardiac

malformations, other malformations, pregnancy history, growth, developmental

progress and any prior investigations. Permission was obtained to review

hospital medical records when possible. For adult subjects recruited at St

Vincent’s Hospital or St Vincent’s Private Hospital, a more limited history was

taken by cardiology staff which included cardiac history (malformations,

77

arrhythmias, surgical or other procedures), and family history. Ethnicity was not

recorded initially but part way through recruitment this began to be recorded

and previously-recruited subjects were re-contacted to obtain this information.

Age and sex were recorded for all subjects.

2.2.4 Examination All children recruited at Sydney Children’s Hospital had a full clinical

examination by EK and/or Dr Fiona McKenzie. This included attention to

presence of dysmorphic features, examination of limbs including palmar and

plantar creases, palate, genitalia, skin, nails, teeth and hair. Examination of the

hands included close inspection for radial ray anomalies, with the thenar

eminences, thumbs and nails being closely examined, and comparison being

made between the two sides for evidence of asymmetry. A record was kept of

all examination findings.

Adults generally had a more limited clinical examination. All probands were

examined by a cardiologist. Relatives of probands were examined by a clinical

geneticist (the author) but it was often not appropriate to do as thorough an

examination as was possible for children. This was in part because many of

these examinations were carried out during the course of home visits without a

chaperone present. In some cases examinations were conducted in unusual

circumstances; several members of one family were examined (and had blood

taken) in the food storage and preparation area of the family’s grilled chicken

shop. Another woman had history taken, limited clinical examination done and

blood taken, all in the lobby of a large hotel, where she was attending a

conference. The reason for this was that she was seen during a short trip to

Brisbane and there was a limited window of opportunity for meeting her. Two

other members of the same family were examined in a back room of the family

business, a carpet warehouse. Notwithstanding this, the minimum examination

for such patients included examination of upper limbs (with attention to the

radial ray as described above) and a dysmorphological assessment of the face,

head and neck, plus inspection of palate, exposed skin, teeth, nails and hair.

78

Trips were made to Wollongong, Nowra, Canberra, Newcastle, Grafton,

Lismore, the Gold Coast and Brisbane for the purpose of recruiting subjects.

Numerous home visits were also done within the Sydney metropolitan area.

Despite this a small number of subjects could not be examined because they

lived in remote areas. For these individuals, a history was taken by telephone

and medical records were obtained.

2.2.5 Investigations All subjects had at least electrocardiography (ECG) and transthoracic

echocardiography. Most of the adult probands had TOE as well. These

investigations were interpreted wherever possible by one of the collaborating

cardiologists – Drs Owen Jones and Robert Justo and the cardiologists at

Children’s Hospital at Westmead, especially A/Prof Gary Sholler (paediatric

patients) and Prof Michael Feneley (adult patients), however in some instances

access was gained to the records of assessments done by other cardiologists.

Dr Michael Tsicalas assessed several members of the family described in

Chapter 4. Other investigations were arranged from time to time, if clinically

indicated. Two subjects had a standard blood karyotype done using routine

methods in service laboratories, and one had FISH for 22q11 deletion.

2.3 Molecular genetics methods 2.3.1 Extraction of DNA from human blood and mouse spleens Genomic DNA was extracted from human blood and mouse spleens using

modified salt precipitation protocols derived from the method of Miller et al

(Miller et al., 1988).

2.3.1.1 DNA extraction from mouse spleens All tubes used at each stage of this procedure were pre-labelled with the unique

identification number assigned to the mouse from which the spleen had been

taken.

1. Preparation of spleen tissue

Following sampling (described above), the spleens were stored in liquid

nitrogen until the time of DNA extraction. On removal from the storage tube, the

79

spleen was placed on a dissection platform lined with clean foil. A segment of

partially-thawed spleen weighing approximately 30mg (corresponding to a piece

roughtly 3mm x 3mm x 3mm in size) was cut off using a new surgical blade.

The remainder of the spleen was re-frozen.

2. Differential red cell lysis.

The 30mg sample of spleen was transferred into a 1.5mL Eppendorf tube and

soaked in 600�L of NH4Cl lysis buffer (NH4Cl 160mM, KHCO310mM, di-sodium

EDTA 0.4mM) for 24 hours at room temperature.

3. Nucleated cell lysis

The supernatant (which contained the red cell lysate) was removed. The

remaining splenic tissue was then homogenized using a DNAase-free

micropestle (SST). 600�L of TNES (50mM Tris-HCl pH 7.5, 400mM NaCl,

20mM EDTA, 0.5% SDS) was added, with 30�L of proteinase K (20mg/mL).

The sample was then briefly vortexed and placed in a 500C water bath for

overnight incubation.

4. Protein precipitation

200�L of 5M NaCl solution was added to the Eppendorf tube and the sample

was mixed by vortexing, and then centrifuged at 13 000 rpm for 8 minutes.

5. DNA precipitation

The supernatant (which should at this point be clear) was transferred to a

second 1.5mL Eppendorf tube, with care being taken to avoid transferring any

precipitate. 600�L of 100% ethanol was added to the supernatant and inverted

gently several times. At this point condensed DNA strands would be visible

within the liquid in the tube.

6. Removal of excess salt

200�L of 70% ethanol was placed in a third Eppendorf tube. A clean yellow

disposable pipette tip was used to transfer the DNA from the previous tube into

the 70% ethanol. The tube which now contained the DNA was then centrifuged

at 13 000 rpm for 3 minutes in a benchtop centrifuge, leaving a DNA pellet

firmly attached to the bottom of the tube. The ethanol was gently tipped out of

the tube and the sample left to air-dry for 30-60 minutes.

80

7. Dissolving and storage of DNA

300�L of 1x TE buffer was then added to the DNA pellet, and the DNA was left

to stand at room temperature for 1-2 weeks.

The concentration of DNA in the sample was then determined by

spectrophotometry.

2.3.1.2 DNA extraction from blood 1. Sample collection, transport and storage

Peripheral blood samples were collected by venesection using standard

techniques, and placed in tubes containing EDTA as an anticoagulant. Sample

collection from children was facilitated by the use of Emla cream (lignocaine

25mg/g and prilocaine 25mg/g, Astrazeneca) as a local anaesthetic agent,

applied 60 minutes before venesection. In some instances local pathology

services collected blood from subjects resident in remote areas. However,

wherever possible, arrangements were made to visit areas where subjects lived

in order to examine subjects and collect blood, as described in section 2.2

above. Samples were transported at room temperature.

2. Sample labelling

The initial sample tube was labelled with at least two identifying pieces of

information – usually study identification number, name and date of birth.

Intermediate tubes used during the process of DNA extraction were labelled

with the identification number only. The final tube in which the extracted DNA

was stored was labelled with the identification number, date of birth of the

subject, date of extraction and concentration of DNA as determined by

spectrophotometry.

3. Red cell lysis

A biological safety cabinet class II was used for handling blood samples. The

blood was transferred from the EDTA-containing tube into a 50mL screw-cap

centrifuge tube. For every 10mL of whole blood, 40mL of NH4Cl lysis buffer was

added (NH4Cl 160mM, KHCO3 10mM, di-sodium EDTA 0.4mM). The sample

and lysis buffer were mixed well by inversion and allowed to stand for 30-120

minutes at room temperature, inverted once more during this time to ensure

81

thorough mixing. The sample was then centrifuged for 10min at 3500rpm. The

supernatant was discarded, leaving a white cell pellet in the bottom of the tube.

5mL of NH4Cl lysis buffer was then added and the pellet was resuspended by

vigorous shaking or vortexing. A further 30mL of NH4Cl lysis buffer was added

and the tube shaken hard to ensure resuspension of the pellet. The sample was

again centrifuged for 10min at 3500rpm, and the supernatant discarded.

Optional: at this stage, if required, the sample could be stored at –200C for

future completion of DNA extraction.

4. Nucleated cell lysis

The white cell pellet was resuspended in 1-2mL (1mL if the pellet was small) of

TE lysis buffer (Tris-HCl 200mM, di-sodium EDTA 5mM). 30�L of Proteinase K

(10mg/mL) was added and the sample mixed with gentle shaking. 100�L of

10% SDS solution was added and mixed by gentle shaking. The sample was

then incubated overnight in a water bath at 500C.

5. Protein precipitation

The next day, the sample was taken from the water bath and cooled to 40C in a

refrigerator. 700�L of ammonium acetate (5M) was added to the sample and

the sample was shaken vigorously for approximately 20 seconds. It was then

centrifuged for 10min at 5000rpm.

6. DNA precipitation

The supernatant was transferred to a clean 20mL screwcap container and two

volumes (4mL) of 95% ethanol added. Gentle mixing caused strands of DNA to

condense and become visible. The DNA was then removed using a 1�L

disposable microbiological loop and washed with 70% ethanol while still on the

loop to remove excess salt. The DNA was then transferred to a 1.5ml screwcap

tube and air dried for 1-5 minutes. Sterile TE (100�L per 5mL of blood in the

original sample) was then added and the sample allowed to stand overnight to

allow the DNA to dissolve.

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The DNA concentration was then measured by spectrophotometry, and the

sample stored at –200C.

2.3.4 Polymerase chain reaction (PCR) and sequencing of NKX2-5 andGATA4

2.3.4.1 PCR of NKX2-5

Initially the two exons of NKX2-5 were amplified in separate PCR reactions.

Results for exon 1 were generally good but amplification and sequencing of

exon 2 were problematic, possibly due in part to the high GC content of this

exon. Subsequently the Expand PCR system (Roche) was used to amplify both

exons and the intron in a single amplicon. Genomic DNA was diluted to a

working concentration of 100ng/�L and this stock was used in PCR.

Reaction mixture (per sample):

Template DNA 1�L

Expand polymerase 0.75�L

10x Expand buffer 5�L

10mM dNTPs 2.5�L

Oligonucleotide PF1

(concentration 50ng/�L) 2.5 �L

Oligonucleotide PR1

(concentration 50ng/�L) 2.5 �L

dH2O 35.75 �L

83

The following cycling conditions were used:

950C for 1min: 30sec

940C for 30 sec

680C for 2 min

(10 cycles)

940C for 20 sec

680C for 2 min +5 sec/cycle

(24 cycles)

720C for 5 min

(1 cycle)

DNA purification

The PCR products were purified using the Qiaquick PCR purification kit

(Qiagen) according to the manufacturer’s instructions.

Sequencing of NKX2-5: second method

Sequencing was done using the BigDye sequencing mix (ABI). Oligonucleotide

concentration was 25ng/�L


PCR product 6�L

Oligonucleotide

(S1R, S1F, S2R, or S2F) 1�L

CSA buffer 3�L

BigDye sequencing mix 4�L

dH2O 6�L

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CSA buffer: 200mM Tris pH9, 5mM MgCL

Cycling conditions:

960C for 2 minutes

960C for 10 sec

500C for 10 seconds

600C for 4 min

(25 cycles)

2.3.4.2 PCR of Exon 5 of GATA4

The AmpliTaq Gold PCR system (ABI) was used to amplify exon 5 of GATA4.

Genomic DNA was diluted to a working concentration of 100ng/�L and this

stock was used in PCR.


Template DNA 0.7�L

AmpliTaq polymerase 0.1�L

10x buffer (Buffer II) 1.5�L

5mM dNTPs 0.6�L

Oligonucleotide G1

(concentration 50ng/�L) 0.5�L

Oligonucleotide G2

(concentration 50ng/�L) 0.5�L

MgCl2 0.8�L

dH2O 10.5�L

85

The following cycling conditions were used:

940C for 11min

(1 cycle)

940C for 10 sec

600C for 45 sec

720C for 1 min

(35 cycles)

720C for 5 min

(1 cycle)

DNA purification

The PCR products were purified using the Qiaquick PCR purification kit

(Qiagen) according to the manufacturer’s instructions.

Sequencing of GATA4

Sequencing was done using the BigDye sequencing mix (ABI). Oligonucleotide

concentration was 25ng/�L


PCR product 5�L

Oligonucleotide (G1) 1�L

CSA buffer 2�L

BigDye sequencing mix 2�L

CSA buffer: 200mM Tris pH9, 5mM MgCL

Cycling conditions:

960C for 2 minutes

(1 cycle)

960C for 20 sec

500C for 10 sec

600C for 4 min

(25 cycles)

2.3.5 Sequence analysis For both NKX2-5 and GATA4, the products of the sequencing reactions were

analysed using an ABI 3700 sequencer at the sequencing facility of the

University of New South Wales. Lasergene DNAstar (Wisconsin) software was

used to read the resulting sequence chromatograms.

Table 2.2: Primers used for PCR and sequencing

Gene Primer Sequence (5’=>3’)

NKX2-5 PF1 gcaccatgcagggaagctgcc

PR1 tcattgcacgctgcataatcgcc

S1F tgagactggcgctgccacc

S1R ctttcttttcggctctagggtcc

S2F agctggagcggcgcttcaag

S2R tggccggctgcgctggggaac

GATA4 1F atcgttgttgccgtcgttttctct

1R gccctcgcgcgctcctactcacc

2F gagagctgggcataaacaaagaat

2R ccccgatgcacaccctcaag

86

GATA4 3F acgcgaggtggaagggcagtg

3R caaaggaagaagacaagggaggac

4F cttctcgcagcaggtgtg

4R tgaaaggccagggatgtc

5F tgtccccggcaaatgtagataaag

5R cagtcggcctccccacaaacagc

6F tgggcctcatcgtgtgctttctgc

6R tccaacacccgcttcccctaacca

TBX20 1F acccttttccctgaacctgt

1R tcatggcttgagcatcagac

2F catttggttatgctgttctttcc

2R ctacccagggagtgtcctg

3F gagtcagaccctttccctcc

3R aggcttggaatgctctcttg

4F cccacttatatatggtttatgtgttcc

4R agatagaaggtgggaagggg

5F cactgtaatttggcctgtttagc

5R aatataagaacctcctaaatccttctc

6F ttccacccttctcaggacac

6R aggcctgcctgatgtctct

NKX2-5 primers PF1 and PR1 were used only for PCR. Primers S1F, S1R,

S2F, S2R were used only for sequencing. For GATA4, all primers were used for

PCR and primers 1F, 2F, 3F, 4F, 5F and 6R were also used for sequencing. For

87

88

TBX20, all primers were used for PCR and 1F, 2R, 3F, 4R, 5R and 6R were

used for sequencing

2.4 Microsatellite analysis All microsatellite analyses were performed by a commercial service, the

Australian Genome Research Facility (AGRF), in Melbourne, Australia.

2.5 Marker selection 2.5.1 Human Markers A standard set of 382 autosomal markers used by the AGRF (selected from the

Genethon linkage map) were used in human mapping studies. An additional 5

markers were used in a second round of (fine) mapping, to investigate an area

of interest on chromosome 1. The average intermarker distance in the initial

genome screen was 9.0cM. The markers used are shown in Chapter 4, tables

4.1a –4.22.

2.5.2 Mouse Markers Candidate microsatellites were selected from the Whitehead Institute database

and from local resources, and their informativeness confirmed by screening with

DNA from the parental mouse strains. This was done by gel electrophoresis at

the AGRF (as detailed below) and by polyacrylamide gel electrophoresis and

autoradiography at the Victor Chang Cardiac Research Institute, with this being

done largely by Dr Changbaig Hyun. Eighty-nine markers were selected to span

the mouse genome, yielding an average intermarker distance of ~17cM. The

markers used are shown in Chapter 6 (Table 6.1).

2.6 Laboratory methods used at AGRF Microsatellites were amplified under standard conditions. Each PCR reaction

was carried out in a total volume of 6ul. Reactions were amplified using a PTC-

225 DNA Engine Tetrad (MJ Research) and PCR products pooled to run more

than one marker per lane. Primers were fluorescently labelled with the

fluorescent dyes FAM, HEX and TET. Gel electrophoresis was done using

0.2mm denaturing polyacrylamide gels, 4.5%. PCR product (1uL) was

89

electrophoresed for 2.8 hours on an Applied Biosystems 377 DNA Sequencer,

with the size standard TAMRA 500 (Red) applied to each gel. Genescan

software Version 3.1.2 (AB) was used to assign tracking for each sample lane.

Files were then imported into Genotyper Version 2.1 (AB) software in order to

interpret the electropherogram and assign genotypes.

2.7 Error checking All genotypes were machine scored and then manually checked by AGRF staff.

The majority of the markers used performed well and it was possible to use the

genotype information directly as provided by the AGRF, following this process.

However, the data quality was checked, as described below.

2.7.1 Error Checking of Human Data In the analysis of the human data the main assurance of data quality was

confirmation of Mendelian segregation of markers within the family. There were

only 8 genotypes with apparent non-Mendelian segregation. These were re-

examined and four were found to be due to incorrect reading of the original

trace, two were probable new mutations and two were probable null alleles. The

data were adjusted appropriately before analysis.

2.7.2 Error Checking of Mouse Data It was possible to examine the data quality for each mouse marker by

comparing the observed ratios of the three possible genotypes (homozygous for

the QSi5 allele, heterozygous, homozygous for the 129T2/SvEms allele) with

the expected ratio of 1:2:1. A Chi-square test was performed to compare the

observed with expected ratios. On review of the data, 9 markers gave results

not consistent with Mendelian segregation of alleles. The expected ratio was

1:2:1 and the following markers had results which were highly significantly

different from this ratio: D1Mit26, D3Mit41, D16Mit38, D2Mit83, D2Mit265,

D5Mit125, D7Mit67, D11Mit62, and D14Mit125. On review of results for these

markers it emerged that 96 of the genotypes for D1Mit26 had been incorrectly

copied from proprietary software to Microsoft Excel; correction of these

genotypes resulted in a return to Mendelian ratios.

90

In addition to the manual checking of the machine calls described above, all

genotypes for D16Mit38 and a selection (~10% of genotypes) for each of the

remaining 8 markers were manually checked in Sydney (by the author). It was

found that D16Mit38 performed well and that calling by AGRF of the genotypes

had been accurate. The deviation from Mendelian patterns in this case was less

marked than in most of the other suspect markers and it appears that in this

instance the skewed distribution of alleles occurred by chance. It is unlikely to

have been caused by overrepresentation of a phenotype-associated allele, as

both extremes of the phenotype distributions were selected for genotyping –

thus any such effect should have been balanced out.

The remaining markers, D3Mit41, D2Mit83, D2Mit265, D5Mit125, D7Mit67,

D11Mit62, and D14Mit125 all proved to have performed poorly at the

genotyping stage despite having appeared suitable during marker evaluation. In

each case one or both alleles amplified inconsistently, leading to a high number

of miscalls which could not be corrected with repeated genotyping.

Replacement markers were therefore selected for D3Mit41, D2Mit83, D2Mit265,

D5Mit125, D7Mit67, D11Mit62, and D14Mit125. The replacement markers were

D3Mit147, D2Mit235, D2Mit517, D5Mit71, D7Mit74, D11Mit71, and D14Mit7,

respectively.

2.8 Statistical methods 2.8.1 Basic statistical analyses All basic statistical analyses including calculations of mean values, standard

deviations, correlation coefficients, chi square calculations, t-tests and analysis

of variance (ANOVA) using the general linear model (GLM) were performed

using Minitab V14.1 (Minitab, Inc).

91

2.9 Linkage analysis 2.9.1 Linkage analysis for autosomal dominant trait Linkage analysis in the family with dominant ASD and Marcus Gunn

Phenomenon was done using the MLINK and LINKMAP programs within the

LINKAGE software package (Lathrop and Lalouel, 1988). Allele frequency data

generated in Australian subjects were used. These were generously provided

by the CRC for Discovery of Genes for Common Diseases.

2.9.2 QTL analysis 2.9.2.1 Selective Genotyping As discussed in section 1.4.2.3, selective genotyping of animals at the

extremes of the phenotypic distribution has advantages in terms of cost and

study power (Lander and Botstein, 1989) In this study, therefore, the top and

bottom deciles for each of FVL and FOW (corrected for sex and week of

gestation) were genotyped, a total of 466 mice, selected from the 1328 F2 mice

with complete records.

The relationship between other traits for which data were recorded (age, sex,

week of dissection, coat colour, body weight and heart weight) and the cardiac

phenotypes of interest is discussed in detail in Chapter 5.

2.9.2.2 Linkage analyses Linkage analyses were performed using the Mapmaker/QTL package (Lander

and Botstein, 1989). This program performs interval mapping using a maximum

likelihood estimation algorithm, as discussed in section 1.4.2.4. It is particularly

advantageous for analysis of a selectively genotyped population, because

provided that the phenotypic data for all individuals are included (with the

genotypes for individuals not in the extremes entered as “missing”), the program

can accurately estimate the effect sizes of QTL. This is in contrast to most other

available QTL mapping programs. Important consequences would arise from

the use of a program not designed with selective genotyping in mind. The main

issue is that as all the phenotype data is from individuals with extreme

phenotypes, effect sizes will be overestimated. This was seen in practice when

the linkage analyses were performed in parallel with the Mapmanager QTXb13

software (Manly and Olson, 1999), which uses a regression-based algorithm but

cannot handle missing genotype data. Although this program confirmed the

location of the QTL detected using Mapmaker/QTL, the effect sizes were

overestimated as predicted.

2.9.2.3 Binary trait analysis As discussed in Chapter 1.4.1.1, PFO can be viewed as essentially a binary

trait (PFO is either present or absent). As such it is intrinsically less informative

and more difficult to analyze from a quantitative genetic perspective than a

continuously distributed trait. However, the large amount of data available in this

study made it practicable to conduct such an analysis. This was done by Dr

Peter Thomson, using an approach (detailed below) and software developed by

him.

For binary analysis of PFO, the model took the form

( ) ( ) ( )log sex1

QQ Qq qqiM i i i i

i

p ax dx axp

� ��

or equivalently,

( ) ( ) ( )

11 exp[ ( sex )]i QQ Qq qq

M i i i i

pax dx ax

��

where pi is the probability that an animal has PFO, sexi is a 0 or 1 indicator

variable for sex (male = 1, female = 0), and ( )QQix , ( )Qq

ix and ( )qqix are u

0 or 1 indicator variables indexing the QTL genotype (QQ, Qq, or qq) with ( ) (QQ Qqi ix x� hat the Q allele refers to the 129T2/SvEms line,

while the q allele refers to the QSi5 line. The parameters a and d refer to

additive and dominance effects of the 129T2/SvEms allele, on the logit scale.

nobserved

Note t) ( ) 1qqix� � .

92

Because the QTL genotypes indicator variables are unobserved, the model is

fitted as a three-component mixture, with mixing probabilities

� �( ) ( ) 1|QQ QQi iP x� � � mi , � �( ) ( ) 1|Qq Qq

i iP x� � � mi , and � �( ) ( ) 1|qq qqi iP x� � � mi , with

, where these are the conditional probabilities of the QTL

genotype, given the flanking marker genotypes, mi. These are calculated in a

standard way for an inbred F2 design.

( ) ( ) ( ) 1QQ Qq qqi i i� � � � � �

As a protection against spurious results, QTL “peaks” with LOD > 2 were

checked by selecting the nearest marker and performing a standard (non-

mixture) logistic regression,

( ) ( ) (log sex1

)MM MmiM i i i i

i

p ax dx axp

� ��

mm ,

where the ( )MMix , ( )Mm

ix and ( )mmix are the (observed) 0-1 indicator variables for

the marker genotypes.

93

3. The role of mutations in the cardiac transcription factors NKX2-5, GATA4 and TBX20 in causing CHD and cardiomyopathy

3.1 Introduction It is now 10 years since the genetic basis of rare Mendelian forms of CHD

began to be elucidated, with the identification of mutations in TBX5 in Holt-

Oram syndrome (Basson et al., 1997). Subsequently, mutations were found in

NKX2-5 (Schott et al., 1998), GATA4 (Garg et al., 2003), MYH6 (Ching et al.,

2005) and ACTC (Matsson H et al., 2005) in families with nonsyndromal

autosomal dominant ASD. The discovery of causes for rare forms of CHD

naturally raises the question: could mutations in the same genes cause

common CHD? Moreover, the fact that TBX5, NKX2-5 and GATA4 interact

during early development, and MYH6 and ACTC are downstream targets of the

other known ASD genes, raises the question: could other genes which interact

with them also be associated with dominant ASD? This chapter represents a

partial answer to these questions, with investigation of a large number of

individuals with ASD and other forms of CHD to determine whether they

harbour mutations in each of NKX2-5, GATA4 and TBX20. The latter was a

candidate gene based on its known interactions with NKX2-5 and TBX5 (see

section 1.5.3).

The chapter is divided into three sections, one for each of these three genes.

Because the work has been progressing over a period spanning the years

2000-2007, the number of subjects studied for each gene was different.

3.2 Mutations in NKX2-5 cause autosomal dominant CHD and AV conduction blockMutations in human NKX2-5 were first identified by Schott and colleagues in

1998 (Schott et al., 1998). They described four families, each with multiple

individuals affected by CHD and/or AV conduction block, in which NKX2-5

mutations segregated with the cardiac phenotype. In this and subsequent

94

95

reports, the AV block associated with NKX2-5 mutations has been noted to

become progressively more severe with age (Schott et al., 1998; Gutierrez-

Roelens I et al., 2002). Although ASD was the main form of CHD in all four

families, there were a number of other malformations including VSD, tetralogy

of Fallot, subvalvular aortic stenosis, pulmonary atresia and redundant mitral

valve leaflets with fenestrations. This and subsequent reports have led to

identification of a total of 35 germline mutations in NKX2-5. The mutations

identified to date are summarized in Table 3.1.

The results of the study described here (reported in (Elliott et al., 2003)) are not

included in the table. The intriguing findings of Dentice and colleagues (Dentice

et al., 2006), are not included because the study was not of CHD. They noted

expression of Nkx2-5 during thyroid development in the mouse. This led them to

screen 241 individuals with thyroid dysgenesis for mutations in NKX2-5. Three

missense mutations were found, two of which were not found in 561 healthy

controls, and one of which was found once among the 561 controls. One of the

affected individuals had mild mitral valve regurgitation, but otherwise they did

not have CHD or conduction abnormalities. In each family, there was at least

one heterozygote who was clinically normal. Thus, if these are pathogenic

changes, the penetrance for thyroid disease associated with them must be low.

Mutations in the related gene NKX2-1 have been identified in patients with

congenital hypothyroidism (Devriendt et al., 1998). A homozygous mutation in

NKX2-6 has also been found to cause common arterial trunk in a large

consanguineous family (Heathcote et al., 2005).

Also not included in the table are the reports by Reamon-Buettner and

colleagues of multiple somatic NKX2-5 mutations in complex CHD seen in

fomalin-fixed hearts, part of a museum collection (Reamon-Buettner and Borlak,

2004; Reamon-Buettner et al., 2004; Reamon-Buettner and Borlak, 2006). The

authors argue that the lack of mutations in healthy tissue and the lack of

mutations in other, non-cardiac specific genes in the same samples confirm that

these are genuine mutations and not artefact. Nonetheless, testing of formalin-

fixed tissues, stored for decades, is fraught with difficulty and these results must

96

be viewed with caution until they are replicated by another group, preferably in

fresh tissue obtained at surgery. The only attempt to do this to date has been

inconclusive, with no somatic mutations found in 19 subjects undergoing

surgery for bicuspid aortic valve (Majumdar et al., 2006).

This represents only weak evidence against a role for somatic NKX2-5

mutations in CHD, however, as bicuspid aortic valve is not one of the typical

lesions associated with NKX2-5 mutations, although it is more frequent in mice

heterozygous for an Nkx2-5 mutation (Biben et al., 2000). Of the 35 NKX2-5

mutations summarized above, 21 are missense mutations, 6 are nonsense

mutations, 6 are frameshift mutations, one a splice site mutation and one an in-

frame deletion of a single amino acid. However, of the missense mutations,

there is currently limited evidence for pathogenicity for R25C, E21Q, A219V,

K15I, Q22P, A63V, A127E, R126C, and P275T. The single amino acid deletion

�N291 is also of uncertain significance. For all of these mutations, there is

either demonstrated nonpenetrance in the setting of only a small number of

affected individuals (in most cases only one), or no information is available

about the mutation status of family members other than the proband, with no

family history of CHD. It is striking that all of the other 12 missense mutations,

including all missense mutations identified as the result of study of families with

multiple individuals affected by CHD, are located within the homeodomain.

It remains possible that the missense mutations located outside the

homeodomain are pathogenic. The identification of these sequence variants in

a relatively large number of individuals with CHD, but not in controls, makes it

likely that this is the case for at least some of them. The absence of family

history does not exclude the possibility that other family members have

unrecognised CHD. Some or all may be proven pathogenic on study of

Tabl

e 3.

1 M

utat

ions

in N

KX2

-5

Mut

atio

nT1

78M

T178

MT1

78M

Q17

0XQ

198X

Q19

8XQ

149X

R18

9G

Ref

eren

ce

(Sch

ott e

t al

., 19

98)

(Sch

ott e

t al

., 19

98)

(Hira

yam

a-Y

amad

a et

al

., 20

05)

(Sch

ott e

t al

., 19

98)

(Sch

ott e

t al

., 19

98)

(Hos

oda

et a

l.,

1999

) (B

enso

n et

al

., 19

99)

(Ben

son

et

al.,

1999

)

#het

eroz

ygou

s

75

24

31

65

ASD

98

44

62

44

VSD

2-

--

--

3-

AV

bloc

k 10

86

33

25

5

Non

pene

tran

t-

-(2

)-

--

(1)

(1)

Oth

er C

HD

(n

o)TO

F(2)

,S

VA

HS

,P

A

--

-M

VF

--

TVab

n

Loca

tion

Hom

eobo

xH

omeo

box

Hom

eobo

xH

omeo

box

Hom

eobo

xO

ne A

A 3

’ to

hom

eobo

xH

omeo

box

Hom

eobo

x

Com

men

tP

roba

nd’s

fath

er d

ied

sudd

enly

at

42, s

on d

ied

at

18(p

neum

onia

)

3 w

ith

decr

ease

dLV

func

tion

97

Mut

atio

nY2

59X

Y191

CN

188K

Int 1

DSG

+1T

R25

CR

25C

R25

C

Ref

eren

ce

(Ben

son

et

al.,

1999

)d}

(Ben

son

et

al.,

1999

)d}

(Ben

son

et

al.,

1999

)d}

(Ben

son

et a

l.,

1999

)d}

(Ben

son

et a

l.,

1999

)d}

(Gol

dmun

tz

et a

l.,

2001

)d}

(Gol

dmun

tzet

al.,

20

01)d

} (M

cElh

inne

yet

al.,

20

03)d

} #h

eter

ozyg

ous

7

15

11

21

x 7

ASD

61

5-

--

-

VSD

31

--

-1

-A

V bl

ock

71

51

--

-

Non

pene

tran

t(1

)-

-(1

)-

-2+

Oth

er C

HD

(no)

-

-E

bste

inan

omal

y(3

)

-TO

FTO

FTO

F(4)

,TA

,IA

A, H

LHS

Loca

tion

3’ c

odin

g re

gion

Hom

eobo

xH

omeo

box

Spl

ice

site

5’

cod

ing

regi

on

5’ c

odin

g re

gion

5’ c

odin

g re

gion

Com

men

tde

nov

om

utat

ion

in p

roba

nd

1 w

ith

decr

ease

dLV

func

tion

Pro

band

asce

rtain

edbe

caus

e of

AV

bl

ock.

Fat

her

died

sud

denl

y ag

ed 2

9.

Oth

er fa

mily

m

embe

rs n

ot

geno

type

d

7 is

olat

ed

case

s lis

ted

here

(“x7

”) Se

edi

scus

sion

98

Mut

atio

nE2

1QA

219V

C26

4X21

5-22

1del

22

3-22

4del

R

142C

Ref

eren

ce

(Gol

dmun

tz e

t al

., 20

01)

(Gol

dmun

tz e

t al

., 20

01;

McE

lhin

ney

et

al.,

2003

)

(Iked

a et

al

., 20

02)

(Wat

anab

e et

al.,

200

2)

(Wat

anab

e et

al

., 20

02)

(Gut

ierr

ez-

Roe

lens

I et

al

., 20

02)

#het

eroz

ygou

s

32

15

413

ASD

--

45*

410

VSD

--

-1

-3

AV

bloc

k -

-6

55

11N

onpe

netr

ant

21

(2)

1(1

)(3

)O

ther

CH

D

(no)

-TO

F-

--

TOF,

PS

,P

DA

Loca

tion

5’ c

odin

g re

gion

NK

2 do

mai

n 3’

cod

ing

regi

onFr

ames

hift

in e

xon

1 Fr

ames

hift

in

exon

1H

omeo

box

Com

men

tN

onpe

netra

nce

? pa

thol

ogic

al

sign

ifica

nce

Non

pene

tranc

e?

path

olog

ical

si

gnifi

canc

e

Non

pene

trant

indi

vidu

al

had

AF

aged

46

but n

o A

V b

lock

and

no

CH

D.

One

affe

cted

indi

vidu

al

had

visc

eral

situ

s in

vers

us w

ith p

olys

plen

ia

but n

ot d

extro

card

ia

4 su

bjec

ts

had

CH

D b

ut

no A

V b

lock

, bu

t 3 w

ere

child

ren

at

the

time

of

stud

y

99

Mut

atio

nQ

187H

K15

IQ

22P

A63

VA

127E

R21

6C

InsT

CC

CT7

01

Ref

eren

ce

(Gut

ierr

ez-

Roe

lens

I et

al.,

20

02)

(McE

lhin

ney

et

al.,

2003

) (M

cElh

inne

yet

al.,

200

3)

(McE

lhin

ney

et a

l., 2

003)

(M

cElh

inne

y et

al

., 20

03)

(McE

lhin

ney

et a

l., 2

003)

(M

cElh

inne

yet

al.,

200

3)

#het

eroz

ygou

s

62

11

31

2A

SD6

1-

-1

-1

VSD

--

--

--

-A

V bl

ock

7-

--

--

1N

onpe

netr

ant

(1)

1?

?1

?1

Oth

er C

HD

(n

o)A

nom

alou

s sy

stem

icve

nous

retu

rn (2

)

-TO

FL-

TGA

BA

VTO

F-

Loca

tion

Hom

eobo

xTN

dom

ain

5’ c

odin

g re

gion

5’ c

odin

g re

gion

5’ c

odin

g re

gion

NK

2 do

mai

n3’

cod

ing

regi

onC

omm

ent

Non

pene

tranc

e ?

path

olog

ical

si

gnifi

canc

e

No

fam

ily

hist

ory

of

CH

D,

pare

nts

not

geno

type

d

No

fam

ily

hist

ory

of

CH

D,

pare

nts

not

geno

type

d

Non

pene

tranc

e,on

e af

fect

ed

only

has

BA

V,

?sig

nific

ance

No

fam

ily

hist

ory

of

CH

D,

pare

nts

not

geno

type

d

100

Mut

atio

nP2

75T

�N

291

A32

3T

605-

606d

el

W18

5L

498i

nsC

L171

PR

efer

ence

(M

cElh

inne

yet

al.,

200

3)

(McE

lhin

ney

et

al.,

2003

) (M

cElh

inne

yet

al.,

200

3)

(Sar

kozy

A e

t al

., 20

04)

(Sar

kozy

A e

t al

., 20

04)

(Sar

kozy

A

et a

l., 2

004)

(K

asah

ara

and

Ben

son,

20

04)

#het

eroz

ygou

s

12

13

31

9A

SD-

--

74

17

VSD

--

-2

3-

1A

V bl

ock

--

-7

21

9N

onpe

netr

ant

?1

?-

--

(2)

Oth

er C

HD

(n

o)C

oarc

tD

OR

V

TOF

-M

VP

, LV

no

ncom

pact

ion

--

Loca

tion

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odin

g re

gion

3’ c

odin

g re

gion

3’ c

odin

g re

gion

Hom

eobo

xH

omeo

box

5’co

ding

regi

onH

omeo

box

Com

men

tN

o fa

mily

hi

stor

y of

C

HD

, par

ents

no

tge

noty

ped

Non

pene

tranc

e?

path

olog

ical

si

gnifi

canc

e

No

fam

ily

hist

ory

of

CH

D, p

aren

ts

not

geno

type

d

de n

ovo

mut

atio

n7d

ecea

sed

indi

vidu

als

had

CH

D,

type

not

co

nfirm

ed

101

102

Mut

atio

nR

190H

c.26

2del

R19

0CY2

56X

Q16

0PR

efer

ence

(K

asah

ara

and

Ben

son,

200

4)

(Hira

yam

a-Y

amad

a et

al

., 20

05)

(Hira

yam

a-Y

amad

a et

al.,

200

5)

(Gut

ierr

ez-

Roe

lens

et a

l.,

2006

)

(Rifa

i L e

t al.,

20

07)

#het

eroz

ygou

s

34

15

4A

SD3

32

24

VSD

2-

--

-A

V bl

ock

34

15

4N

onpe

netr

ant

-(1

)?2

(2)

-O

ther

CH

D

(no)

--

-M

VP

-

Loca

tion

Hom

eobo

x5’

codi

ngre

gion

, FS

H

omeo

box

3’co

ding

regi

onH

omeo

box

Com

men

tTw

o de

ceas

ed

indi

vidu

als

had

CH

D, t

ype

not

conf

irmed

Fath

er o

f 4

affe

cted

child

ren

unav

aila

ble

for s

tudy

Pro

band

’s fa

ther

die

d ag

ed 6

3 of

su

bara

chno

id h

aem

orrh

age,

co

usin

with

AS

D n

ot

geno

type

d/un

cle

not s

tudi

ed

Mut

atio

ns a

re li

sted

in o

rder

of p

ublic

atio

n, e

xcep

t tha

t a s

econ

d or

sub

sequ

ent r

epor

t of a

mut

atio

n is

list

ed a

djac

ent t

o th

e or

igin

al re

port.

Whe

re

mul

tiple

fam

ilies

with

the

sam

e m

utat

ion

have

bee

n re

porte

d, e

ach

is li

sted

sep

arat

ely.

# h

eter

ozyg

ous:

num

ber p

rove

n he

tero

zygo

us fo

r the

mut

atio

n.

Num

ber w

ith A

SD

or o

ther

abn

orm

aliti

es m

ay b

e gr

eate

r tha

n th

is if

ther

e ar

e m

ultip

le a

ffect

ed in

divi

dual

s in

the

pedi

gree

who

cou

ld n

ot b

e ge

noty

ped

(eg

dece

ased

indi

vidu

als)

. AV

blo

ck: C

onfir

med

AV

con

duct

ion

bloc

k. N

onpe

netra

nt: c

onfir

med

mut

atio

n po

sitiv

e O

R o

blig

ate

heto

rozy

gote

, kno

wn

norm

al c

ardi

ac s

tatu

s. N

umbe

rs in

bra

cket

s re

fer t

o in

divi

dual

s w

ith A

V b

lock

but

stru

ctur

ally

nor

mal

hea

rt. O

ther

CH

D: m

ay o

verla

p w

ith A

SD

/VS

D

cate

gorie

s if

>1 c

ardi

ac d

iagn

osis

(no)

num

ber i

f mor

e th

an o

ne a

ffect

ed in

ped

igre

e. T

OF

tetra

logy

of F

allo

t (in

divi

dual

s w

ith T

OF

not l

iste

d se

para

tely

as

hav

ing

VS

D),

SV

AH

S s

upra

valv

ular

aor

tic s

teno

sis,

PA

pul

mon

ary

atre

sia,

MVF

mitr

al v

alve

fene

stra

tion,

TV

abn

tric

uspi

d va

lve

abno

rmal

ity, L

V le

ft ve

ntric

le, P

S p

ulm

onar

y st

enos

is, T

A tr

uncu

s ar

terio

sus,

IAA

inte

rrupt

ed a

ortic

arc

h, L

-TG

A L

-tran

spos

ition

of t

he g

reat

arte

ries,

BA

V b

icus

pid

aorti

c va

lve.

Coa

rct c

oarc

tatio

n of

the

aorta

, DO

RV

dou

ble

outle

t rig

ht v

entri

cle,

MV

P m

itral

val

ve p

rola

pse,

LV

left

vent

ricul

ar. A

A, a

min

o ac

id. F

S fr

ames

hift.

*3

had

sin

us v

enos

us A

SD

, 2 h

ad ty

pe n

ot s

peci

fied

103

additional families. Even if the family history is correct, some or all of these

mutations may be pathogenic but with reduced penetrance. Alternately, these

may be rare polymorphisms (they are not common as they were not identified in

100 control chromosomes) or may be participating in multifactorial causation of

CHD.

The recurrent mutation R25C is particularly noteworthy. The mutation is not in a

recognised functional domain of the gene. It has mainly been reported in

individuals with TOF. None of the affected individuals with this mutation have

had ASD or conduction abnormalities. In most of the families in which it has

been reported, studies of family members other than the proband have not been

possible. There are three families in which other family members have been

studied. In two, a healthy parent was heterozygous for the mutation. In the third,

the father of the proband was heterozygous for the mutation and had VSD

(Goldmuntz et al., 2001; McElhinney et al., 2003). Although the mutation was

not identified in 50 healthy individuals not selected for racial background, 2/43

healthy African-American controls were heterozygous for the mutation. Of the 7

probands identified with the mutation, 5 were of African-American ancestry, one

Hispanic and one Caucasian (McElhinney et al., 2003). In functional studies in

vitro, NKX2-5 protein with the R25C mutation localised normally to the nucleus,

bound to DNA normally at low concentrations but was subtly abnormal in

formation of dimers at higher concentrations, and had similar effects on

transcriptional activation to wild-type protein (Kasahara and Benson, 2004). The

clinical data, including identification of the mutation in two healthy controls,

suggest that this mutation is likely to have low penetrance. The in vitro data

show only subtle differences between wild-type and mutant protein. On the

other hand, the identification of a family with two affected individuals (one with

TOF and one with VSD) with CHD segregating with the mutation represents

evidence in favour of its pathogenicity. It is possible is that this is not a benign

polymorphism, but rather is a mutation which participates in multifactorial

causation of CHD.

104

The E21Q mutation was identified in a proband with TOF and was also present

in the proband’s mother and maternal grandmother, both of whom had normal

hearts (McElhinney et al., 2003). Its status is therefore uncertain.

Studies of DNA binding in homeodomain NKX2-5 missense mutations indicates

that DNA binding is impaired by these mutations (Kasahara and Benson, 2004).

This, together with the nonsense and frameshift mutations, and the observation

of CHD and AV conduction defects in children with deletions of the NKX2-5

locus (Baekvad-Hansen et al., 2006), suggest that the mechanism by which

NKX2-5 mutations cause disease is haploinsufficiency. However, dominant

negative inhibition of other transcription factors or transcription complexes may

also play a role.

The penetrance associated with mutations other than those listed above as

being of uncertain significance is very high. Penetrance for AV conduction

disease is almost complete, with the exceptions being children at the time of

assessment, implying a possibility that they will develop AV conduction defects

later in life. This has important clinical implications, implying a need for long

term cardiac follow-up for individuals found to be heterozygous for an NKX2-5

mutation. Penetrance for CHD, although lower than penetrance for AV

conduction defects, is likewise high.

3.2.1 Subjects screened for NKX2-5 mutationsSubjects screened for mutations in NKX-5 were as described in section 2.2.2.3.

Patient characteristics are shown in Table 3.2.

Contributions by EK to this part of the study include participation in study

design, patient recruitment (recruitment of all paediatric subjects, assisted by Dr

Fiona MacKenzie, but also including review of medical records and reading of

ECGs for many of the adult subjects), DNA extraction, sequencing of samples

from paediatric subjects and some of the adult subjects (the majority of

sequencing of adult subjects was performed by Dr David Elliott), clinical

evaluation of family members, and data analysis.

Tabl

e 3.

2 Pa

tient

cha

ract

eris

tics

(NK

X2-5

)

AS

D (n

=102

) P

FO (n

=25)

H

LHS

(n=1

9*)

Mal

e/fe

mal

e37

/65

12/1

311

/8

Mea

n ag

e in

yea

rs (r

ange

)�31

.9 (b

irth

to 8

0)48

.7 (1

2-73

)0.

9 (b

irth

to 4

)

Pos

itive

fam

ily h

isto

ry†

121

12

AV

con

duct

ion

bloc

k�4

10

Oth

er c

ardi

ac c

ondi

tions

A

nom

alou

s pu

lmon

ary

veno

us d

rain

age

20

0

T

ricus

pid

annu

lar d

ilata

tion

10

0

D

oubl

e-ou

tlet r

ight

ven

tricl

e 0

02

C

ompl

ete

hete

rota

xy

00

1

NK

X2-

5 m

utat

ion

1�0

1�

* Inc

lude

s on

e un

affe

cted

indi

vidu

al w

ho w

as a

n ob

ligat

e ca

rrier

of a

fam

ilial m

utat

ion

# A

t the

tim

e of

recr

uitm

ent i

nto

the

stud

y † Fi

rst o

r sec

ond-

degr

ee re

lativ

e w

ith c

onge

nita

l hea

rt di

seas

e �

Bot

h m

embe

rs o

f fam

ily 1

024

105

106

3.2.2 Results The coding regions and intron-exon boundaries of NKX2-5 were sequenced in

102 subjects ascertained only because of their need for ASD repair and 25 with

PFO diagnosed following cryptogenic stroke. Thirty-two were children (age

range birth to 14 years, mean 3.9 years) and the remainder were adults (age

range18-80 years, mean 44.7 years). Within the cohort, five individuals (3.9%)

also had first-degree heart block and 3 (2.9%) had some other form of CHD

(table 3.2). Thirteen patients (10%) had a family history of ASD in at least one

first or second-degree relative, although no family members had evidence of AV

conduction block. Clinical and laboratory methods were as described in sections

2.2 and 2.3.

Two individuals had sequence changes in NKX2-5 identified, both of which had

been reported previously (Figure 3.1). These were 61G>C, leading to the amino

acid change E21Q, and 532C>T, leading to the amino acid change T178M.

3.2.2.1 Family 1024: T178M This mutation has been reported previously, in two of the families in the original

report of NKX2-5 mutations in human disease (Schott et al., 1998) and in a

more recent report by Hirayama-Yamada and colleagues (Hirayama-Yamada et

al., 2005). The substitution of a threonine residue with methionine in the

homeobox between the second and third helix probably changes the angle of

the third helix, reducing contact to the major groove of DNA (Kasahara and

Benson, 2004).

It has been shown to markedly impair the ability of NKX2-5 protein to bind DNA

(Kasahara et al., 2000; Kasahara and Benson, 2004) .

In the family reported here, the proband was a girl aged 10 at the time of

recruitment into the study. A cardiac murmur was noted on clinical screening six

weeks after birth and the diagnosis of ASD was made. Surgical repair was

done at age 4 and was complicated by pulmonary oedema; however, she made

532C

>T61

G>C

61G

>C =

E21

Q

532C

>T =

T17

8M

532C

>T

Figu

re 3

.1 F

amili

es w

ith A

SD a

nd N

KX2

-5se

quen

ce c

hang

es. A

t lef

t is

fam

ily 1

024,

with

AS

D a

nd H

LHS

. Nla

III re

stric

tion

dige

st o

f PC

R p

rodu

cts

(bel

ow p

edig

ree)

dem

onst

rate

d th

at a

ll af

fect

ed a

nd o

ne c

linic

ally

nor

mal

indi

vidu

al (I

I:4) c

arrie

d th

e m

utat

ion.

At r

ight

is fa

mily

AF1

, with

AS

D in

thre

e

gene

ratio

ns; h

owev

er, o

nly

one

affe

cted

indi

vidu

al w

as h

eter

ozyg

ous

for t

he 6

1G>C

cha

nge

(fam

ily A

F1 s

eque

ncin

g an

d fa

mily

102

4 re

stric

tion

dige

st

by D

r Dav

id E

lliot

t) A

ll se

quen

ce c

hang

es il

lust

rate

d w

ere

conf

irmed

by

bidi

rect

iona

l seq

uenc

ing

107

108

a good recovery and at age 10 was in good health. Clinical examination was

normal other than the presence of the surgical scar. On review at age 13,

transthoracic echocardiogram and ECG were normal. Her father (I:2 on the

pedigree) had had an ASD diagnosed and surgically repaired at the age of 27;

he also had mitral valve replacement for mitral regurgitation at the same time.

Twelve months later he developed heart failure due to restrictive pericarditis,

which was managed surgically. At the age of 37, he had a cardiac arrest while

at a concert, and was successfully treated for ventricular fibrillation (VF) with DC

cardioversion. He had consumed approximately 5L of beer just before the

concert, which his cardiologist regarded as a possible contributing factor.

Electrophysiological studies did not induce any arrythmias and an implantable

defibrilllator was placed. He also developed atrial fibrillation, for which he was

successfully treated with flecainide. There was little extended family history

available.

The proband had three sibs. The first child in the family (II:1) had died aged 9

weeks of hypoplastic left heart syndrome with an associated ASD. With the

consent of the parents, this child’s newborn screening card was accessed and

DNA extracted from it. An NlaIII restriction enzyme digest performed by Dr

David Elliott confirmed that she was heterozygous for the T178M mutation.

Of the other two sibs, one was found not to carry the mutation. The other (II:4)

was heterozygous for the mutation. However, clinical examination,

echocardiography and ECG were normal when she was last seen, aged 10

years.

The finding of this mutation in a child with HLHS prompted sequencing of the

gene in an additional 18 individuals with HLHS, none of whom had identifiable

mutations in NKX2-5.

3.2.2.2 Family AF1: E21Q The sequence change 171G>C, leading to the amino acid change E21Q, was

identified in one member of a family in which four individuals had ASD without

109

AV conduction abnormalities. However, E21Q did not segregate with the

cardiac phenotype in the family – the other three affected individuals did not

carry the mutation. The mother of the individual who was heterozygous for

E21Q (II:1) was not available for testing, so it is not certain whether this was a

de novo change or inherited. Although II:1 was said not to have cardiac disease

it is possible that she did have unrecognised CHD. Nonetheless, this finding

adds weight to the suggestion that this may be a benign polymorphism (see 3.2

above).

3.2.3 Role of NKX2-5 mutations in nonsyndromal ASD The findings reported here are consistent with those of other investigators.

Considering ASD alone, among 102 individuals with ASD one definite mutation

was identified, in an individual with a strong family history of CHD and

conduction disease; this represented 1/12 subjects with a family history (or 1/13

if PFO is considered to represent CHD). Ikeda and colleagues found that 1/109

individuals with ASD had a mutation in NKX2-5; that proband had a family

history of ASD and conduction block (Ikeda et al., 2002). This paper does not

state how many of the individuals studied had a family history. Gutierrez-

Roelens and colleagues found mutations in 2/50 subjects, both of whom had

extensive family histories of CHD and conduction block (Gutierrez-Roelens I et

al., 2002). There were a total of 16 probands with a family history of CHD.

McElhinney and colleagues studied a total of 608 individuals, of whom 18 had a

mutation identified (although, as discussed above, a number of these are of

uncertain significance; 9/18 had tetralogy of Fallot including 7/9 with R25C)

(McElhinney et al., 2003). Considering ASD alone, 3/71 had mutations. One of

these individuals was heterozygous for the K15I mutation. This subject had no

conduction abnormality and one parent was heterozygous for the mutation but

had no CHD. A second individual had the A127E mutation. This individual had

ASD with no conduction defect, a parent with a normal heart and a grandparent

with bicuspid aortic valve (BAV). Given the high frequency of BAV, it is hard to

be certain that this confirms pathogenicity of the mutation. However, mice with

Nkx2-5 mutation have an increased frequency of bicuspid aortic valve (Biben et

110

al., 2000) so it is possible that the bicuspid valve in the grandparent was caused

by the A127E variant and that this is indeed a pathogenic mutation. The third

individual had an unequivocal mutation; this subject had ASD and AV

conduction disease and had a de novo frameshift mutation, InsTCCCT701

(McElhinney et al., 2003). Assuming that these latter two mutations are

pathogenic but R25C is not, 2/71 subjects with ASD had mutations. It is not

stated how many of the 71 subjects with ASD had a family history.

Combining these figures, 6/332 individuals ascertained purely on the basis of

ASD have been shown to have mutations in NKX2-5 (1.8%). However, 3/28

individuals with a family history of ASD had mutations (10.7%). It is possible

that publication bias is at work, with negative studies not having been published.

Nonetheless, these findings suggest that where there is a family history of ASD,

particularly with conduction defects, the yield from screening for NKX2-5

mutations is high enough to warrant testing, particularly as this a relatively small

gene (two exons). These figures do not support screening individuals without

family history for mutations in NKX2-5; of the 6 individuals with mutations, 5 had

a family history of CHD (if the bicuspid aortic valve is included) and the sixth

had AV conduction block in addition to ASD.

Supporting this conclusion, Benson and colleagues (Benson et al., 1999) found

mutations in all of five individuals with CHD and AV block, four of whom had an

extensive family history. In addition, 1/10 individuals with idiopathic 2nd or 3rd

degree heart block but no CHD had a mutation (Benson et al., 1999). The latter

part of the study has not been replicated and it remains to be seen whether this

of itself is sufficient indication for mutation testing, but based on these studies

the combination of CHD (particularly ASD) and AV conduction block does

represent sufficient indication for NKX2-5 mutation testing, even in the absence

of family history.

3.2.4 Implications for asymptomatic mutation-positive individuals The identification of an asymptomatic child with an NKX2-5 mutation (and, of

course, her sister whose ASD has been successfully treated) raises the

111

question of the implications for the future health of such individuals. As

discussed above, conduction disease in patients with NKX2-5 mutations is often

progressive, and sudden death has been reported. Penetrance for conduction

disease in adults is very high. Individual I:2 would probably not have survived

his episode of VF if it had not occurred at a concert, with paramedics equipped

with a defibrillator stationed close at hand. Normal ECGs in childhood are

therefore not reassuring. Currently, these children are being monitored with

annual ECG. This regimen is not evidence-based, as evidence for a specific

monitoring program does not yet exist. It may need modification as more

experience with the phenotype associated with NKX2-5 mutations accumulates.

3.3 The role of mutations in GATA4 in ASD and PFO As discussed in section 1.7.1.2, a role for GATA4 in human CHD was first

suggested by the observation that patients with deletions of 8p23, where

GATA4 is located, commonly have CHD. Mutations in GATA4 were first

reported in 2003 (Garg et al., 2003). The mutations G296S and c.1075delG

segregated with CHD, predominantly ASD, without conduction abnormalities or

extracardiac manifestations. Since then, an additional three germline mutations

have been reported (Table 3.3). Reamon-Buettner and colleagues have

reported multiple somatic GATA4 mutations in formalin-preserved cardiac

specimens. These mutations are not included in Table 3.3. This study used the

same set of hearts in which the same investigators identified numerous somatic

NKX2-5 mutations (Reamon-Buettner and Borlak, 2005). As for the NKX2-5

mutations, these somatic GATA4 mutations may be artefactual, and until the

findings are replicated by another group, preferably in fresh cardiac tissue, their

status will be uncertain.

The five previously reported mutations include two single basepair deletions

causing frameshifts, and three missense mutations. Apart from the missense

mutation E216D, which was found to occur de novo in two unrelated individuals

with TOF (Nemer G et al., 2006), all of them segregate with disease in the

families in which they have been reported, and none have been found in large

numbers of controls.

Tabl

e 3.

3: M

utat

ions

in G

ATA

4.M

utat

ion

G29

6SG

296S

G29

6Sc.

1075

delG

c.

1074

delC

S5

2F

E216

DR

efer

ence

(G

arg

et a

l.,

2003

) (S

arko

zy A

et

al.,

2004

) (S

arko

zy A

et

al.,

2004

) (G

arg

et a

l.,

2003

;H

iraya

ma-

Yam

ada

et

al.,

2005

)

(Oku

bo e

t al.,

20

04)

(Hira

yam

a-Y

amad

a et

al

., 20

05)

(Nem

er e

t al

., 20

06)

#het

eroz

ygou

s

132

35

93

1x2

ASD

162

67

113

-VS

D3

-1

--

--

PS6

23

-2

--

Non

pene

tran

t-*

--

--

--

Oth

er C

HD

(n

o)A

R, M

R,

PD

A, A

VS

D

--

Dex

troca

rdia

-

-TO

F(2)

Loca

tion

Adj

acen

t to

zinc

fing

er

and

nucl

ear

loca

lizat

ion

sign

al (N

LS)

Adj

acen

t to

zinc

fing

er

and

nucl

ear

loca

lizat

ion

sign

al (N

LS)

Adj

acen

t to

zinc

fing

er

and

nucl

ear

loca

lizat

ion

sign

al (N

LS)

3’ c

odin

g re

gion

3’ c

odin

g re

gion

TAD

1Zi

nc fi

nger

Com

men

t

Mut

atio

ns a

re li

sted

in o

rder

of p

ublic

atio

n, e

xcep

t tha

t a s

econ

d re

port

of a

mut

atio

n is

list

ed a

djac

ent t

o th

e or

igin

al re

port.

# h

eter

ozpr

oven

het

eroz

ygou

s fo

r the

mut

atio

n. N

umbe

r with

AS

D o

r oth

er a

bnor

mal

ities

may

be

grea

ter t

han

this

if th

ere

are

mul

tiple

affe

cted

indi

vidu

als

in th

e pe

digr

ee w

ho c

ould

not

be

geno

type

d (e

g de

ceas

ed in

divi

dual

s). N

onpe

netra

nt: c

onfir

med

mut

atio

n po

sitiv

e O

R o

blig

ate

heto

rozy

gote

, kno

wn

norm

al

card

iac

stat

us. O

ther

CH

D: m

ay o

verla

p w

ith A

SD

/VS

D c

ateg

orie

s if

>1 c

ardi

ac d

iagn

osis

; (no

) num

ber i

f mor

e th

an o

ne a

ffect

ed in

ped

igre

e. A

R, a

ortic

re

gurg

itatio

n; M

R, m

itral

regu

rgita

tion;

PD

A, p

aten

t duc

tus

arte

riosu

s; P

S, p

ulm

onar

y st

enos

is, T

OF,

tetra

logy

of F

allo

t; TA

D1,

tran

sact

ivat

ion

dom

ain.

*t

wo

oblig

ate

hete

rozy

gote

s no

t kno

wn

to h

ave

CH

D w

ere

unav

aila

ble

for e

valu

atio

n

ygou

s: n

umbe

r

2un

rela

ted

indi

vidu

als

with

deno

vom

utat

ion

112

114

In expression studies, both G296S and c.1075delG showed reduced activity,

with the effect being most pronounced with the frameshift mutation (Garg et al.,

2003). GATA4 protein with the hypomorphic G296S allele showed reduced

DNA binding compared with wild-type protein, and there was evidence that

interaction with TBX5 was disrupted. This mutation appears to be particularly

associated with pulmonary stenosis (PS), with 11/24 reported affected

individuals having PS (all of them also have ASD). Somewhat confusingly, the

family with c.1075delG from the first report of GATA4 mutations was reported a

second time, in a paper first-authored by one of the authors of the original paper

(Hirayama-Yamada et al., 2005). No additional clinical information or laboratory

information was provided in this subsequent paper, however.

No functional assays have been done on the single base-pair deletion

c.1074delC (Okubo et al., 2004). However, this mutation results in a frameshift,

with a premature stop codon resulting at amino acid 403. This, combined with

the location of the mutation very close to the well-studied and very similar

c.1075delG, plus the segregation of the mutation with CHD in the family in

which it is reported, leave little doubt as to its pathogenicity.

The missense mutation S52F is located in a known functional domain (the

transcriptional activation domain (TAD1)) and segregated with CHD in a small

family (3 affected individuals in two generations) (Hirayama-Yamada et al.,

2005). No functional assays have been reported on this sequence change but it

is likely to be pathogenic.

The missense mutation E216D, identified as a de novo change in two unrelated

individuals with TOF, showed normal cellular localization and binding to the

consensus GATA binding site in assays in rat cells. However, transcriptional

activity was modestly, but statistically significantly, reduced, by about 50%

(Nemer G et al., 2006). This is a highly conserved residue. although the amino

acid change is relatively conservative – both glutamate and aspartate are polar

amino acids, although glutamate is larger than aspartate. Overall, the status of

this mutation is uncertain based on currently available evidence.

115

3.3.1 Subjects screened for GATA4 mutationsSubjects screened for GATA4 mutations were as described in section 2.2.2.3.

There were two control groups. The first group had had trans-oesophageal

echocardiography (TOE) for a variety of indications and were known to have

structurally normal hearts, and in particular intact atrial septa (“TOE controls”).

The second group were ascertained by Dr Lyn Griffiths and were unselected

except for Caucasian ancestry (see below). Results of testing for the S377G

variant (see below) were confirmed by commercial SNP analysis (Genera

Biosystems). Contributions by EK to this work were participation in study

design, patient recruitment, management of clinical data including coordination

of a retrospective data collection exercise to obtain ethnicity information (with

the assistance of Ms Haley Crotty and Ms Janan Fornusek); follow-up and

clinical assessment of family members, and data analysis.

3.3.2 Results of sequencing and cytogenetic analysis Two missense changes, A411V and D425N, were identified, in subjects with

ASD and large PFO + mitral regurgitation respectively (Figure 3.2).

Additionally, when the proband in family 1006 was evaluated, the history and

examination findings (see below) raised the possibility of a chromosomal

abnormality. Cytogenetic analysis confirmed the presence of a deletion of 8p23.

A previously reported sequence variant, S377G, was identified in a number of

cases and controls and further studies were done to investigate the possibility

that this may be a contributor to CHD (see below; Tables 3.4 and 3.5).

3.3.2.1 Family 1012 – GATA4 variants A411V and S377G In this family (Figure 3.2a), the proband (II:1) had ASD diagnosed aged 9 weeks

following identification of a cardiac murmur at routine examination aged 6

weeks. The pregnancy and birth history were unremarkable. At 20 months a

10mm diameter lesion was repaired surgically. Subsequently he was in good

health apart from mild asthma. The only family history of note was that his

116

mother had unilateral breast cancer diagnosed at the age of 29. There was no

other family history suggestive of a familial cancer syndrome.

On examination at the age of 21, II:1 was not dysmorphic. He had mild bilateral

fifth finger clinodactyly. Individual II:1 was found to be heterozygous for both

A411V and S377G. The latter variant is discussed in more detail below.

Sequencing of GATA4 in all first-degree relatives showed that both of the

proband’s sisters (II:2 and II:3) were heterozygous for S377G but not for A411V.

The proband’s father, I:I, proved to be homozygous for both S377G and

heterozygous for A411V. He was a healthy 53 year old man, with no past

medical history of note. Transoesophageal echocardiography by Prof Michael

Feneley was normal, and in particular the atrial septum appeared intact. A

bubble study did not reveal any evidence of PFO.

The alanine at position 411 is not in a recognised functional domain. The

change to valine is relatively conservative, substituting a large nonpolar amino

acid for a small nonpolar amino acid. This residue is not evolutionarily

conserved – at the same position, mice share the alanine with humans, but in

rat a proline is sustituted, in chicken a glutamine and in Xenopus histidine. This

variant has been reported previously as a rare polymorphism (minor allele

frequency <0.01) (Poirier et al., 2003). In this family, one of two individuals

heterozygous for A411V has a structurally and functionally normal heart. Taking

all these factors into consideration, it is likely that A411V is a polymorphism with

no pathogenic impact. A role in multifactorial causation of ASD cannot be

excluded.

BC

A

Figu

re 3

.2: F

amili

es w

ith G

ATA

4 va

riant

s an

d 8p

23 d

elet

ion.

Seq

uenc

ing

done

by

Dr C

hang

baig

Hyu

n.

A.F

amily

101

2 (A

411V

+ S

377G

) B.F

amily

z10

(D42

5N) C

.Fam

ily 1

006

(del

etio

n). A

rrow

s in

dica

te re

sidu

es a

ffect

ed b

y m

utat

ion.

WT

= w

ild ty

pe,

de

l = d

elet

ion

117

118

3.3.2.2 Family z10 – GATA4 variant D425N In this family (Figure 3.2b) the proband, I:2, had mitral valve replacement aged

65 for mitral stenosis presumed to be due to rheumatic heart disease. A large

PFO with bidirectional shunting was identified during pre-operative evaluation

for this surgery. Other cardiac findings included atrial fibrillation, severe tricuspid

regurgitation and mild thickening of the aortic valve leaflets with normal function.

The only other known medical history was cholecystectomy. It was not clear

whether or not she had actually had rheumatic fever in childhood. Unfortunately,

II:1 died aged 76, between the time that she was recruited to the study and the

finding that she was heterozygous for D425N. Her sons (II:1 and II:2) were

uncertain of the cause of death. They agreed to venepuncture (collected during

a home visit) for the purpose of the study, but were unwilling to travel to a

hospital in order to have echocardiography (clinical assessment and

venepuncture were conducted at the home of one of them). This was

disappointing, particularly given that both proved to be heterozygous for D425N.

On clinical examination, neither was dysmorphic and neither had clinical

evidence of CHD. II:1 had a unilateral single transverse palmar crease. There

was no other family history of note.

D425N has not been reported previously. The aspartate residue at position 425

is not in a recognized functional domain. It is highly conserved, being invariate

in mouse, rat, chicken and frog. Nonetheless, the lack of definitive cardiac

assessment in individuals II:1 and II:2 means that the status of this sequence

variant is uncertain.

3.3.2.3 Family 1006 – 8p23 deletion At the time of recruitment to the study, the proband in this family (II:2) was 17

years old. She had been born at term following an uncomplicated pregnancy.

Birth weight and length were on the 10th centile, but head circumference was

on the 50th. ASD and PDA were identified in the newborn period, as was a left-

sided diaphragmatic hernia. The diaphragmatic hernia was repaired in the

newborn period and the ASD at the age of 4 years. Medical problems in

childhood included mild asthma and several episodes of pneumonia. Cognitive

119

development was delayed. Formal developmental assessment at ages 6 and 8

placed her in the mild range of intellectual disability (IQ 60-70), and she

required special schooling. Cerebral CT scan and karyotype at 6 years were

normal.

There was a steady increase in weight during her teenage years. She was not

hyperphagic. Aged 17, she had a mildly low serum calcium at 2.17 mmol/L (NR

2.25-2.58) and fasting insulin was high at 47.4mU/L (NR 0.8-16), suggesting

insulin resistance. Other endocrine investigations were normal and treatment

with modified diet and metformin were commenced.

On examination aged 17, weight was 71.1kg (90th centile) , height 141.8cm

(<3rd centile) and head circumference 52.5cm (2nd centile). She had deepset

eyes, short palpebral fissures and a smooth philtrum. She had truncal obesity.

Her 4th and 5th metacarpals were mildly short bilaterally. She had striae on her

shoulders but no cutaneous calcification. She was pubertal, at Tanner stage 5

for both breast and pubic hair development.

These features raised the possibility of a chromosomal disorder. It was more

than 10 years since the previous karyotype and given advances in cytogenetic

techniques, chromosomal analysis on blood was repeated. This showed a

deletion at chromosome 8p23, with the karyotype being

46,XX,del(8)(p23.1p23.3). This includes the locus of GATA4. Because of the

similarities of the phenotype to that seen in Albright Hereditary Osteodystrophy

(AHO), sequencing of GNAS1 was also requested and this was kindly done by

Dr Eileen Shore; no mutation was identified.

The S377G variant was also identified in members of this family and in

particular the proband was found to be hemizygous for S377G.

Deletions of 8p23, encompassing GATA4, were discussed in section 1.7.1.2. It

is likely that the high incidence of CHD in patients with these deletions is at

least in part due to haploinsufficiency for GATA4. Although a phenotype similar

to AHO has not previously been reported in patients with such deletions, other

120

features seen in II:2 have been previously reported. Apart from intellectual

handicap, which is common to most chromosomal deletions, it is noteworthy

that congenital diaphragmatic hernia (CDH) has been repeatedly reported in

association with deletions of 8p23 – there have been more than 10 such cases

reported (Holder et al., 2007). Since CDH has not been reported in families with

GATA4 mutations, it is likely that deletion of another gene or genes within the

region is responsible for CDH.

3.3.3 The common variant S377G – possible role in PFO with stroke The GATA4 variant S377G has previously been reported as a common SNP

with a minor allele frequency of 0.11. Given the role of GATA4 in dominant

CHD, this allele is a candidate for involvement in multifactorial causation of

CHD. Allele frequency can vary between ethnic groups due to selection or

founder effects. Therefore, before performing an association study, the allele

frequency was determined in a previously established set of globally distributed

indigenous populations (Martinson et al., 2000). This was done by Dr Jeremy

Martinson (Table 3.4).

High allele frequencies were observed in populations of European and

American Caucasian, Middle Eastern and – to a lesser extent – Indian and

Hispanic-American descent. African, East Asian and Pacific Islander

populations had very low frequencies. These data suggest a relatively recent

and Caucasian origin for S377G.

The association study was therefore restricted to Caucasian subjects (Table

3.5). There was some variation in both heterozygosity and allele frequency

between groups. The differences between the ASD and “other CHD” groups

and controls were not statistically significant. However, an excess of S377G

was observed in subjects with PFO with stroke, with an allele frequency of 0.18.

Tabl

e 3.

4S3

77G

alle

le d

istr

ibut

ion

in in

dige

nous

hum

an p

opul

atio

ns

Tota

l n

Wild

type

* S3

77G

het

S3

77G

hom

S3

77G

H

eter

ozyg

osity

* Hom

ozyg

osity

* A

llele

hehe

te

Freq

uenc

y*__

____

____

____

____

___

____

____

____

____

____

_ __

____

____

____

____

__

n %

n

%

n %

%

Cau

casi

an

Cau

casi

an U

S

480

382

79.6

%

88

18.3

%

10

2.1%

11

.3%

UK

42

27

64

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13

31

.0%

2

4.8%

20

.2%

Cyp

rus

37

27

73.0

%

9 24

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1

2.7%

14

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Rus

sian

Cau

casu

s 11

2 84

75

.0%

26

23

.2%

2

1.8%

13

.4%

Rus

sia

47

33

70.2

%

14

29.8

%

0 0.

0%

14.9

%

Asi

an

Yem

en

89

64

71.9

%

24

27.0

%

1 1.

1%

14.6

%

In

dia/

Pak

ista

n 11

1 94

84

.7%

17

15

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0

0.0%

7.

7%

H

ong

Kon

g 57

57

10

0.0%

0

0.0%

0

0.0%

0.

0%

Taiw

an

92

9210

0.0%

00.

0%0

0.0%

0.0%

Afr

ican

M

adag

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r 11

7 11

6 99

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1

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l Afri

can

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44

44

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0 0.

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Paci

fic Is

land

er

Pap

ua N

ew G

uine

a 88

88

100.

0%

0 0.

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0 0.

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0.0%

*Ref

lect

s th

e pr

eval

ence

of t

he A

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the

S37

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ata

prov

ided

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Dr J

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het

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m =

hom

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121

122

$ In

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TA

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2.1

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Tabl

e 3.

5: S

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in C

auca

sian

Sub

ject

s A

SD$

Oth

er C

HD

*PF

OSt

roke

/no

TOE

Con

trol

s (n

oC

auca

sian

PF

O/A

SD^

PFO

/ASD

/Str

oke)

%Po

pula

tion

Con

trol

s (n

=131

) (n

=109

) (n

=66)

(n

=113

) (n

=391

) W

ith S

trok

e#W

ithou

t Str

oke##

(n=5

9)

(n=2

9)

Age

at S

tudy

0-

77

0.1-

44.3

19

-86

38-8

8 29

-87

21-8

9 16

-84

(yrs

) (m

ean:

21.4

) (m

ean:

3.9)

(mea

n:51

.7)

(mea

n: 6

5.9)

(m

ean:

65.

9)(m

ean:

63.3

) (m

ean:

53.

1)

Mal

e (%

) 51

(39.

2)

69(6

3.3)

33

(56.

9)

20(6

9.0)

44(6

6.7)

66

(58.

4)

200(

50)

Fam

ily H

isto

ry o

f C

HD

(%)

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29(1

4.7)

(2un

k)13

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9)

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(10.

5)(1

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4/27

(14.

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(10.

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) 18

(13.

7)

24(2

2.0)

15

(25.

4)

8(27

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16(2

4.2)

17

(15.

0)

83(2

1.2)

GA

TA4

S377

G

Hom

ozyg

ous

(%)

3(2.

3)

3(2.

8)

3(5.

1)

0(0.

0)

0(0.

0)

2(1.

8)

7(1.

8)

GA

TA4

S377

G

Alle

le F

requ

ency

0.

092

0.14

0.

18**

0.14

0.

12

0.09

3 0.

12

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re A

ther

oma

N/A

N/A

0/44

(0.0

)(14u

nk)

3(10

.3)

18(2

7.3)

18

/109

(16.

5)(4

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N/A

Atr

ial F

ibril

latio

n N

/AN

/A1(

1.7)

N

/A13

(19.

7)

N/A

N/A

124

When compared with the TOE control group, this was statistically significant

(p=0.022, chi square test) . This is arguably the most appropriate comparison

given the high prevalence of PFO in the general population – it is likely that

approximately 25% of the unselected Caucasian controls have PFO (Hagen et

al., 1984).

These findings suggest the possibility that S377G may be implicated in the

causation of PFO. This finding should be interpreted with caution. Multiple

comparisons were made. ASD did not show an increase in S377G as might be

expected given the presumed shared aetiology of ASD and PFO (see section

1.6.5). It is possible that if there is a relationship between S377G and PFO with

stroke, the effect is not an increase in the risk of PFO but rather a direct effect

on stroke risk. GATA4 is expressed in liver as well as heart, and has been

implicated as a transcriptional repressor of the gene for apolipoprotein(a), high

levels of which are an independent risk factor for atherosclerosis and stroke

(Negi et al., 2004). However, if there were such an independent effect, an

increased allele frequency would be expected in the “stroke/no PFO/ASD”

group and this was not seen.

At present, despite these data, S377G is most likely to represent a

polymorphism of no clinical significance. However, a replication study is under

way and if this finding can be reproduced it will have important implications for

the pathogenesis of cryptogenic stroke.

3.3.4 Role of GATA4 mutations in nonsyndromal ASD The findings reported here are consistent with previous studies. It appears that

GATA4 mutations are somewhat less common than mutations in NKX2-5. If

A411V and D425N are considered polymorphisms, 1/131 subjects with ASD

had a whole gene deletion, and 0/19 with a family history of CHD had a GATA4

mutation identified. Sarkozy and colleagues found mutations in 2/29 probands,

including 16 familial cases (Sarkozy A et al., 2004). In a subsequent study, the

same group studied 42 subjects with AVCD for mutations in GATA4 and

125

CRELD1 but found no mutations (Sarkozy et al., 2005) (not included in the

combined figures below). Zhang and colleagues found mutations in 0/99

subjects with CHD, of which however only 6 had ASD (no information available

regarding family history) (Zhang L et al., 2006). Schluterman and colleagues

found no mutations in 157 probands with CHD, of whom 14 had ASD (no family

history information available) (Schluterman M et al., 2007). Nemer and

colleagues found mutations in 0/94 subjects with a variety of forms of CHD,

including 0/12 with ASD (their findings in TOF are discussed above) (Nemer G

et al., 2006). Hirayama-Yamada and colleagues found mutations in 2/16 familial

cases (Hirayama-Yamada et al., 2005), but this was not part of a larger study of

unselected cases.

Combining all of these data, 3/192 unselected cases (the majority being from

this study) had a GATA4 mutation or deletion (1.5%), and 4/51 cases with a

family history had a GATA4 mutation (7.8%). Of the three unselected cases,

one had extracardiac malformations, intellectual disability, short stature and

dysmorphic features as clues to the chromosomal basis of her problems, and

the other two both had a family history of CHD. Thus, screening for mutations in

GATA4 appears indicated in affected individuals with a family history of CHD,

particularly in families in which the predominant phenotype is ASD +/- PS.

3.4 Mutations in TBX20 are associated with diverse cardiac pathologies, including abnormal septation and valvulogenesis, and cardiomyopathy As discussed in 1.5.3, the T-box transcription factors share a highly conserved

DNA-binding domain called the T-box. TBX20 is an ancient member of this

family of genes, related to TBX1. In mice, Tbx20 is expressed in cardiac

progenitor cells, in the developing myocardium and in endothelial cells

associated with the endocardial cushions, which are the precursors for the

cardiac valves and AV septum (Stennard et al., 2003). The Tbx20 protein

contains both transcriptional and repression domains, and it physically or

genetically interacts with Nkx2-5, Gata4, and Tbx5 (Brown et al., 2005). Tbx20

null mice have profoundly abnormal cardiac development (Stennard and

Harvey, 2005) , with a rudimentary heart lacking chamber myocardium. There is

126

evidence that Tbx20 directly represses Tbx2, which in turn functions as a

repressor in the development of both chamber and nonchamber myocardium

(Stennard and Harvey, 2005). Mice heterozygous for a Tbx20 null mutation

have an atrial septal phenotype similar to mice heterozygous for Nkx2-5

mutations. They have mild atrial septal abnormalities, including an increased

prevalence of PFO, atrial septal aneurysm (ASA) and ASD, as well as mild

dilated cardiomyopathy (Stennard and Harvey, 2005).

TBX20 mutations have not previously been associated with human disease, but

the information described above, plus the knowledge that mutations in TBX5,

NKX2-5 and GATA4 are associated with CHD above made TBX20 a logical

candidate gene for study in human subjects with CHD.

3.4.1 Subjects screened for TBX20 mutationsA total of 353 individuals with CHD were screened for mutations in TBX20 by

sequencing of the coding regions of the gene and intron-exon boundaries. This

sequencing was done by Leticia Castro, Changbaig Hyun and Andrew Cole. At

this stage of the study, subjects were recruited mainly from the Children’s

Hospital at Westmead and St Vincent’s Hospital, with only 10% being recruited

from Sydney Children’s Hospital. Contributions by EK to this part of the study

were limited to participation in study design, patient recruitment, particularly

recruitment and clinical evaluation of family members of probands with possible

mutations, and data analysis and figure preparation (but not laboratory

benchwork) for the transcriptional assays, and to a lesser extent the Xenopus

studies.

Patient characteristics are summarised in Table 3.6

127

Table 3.6 Characteristics of subjects sequenced for TBX20 mutations

Phenotypes ASDonlya

ASD+Other CHDb

VSDonly

VSD+other CHDc

OtherCHD

No of subjects: Total 151 24 41 22 115 Male 53 16 23 10 70 With positive family historye

20 5 4 2 8

With AV conduction blockf

5 3 0 1 0

With atrial fibrillation

8 0 0 0 0

With LV dysfunction

5g 1h 0 0 0

Mean (range) age at enrolment, in years

26 (0-79)

12(0.2-62) 6 (0-68)

6(0-59) 4 (0-16)

a. Three adults had mitral valve prolapse.

b. Including sinus venosus ASD (n = 13; all others are secundum ASD); partial

anomalous pulmonary venous connection (n = 6); left SVC (n=2); valvular lesions (n =

5), including one example of supravalvar mitral ring; coarctation of the aorta (n = 1).

c. Including ASD (n = 7), left SVC (n = 5), aortic valve abnormalities (n = 5), coarctation

of the aorta (n = 4), double-chambered right ventricle

(n = 2), pulmonary stenosis (n = 1), patent ductus arteriosus (n = 1), and partial

anomalous venous connection (n = 1). One subject had mitral valve prolapse, and one

had supravalvar mitral ring.

d. Including outflow tract lesions (n = 75), atrioventricular septal defect and variants (n

= 18), functional single ventricle (n = 17, including 2

with mitral valve atresia), heterotaxy (n = 2), cor triatriatum (n = 1), and Ebstein

anomaly (n = 1).

e. Positive family history was defined as at least one first-degree relative affected with

CHD. Thirty-seven subjects were found to have syndromes known to be associated

with CHD, including trisomy 21 (n = 20) and 22q microdeletions (n = 12). However, only

two subjects with a positive family history were from this group.

f. First-degree or complete heart block. Complete and partial right bundle-branch block

were not included in this group. Two subjects with ASD had left bundle branch block.

g. All subjects were aged 1-55 years. Subjects had normal LV size and contractility but

impaired diastolic relaxation (n = 2) or impaired systolic function with (n = 2) or without

(n = 1) LV dilation.

h. This patient (family 2, individual III:4) was positive for the TBX20 mutation Q195X.

3.4.2 TBX20 mutations Mutations in TBX20 were identified in two families. These were 456C>G, coding

for the protein change I152M, and 583C>T, coding for Q195X. In addition, a

third change, 626C>T, coding for T209I, was identified in another family, but did

not segregate with CHD in that family (Figure 3.3).

Family 9001

Family z103

Family WM1

Figure 3.3 Families with TBX20 mutations. Relevant sequence

chromatograms of patient and wild-type DNA are shown. The arrow under the

sequence indicates the detected single-nucleotide change or the corresponding

normal sequence. Individual II:4 in family z103 was not available for genotyping.

128

129

3.4.2.1 Family 9001: TBX20 mutation 152M This mutation was identified in a family in which septal defects affected

members of three generations. The proband (III:1) had ASD, which was

corrected surgically in childhood. Her mother (II:2) had a large PFO with a

permanent left to right shunt. The proband’s grandmother (I:2) had a small VSD.

Cardiac valves and LV function were normal in all family members. There were

no limb anomalies or other malformations, and the affected individuals did not

have AV conduction abormalities or arrhythmias. The mutation was absent in

>450 controls.

3.4.2.2 Family z103: TBX20 mutation Q195XIn family 2, in which the Q195X mutation was identified, the phenotype was

more complex and varied considerably between affected family members.

Congenital heart disease included atrial septal defect and coarctation of the

aorta (in the proband, III:4), and a strongly suggestive history of congenital

heart disease in individual II:6. Although records for this woman are no longer

available, she was said to have been born with a “hole in the heart” and was

scheduled for corrective cardiac surgery at the time of her death in a motor

vehicle accident, aged in her early 20s. Valvular dysfunction, primarily affecting

the mitral valve, was present in several affected family members; individual II:2

has marked mitral valve prolapse (but only mild mitral regurgitation), and

individual I:2 had a mitral valve replacement in the 1960s for presumed

rheumatic heart disease. While it is possible that this was a correct diagnosis,

the presence of mitral valve disease in other family members suggests that this

may have been a manifestation of the family’s TBX20 mutation.

Individual III:2 died of cardiac failure in 1967, aged 11 months. At 10 months,

cardiac catheterization showed evidence of severe pulmonary hypertension,

right ventricular hypertrophy and and enlarged left atrium, presumably due to

mitral stenosis. At post mortem examination, she had a small mitral valve ring

and thickened valve leaflets. In addition, she was found to have endocardial

fibroelastosis, more pronounced in the left ventricle than the right, right

ventricular hypertrophy and hypoplasia of the left ventricle. The atrial septum

was intact. Her sister, III:3, died in 1977, aged 7, of heart failure following a

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long history of pulmonary hypertension. She was first investigated with cardiac

catheterization aged 3, at which time she was asymptomatic but was shown to

have moderate pulmonary hypertension. This progressed over the next 4 years.

No post mortem examination was done. The proband, III:4, also had pulmonary

hypertension in childhood (in addition to his cardiac structural malformations),

but this resolved by early adulthood. However, at the age of 34 he was found to

have a mildly dilated left ventricle with mild global impairment of systolic

function. His mother, II:2, also has evidence of a mild left ventricular dilated

cardiomyopathy, with unusual apico-lateral hypertrophy recorded. Q195X was

not identified in >300 controls. There are no noncardiac malformations in

members of this family.

3.4.2.3 Family WM1: TBX20 polymorphism T209IIn family WM1, in which the TBX20 mutation T209I was identified, the proband

(II:3) had an ASD, one brother (II:4) had a VSD and one sister (II:6) had an

ASD. Several members of this family declined to take part in the study (clinical

studies of this family were done by A/Prof David Winlaw and members of his

lab). Both II:3 and II:4 were heterozygous for the T209I mutation, but II:6 was

homozygous for the wild-type allele. II:3 also had features of Klippel-Feil

syndrome, in which CHD is relatively common (Tracy et al., 2004). Thus, the

T209I mutation is not segregating with the cardiac phenotype in this family.

While it is possible that II:6 represents a phenocopy, the significance of the

mutation in this family is uncertain, based on pedigree analysis. It seems most

likely that this is a polymorphism of no pathological significance, although it is

possible that it does play some role in the pathogenesis of CHD in the family

members who carry it. If it is a polymorphism, it is a rare one – it was not found

in >300 controls.

3.4.3 Functional and other studies of the TBX20 mutationsWork done by a number of others, outlined below, supported the pathogenicity

of the I152M and Q195X mutations, and provided some evidence of abnormal

function associated with T209I (Kirk et al., 2007). Specifically, transcriptional

assays were done by members of Prof Richard Harvey’s lab, including Drs

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Mauro Costa, Orit Wolstein and Guanglan Guo. Work in Xenopus embryos was

done by Drs Aaron Zorn and Scott Rankin. Biophysical studies were performed

by Drs Margaret Sunde and Joel Mackay.

3.4.3.1 Transcriptional assays of Tbx20 functionThe mouse Tbx20 protein exists in long (Tbx20a) and short (Tbx20c) isoforms .

The long isoform has weak transcriptional activity, when assayed alone,

compared with the short isoform, because of dominant effects of its C-terminal

trans-repression domain (absent in the short isoform) (Stennard et al., 2003).

However, when collaborating with Nkx2-5 and Gata4, the long isoform has

strong transcriptional activity. In a transcriptional assay in 293T cells (Figure

3.4), measuring activation of the Nppa promotor in the presence of Tbx20c,

activity of Tbx20 I152M was significantly reduced (p=0.05), and the activity of

Tbx20c with the Q195X was severely impaired. Interestingly, in this assay the

T209I change was also associated with reduced transcriptional activity

(p=0.008). When assayed together with Nkx2-5, Tbx20a I152M activity was

slightly elevated (p=0.05); Tbx20a T209I activity was unchanged, and not

surprisingly Tbx20a Q195X activity was again severely reduced (p=0.02).

3.4.3.2 Xenopus embryo gastrulation assay Microinjection of synthetic Tbx20a wildtype mRNA into Xenopus laevis embryos

severely disrupts gastrulation (figure 3.4) . This capacity was preserved with

Tbx20a I152M and Tbx20a T209I mRNA, but was abolished when Tbx20a

Q195X mRNA was used.

3.4.3.3 Protein modellingA model of the Tbx20 T-box was constructed by Drs Margaret Sunde and Joel

Mackay, using the known crystal structure of the human TBX3 T-box. This

model placed the side chain of the I152 residue in the core of the T-box, packed

against other hydrophobic residues. Replacement of isoleucine by methionine in

other proteins has been shown to have a destabilising effect (Gassner et al.,

1996; Ohmura et al., 2001). The T209 residue is also located in the T-box, at

the DNA-interaction face. Substitution of threonine with isoleucine at this point

has the potential to disrupt H bonds which stabilise the chain in the DNA binding

region.

The Q195X mutation results in truncation of the protein within the T-box – thus

its severe effects on Tbx20 function in the transcription and Xenopus assays

are not surprising.

Figure 3.4 Transcription studies and Xenopus embryo gastrulation assay. a, 293T cell-transfection assay measuring activation of the Nppa promotor in the

presence of Tbx20c (short isoform without C-terminal trans activation and trans

repression domains). b, COS cell-transfection assay measuring activation of the Nppa

promotor in the presence of Tbx20a (full-length isoform), Nkx2-5 and Gata4, alone or in

combination. The combination of all three factoris is required for synergistic activation.

c. Ability of wild type and mutant Tbx20a to disturb gastrulation in Xenopus embryos

after microinjection of mRNAs into fertilized eggs. Assays done by Mauro Costa, Orit

Wolstein and Guanglan Guo (transcription assays) and Aaron Zorn and Scott Rankin

(Xenopus). Statistical analysis of transcription assays and figure preparation by EK

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3.4.4 Significance of mutations in TBX20There is strong evidence that the cardiac pathology seen in members of families

1 and 2 is caused by the unique mutations in TBX20 found in each. Neither

mutation was found in >300 controls. The missense mutation, I152M, affected a

highly conserved amino acid in the T- box DNA binding domain. It segregated

with cardiac septal abnormalities over three generations in the affected family.

Biophysical studies by Drs Margaret Sunde and Joel Mackay showed

abnormalities including a fourfold reduction in the DNA-binding “on” rate (Kirk et

al., 2007)). Transcriptional activity of the short isoform of mouse Tbx20 was

significantly reduced, by ~40%, although the overexpression assays relying on

a synergistic interaction with other transcription factors did not confirm this.

These data point to I152M causing reduced TBX20 function, although it is

clearly not a null allele.

By contrast, the Q195X mutation results in a functionally inactive protein. It

introduces a stop mutation within one of the exons coding for the T-box DNA-

binding domain, a key functional domain of the protein. The resulting TBX20

protein is truncated within the T-box and completely lacks the trans activation

and trans repression domains located in the C terminus of the protein (Stennard

et al., 2003). In all functional assays the Q195X mutation had severely reduced

activity. Although only two affected individuals within the family were alive and

available for genotyping, both are heterozygous for the mutation. Moreover, the

pattern of congenital heart disease and cardiomyopathy within the family is

consistent with autosomal dominant inheritance.

The range of phenotypes seen in this family is remarkable for its range, and

particularly the occurrence of CHD and cardiomyopathy. Mitral valve structural

malformations were prominent, with both mitral valve stenosis and prolapse

occurring in different members of the family. Congenital mitral valve stenosis is

a rare but serious malformation, generally associated with poor prognosis,

whereas mitral valve prolapse is common, and is usually detected later in life.

The causes of both of these types of mitral valve pathology are unknown, but it

appears that both can arise from loss of TBX20 function. As discussed in

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Chapter 1, mouse Tbx20 is expressed strongly in cells of the endocardial

cushions and, subsequently, the cardiac valves (Stennard and Harvey, 2005;

Stennard et al., 2003). Consistent with a functional role for TBX20 in this tissue,

mouse embryos with a partial RNAi-mediated knockdown of Tbx20 expression

show severely hypoplastic and/or immature atrioventricular valves (Takeuchi et

al., 2005).

Dilated cardiomyopathy (DCM) was present with structural CHD in two

individuals heterozygous for the Q195X mutation. While this could reflect

pathological decompensation after functional adaptation to structural defects,

the impression of the treating cardiologists was that the degree of DCM was

greater than could be explained on this basis in both affected individuals.

Consistent with the idea that the TBX20 mutation may cause DCM

independently of structural anomalies, mild DCM was observed in mice

heterozygous for Tbx20 mutation but without signficant structural anomalies

(Stennard and Harvey, 2005). A possible explanation is that the TBX20

mutation provides a sensitized developmental template for adult-onset DCM.

Heart failure is also seen in some patients carrying NKX2-5 mutations, years

after correction of structural CHD (Schott et al., 1998; Benson et al., 1999). In a

mouse model of Nkx2-5 deficiency, DCM is present even in fetal life (Elliott et

al., 2006). Familial DCM is highly genetically heterogeneous, with most

mutations occurring in genes encoding myofilament, cytoskeletal, energy, and

Ca2+ handling proteins (Fatkin and Graham, 2002), rather than transcription

factors. However, mutation of the transcriptional coactivator, EYA4 (MIM

603550), causes familial DCM and sensorineural hearing loss (Schonberger et

al., 2005).

The pathology seen in the family with the Q195X mutation is generally more

severe than in the family with the I152M mutation, and this may reflect the more

severe disruption to the protein. It seems likely that for this mutation at least, the

pathogenic effects of the mutation result from haploinsufficiency, although a

dominant-negative effect of the mutant protein cannot be excluded.

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The third change detected in this study, T209I, did not segregate with pathology

in the proband’s family – similarly to the E21Q mutation in NKX2-5 described in

section 3.2.2.2. The affected residue is located within the T-box, subtle

transcriptional defects and instability in bacteria were observed for this allele

and it was not found in controls. However, the lack of segregation with

phenotype makes it difficult to ascribe it a role in causing pathology in this

family. Although every effort was made to prevent this, the possibility that the

non-segregation was due to one or more sample handling errors cannot be

excluded. Additional samples from the family members were not available to

check this.

Mutations in TBX20 have not previously been linked to human disease, but from

the data presented here there is little doubt that, in some families at least,

TBX20 mutation is responsible for CHD, particularly septal and mitral valve

abnormalities, and DCM. Although mutations were present in only 0.6% (2 of

352) of probands with CHD, among those with a family history 5.1% (2/39)

carried a TBX20 mutation.

3.5 Conclusions: the role of mutations in NKX2-5, GATA4 and TBX20 inhuman disease The hypothesis that mutations in genes responsible for dominant forms of ASD

might contribute to common forms of ASD is only partially borne out by the

studies reported here. Mutations in NKX2-5, GATA4 and TBX20 accounted for

1.8%, 1.5% and 0.6% respectively of unselected cases of ASD. The great

majority of individuals with mutations had a positive family history, and this is

reflected in the finding that 10.7%, 7.8% and 5.1% of familial cases respectively

(including all reported studies) had mutations in one of these three genes.

Combining these figures, nearly a quarter of familial ASD is accounted for by

mutations in one of these three genes. It seems likely that studies of the other

known nonsyndromal ASD genes, MYH6 and ACTC, will produce similar

results. This will leave a considerable percentage of familial ASD unaccounted

for. Some cases will represent recurrences in families due to multifactorial

inheritance, rather than autosomal dominant inheritance, but it is likely that

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there is considerable genetic heterogeneity yet to be unravelled, accounting for

the bulk of familial cases. Chapter 4 describes an effort to identify an additional

gene contributing to this heterogeneity.

There are tantalizing hints here that variation in these genes may play a role in

the multifactorial causation of ASD. In each of these three genes, sequence

changes resulting in altered amino acid structure have been identified, which

although not found in large numbers of controls, do not segregate with disease

or meet other criteria for Mendelian mutations. Determining whether such

variants are truly relevant to common forms of ASD will require large scale

association studies of a type not yet conducted. The exception is the S377G

variant, which remains of uncertain significance despite the association study

reported here, but may contribute to PFO and cryptogenic stroke.

4. Atrial septal defect and Marcus Gunn phenomenon: further evidence for clinical and genetic heterogeneity in autosomal dominant atrial septal defect

4.1 Introduction As discussed in Chapters 1 and 3, to date mutations in six genes have been

associated with autosomal dominant ASD – TBX5 (as part of Holt-Oram

syndrome), NKX2-5, GATA4, MYH6, ACTC and now TBX20. In addition,

linkage to 5p has been reported in a single family, with the responsible gene yet

to be identified (Benson et al., 1998). Four of the genes identified to date have

been transcription factors and two (MYH6 and ACTC) structural proteins. There

is thus considerable genetic heterogeneity in dominant ASD. This chapter

describes a large autosomal dominant ASD family without conduction defects,

in which 5 of 10 affected family members also have the Marcus Gunn jaw

winking phenomenon. Although the effort to map the ASD gene in this family

was unsuccessful, there was no evidence of linkage to any of the known ASD

loci, indicating that this is an eighth form of dominant ASD. No association

between ASD and Marcus Gunn phenomenon (MGP) has previously been

reported – thus the phenotype in this family represents a new syndrome.

4.2 Marcus Gunn phenomenon MGP consists of synkinetic upper eyelid motion with stimulation of the ipsilateral

pterygoid muscles, in association with varying degrees of congenital ptosis. This

manifests as rapid movements of the affected eyelid with movement of the

mandible during jaw protusion or opening (eg during chewing). It is most

pronounced in the newborn period, when the eye movements are noted during

sucking, but usually persists into adult life. It is thought that the disorder results

from abnormal connection of axons which would normally travel within the

motor branch of the trigeminal nerve, innervating the pterygoid muscle

(Freedman H and Kushner B, 1997). The abnormal connection is to the levator

superioris muscle (normally innervated by a branch of the oculomotor nerve).

137

138

Although most cases of MGP are sporadic, there have been several reports of

familial occurrences of the condition, usually consistent with autosomal

dominant inheritance (Falls et al., 1949; Kuder GG and Laws HW, 1968;

Kirkham, 1969; Mrabet et al., 1991; Pratt et al., 1984). Incomplete penetrance

has been observed in dominant MGP (Mrabet et al., 1991), but no associated

malformations have been reported in these families. The only previous report of

an association between CHD and MGP was in a case report of a child with

MGP and complex CHD involving Tetrallogy of Fallot, with left heart hypoplasia

and total anomalous pulmonary return (Festa et al., 2005). The MGP is

mentioned only in the abstract of the paper and there is no mention of family

history.

There has been a single report of KIF21A mutations in four patients with

congenital fibrosis of the extra-ocular muscles (CFEOM) who also have MGP

(Yamada et al., 2005). CFEOM is an autosomal dominant disorder

characterised by nonprogressive ophthalmoplegia, bilateral ptosis and a

downward primary position of the eyes, with limited ability to elevate (supraduct)

the eyes, and is caused by mutations in KIF21AI, a kinesin motor protein

located at 12q12 (Yamada et al., 2003). CFEOM is associated with abnormal

development of the oculomotor axis, including the nucleus of the oculomotor

nerve, the superior division of the nerve itself and the levator and superior

rectus muscles (Yamada et al., 2005), and thus its pathogenesis overlaps with

that of MGP. In a total of four patients with CFEOM and MGP, two had de novo

mutations in KIF21A and two had familial mutations. In the latter two families

there were other family members who had CFEOM but did not have MGP

(Yamada et al., 2005). To date, there have been no reports of mutation

screening in KIF21A in subjects with isolated MGP, familial or otherwise.

Doco-Fenzy and colleagues (Doco-Fenzy et al., 2006) reported a child with a

chromosomal duplication involving 12q24.1-q24.2 who had MGP as well as

multiple congenital anomalies. The cardiac phenotype of this child was of

multiple small VSDs, which closed spontaneously, and pseudo-coarctation of

the aorta. TBX5 is one of the more than 75 genes within the duplicated region. It

139

is not clear what link, if any, this has to the association of ASD and MGP

reported here. Holt-Oram syndrome is caused by loss of function of TBX5

(Basson et al., 1999), rather than gain of function as would be expected in a

duplication; however, in animal models cardiac development is disrupted by

Tbx5 overexpression (Hatcher et al., 2004), and it is plausible that TBX5

duplication could cause CHD.

There has been a single report of MGP in a child with CHARGE syndrome

(Weaver et al., 1997). This antedated the identification of CHD7 mutations in

individuals with CHARGE syndrome (Vissers et al., 2004) so the molecular

basis of CHARGE syndrome in this patient is not confirmed although a CHD7

mutation is likely. Facial nerve palsy, usually unilateral, is a common feature of

CHARGE syndrome, and indeed abnormalities of all other cranial nerves have

been reported (Sanlaville and Verloes, 2007). It is plausible that the MGP in the

patient reported by Weaver et al (Weaver et al., 1997) is an unusual

manifestation of a common component of the syndrome, i.e. abnormal

development of cranial nerves and their nuclei. It seems unlikely that CHD7

mutations would account for MGP in patients without other features of CHARGE

syndrome.

4.3 Phenotypes of affected family members The pedigree is shown in Figure 4.1. Ten individuals in four generations were

affected by congenital heart disease. All of these had secundum ASD but

otherwise structurally normal hearts, except for individuals II:7 (primum and

secundum ASD), III:4 (VSD, no ASD), and III:5 (TOF and ASD). The severity of

ASD ranged from a defect only a few mm in size in an asymptomatic 88 year

old man (I:1) diagnosed as part of the study, to a lesion 4cm in diameter

requiring surgical closure (II:7). No affected family members had cardiac

conduction abnormalities, but II:7 had atrial fibrillation diagnosed at the age of

55. In addition, 5 of the 10 individuals with congenital cardiac malformations

had MGP. Individual III:6 had very pronounced bilateral MGP and required

surgery for ptosis and to eliminate the "wink". Other individuals were less

severely affected. No family members with MGP in the absence of congenital

140

heart disease were identified. Individual II:1 is an obligate gene carrier, since he

has an affected parent and child, but is apparently non-penetrant for the

disorder, having a normal heart on echocardiography and lacking MGP.

Individual III:7 had unilateral cleft lip and palate which were repaired in

childhood, in addition to ASD. All family members were assessed by a clinical

geneticist (EK). None had significant craniofacial dysmorphism or other features

suggestive of known syndromes. In particular, none had features of

velocardiofacial syndrome, or subtle limb abnormalities suggestive of Holt-Oram

syndrome. Individuals II:4 and III:4 declined to take part in the study, and

information about their cardiac lesions and the MGP in III:4 is based on history

provided by other family members. The family was originally ascertained by Prof

Ian Glass, and he played an important role in recruitment of subjects, including

doing many of the venepunctures. Dr Rob Justo and Dr Michael Tsicalis did the

cardiac assessments and Dr Tim Sullivan provided opthalmological advice.

4.4 Cytogenetics Although no family members were felt to have features consistent with

velocardiofacial syndrome, a karyotype and FISH for 22q11 microdeletion was

performed in individual IV:2. This was normal.

4.5 Sequencing of cardiac genes No abnormalities in the coding sequences of NKX2-5, GATA4 or TBX20 were

detected in individual II:7 (sequencing of NKX2-5 by EK, GATA4 by Dr

Changbaig Hyun and TBX20 by Ms Leticia Castro). DNA extraction was done

by EK, assisted by Dr Fiona McKenzie.

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141

142

4.6 Mapping results The names and map locations of the markers used in this study, together with

the 2-point LOD scores obtained for each marker at � = 0, are shown in

Appendix 1. Analysis was also done at � = 0.025, 0.05, 0.075, 0.1, 0.15, 0.2,

0.3 and 0.4 (data not shown) but no loci had substantially higher LOD scores at

� > 0 than at � =0. Two LOD scores are shown for each marker. The first

(“LOD score all”) is the score obtained using all available phenotype data.

Because this was inconclusive, the genome scan was re-run with all unaffected

individuals coded as “unknown”, apart from the unaffected spouses II:5, II:8 and

III:9. The results are in the columns headed “LOD score affected”. The rationale

for this was that if one of the individuals who was phenotypically unaffected was

in fact heterozygous for the disease-causing mutation, this would manifest in

the mapping results as a recombinant, lowering the LOD score. Although the

maximum obtainable LOD score using only affected individuals would be lower,

this problem would be avoided. Genetic distances from the Généthon linkage

map (Gyapay et al., 1994) are given in cM from the p telomere. As there are 6

instances of male to male transmission in the pedigree, the X chromosome was

not analysed. Dr Kyall Zenger did the initial analysis of the data (LOD score all)

and the multipoint analysis of chromosome 5; EK did all other analyses.

4.6.1 Chromosome 1 Marker D1S2878 was the only marker studied to give a result suggestive of

linkage, with a LOD score of 2.03 – just above the threshold of 1.9 proposed by

Lander and Kruglyak as the threshold for suggestive linkage. Interestingly,

when non-affected individuals are removed from the analysis the LOD score at

this marker actually goes down to –1.23. Nonetheless, as the best candidate

locus further study was warranted and additional markers closely flanking

D1S2878 were genotyped. The results are in appendix 1, Table A1.1b. Note

that the map locations given in the key are from the Marshfield map, as two of

the additional markers selected (D1S382 and D1S1679) are not listed on the

Généthon map. This is important because D1S382 is at the same position on

the genetic map as D1S2878, but has a slightly different map position listed in

table 1b. Given the results for these closely flanking markers, and the fact that

I:1 was homozygous for D1S2878, linkage to this region can be regarded as

excluded.

4.6.2 Chromosome 5 No mutation was found in NKX2-5, which is located on 5q at 180cM, in an

affected family member. However, additional analysis was done on this

chromosome because of the previously reported linkage (with no gene yet

identifed) on the short arm of chromosome 5. The results of multipoint linkage

analysis are shown in Figure 4.2. Linkage to both loci is excluded.

Figure 4.2 Multipoint mapping of chromosome 5. The locations of NKX2-5

and D5S208, the marker at which maximum linkage was found by Benson et al

(Benson et al., 1998), are indicated.

143

144

4.6.3 Chromosome 6Mohl and Mayr (Mohl and Mayr, 1977) reported linkage of ASD to the HLA

complex, which is at about 44cM on the Généthon map. As discussed above, it

is difficult to assess their very brief report, and the finding has not been

replicated in the 30 years since its publication. Although the data shown do not

suggest linkage to 6p is likely in this family, the possibility has not been

completely excluded.

4.6.4 Chromosome 7 TBX20 is located at 55.6cM, between markers D7S516 and D7S484. Given the

low LOD scores at these markers, it is not surprising that no mutation was found

in the coding regions of TBX20 in an affected family member.

4.6.5 Chromosome 8 GATA4 is located at 21.2cM, between D8S550 and D8S549. Given the low

LOD scores in this region, it is not surprising that sequencing of the coding

regions of this gene in an affected family member did not reveal a mutation.

4.6.6 Chromosome 12 TBX5 is located at 125cM, between D12S78 and D12S79. Given their very low

LOD scores it is unlikely that this family is linked to TBX5. Clinically there is no

evidence of even subtle limb changes in any family member, which is additional

strong evidence against a role for TBX5 mutation in this family. Penetrance for

limb anomalies is very high in Holt-Oram syndrome (Newbury-Ecob et al.,

1996).

4.6.7 Chromosome 14 MYH6 is located at 10.97cM, close to D14S283. The low LOD score at this

locus indicates a mutation in MYH6 is unlikely in this family.

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4.6.8 Chromosome 15The dominant ASD gene ACTC is located at 32cM, between markers

D15S1007 and D15S1012. Given the linkage results for these markers it is

unlikely that an ACTC mutation is responsible for the phenotype in this family.

4.7 Discussion 4.7.1 Linkage results The size of the family reported here is, unfortunately, marginal at best for a

successful mapping effort. In principle, mapping in an autosomal dominant

pedigree with 10 affected individuals could yield a LOD score of 3. However,

this relies on a fully informative marker, positioned at or very close to the gene

of interest, and in practice analysis of a rather larger number of affected

individuals is likely to be required (Anderson NH, 2002). Although there are

unaffected individuals in the pedigree, they contribute relatively little linkage

information, because their status is uncertain (Lander and Botstein, 1989). The

exception is II:1, who is an obligate heterozygote on the basis of having an

affected parent and an affected child. The decision by two affected family

members (II:4 and III:4) not to participate in the study also reduced the

likelihood of success.

Non-penetrance, as seen in II:1, may also have been a problem. If one of the

individuals classed as unaffected was in fact heterozygous for the mutation but

was non-penetrant for a cardiac phenotype or for MGP, he or she would have

represented an apparent recombination event, even with a perfect marker, and

would have lowered the LOD score obtained. Similarly, with a common class of

malformations like CHD, there is the possibility of a phenocopy occurring – i.e. a

member of the family having CHD for reasons unconnected with the familial

mutation. This would have serious consequences for success of the linkage

analysis. Re-analyzing the data with clinically unaffected individuals marked as

“unknown” was an attempt to control for the possibility of non-penetrance in one

or more of those people. Although this had a significant effect on many of the

LOD scores obtained, none rose substantially above zero.

146

Although no definite linkage was detected, and the only suggestive locus – on

chromosome 1 – was not supported by additional fine mapping – the results of

this linkage analysis do have something to tell us about the genetics of

dominant ASD. All 6 of the known ASD genes were excluded, as was the only

locus which has been convincingly mapped without the gene yet being identified

(on 5p). Linkage to the HLA locus on 6p was not excluded but appears unlikely,

and in any case the status of that linkage result is in doubt, as discussed above.

Thus, this family provides firm evidence for further genetic heterogeneity in

dominant ASD. Including TBX5 and the locus responsible for this family’s CHD,

there are at least 8 different dominant ASD loci. It seems likely that ultimately

there will prove to be even more than this.

4.7.2 ASD and MGP The association of ASD and MGP represents a new dominant ASD syndrome.

Non-penetrance for MGP is common, with 5/11 presumed mutation carriers

(counting II:1) manifesting MGP. Non-penetrance has been observed in

previous families with dominant MGP (Mrabet et al., 1991). Although CHD has

not previously been reported in association with dominant MGP, ASD can easily

be missed – as evidenced by the fact that several members of this family had

their ASDs identified as a result of participation in the study – and it is possible

that this disorder may sometimes present as a primarily ophthalmological

phenotype.

4.7.3 Clefting Individual III:7 had cleft lip and palate repaired as a child. All other family

members had normal lips and palates on examination, and it is possible that

this is a chance association rather than representing a part of the phenotype of

this disorder. There have been two previous reports of an association between

MGP and orofacial clefting. In one case, the father of a girl with MGP had cleft

lip and palate (Brooks, 1987). In another, a boy with MGP had cleft lip, as did

two of his six sibs (neither of whom was affected by MGP) (Awan, 1976). This

raises the possibility that the cleft lip and palate seen in individual III:7 in the

147

family reported here may not be a chance association, but could in fact be an

uncommon feature of the disorder.

4.7.4 Future studies The majority of the work reported here was completed in 2000 and 2001. Since

then, there have been considerable technological advances in mapping

techniques. Specifically, SNP microarray is now becoming cheap enough to be

an accessible alternative to microsatellite markers, offering the advantage of

very dense map coverage – ranging upwards from 10,000 markers, compared

with the 382 microsatellite markers used in this study. This increases the

chance of finding the “perfect marker” or combination of closely linked markers

which would allow the maximum possible LOD score to be obtained from the

pedigree. While the spacing of markers used here was dense enough to make

double recombinants in between markers unlikely (another possible cause for

the lack of success in establishing linkage) it still seems possible that repeating

the whole genome screen at the density allowed by microarray technology may

be the key to future success.

5. Cardiac atrial septal morphology and risk of patent foramen ovale in inbred laboratory mice

5.1 Introduction Chapters 3 and 4 described studies of the Mendelian variants of ASD, which

proved to be rare. In affected individuals without a family history of CHD, the

chance of finding a mutation in one of the genes known to be associated with

ASD is low. It is unlikely that similar studies of other such dominant genes will

substantially advance our understanding of the causes of the majority of ASD.

An alternate approach is therefore required. This chapter and the next describe

studies of an animal model of atrial septal dysmorphogenesis. Different strains

of inbred laboratory mice have different susceptibilities to PFO, a point first

demonstrated by Biben and colleagues (Biben et al., 2000). The Biben study

formed the basis on which this study stands and will be discussed in detail in

section 5.2, below. An important finding of that study was that there are features

of the mouse atrial septal wall which correlate with the presence of PFO, with

strain to strain variation in these quantitative traits being closely related to the

risk of PFO in each strain. Biben and colleagues studied the length of the

septum primum, or flap valve length (FVL). The traits foramen ovale width

(FOW) and crescent width (CRW) were found in the course of this study to also

be associated with the risk of PFO (descriptions of all of these traits are to be

found in section 2.1.4.2, especially Figure 2.3).

While PFO itself is a binary trait, which offers relatively little power for mapping

studies, the identification of quantitative traits which are associated with risk of

PFO made a QTL mapping study a viable option. The underlying hypothesis of

this study was that if QTL relevant to FVL, FOW and CRW could be mapped,

these would also influence risk of PFO. Identification of the underlying genetic

basis of any such QTL should provide insight into normal and abnormal

morphogenesis of the atrial septal wall, and may have wider significance in the

study of CHD.

148

149

Chapter 6 reports the identification of QTL relevant to PFO risk in two strains of

inbred laboratory mice, QSi5 and 129T2/SvEms. For the QTL mapping study,

85 [QSi5 x 129T2/SvEms] F1 mice and 1437 F2 mice were dissected.

Subsequently, an advanced intercross line (AIL) was established from the same

parental strains and bred for 14 generations; 1003 mice from the F14

generation were dissected. These experiments generated a large amount of

quantitative data relating to atrial septal wall morphology. This chapter presents

analyses of those data, with a focus on the relationship between septal

morphology and PFO. Independently from the QTL analysis, the morphological

data provide insight into the relationships between PFO and each of FVL, FOW

and CRW, as well as information about interaction between the traits and their

relationship to other variables such as sex, body weight and heart weight.

All dissections, measurements and statistical analyses including LOD score

analysis presented in chapters 5 and 6 were done by EK.

5.2 The relationship between atrial septal morphology and PFO: previous work The only previous investigation of the relationship between atrial septal

morphology and incidence of PFO in laboratory mice was that of Biben and

colleagues (Biben et al., 2000). As part of an exploration of the effects of

heterozygous mutations of Nkx2-5 on the murine heart, Biben and colleagues

noted that the incidence of PFO varies considerably between strains of inbred

laboratory mice. Moreover, they found a relationship between measures of

atrial septal morphology and strain-specific incidence of PFO. In particular, the

mean FVL was inversely proportional to the incidence of PFO in a given strain.

Mice heterozygous for Nkx2-5 mutations had shorter FVL and a higher

incidence of PFO than wild-type mice of the same genetic background. There

was a very strong correlation between these traits, with a correlation coefficient

of –0.97 (Biben et al., 2000). The authors made the point that it was not clear

whether there was a causal relationship between short FVL and high risk of

PFO, or even in which direction such a causal relationship might act. Either the

shortness of the septum primum might reduce the chance of the flap valve

forming an effective seal, or the presence of the PFO might lead to increased

150

bloodflow which could place additional stress on a fragile structure, resulting in

a shorter flap valve in postnatal life.

Mice heterozygous for the Nkx2-5 mutation were found to have other

abnormalities, including (in 129T2/SvEms mice) a high incidence of ASD and

some 17% having borderline ASD (only 6% had no PFO or ASD). In C57Bl/6

mice, 1.4% of wild-type mice had bicuspid aortic valve, compared with 11% of

Nkx2-5+/- mice; and female heterozygotes had a mild but significant prolongation

of the PR interval. There was also an increased incidence of atrial septal

aneurysm (ASA) in heterozygotes.

This paper was a vital precursor to all of the mouse work described in this

chapter and the next. It showed 1) a relationship between FVL and PFO; 2) that

the mouse heart responds to Nkx2-5 haploinsufficiency in a similar way to the

human heart, albeit with a milder phenotype in mouse than man; and 3) clear

evidence of a genetic link between ASD and PFO. In short, the findings of

Biben and colleagues validated a quantitative analysis of the mouse atrial

septum, and particularly of PFO as a model for human ASD.

In addition to FVL, Biben and colleagues studied the relationship between the

width of the patent corridor behind the flap valve in cases of PFO, showing that

this was wider in Nkx2-5+/- than in wild-type mice. As this can only be studied in

mice with PFO, alternative measures were sought during initial dissections for

this study, and FOW and CRW were identified as being worthy of investigation.

5.3 Selection and breeding of mice for study The process of strain selection for the QTL mapping study reported in chapter 6

is described in section 2.1.5. The strains chosen for study, QSi5 and

129T2/SvEms, had extreme values for FVL, with QSi5 having FVL of

1.13±0.11mm (mean±SD), compared with 0.60±0.11mm for 129T2/SvEms. The

difference between the strains, which is 4.8 times the standard deviation of the

strains, is large enough that a QTL mapping exercise would be expected to

have good prospects of success. By way of comparison, Kirkpatrick and

151

colleagues (Kirkpatrick et al., 1998) identified 3 QTL for fecundity using just 41

informative markers across all chromosomes in an F2 resource of only 200

mice, where phenotypic means were separated by 4-5 SD. The study reported

here was designed to use much larger numbers of mice and a denser marker

map, suggesting that the prospects of success were excellent.

The F1 and F2 mice were bred by Dr Ian Martin as part of a mapping protocol,

as described in sections 1.4.2 and 2.1.3.1. A total of 85 F1 mice were dissected

early in the study, in order to gain an impression of overall dominance effects.

At that stage, the decision to measure FOW and CRW had not yet been made

and only FVL was measured.

Following the success of the QTL mapping project (Chapter 6), approaches to

fine mapping were considered. One such approach is the use of an Advanced

Intercross Line (AIL). The principles behind and generation of an AIL are

described in section 2.1.3.2. The advantages of this approach include the ability

to do fine mapping on multiple regions of interest simultaneously, and the ability

to breed over a number of generations and only phenotype and genotype at the

final generation. The gain in resolution of mapping diminishes with increasing

generation number and after about 10 generations, the confidence interval (i.e.

the size of the area of interest) is reduced only modestly for each additional

generation (Darvasi and Soller, 1995). As it happened, it was not possible to

devote the necessary time for phenotyping a large number of mice when the

10th generation of AIL mice were being born, and breeding (which was much

less time-consuming than phenotyping) was thus continued until the 14th

generation, when 1003 mice were dissected. These mice have yet to be

genotyped (see chapter 8 for a discussion of future plans). However, the

phenotype data generated by their dissection is of interest and is presented

here. All mouse breeding, dissections and measurements for the AIL were

done by EK.

152

5.4 Analysis of data from QSi5, 129T2/SvEms, and the [QSi5 x 129T2/SvEms] F1, F2 and F14 mice In this section, data are presented from the parental strains used in the QTL

mapping experiment, and from strains derived from crossing them at various

generations. Wherever possible, all available data for each trait are analysed,

but for the more complex comparisons, analyses are restricted to the subset of

mice with complete data. Selection of mice for genotyping was based only on

mice with complete data available. Data were missing for some mice for a

variety of reasons, most commonly inadvertent damage to the atrial septum

during dissection. Uncommonly, atypical anatomy made it impossible to do the

full set of measurements. For example, occasionally a PFO would be observed

with two separate outlets in the left atrial septal wall, making it possible to

measure FOW, but not CRW or FVL as there would be two values for each of

these.

5.4.1 Descriptive statistics Table 5.1 shows the means and standard deviations for the continuous traits

which were measured – body weight, heart weight, FVL, FOW, CRW, body

weight and heart weight – and the percentage of PFO in each set of mice. QSi5

mice had a low prevalence of PFO, associated with a long FVL, small FOW and

large CRW when compared with 129T2/SvEms. They were also heavier and

had correspondingly greater heart weight compared with 129T2/SvEms.

Interestingly, the atrial septa of the F1 mice were strikingly similar to QSi5 mice,

with no PFOs identified in 85 F1 mice, and with very similar FVL to QSi5 mice,

although body weight and heart weight were intermediate between the two

parental strains. This similarity may represent the effect of alleles with

substantial dominance effects in the direction of the QSi5 phenotype. FOW and

CRW were not measured for the F1 mice as the decision to include these

measures had not been made at the time that these mice were dissected. The

F2 mice were intermediate between the parental strains for %PFO (albeit closer

to QSi5 than 129T2/SvEms), for FVL and bodyweight. They were similar to

QSi5 for FOW and heart weight, and similar to 129T2/SvEms for CRW. It is

153

hard to interpret this information with confidence, but again, it is possible that

this represents dominance effects.

In theory, the F14 mice should be very similar to F2 mice for all measures. In

practice, although all the continuous variables for the F14 mice were within one

standard deviation of the values for the F2 mice, there was a substantial

difference in prevalence of PFO.

Of the F14 mice, 34% had PFO, double the value for the F2 mice (p<0.0005,

chi square test). The most likely explanation for this is that during the breeding

of successive generations of mice a significant amount of genetic drift occurred

– i.e. the random loss of some alleles from the population. The experiment was

structured to minimize the risk of this occurring, with the use of 48 pairs of mice

and with great care taken to avoid inbreeding at each generation. Nonetheless,

it is possible that the continuation of the experiment past the 10th generation

contributed to this unwanted outcome - if this is indeed the explanation for the

changes from the F2 to F14 generations. The reason this is undesirable is that

there is a risk that at one or more of the QTL detected in the F2 mapping

experiment (see chapter 6), the F14 mice have become fixed for one of the

parental strain alleles, or at least there may be heavy skewing towards one of

the parental strain alleles. If this has happened the experiment will be unable to

confirm and refine any such QTL (if fixed) or will have reduced power to do so

(if not fixed but skewed). The large number of QTL which were detected mean

that it is unlikely that all of the QTL will have been affected by genetic drift. It is

also possible that none have been affected, and that the difference in PFO

prevalence between F2 and F14 results from genetic drift affecting QTL which

were not detected in the original experiment. Other possible explanations for

this difference, such as a change in environmental factors in the interval

between the breeding and dissection of the F2 and F14 mice (several years),

are less likely. Animal husbandry practices in the animal house did not change

over the period of these studies.

Tabl

e 5.

1: C

hara

cter

istic

s of

par

enta

l str

ains

, F1

and

F2 m

ice

QS

i5

129T

2/S

vEm

sF1

F2F1

4

n66

7585

>124

7*

>933

*

PFO

(%)

4.5

800

1734

FVL(

mm

)1.

13�

0.11

0.60

� 0.

11

1.15

� 0.

14

1.0

� 0.

191.

01�0

.16

FOW

(mm

) 0.

21�

0.06

1 0.

24�

0.05

8 N

/R0.

21�

0.07

0.

24�0

.07

CR

W(m

m)

0.51

� 0.

13

0.44

� 0.

12

N/R

0.41

� 0.

12

0.54

�0.1

5

Bod

y W

eigh

t(g)

29.4

� 2.

77

17.5

� 2.

1 21

.9�

2.9

26.6

� 3.

325

.8±2

.9

Hea

rt W

eigh

t(g)

0.18

±0.0

280.

21�

0.02

4 0.

14�

0.02

1 0.

16�

0.02

8 0.

21�

0.03

3

*not

all

mic

e ha

d co

mpl

ete

data

– s

ee te

xt

154

5.4.2 Relationships between the continuous traits Tables 5.2 and 5.3 show the relationships between the continuous variables. In

these tables, the numbers on the diagonals are the mean ± standard deviation,

with the number of mice with complete data for that trait in brackets. To the right

of the diagonal, each cell contains correlation coefficients between the traits at

the left of the row and top of the column which intersect at that cell, with p

values in brackets.

Table 5.2 Basic statistical information and correlations for F2 mice

Body weight

Heartweight

FlapValvelength

Crescentwidth

Foramenovalewidth

Body weight

26.635

±3.284

(1437)

0.744

(<0.0005)

0.133

(<0.0005)

0.086

(0.002)

0.096

(0.002)

Heartweight

0.20599

±0.03362

(1436)

0.121

(<0.0005)

0.036

(0.209)

0.152

(<0.0005)

FlapValvelength

3.3303

155

±0.6024

(1373)

-0.034

(0.231)

-0.087

(0.001)

Crescentwidth

1.3717

± 0.6237

(1247)

0.121

(<0.0005)

Foramenovalewidth

0.70216

±0.22785

(1344)

Table 5.3 Basic statistical data and correlations for F14 mice

Body weight

Heartweight

FlapValvelength

Crescentwidth

Foramenovalewidth

Body weight

25.801

±2.877

(971)

0.640

(<0.0005)

0.100

(0.002)

0.117

(<0.0005)

0.007

(0.825)

Heartweight

0.182

±0.0277

(953)

0.134

(<0.0005)

0.130

(<0.0005)

0.074

(.023)

FlapValvelength

1.01

±0.1564

(933)

0.005

(0.890)

-0.284

(<0.0005)

Crescentwidth

0.545

± 0.146

(937)

0.117

(<0.0005)

Foramenovalewidth

156

0.2375

±0.074

(1344)

It is not surprising that heart weight and body weight are strongly (and highly

significantly) positively correlated, in both F2 and F14 mice. It would be

surprising if this were not the case – a large mouse would be expected to have

a large heart.

There are significant correlation coefficients between body weight and FVL (in

F2 and F14), body weight and CRW (in both), body weight and FOW (in F2 but

not F14), and between heart weight and FVL (in both), heart weight and

crescent width (in F14) and heart weight and FOW (in both). However, all of

these correlation coefficients are very low and these variables can be viewed as

essentially independent. Figure 5.1, a scatterplot graphing FVL against heart

weight in F2 mice, illustrates this point – although there is a highly significant

correlation between these variables, the graph shows a lack of any clear

pattern. One of the weaknesses of the correlation coefficient is that if there are

very large numbers of observations, as in this study, there is a high chance of a

statistically significant correlation being observed, which is unlikely to be of any

biological significance.

Heart Weight

FVL

0.300.250.200.150.10

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

Figure 5.1: Scatterplot of FVL vs heart weight in F2 mice

Similarly, although several pairs of the measurements of greatest interest here,

FVL, FOW and CRW, have correlation coefficients which are statistically

significant, the size of the correlation coefficient is small in every instance. This

suggests that these variables are likely to be under largely independent genetic

control, a prediction supported by the results of the QTL mapping (see chapter

6).

5.4.3 Analysis of variance for factors affecting FVL, FOW and CRW in F2 miceAnalysis of variance (ANOVA) was used to study the relationships between the

various traits – cardiac and noncardiac – recorded for each mouse. The

General Linear Model (GLM) for ANOVA was used for all analyses of variance,

157

158

because unlike other available models it does not require data to be perfectly

balanced (eg equal numbers of male and female mice).

The full ANOVA computer output is shown below only for the more complex

models with multiple terms. The results of ANOVA for single variables in the F2

mice were as follows. For FVL, sex (p=0.019), coat colour (p=0.04) and heart

weight (p=0.003) all significantly affected FVL (note that here, and from here

onwards in statistical discussion, the words “affected” and “effect” are used in

the statistical sense and do not necessarily imply biological causation). For

FOW, age at dissection (p < 0.0005), coat colour (p=0.03), heart weight

(p<0.0005) and week of dissection (in F2 but not F14) (p<0.0005) were

significant. For CRW, sex (p=0.002), age (p<0.0005), week of dissection (in F2

but not F14 mice)(p<0.0005) and body weight (p=0.038) were significant, but

adjusting for sex removed the effect of weight (p=0.09). Week of dissection was

included to take into account the possibility of inconsistency in measurement

technique, particularly with gains in experience over the course of the very large

number of dissections performed in the study. Although there was no consistent

trend observed to larger or shorter values over the course of the study, the

effect was significant and it was considered prudent to include this as a

covariant in the ANOVA analyses for the F2 mice.

Table 5.4: Comparison between data for 129T2/SvEms mice with and

without PFO

129T2/SvEms (all) 129T2/SvEms

with PFO

129T2/SvEms

without PFO

P

value#

n 75 15 60 -

PFO (%) 80 - - -

FVL(mm) 0.60 � 0.11 0.60�0.11 0.61�0.13 0.69

FOW(mm) 0.24 � 0.058 0.25�0.058 0.21�0.052 0.05

CRW(mm) 0.44 � 0.12 0.44�0.13 0.40�0.09 0.25

Body Weight(g) 17.5 � 2.1 17.4�2.1 18.1�2.0 0.27

Heart Weight(g) 0.14 � 0.021 0.14�0.021 0.14�0.019

# two tailed t-test

0.60

5.4.4 Relationship between PFO and the continuous variables The low frequency of PFO among QSi5 and F1 mice make it difficult to assess

the effect of the measured traits on the risk of PFO in these mice. However,

there were sufficient 129T2/SvEms mice without PFO to allow comparisons

between groups. Table 5.4 shows data for 129T2/SvEms mice with and without

PFO. For most of the measures, the results are very similar in mice with and

without PFO. Only for FOW is there a marginally significant difference, of small

magnitude (<1 standard deviation). Mice with PFO have slightly larger FOW

than mice without PFO. Correction for multiple comparisons has not been done

but would probably render this association non-significant. On the whole,

however, these data suggest that comparisons between inbred strains are more

likely to provide useful information than comparisons within strains.

In the F2 and F14 mice, there were sufficient mice both with and without PFO to

allow statistical comparisons to be made. Analysis of variance was performed

for each of the key variables – FVL, FOW and CRW.

159

160

The tables below are the ANOVA output produced by the statistical package

Minitab V14 (Minitab Inc). In each table, DF = degrees of freedom, Seq SS =

sequential sums of the squares, Adj SS = adjusted sums of the squares, and

Adj MS = adjusted means squares. Adjusted sums of the squares are used

because all factors are considered in the model, without dependence on model

order. The F statistic is the ratio of between-group to within-group variance and

is the basis on which the p value is calculated. The smallest p value reported by

Minitab is 0.000 which is equivalent to <0.0005, as values greater than or equal

to 0.0005 are rounded up to 0.001. In each case the model used includes the

variables which were found to have a significant effect on PFO in single

analyses.

5.4.4.1 Relationship between FVL and PFO In the F2 and F14 mice, the relationship between FVL and PFO demonstrated

by Biben and colleagues (Biben et al., 2000) was resoundingly confirmed. Biben

and colleagues found that strains with short FVL had a high prevalence of PFO

and strains with long PFO had a low prevalence of PFO. The same pattern was

found in the F2 and F14 mice. Tables 5.5 and 5.6 show the results of ANOVA

for FVL.

Table 5.5 Analysis of variance for FVL in F2 mice

Source DF Seq SS Adj SS Adj MS F P

HtWeight 1 0.5927 0.4635 0.4635 16.14 0.000

Sex 1 0.0004 0.0003 0.0003 0.01 0.925

Colour 2 0.1492 0.0511 0.0256 0.89 0.411

PFO 1 4.6488 4.6488 4.6488 161.91 0.000

Error 1322 37.9576 37.9576 0.0287

Total 1327 43.3488

161

Table 5.6 Analysis of variance for FVL in F14 mice


HtWeight 1 0.4105 0.3499 0.3499 19.35 0.000

Sex 1 0.0220 0.0297 0.0297 1.64 0.200

Colour 2 0.0012 0.0058 0.0029 0.16 0.852

PFO 1 5.6147 5.6147 5.6147 310.42 0.000

Error 927 16.7672 16.7672 0.0181

Total 932 22.8156

Note the extremely high F statistics in both F2 and F14 mice. An F statistic of

16.14 in F2 and 19.35 in F14 mice yields p<0.0005. The F statistics for PFO –

even after correction for other potentially significant factors – are very

substantially larger (161.91 and 310.42 respectively), so the true p value for the

significance of this effect must be <<0.0005. Figures 5.2 and 5.3 are histograms

comparing the distribution of FVL in mice with and without PFO.

For F2 and F14 mice, there is an approximately normal distribution of FVL

lengths. There is overlap between the values for mice with and without PFO, but

mice with PFO have generally shorter FVL than mice with PFO.

Figure 5.2: Histogram of FVL in F2 mice with and without PFO

020406080

100120140160180200220240260

0.4 0.6 0.8 1 1.2 1.4 1.6

FVL (mm)

Num

ber o

f Mic

e

With PFOWithout PFO

Figure 5.3: Histogram of FVL in F14 mice with and without PFO

020406080

100120140160180200

0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6

FVL (mm)

Num

ber o

f mic

e

With PFOWithout PFO

162

163

In the F2 mice there were FVL values available for 1328 mice. Of the top decile

for FVL (i.e. the 10% of mice with the longest FVL), 1/133 had PFO. Of the

bottom decile, 62/133 mice had PFO. There were similar figures for the F14

mice. There were 933 FVL values, and of the top decile 1/93 had PFO,

compared with 73/93 in the bottom decile. The somewhat higher proportions in

F14 than in F2 mice reflect the overall higher prevalence of PFO in the F14 than

F2 mice, as discussed above.

5.4.4.2 Relationship between FOW and PFO FOW as defined for this study was not investigated by Biben and colleagues,

and there have been no other reported studies of FOW. As a measure, it too

proved to have a very strong relationship with PFO, with larger FOW being

associated with a higher risk of PFO and vice versa. Tables 5.7 and 5.8 are

ANOVA results for FOW.

Table 5.7: Analysis of variance for FOW in F2 mice


HtWeight 1 0.146315 0.160581 0.160581 40.01 0.000

Week 1 0.050327 0.053148 0.053148 13.24 0.000

Age 18 0.244240 0.237463 0.013192 3.29 0.000

Colour 2 0.044954 0.031331 0.015666 3.90 0.020

PFO 1 0.480393 0.480393 0.480393 119.68 0.000

Error 1304 5.234077 5.234077 0.004014

Total 1327 6.200306

Table 5.8: Analysis of variance for FOW in F14 mice


HtWeight 1 0.028424 0.044196 0.044196 11.42 0.001

Age 14 0.253185 0.107972 0.007712 1.99 0.016

Colour 2 0.003786 0.002914 0.001457 0.38 0.686

PFO 1 1.308869 1.308869 1.308869 338.13 0.000

Error 923 3.572821 3.572821 0.003871

Total 941 5.167085

Figure 5.4: Histogram of FOW in F2 mice with and without PFO

0

50

100

150

200250

300

350

400

450

0.06

0.12

0.18

0.24 0.3 0.3

60.4

20.4

80.5

4 0.6

FOW (mm)

Num

ber o

f mic

e

With PFOWithout PFO

Figure 5.5: Histogram of FOW in F14 mice with and without PFO

0

50

100

150

200

250

300

0.06 0.12 0.18 0.24 0.3 0.36 0.42 0.48 0.54 0.6

FOW (mm)

Num

ber o

f mic

e

With PFOWithout PFO

164

165

As for FVL, there is a very highly significant relationship between FOW and

PFO. Figures 5.4 and 5.5 are histograms comparing the distribution of FOW in

mice with and without PFO. Week of dissection is not included as a variable for

ANOVA of F14 mice because of its lack of significant effect on FOW in the

single variable analysis of these mice (the same applies for CRW).

While the pattern of results is a little less striking than for FVL, it is similar in

essence. There is an approximately normal distribution of results for mice with

and without PFO, and the distributions overlap. The relationship is the opposite

to that seen for FVL in that larger values for FOW are associated with PFO.

Considering the upper and lower deciles, for the F2 mice there were values

available for 1328 mice. In the top decile, 55/133 mice had PFO; in the bottom

decile 4/133 mice had PFO. For the F14 mice, there were 942 values available.

87/94 mice in the top decile and 8/94 in the bottom decile had PFO.

In summary, FOW appears to be nearly as good a predictor of PFO as FVL.

These analyses led to the decision to select mice for genotyping in the QTL

study on the basis of both FVL and FOW.

5.4.4.3 Relationship between CRW and PFO CRW was not studied by Biben or colleagues and there have been no other

reported studies of this measurement. Tables 5.9 and 5.10 are ANOVA results

for CRW.

Table 5.9: Analysis of variance for CRW in F2 mice


Week 1 1.11120 1.06793 1.06793 81.93 0.000

Weight 1 0.50019 0.31863 0.31863 24.45 0.000

Sex 1 0.00033 0.00099 0.00099 0.08 0.783

Age 18 0.58072 0.48260 0.02681 2.06 0.006

PFO 1 0.47049 0.47049 0.47049 36.10 0.000

Error 1205 15.70660 15.70660 0.01303

Total 1227 18.36954

Table 5.10: Analysis of variance for CRW in F14 mice


Weight 1 0.27177 0.19664 0.19664 9.62 0.002

Sex 1 0.10741 0.08047 0.08047 3.94 0.048

Age 14 0.62692 0.61444 0.04389 2.15 0.008

PFO 1 0.06206 0.06206 0.06206 3.04 0.082

Error 919 18.78996 18.78996 0.02045

Total 936 19.85811

In the F2 mice, there was a strong association between CRW and PFO,

although this was much less pronounced than for FVL and FOW. Interestingly,

in the F14 generation the association appears to have been lost with p = 0.08 in

this analysis. This may be the result of the effects of genetic drift, as discussed

above in relation to the increased prevalence of PFO in the F14 mice. Figures

5.6 and 5.7 are histograms comparing the distribution of CRW in mice with and

without PFO.

Figure 5.6: Histogram of CRW in F2 mice with and without PFO

0

50

100

150

200

250

300

350

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

CRW (mm)

Num

ber o

f mic

e

With PFOWithout PFO

166

Figure 5.7: Histogram of CRW in F14 mice with and without PFO

020406080

100120140160180200

0.1 0.3 0.5 0.7 0.9 1.1 1.3

CRW (mm)

Num

ber o

f mic

e

With PFOWithout PFO

Although the values for mice with and without PFO are once again

approximately normally distributed, the distinction between mice with and

without PFO is much more subtle. For the F14 mice the distributions are not

evidently different, consistent with the finding on ANOVA of no significant

association between CRW and PFO. For the F2 mice, the distribution for mice

with PFO is somewhat shifted to the right relative to the distribution for mice

without PFO, but this is much less clear-cut than for FVL and FOW. Considering

the top and bottom deciles as for FVL and FOW, in the F2 mice there is a clear

distinction. Of mice in the top decile (high CRW) 42/123 had PFO, compared

with 12/123 (from 1228 mice with data available). This is a highly significant

difference (p<0.0005, chi square test). By comparison, for the F14 mice, 33/94

from the top decile had PFO, compared with 24/94 from the bottom decile (from

937 mice with data available). This is not a statistically significant difference

(p=0.15, chi square test).

Based on these data, CRW is a much weaker predictor of PFO than either FVL

or FOW, and F2 mice were not selected for genotyping based on CRW. The

167

168

strength of the association in the F2 mice did suggest that it may be possible to

map QTL for CRW, and analyses were done using the genotypes of mice

selected on the basis of FVL and/or CRW (discussed in Chapter 6).

5.4.5 Biological significance of the relationships between FVL, FOW and CRW These figures demonstrate a strong relationship between FVL and PFO

(confirming the findings of Biben and colleagues (Biben et al., 2000)), between

FOW and PFO, and to a lesser extent between CRW and PFO. The biological

bases for these associations are unknown. It is tempting to speculate that a

long flap valve presents greater opportunity for fusion, reducing the chance of

PFO, and that a short flap valve barely covers the foramen ovale, increasing the

likelihood of PFO. For FOW, it is possible that a wide foramen ovale results in

greater bloodflow across the septum in early postnatal life, inhibiting fusion of

the flap valve; or perhaps a wide foramen ovale results in a wider channel which

is less likely to close simply because there is more of it to close off. Likewise, it

is possible that a wide crescent reflects the width of the channel in prenatal life.

However, there is at present no direct evidence for any of these ideas.

Hopefully, identification of the genetic bases for the QTL identified in chapter 6

will provide evidence for one or more of these alternatives, or will suggest an

entirely different mechanism.

6. Quantitative trait loci modifying cardiac atrial septal morphology and risk of patent foramen ovale in inbred laboratory mice

6.1 Introduction Chapter 3 described an hypothesis-driven approach to understanding the

genetics of ASD. The hypothesis can be stated as follows: mutations in genes

associated with rare dominant forms of ASD or which are known to be important

during development of the heart may be responsible for a significant proportion

of ASD. In chapter 4, an effort was made to map an additional ASD gene. If this

had been successful, in turn it would have been logical to screen affected

individuals, as was done with NKX2-5, GATA4, and TBX20. However, the

results of this and similar studies suggest that this kind of candidate gene

approach identifies causative mutations in only a small proportion of cases, and

in most of them the mechanism appears to be autosomal dominant inheritance

rather than by playing a role in multifactorial causation. A possible exception to

this, as discussed in section 3.2.3, is the work of McElhinney and colleagues

(McElhinney et al., 2003) which does suggest a possible role for NKX-5 in the

multifactorial causation of ASD. The role of the GATA4 polymorphism S377G

(see section 3.3.3) may represent another exception to this rule. Nonetheless,

these genes are likely to be relatively minor contributors to the great bulk of

CHD, and ASD in particular.

It follows that a non-hypothesis driven approach is required if we are to

understand the genetic basis of common forms of CHD. One option might be to

conduct a whole-genome association study, studying very large numbers of

affected individuals and unaffected controls with markers densely spaced

through the genome (Carlson et al., 2004). While this is a powerful study design

and has the advantage of working directly with human disease, it remains

dauntingly expensive, and for this reason, in part, most studies to date have

been grossly under-powered. Even with continued reductions in the cost of

169

170

genotyping with improved technology, the sample sizes required for such

studies run to thousands of cases and controls (Wang et al., 2005).

QTL mapping in laboratory animals represents an approach which is non-

hypothesis-driven, allows ready generation of large sample sizes in a way which

is not possible in humans, and (compared with whole-genome association

studies) is relatively inexpensive. For this reason it has been widely used for

study of quantitative cardiovascular risk factors such as hypertension and

hyperlipidemia (as well as numerous other clinically relevant quantitative traits)

(Mashimo et al., 2007; Redina et al., 2006; Ueno et al., 2003).

These studies have shown the relevance of the animal models to human

disease. In QTL studies in humans of hypertension , a major cardiovascular

disease risk factor, 30 of 36 QTL regions identified were predicted by work in

rats or mice (Stoll et al., 2000; Cowley, 2006). This also points to the enormous

genetic complexity which can emerge in the course of QTL studies. In 2006,

Cowley identified 108 hypertension QTL in the human genome, and hundreds

more in rodents (Cowley, 2006).

Stroke and myocardial infarction, like CHD, can be treated as binary traits – a

patient either has one of these phenotypes or not. Although severity can be

clinically graded, the scoring scales are not continuous and thus cannot be used

to map QTL with nearly the same power afforded by QTL analysis. Decades of

intensive study into the causes of these problems has led to the identification of

quantitative risk factors which allow a QTL mapping approach to be applied –

not directly to the phenotype of ultimate interest but to a clinically important

proxy, in accordance with the liability model for binary traits (Falconer, 1965)

(see section 1.7.3). Until now, this has not been possible for CHD because of a

lack of quantitative endophenotypes which can be studied. The work of Biben

and colleagues (Biben et al., 2000) as discussed in sections 1.6.5 and 5.2, has

identified endophenotypes influencing the risk of PFO, which in turn made this

study possible.

171

6.2 Study design As described in 2.1.3.1, the study used an intercross design with parental

strains being represented by 129T2/SvEms males and QSi5 females, and the

F1 mice being crossed to produce the F2 generation. The strains were selected

on the basis of having extreme phenotypes for FVL and PFO. 129T2/SvEms

has short flap valve and high incidence of PFO, and QSi5 has FVL and low

incidence of PFO (see section 5.3). In addition, the strains have significantly

different mean FOW (QSi5 < 129T2/SvEms) and CRW (QSi5 >129T2/SvEms).

QSi5 has the additional advantage of very high fecundity (average litter size

13.4) (Holt et al., 2004), an advantage for a study in which large numbers of

animals are required.

6.3 Selection of mice for genotyping A total of 1437 F2 mice were dissected. Complete data were available from

1328 of these. After correction for sex and week of dissection, the top and

bottom deciles for FVL and FOW were selected for genotyping, amounting to

466 mice. Note that, as anticipated, there was a relatively small overlap

between the mice selected on the basis of FVL and those selected on the basis

of FOW – if there had been no overlap at all, 532 mice would have been

genotyped. This was an expected result because of the low correlation between

the two traits (see section 5.4.2). There was no selection on the basis of CRW

values, because this trait had the weakest association with PFO of any of the

three. However, taking advantage of the ability of MAPMAKER/QTL to deal with

missing data, it was possible to perform QTL mapping for QTL influencing

CRW, albeit with lower power than the mapping for FOW and FVL, because the

genotyped mice were not selected because of extreme values for CRW.

The decision to control for sex and week of dissection was based on the

significant statistical effects each of these had on the risk of PFO (see section

5.5.4). Heart weight (p=0.003) and coat colour (p=0.04) also affected FVL. Age

at dissection (p<0.001), coat colour (p=0.03), and heart weight (p<0.001)

affected FOW, and age (p<0.001) and weight (p=0.038) affected CRW.

However, it was decided not to control for these factors. Although statistically

significant, these effects (including the ones which were controlled for) were of

small size. For example, mean male FVL was 1.01 mm, whereas mean female

FVL was 0.98 mm, a difference of 0.03 mm, compared with an SD for FVL of

0.19. Adjustment for coat colour risked concealing the presence of a QTL that

may be linked to coat colour genes, and was not done for that reason.

Adjustment for heart weight could have masked QTL relevant to chamber

morphology which also affect heart weight. Importantly, although there were

substantial differences between heart weight and body weight in the parental

strains, in the F2 mice there was no apparent direct relationship between PFO

status and body weight or heart weight. Mice with PFO had a body weight of

26.85+/-3.32 g (mean+/-SD) and heart weight of 0.208+/-0.034 g, and mice

without PFO had a body weight of 26.61+/-3.28 g and heart weight of 0.205+/-

0.033 g. Moreover, in F2 mice, there were only weak correlations between each

of body weight and heart weight and FVL, FOW, and CRW (correlation

coefficients, all <0.16).

6.4 Markers usedEighty-nine markers were selected, spanning the mouse genome with an

average intermarker distance of ~17cM. As described in section 2.7.2, it was

necessary to replace 7 of the initial markers selected because although they

appeared to work well in the hands of the AGRF in evaluation, in practice they

were unreliable. The final set of markers used (after replacement of the poorly

performing markers) is listed in Appendix 2. Map positions, in cM, are from the

Whitehead institute database (Dietrich et al., 1996)

(http://www.broad.mit.edu/cgi-bin/mouse/sts_info?database=mouserelease).

6.5 Linkage results The figures and tables below show the results obtained using MAPMAKER/QTL

as described in 2.9.2.2. Each chromosome was treated as an independent

linkage group, and the phenotype data from all F2 animals with complete data

were used for the purposes of estimating QTL effect sizes. In addition to the

results from MAPMAKER/QTL, the figures include the results of a binary trait

172

http://www.broad.mit.edu/cgi-bin/mouse/sts_info?database=mouserelease

analysis performed by Dr Peter Thomson, using a program he wrote for this

explicit purpose using the model described in 2.9.2.3. Results for the X

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100 110 120

chromosome have been calculated separately for males and females and are

graphed separately. In each figure, open triangles indicate FVL, solid triangles,

FOW; solid squares, CRW, open diamonds, PFO (binary analysis) The y-axis

represents LOD scores for that chromosome; the x-axis represents map

distance. Note that the figures are NOT all to the same scale. The highest peak

LOD score is almost 9 times the lowest peak, and the longest chromosome is

nearly three times the length of the shortest. If all the graphs were to the same

scale, either some very large figures or some very small ones would be

required. Thus, each graph has been scaled to fit comfortably within half a

page, although figures sharing a page are kept roughly in scale with one

another as far as possible.

Figure 6.1: MMU1

LOD

Scor

e

Map Distance (cM)

173

0

Figure 6.2 MMU2

174

1

2

3

4

5

0 10 20 30 40 50 60 70 80 90 100

LOD

Scor

e

Map Distance (cM)

Figure 6.3 MMU3

0123

0 10 20 30 40 50 60 70 800123

0 10 20 30 40 50 60 70 80

LOD

Scor

e

Map Distance (cM)

0123456789

10

0 10 20 30 40 50 60 7

Figure 6.4 MMU4 LO

DSc

ore

0

Map Distance (cM)

Figure 6.5 MMU5

0

1

2

0 10 20 30 40 50 60 70 80

LOD

Scor

e

Map Distance (cM)

175

0

1

2

3

4

0 10 20 30 40 50 60

Figure 6.6 MMU6

LOD

Scor

e

Map Distance (cM) Figure 6.7 MMU 7

176

0

1

3

4

5

0 10 20 30 40 50

LOD

Scor

e

2

Map Distance (cM)

0

1

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3

4

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6

0 10 20 30 40 50 60

Figure 6.8 MMU8

LOD

Scor

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Map Distance (cM)

Figure 6.9 MMU9

0123

0 10 20 30 40 50 60 70

LOD

Scor

e

Map Distance (cM)

177

Figure 6.10 MMU10

0

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3

4

0 10 20 30 40 50 60 70

LOD

Scor

e

Map Distance (cM)

Figure 6.11 MMU11

178

0

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0 10 20 30 40 50 60 70

LOD

Scor

e

Map Distance (cM)

01

Figure 6.12 MMU12

3

LOD

Scor

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2

0 10 20 30 40 50 60

Map Distance (cM)

Figure 6.13 MMU13

179

01

23

45

0 10 20 30 40 5

LOD

Scor

e

0Map Distance (cM)

0

1

2

0 10 20 30 40 50 60 7

Figure 6.14 MMU14 LO

DSc

ore

0

Map Distance (cM)

Figure 6.15 MMU15

180

0

1

2

3

4

0 10 20 30 40 50 60 70

LOD

Scor

e

Map Distance (cM)

0

1

0 10 20 30 40 5

Figure 6.16 MMU16

181

0

012

0 10 20 30 40 50

Map Distance (cM)

Map Distance (cM)

LOD

Scor

e

Figure 6.17 MMU17

LOD

Scor

e

0123

0 10 20 30 4

Figure 6.18 MMU 18 LO

DSc

ore

0

182

0

1

2

3

0 10 20 30 40 50Map Distance (cM)

Map Distance (cM)

Figure 6.19 MMU19

4

5

6

7

LOD

Scor

e

Figure 6.20 MMUX (female mice)

183

Figure 6.21 MMX (male mice)

012

0 10 20 30 40 50 60

0

1

2

0 10 20 30 40 50 6

LOD

Scor

e

LOD

Scor

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0Map Distance (cM)

Map Distance (cM)

184

6.6 Chromosomes with noteworthy findings Using the significance thresholds of 2.8 for suggestive linkage and 4.3 for

significant linkage proposed by Lander and Kruglyak (Lander and Kruglyak,

1995), there were a total of seven significant and six suggestive QTL identified

in this study. The findings are summarised in tables 6.1a, 6.1b and 6.1c. The

results for a number of the chromosomes deserve specific comment (see

below).

Table 6.1a: Loci with LOD score >2.8 for FVL

MMU chromosome 6 8 10 13 15 18 19

LOD score 4.11 5.52 3.8 4.64 3.56 3.05 6.04

Maximum binary LOD* 2.44 0.99 1.47 0.17 3.25 0.94 3.35

Maximum FOW LOD* 2.11 2.99

Maximum CRW LOD* 2.1

Position (cM) 59 60 68 10 24 11 9

Estimated physical location

(Mb)

142 114 118 37 53 40 13

Confidence interval (Mb) 118-tel 108-tel 111-tel 6.5-48 21-90 cen-56 cen-46

% attributable phenotypic

variance�

2.4% 3.4% 1.8% 2.6% 2.1% 1.9% 3.5%

�FVL§ -0.22 -0.25 -0.19 -0.22 -0.20 -0.19 -0.26

Dominance effect# +0.017 +0.033 +0.012 +0.009 -0.005 +0.022 +0.005

185

Table 6.1b Loci with LOD score >2.8 for FOW

MMU chromosome 1 2 4 9

LOD score 5.89 4.39 9.05 3.43

Maximum binary LOD* 3.94 1.49 3.87 0.46

Maximum FVL LOD* - - 2.39 -

Position (cM) 34 63 24 13

Estimated physical location (Mb) 68 137 49 37

Confidence interval (Mb) 43-102 115-154 26-80 30-51

% attributable phenotypic variance� 3.6% 3.1% 5.6% 1.6%

�FOW (additive effect)§ +0.033 -0.022¶ -0.043¶ -0.022¶

Dominance effect# -0.017 -0.035 +0.013 -0.012

186

Table 6.1c Loci with LOD score >2.8 for CRW

MMU chromosome 3 7

LOD score 3.49 4.58

Maximum binary LOD* 2.11

Position (cM) 23 43

Estimated physical location (Mb) 62 113

Confidence interval (Mb)

cen-

121

108-tel

% attributable phenotypic

variance�

5.8% 7.0%

�CRW§ 0.23¶ 0.31¶

Dominance effect# 0.079 -0.063

*Maximum LOD score derived from binary analysis of PFO data, within the 1-

LOD dropoff interval for the corresponding FVL, FOW or CRW QTL. LOD

scores >2 within this interval for the other continuous traits are also shown �Percentage of the genetic component of the F2 phenotypic variance explained

by segregation of the QTL, assuming additivity §Estimated impact on the phenotype of an F2 animal when a 129 allele is

present instead of the QSi5 allele at this locus. A positive sign indicates an

increase in FVL, FOW or CRW compared with the mean of QSi5 homozygotes,

and vice versa.

¶ Indicates cryptic QTL

# If positive indicates effect is towards the value for 129T2/SvEms

homozygotes, if negative towards the value for QSi5 homozygotes

Abbreviations: cen: centromere; tel: telomere

187

6.6.1 MMU1 There was significant evidence for linkage for FOW. The results for the binary

analysis of the PFO data produced a strikingly similar pattern to that seen for

FOW, and with a maximum LOD score of 3.94, the binary analysis

independently exceeded the threshold for suggestive linkage. There was no

significant evidence for linkage with the other two traits on this chromosome.

Strikingly, this QTL accounts for 110% of the difference between the parental

means. This overrepresentation almost certainly reflects the presence of cryptic

QTL (see section 6.7.1 below)

6.6.2 MMU2 There was significant evidence for linkage for FOW, with a maximum LOD

score of 5.89. The mouse ortholog of ACTC, a human gene implicated in

autosomal dominant ASD (Matsson H et al., 2005) lies within the 1-LOD drop-

off interval for the position of this QTL. This is the only one of the six reported

human ASD genes (including TBX20, as reported in chapter 3) to have an

orthologue within one of the QTL identified by this study (see table 6.2). The

dominance effect for this QTL was greater than the additive effect, an example

of overdominance (see section 1.3.1.1 for a discussion of dominance and

overdominance).

6.6.3 MMU3 There was suggestive evidence for linkage for CRW. None of the other traits,

particularly PFO, demonstrated a similar pattern.

6.6.4 MMU4 There was highly significant evidence for linkage for FOW. There was evidence

for a QTL affecting PFO from the binary analysis, and although the shapes of

the curves for FOW and PFO are not as similar as for MMU1, some

correspondence between the two is apparent.

188

6.6.5 MMU6 There was suggestive evidence of a QTL for FVL, with a LOD score of 4.11

(only a little below the threshold for significant linkage). Again, the curve for

PFO paralleled that for FVL over the last 20 cM of the chromosome.

6.6.6 MMU7 There was significant evidence for linkage for CRW. Although Tbx20, the

mouse orthologue of human TBX20, is located on MMU7, it is well outside the

confidence interval for this QTL and can be excluded as a candidate gene.

6.6.7 MMU8 There was signficiant evidence for linkage for FVL.

6.6.8 MMU9 There was suggestive evidence of linkage for FOW.

6.6.9 MMU10 There was suggestive evidence of linkage for FVL.

6.6.10 MMU13 There was significant evidence of linkage for FVL.

6.6.11 MMU15 There was significant evidence for a QTL for FVL. There was also suggestive

evidence of a QTL for FOW, and the curves have similar shapes, as does the

curve for the binary analysis – which in turn is also above the threshold for

suggestive linkage, at 3.25. This is the only chromosome for which there was

evidence of a QTL which may affect two of the three continuous traits,

consistent with the observed low correlation between them (section 5.4.2)

6.6.12 MMU18 There was suggestive evidence for a QTL affecting FVL.

189

6.6.13 MMU19 There was significant evidence for a QTL for FVL. In addition, there was

suggestive evidence of a QTL from the binary analysis, with a similarly shaped

curve for PFO and FVL.

Table 6.2: LOD scores at loci of orthologues of reported human ASD

genes

Actc Tbx5 Tbx20 Myh6 Gata4 Nkx2-5

MMU chromosome 2 5 7 14 14 17

Position (cM) 53 54 5 28 34 5.5

Max LOD score*

(phenotype)#

3.38

(FOW)

1.0

(FVL)

1.1

(PFO)

1.75

(FVL)

1.86

(FVL)

1.7

(CRW)

* Highest LOD score of any of the traits at the location of this gene

# phenotype which had the highest LOD score at that location

6.7 Discussion This study has identified 7 QTL with significant and 6 with suggestive evidence

of linkage, affecting 3 distinct atrial septal anatomical phenotypes relevant to

septal dysmorphogenesis in the mouse. For 4 of these loci (on MMU1, MMU4,

MMU6, and MMU19), there is strong supportive evidence for the presence of a

QTL from a binary analysis of the data relating to presence or absence of PFO.

The stringent criteria for evidence of linkage proposed by Lander and Kruglyak

(Lander and Kruglyak, 1995) include a cutoff for “suggestive” linkage expected

to be seen by chance once per whole genome scan. It is therefore likely that

most or all of the “suggestive” loci identified here will be confirmed.

190

6.7.1 Cryptic QTL Several of the QTL identified in this study are “cryptic,” ie, the effect of the QTL

is in the opposite direction to that which would have been predicted from the

phenotypes of the parental strains. These include three of the four for FOW (two

with significant and one with suggestive evidence for linkage) and both for CRW

(one with significant and one with suggestiveevidence for linkage). For example,

mean FOW in QSi5 mice is 0.21 mm, compared with 0.24 mm in 129T2/SvEms

mice. For the FOW QTL with strongest statistical support (a LOD score of 9.05),

homozygosity for the 129T2/SvEms allele is associated with a decreasing effect

on FOW compared with the QSi5 allele. This phenomenon, known as

transgressive segregation, can result in more extreme phenotypes in F2

individuals than in either parental strain. Cryptic QTL are particularly commonly

reported in plant studies (Peng et al., 2003; Lauter and Doebley, 2002), but the

phenomenon is by no means restricted to plants. In a review of 171 studies

conducted in a variety of plant and animal species, 44% of 1229 traits studied

were transgressive (Rieseberg et al., 1999).

6.7.2 Binary trait analysis It is likely that the liability model for binary traits (Falconer, 1965), as applied

here using the software developed by Dr Peter Thompson for the purpose, is

applicable to most forms of CHD. The identification of continuous traits that act

as proxies for the phenotype of interest provides considerably more power than

would be available using a binary analysis alone. This is illustrated by our

results; even where the binary analysis closely conforms to the results for one

of the three continuous traits, the strength of evidence for linkage is always

substantially lower for the binary than for the continuous trait. However, these

results show that binary analysis can provide important independent information

in support of standard QTL analysis. In a sense, as discussed in section 6.1

above, this approach is already in use for the study of phenotypes related to

atherogenic vascular disease. Even if there were a useful rodent model of

stroke or myocardial infarction, direct study of these phenotypes would be much

less powerful than the current approach to dissecting quantitative risk factors

191

such as hypertension and hyperlipidaemia. The results reported here vindicate

this approach.

6.7.3 Genetic relationship between FVL, FOW and CRW In section 5.4 the relationships between FVL, FOW and CRW were discussed.

Each has an influence on the likelihood of PFO, FVL and FOW more strongly

than CRW. However, they appear to be largely independent traits, with very

low correlation coefficients between each pair of traits. This suggests they are

likely to be under largely separate genetic control. The results of the linkage

studies support this idea. Of all of the QTL identified, in only one instance (on

MMU15) was there suggestive evidence of linkage for a second trait (FOW) at a

locus where a QTL for another trait (FVL) had been identified. It remains

possible that other identified QTL do in fact contribute to more than one trait and

that the study has simply not detected this effect. Nonetheless, given the power

of this analysis, with a very large number of mice phenotyped and genotyped, it

is likely that these traits are indeed largely under separate genetic control.

6.7.4 Contribution of the identified QTL to the phenotypes under study Assuming that none of the “suggestive” loci are chance findings and taking the

conservative position that QTL are additive, the QTL identified account for 18%,

14%, and 13% of the phenotypic variance of FVL, FOW, and CRW,

respectively. However, phenotypic variance includes nongenetic variance

attributable to measurement error (which may be significant for this type of

study, although the replication of measurements described in section 7.7

suggests a fairly reliable technique), environmental effects, and biological noise.

Another way of considering the magnitude of the genetic effect on phenotype of

these QTL is to consider their contribution to observed differences in mean

phenotypic values between the parental strains. For example, for FVL, each of

the 7 QTL identified accounts for 13% to 18% of the difference between

parental means. Assuming additivity, this accounts for the complete difference

between those strains. Strikingly, a single QTL for FOW (on MMU1) accounts

for 111% of the difference between parental means. This overrepresentation

almost certainly reflects the presence of cryptic QTL for this and other traits

192

contributing to the parental means. Cryptic QTL detected for FOW and CRW

individually impact on these phenotypes by 73% to 143% relative to the

difference between parental means. The seemingly exaggerated effects testify

to the fact that QTL mapping can reveal genetic information that individually can

contribute to trait variation to an extent far beyond that seen as the difference

between parental strains. Thus, although it is difficult to precisely quantify the

effects of individual QTL on the genetic component of variation, it can be

concluded that QTL detected in this study are all of relatively strong effect. The

complexity of the effects of QTL is further emphasized by the figures for

dominance effects. While generally fairly modest, several (specifically those on

MMU1 MMU6, and MMU9) are of comparable magnitude to the additive effects

and one QTL (for FOW on MMU2) exhibits overdominance.

6.7.5 Candidate genes The very large confidence intervals of the QTL reported here mitigate against

immediately moving to a candidate gene approach in an effort to identify the

underlying genetic basis of the QTL. There are a total of 4964 genes listed

within the 1 LOD drop-off confidence interval of the 13 QTL, a not-

inconsiderable proportion of the entire mouse genome. Of these, 185 were

annotated as “heart” on the mouse genome informatics website of The

Jackson Laboratory (http://www.informatics.jax.org , accessed October 2005).

Undoubtedly many more genes than this are expressed in the developing heart,

but this selection gives a flavour of the problem of candidate gene selection in

the face of very large regions of interest. The 185 genes are listed in Appendix

3. Among them are many plausible candidate QTL genes, including members

of the bmp and fgf growth factor pathways involved in cardiac induction and

proliferation, as well as a host of others governing transcriponal regulation,

signaling, cell cycle, cell death, and extracellular matrix biology.

Notwithstanding this, given the relationship between ASD and PFO, and

particularly the known effect of heterozygosity for a mutation in NKX2-5 on

incidence of PFO and FVL (Biben et al., 2000), it is worth considering the

http://www.informatics.jax.org

193

possibility that variants in murine orthologues to one or more of the 6 known

human ASD genes may be responsible for the QTL identified here.

Table 6.3 lists the locations of these genes. Of the QTL regions mapped in this

study, only the one for FOW on MMU2 contains a mouse ortholog (Actc) of 1 of

the 6 known human dominant ASD. Dr Donna Lai sequenced exons of the Actc

gene from the QSi5 and 129T2/SvEms strains and compared levels of Actc

mRNA in the atria and remaining portions of dissected E9.5 embryonic hearts

by quantitative RT-PCR (Kirk et al., 2006). There were no nonsynonymous

polymorphisms in coding regions of Actc or significant difference in mRNA

levels that would implicate Actc as the gene underlying the MMU2 QTL. The

mouse orthologues of the other 5 known ASD genes, Tbx5, Tbx20, Myh6,

Gata4, and Nkx2–5, all fall within regions where there is little evidence for

linkage.

6.7.6 Future studiesFrom the above it is clear that even a successful QTL mapping study like this

one can only represent a first step towards a deep understanding of the genetic

architecture of a complex trait. Work has already commenced on two different

approaches to refining the QTL reported here, using an Advanced Intercrossed

Line (AIL) and using an association technique based on the Mouse Hapmap

Project. These are discussed in Chapters 5 and 7.

7. Comparisons of atrial septal anatomy in 12 strains of inbred laboratory mice reveal unexpected complexity

7.1 Introduction The problem of moving from identification of a QTL to discovery of its underlying

genetic basis was raised in section 1.4.3. The results of the QTL mapping study

discussed in Chapter 6 highlight this issue: the QTL identified encompass a

sizeable fraction of the entire mouse genome. How, then, to find the genetic

needle in the genomic haystack? One approach, the AIL, has been discussed

already in sections 1.4.3, 2.1.3.2 and 5.3, with results from phenotypic analysis

of the F14 AIL mice forming a substantial part of Chapter 5. This approach was

chosen in preference to the generation of a congenic mouse line because

generation of an AIL allows simultaneous investigation of multiple QTL. This

was an attractive option because of the large number of QTL identified in the

mapping study (Chapter 6). Another advantage of the AIL is that phenotyping

and genotyping are not required until the final generation of mice.

During the course of this project, a new approach to fine mapping of QTL

emerged. This was the use of SNP data generated for a large number of inbred

strains of laboratory mice as a mapping tool, often without the need to

phenotype additional mice (for well studied phenotypes such as body weight

and haematological characteristics). Such SNP data have been generated and

made freely available by the Inbred Laboratory Mouse Haplotype Map project

(also referred to as the “mouse Hapmap project”) (Frazer KA et al., 2007)

(http://www.broad.mit.edu/mouse/hapmap). This approach was introduced in

section 1.4.4 and will be discussed in more detail below. It has the advantage

of not requiring genotyping (as this has already been done), requiring only

phenotyping of sufficient mice from each strain to accurately establish the mean

strain value for the trait of interest. It has the potential to play a complementary

role with the AIL, and this chapter reports the results of phenotyping of 10 of the

Hapmap strains, combined with data from QSi5 and 129T2/SvEms.

194

http://www.broad.mit.edu/mouse/hapmap

195

7.2 History of the inbred laboratory mouse The potential success of the haplotype-based approach to mapping in inbred

laboratory mice rests in large part on the unique history of the laboratory

mouse. Breeding of “fancy” mice in Asia from the 1700s, and subsequently in

Europe and the United States, provided a stock of relatively inbred strains. The

Asian strains originated from the subspecies Mus musculus musculus and Mus

musculus molossinus (the latter in turn being a hybrid of M.m.musculus and

M.m.castaneous). The European strains were bred from imported Asian strains

and from locally obtained wild mice of the subspecies Mus musculus

domesticus (Wade et al., 2002). In the early 20th century, the systematic use of

mice in laboratory research began with the work of pioneers such as Castle and

Little, who obtained mice from Abbie Lathrop – a breeder in Granby,

Massachusetts, who bred both Asian and European-derived strains (Wade and

Daly, 2005). Deliberate crossing and then inbreeding of these strains led to the

derivation of the classical inbred strains which are ancestors of many of today’s

laboratory mice. Recent work has shown an overall ancestral contribution to the

classical inbred laboratory strains of 68% from M.m.domesticus, 10% from

M.m.molossinus, 6% from M.m.musculus, 3% from M.m.castaneous and 13%

of unknown origin (Frazer KA et al., 2007).

One consequence of this history is that, to a greater or lesser extent, most

commonly used strains of laboratory mice share common ancestry. Analysis of

the fine structure of variation of the mouse genome has revealed long

interspersed regions of high and low sequence identity between pairs of strains

of laboratory mice (Wade and Daly, 2005). This reflects segments of recently

shared ancestral origins (high sequence identity) and differing ancestral origins

(low sequence identity). In a pairwise comparison, the frequency of SNPs can

range from 1 SNP per 20kb to 1 per 250-300bp (Wade et al., 2002).

Comparison of multiple strains reveals a mosaic of overlapping blocks of SNPs.

In a recent report, identification of 8.27 million SNPs in 15 mouse strains (4

wild-derived and 11 classical) led to generation of a haplotype map of 40,898

segments (Frazer KA et al., 2007).

196

7.3 Application of mouse haplotype data to mapping The properties of the genomic structure of common strains of laboratory mice,

as outlined above, have the potential to be a powerful resource for gene

mapping. Using the tyrosinase locus as an example, Wade and colleagues

showed that using then-available (in 2002) haplotype information in just 12

strains of mice, it was possible to map the tyrosinase locus to within 500kb,

assuming previous assignment of the gene to MMU7. This was done simply by

sorting the mice into albino and non-albino strians and identifying a region

shared by descent in all of the albino mice, which was not shared by any of the

non-albino mice (Wade et al., 2002).

While such a simple approach is unlikely to be effective in studying complex

traits, statistical methods have been developed which allow the use of

haplotype data to map QTL in laboratory mice, and this approach has already

been used to map QTL (Pletcher et al., 2004; McClurg et al., 2006). A simple

but effective approach is the single-marker mapping (SMM) approach. This

exploits the fact that each SNP is biallelic across inbred strains. A SNP by SNP

calculation of association is done, using a t-test to measure strength of

association between phenotype and genotype (McClurg et al., 2006). Because

this results in thousands of statistical tests being performed, correction for

multiple testing, for example using a false discovery rate (FDR) threshold, is

required (McClurg et al., 2006). This is also a requirement for the more complex

haplotype-based approaches, although the effective number of individual

comparisons is lower than if a SNP by SNP analysis is done.

The SMM approach is computationally simple, but it risks discarding a great

deal of useful information. In particular, while SNPs are biallelic, there are

multiple possible haplotypes in most regions and more information can be

obtained using analyses which consider multiple SNPs simultaneously. McClurg

and colleagues (McClurg et al., 2006) review several possible approaches to

mapping by inferred haplotype structure.

197

7.4 Number of mice to phenotype There is a considerable amount of publicly available information about

characteristics of commonly used strains of laboratory mice, opening the way to

in silico mapping without the requirement for the researcher to examine or

genotype a single animal. However, FVL, FOW and CRW have not been

studied by other investigators, and using the available mouse SNP data for

mapping requires phenotyping sufficient animals to provide an accurate

estimate of the mean values for each trait. An estimate of the required number

of animals can be made based on the equation

n = (Z�/d)2 (Kuzma JW and Bohnenblust SE, 2001)

where Z is the standardized normal score for the required confidence interval, �

is standard deviation of the trait and d is the number of units from the mean by

which a deviation from the true population mean is acceptable. Put another

way, if we wish to have 95% confidence that the mean of the animals scored is

within one standard deviation of the true population (or in this case, strain)

mean, the calculation resolves as follows:

n = (1.96 x 1 / 1 )2

(note that 1.96 is the standardized normal score for a 95% confidence interval)

i.e. n � 4. Estimation of the population to within half a standard deviation

requires � 15 mice, and within a quarter of a standard deviation requires � 61

mice. Each time the accuracy required is doubled, four times as many mice are

needed (the progression 4, 15, 61 represents the effects of rounding rather than

inaccuracy in this statement). Thus, the aim was to dissect a minimum of 15

mice from each of as many strains as possible, in order to achieve a mean

within half a standard deviation of the true mean for the strain. This represents a

balance between the time and resources required to phenotype large numbers

of mice and a reasonable level of accuracy. So far, 10 strains have been

assessed, and in addition Dr Claire Wade has generously arranged for SNP

data to be generated for both QSi5 and 129T2/SvEms, allowing the use of the

existing phenotype data for these strains in any analysis. For the remainder of

this chapter, reference to the Hapmap strains refers to these 12 strains

(including QSi5 and 129T2/SvEms), unless otherwise specified.

198

7.5 Analyses of Hapmap strains Data from 403 mice, representing 12 strains of inbred laboratory mice, are

presented in this section. This includes the QSi5 and 129T2/SvEms mice

described in section 5.4, and 262 mice from 10 strains selected from the 49

strains genotyped by the Inbred Laboratory Mouse Haplotype Map project

(http://www.broad.mit.edu/mouse/). These were 129X1/SvJ, A/J, AKR/J,

C3H/HeJ, C57Bl/6J, DBA/1J, DBA/2J, FVB/NJ, SJL/J and CBA/J.

7.6 Training of a second observer The original intention in this part of the project was to genotype most of the 49

strains studied by the Mouse Hapmap consortium. Phenotyping a minimum of

15 mice from each of 49 strains would mean dissecting at least 750 mice, an

effort comparable in scale to the work required for study of the F2 and F14

mice. This made training a second observer to do dissections and

measurements a worthwhile option, and Ms Noelia Lopez was recruited and

trained as part of a Master’s project. Unfortunately, delays in obtaining mouse

strains meant that there was insufficient time for her to complete the large scale

dissection exercise. However, there was time for her to co-measure 147 hearts,

with the two observers blinded to one another’s results. The availability of these

data is useful for assessing the reliability of the measurements.

7.7 Assessments of the reliability of measurement of atrial septal anatomy The atrial septum of a mouse is a tiny, delicate structure which is very easily

stretched or distorted during measurement. Small amounts of damage to the

surrounding structures would be expected to affect the quality of the

measurements. The structures under study ranged in size from 0.2-1.5mm. The

possibility of significant error was therefore a real concern.

Consideration was given to the option of fixing hearts in paraformaldehyde prior

to measurement, which would have had the added benefit of allowing later

checking of measurements. When this was attempted, hearts did not prove

suitable for study. The tissues became stiff and difficult to align for

measurement. Their pallor also made identification of landmarks more difficult.

http://www.broad.mit.edu/mouse/

Also, assessment for PFO in the absence of liquid blood to act as a marker (by

passing through the foramen ovale to the left side of the atrial septum) was

much more difficult, requiring the injection of dye prior to assessment for PFO.

Table 7.1 shows measures of inter-rater reliability for each atrial septal trait.

Table 7.1: Measures of inter-rater reliability

Coefficient FVL FOW CRW

Pearson’s r

(p value) 0.80 (<0.0005) 0.72 (<0.0005) 0.53 (p<0.0005)

ICC(individualvalues)

0.80 0.72 0.53

ICC (means)* 0.89 0.84

*ICC = Intra-class Correlation

0.69

Comparing observers, the correlation coefficients for FVL and FOW are high,

the correlation coefficient for CRW is moderately high and all three achieve very

high levels of statistical significance – in short, there is good agreement

between the observers for FVL and FOW, and fair agreement for CRW. One

limitation of Pearson’s correlation coefficient is that it does not take into account

any systematic difference between values (for example if one observer

consistently measures higher than the other). Intra-class correlation (ICC) is a

measure of homogeneity within groups relative to total variation, taking into

account random variation between groups (as for Pearson’s correlation

coefficient) and systematic variation. Like the Pearson correlation coefficient,

the ICC ranges from –1 to +1, with values close to zero indicating low

agreement between raters. The ICC is not calculated by Minitab, so a free

online calculator (at

http://department.obg.cuhk.edu.hk/researchsupport/IntraClass_correlation.asp)

was used. There are three classes of ICC, termed Case 1, Case 2 and Case 3

(Shrout and Fleiss, 1979) – referring to different relationships between raters

and data. Case 2 refers to a situation in which the same raters rate each case,

199

http://department.obg.cuhk.edu.hk/researchsupport/IntraClass_correlation.asp

200

and the cases are a random sample. This is the best match with the mouse

data under analysis, so Case 2 was used for these analyses.

The Pearson’s correlation coefficient and ICC are identical for all measures

(there are in fact slight differences but only in the third decimal place, i.e. they

are of no consequence), indicating no systematic error. The ICC for means is of

particular interest here – this compares the means of the three measures. Since

the intention of the mapping exercise is to generate accurate approximations of

the true mean for the strains under study, the high values for the meaned ICC

are very encouraging, particularly for FVL and FOW. Given this encouraging

result – close agreement with high measures of correlation and no evidence for

systematic bias - for the 68 mice measured by Ms Lopez and not by EK, Ms

Lopez’s data are used in the subsequent analyses.

Overall, these analyses provide reassurance that the measures used

throughout this chapter, and which form the basis for the QTL mapping study,

are reliable and represent a reasonably accurate measure of the actual

anatomy of the mice which were studied.

7.8 Descriptive statistics for the Hapmap strains (including 129T2/SvEms and QSi5) Table 7.2 shows descriptive statistics for all 12 strains of inbred laboratory mice

which have been studied to date.

Tabl

e 7.

2: D

escr

iptiv

e st

atis

tics

for 1

2 st

rain

s of

inbr

ed la

bora

tory

mic

e

QS

i512

9T2/

SvE

ms

C57

/Bl6

FVB

/J

A/J

SJL

/J

n66

7522

2637

44

PFO

(%)

4.5

8059

859

55

FVL(

mm

)1.

13�

0.11

0.60

� 0.

11

1.00

�0.1

01.

19�0

.11

1.08

�0.1

30.

81�0

.11

FOW

(mm

)0.

21�

0.06

1 0.

24�

0.05

8 0.

25�0

.087

0.35

�0.0

550.

26�0

.084

0.26

�0.0

78

CR

W(m

m)

0.51

� 0.

13

0.44

� 0.

12

0.53

�0.1

00.

56�0

.11

0.59

�0.1

20.

54�0

.12

Bod

y W

eigh

t(g)

29.4

� 2.

77

17.5

� 2.

1 20

.8�1

.921

.7�2

.519

.5�2

.219

.7�2

.1

Hea

rt W

eigh

t(g)

0.21

� 0.

024

0.14

� 0.

021

0.17

�0.2

80.

16�0

.028

0.13

�0.0

160.

15�0

.019

201

129X

1/S

vJ

DB

A/1

J D

BA

/2J

C3H

/HeJ

A

KR

/J

CB

A/J

n28

2524

2314

25

PFO

(%)

5756

*96

100

8664

FVL(

mm

)0.

73�0

.097

1.07

�0.0

920.

90�0

.14

0.94

�0.1

31.

05�0

.13

1.07

�0.0

92

FOW

(mm

)0.

29�0

.10

0.24

�0.0

640.

32�0

.20

0.29

�0.1

00.

37�0

.093

0.24

�0.0

64

CR

W(m

m)

0.50

�0.1

30.

63�0

.14

0.54

�0.1

10.

58�0

.15

0.60

�0.1

10.

63�0

.14

Bod

y W

eigh

t(g)

21.8

�1.8

18.5

�2.3

20.6

�3.3

19.3

�4.1

25.8

�4.5

18.5

�2.3

Hea

rt W

eigh

t(g)

0.16

�0.0

190.

12�0

.01

0.18

�0.0

370.

12�0

.060

0.21

�0.0

36

*In

clud

es 5

mic

e w

ith A

SD

0.12

�0.0

12

202

203

All of the traits under study varied widely between strains. Evaluation of these

additional strains did reveal some with more extreme phenotypes than those

chosen for the QTL mapping study, 129T2/SvEms and QSi5. However, these

strains were at or close to the extremes for FVL and frequency of PFO. QSi5

had the lowest frequency of PFO at 4.5%, and 129T2/SvEms had the fourth

highest at 80%. QSi5 had the second longest mean FVL (after FVB/J) and

129T2/SvEms had the shortest. As the strains were not selected on the basis of

FOW or CRW, it is not surprising that 129T2/SvEms had an unexceptional

FOW, but QSi5 had the smallest FOW of any strain studied to date, and

129T2/SvEms had the shortest CRW. The data for DBA/1J mice presented here

include mice with ASD (see below) and may be artefactually low – the ASDs

seen in this strain had a residual lip of flap valve, making it possible to record

measurements for each of FVL, FOW and CRW, but it is possible that these

traits have been modified to some degree by the presence of ASD.

7.9 ASD in DBA/1J mice Study of DBA/1J mice yielded a completely unexpected result, the presence of

frank ASD in 5/25 mice. Figure 7.1 illustrates ASD in a DBA/1J mouse. In

addition, atrial septal aneurysm (ASA) was very common, with 6/20 mice

without ASD having ASA. By way of comparison, among the 1438 F2 mice, one

had an ASA and one had TGA. Among the 1003 F14 mice, 4 had ASA and one

had an ASD. Among the other inbred strains, 2/23 C3H/HeJ had ASA, but none

of the other strains had any abnormalities recorded. Even mice heterozygous

for a mutation in Nkx2-5 have an incidence of ASD of only ~1% (Biben et al.,

2000).

A

B

Figure 7.1: ASD in a DBA/1J mouse. A. View of heart with left atrium opened

to show ASD. B. High power view. A residual rim of flap valve is visible (arrow)

204

205

The dissections were targeted at atrial septal anatomy and it is possible that

some CHD was missed – for example, the ventricular septum was not routinely

examined and nor were the great vessels. The F2 mouse with TGA was

identified because of the observation during routine dissection that there was

massive right ventricular hypertrophy, which led to performance of a more

detailed fine dissection than usual. Nonetheless, it is clear that among inbred

laboratory mice, CHD is rare and the DBA/1J strain is exceptional in this regard.

This opens the possibility of further studies to determine whether other forms of

CHD occur more commonly in this strain, and if possible to identify the genetic

basis of this propensity to ASD.

7.10 Relationships between PFO and other traits in the 12 strains of inbred miceBased on the work of Biben and colleagues, and the results of the studies of

129T2/SvEms, QSi5 and the F1, F2 and F14 mice reported in chapter 5, it was

anticipated that study of the additional 10 strains would yield similar results.

Specifically, associations between PFO and each of FVL, FOW and CRW were

expected. Table 7.3 shows correlations between the traits in the 12 strains.

Remarkably, none of the relationships relevant to PFO established in studies of

129T2/SvEms, QSi5 and the strains derived by mating them are confirmed by

this analysis. The only significant correlations are between FVL and CRW

(r=0.684) (p=0.01), traits which had very low correlation in the earlier analyses.

For FVL and %PFO, the correlation coefficient is negative, as would be

expected, and moderately large at –0.485, but the correlation is not statistically

significant. The strongest correlation is between weight and heart weight – as

expected, heavier mice have heavier hearts. The correlation coefficient for

these traits is high and the correlation is highly statistically significant. Figures

7.2 – 7.4 are scatterplots with regression fit for percentage of PFO for each

strain, plotted against the strain means for each of FVL, FOW and CRW. These

figures illustrate the looseness of the relationship between these traits. By way

of comparison, figure 7.5 shows the strong relationship between weight and

heart weight.

206

Table 7.3 Correlation between mean values for %PFO, FVL, FOW, CRW, weight and heart weight

%PFO FVL FOW CRW Weight

FVL -0.485

(0.11)

FOW 0.235

(0.46)

0.134

(0.68)

CRW 0.098

(0.76)

0.684

(0.01)

0.186

(0.56)

Weight -0.471

(0.12)

0.398

(0.20)

0.15

(0.64)

-0.114

(0.72)

HeartWeight

-2.51

(0.43)

0.152

(0.64)

0.33

(0.29)

-0.31

(0.32)

0.87

(<0.0005)

Cells contain Pearson’s correlation coefficient r for the correlation between traits in the

row and column headers intersecting at each cell. The p values for the correlation

coefficients are in brackets. %PFO, percentage of mice with PFO.

FVL

%P

FO

1.21.11.00.90.80.70.6

100

80

60

40

20

0

Figure 7.2: Scatterplot of %PFO vs FVL

FOW

%P

FO

0.380.360.340.320.300.280.260.240.220.20

100

80

60

40

20

0

Figure 7.3: Scatterplot of %PFO vs FOW

207

CRW

PFO

%

0.650.600.550.500.45

100

80

60

40

20

0

Figure 7.4: Scatterplot of %PFO vs CRW

Heart Weight

Wei

ght

0.210.200.190.180.170.160.150.140.130.12

30

28

26

24

22

20

18

16

Figure 7.5: Scatterplot of Weight vs Heart Weight

208

209

However, analysis of variance for the entire dataset for the 12 inbred strains (as

opposed to correlations between means for the strains) does show a

relationship between each of FVL, FOW and CRW and PFO. Table 7.4 shows

the results of analysis of variance calculations for FVL, FOW and CRW, using

PFO as the single term in the model. In order to account for the possibility that

the results were skewed by the inclusion of the original strains, particularly as

there were considerably more 129T2/SvEms and QSi5 mice dissected than for

any other strain, the analysis was repeated after removal of the data for these

strains.

Table 7.4: Results of ANOVA for FVL, FOW and CRW – single analyses with PFO as the model

All strains With 129T2/SvEms and QSi5 removed

F statistic p value F statistic p value

FVL 55.10 <0.0005 19.29 <0.0005

FOW 31.88 <0.0005 19.84 <0.0005

CRW 3.76 0.024 4.68 0.010

Table 7.5 Combined mean values for 12 mouse strains with and without PFO

With PFO Without PFO

FVL 0.89�0.26 1.04�0.22

FOW 0.26�0.098 0.22�0.059

CRW 0.54�0.16 0.51�0.12

Mean � standard deviation

Table 7.5 shows the mean FVL, FOW and CRW for mice with and without PFO.

These figures may be distorted by variation in the number of mice dissected

from each strain, but the results are consistent with the earlier findings – PFO is

associated with short FVL, large FOW and wide CRW.

210

There is an apparent conflict between these two analyses – comparison

between the strain means and grouped analysis of all data, with the former not

confirming the expected relationships between PFO and each of FVL, FOW and

CRW but the latter in agreement with the studies reported in Chapter 5. It

appears that the relationship between aspects of atrial septal morphology and

PFO is complex. It is likely that for some of the strains studied, the main

determinants of the presence of PFO are not causally connected with the

determinants of FVL, FOW and CRW. The wording used here is deliberately

cautious. It is not certain that having a short FVL (for example – the same

argument applies to the other measures) causes PFO. It may be, for example,

that short FVL is a consequence of the haemodynamic effects of an existing

PFO.

If it is true that for at least some strains, factors unconnected to FVL, FOW and

CRW are the main determinants of PFO, then it is to be expected that some

strains with high frequency of PFO will have septal morphology similar to others

with low frequency of PFO. This would be consistent with the lack of a clear

relationship between the strain means for PFO and the other measures. To take

one strain as an example, 100% of C3H/HeJ mice had PFO. But this strain has

only a moderately short FVL and moderately wide FOW. Perhaps C3H/HeJ

mice have PFO for other reasons, e.g. mildly abnormal function of cell adhesion

factors during early postnatal life.

7.11 Comparison with the study by Biben and colleagues It is puzzling that Biben and colleagues found such a close correlation between

PFO and FVL (Biben et al., 2000). A partial explanation for this may be that they

studied relatively few different types of wild-type mice – outbred Swiss QS,

FVB/J, C57Bl/6, and 129T2/SvEms. The other strains on which the correlation

coefficient of – 0.97 was based were crosses between these strains, and/or

were heterozygous for an Nkx2-5 mutation. FVB/J and Swiss QS mice are from

a similar background and might be expected to have similar determinants of

PFO, and Nkx2-5 haploinsufficiency has a substantial effect on FVL and PFO.

By contrast, the figures for strain means here are based on 12 separate strains

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(although some, such as the two 129 strains, are from a similar background), all

of which are pure-bred and wildtype with respect to Nkx2-5.

7.12 Conclusions Comparisons between two independent observers show that the dissection and

measurement technique used is robust and does not suffer from systematic

bias. This is an important finding for this thesis given the dependence of

Chapters 5, 6 and 7 on the results of several thousand dissections.

The simple correlation between FVL and percentage of mice with PFO found by

Biben and colleagues (Biben et al., 2000) appears to break down with the

addition of further strains. However, on more detailed statistical analysis a

highly significant relationship remains, suggesting considerable and unexpected

complexity in the relationships between FVL, FOW and CRW. This finding was

rendered even more surprising by the strong confirmation of the findings of

Biben and colleagues in studies of QSi5 and 129T2/SvEms, including F2 and

F14 mice derived from crosses of these strains.

The contrast between the results in two closely studied strains (QSi5 and

129T2/SvEms) and the results in a larger group of strains is striking, and may

have implications for the use of the mouse Hapmap data for fine mapping. If the

genetic determinants of the atrial septal traits studied here vary importantly

between strains, the power of this mapping approach would be expected to be

reduced. It is possible that the genetic determinants of FVL, FOW and CRW are

common to all of the strains, even if these traits are not importantly related to

the risk of PFO in a substantial proportion of them. If this were the case, the

power of a mapping study directed at these quantitative traits would not be

affected, although an analysis designed to look only at the binary trait (presence

or absence of PFO) would still be weakened. However, preliminary mapping

based on the results to date indicates that in practice, this may not be an

important issue (see section 8.5).

8. Conclusions and Future Directions

8.1 Genetic heterogeneity and its clinical implications Previous work had established that dominant autosomal ASD is one of a

number of genetic disorders which is highly genetically heterogeneous. Other

examples include autosomal dominant dilated and hypertrophic

cardiomyopathy, long QT syndrome, autosomal dominant and recessive

sensorineural deafness, dominant and recessive retinitis pigmentosa and

nonspecific mental retardation (X-linked and other forms).

The studies reported in chapters 2 and 3 reinforce this point. Mutations in

NKX2-5 and GATA4 account for a relatively small proportion of cases, and even

when only individuals with a family history are considered, mutations in these

genes are found in 10.7% and 7.8% respectively. Mutations in TBX20, identified

in the course of this study as contributing to human CHD, possibly account for

another 5.1% of familial cases, leaving more than 75% of familial ASD

unaccounted for. Based on the figures for these three genes, it is unlikely that

studies of MYH6 and ACTC will account for more than another 10% of familial

cases each. Mutations in TBX5 are unlikely to account for more than a very

small percentage of cases, if cases of definite Holt-Oram syndrome are

excluded, given the high penetrance of limb anomalies in HOS (Newbury-Ecob

et al., 1996). Thus, the majority of the genetic basis of familial ASD is yet to be

elucidated, and it would not be at all surprising if there were 10 or more

dominant ASD genes yet to be discovered. The identification of syndromal

forms of ASD with relatively subtle extra-cardiac components, such as the

syndrome of ASD with Marcus Gunn phenomenon reported in Chapter 3, is

unlikely to be an aid to genetic diagnosis in more than a small percentage of

cases, even once the gene for this condition is mapped.

Conditions with this much genetic heterogeneity present a challenge in genetic

counselling, particularly if there is a strong indication for genetic testing in a

family. Even where known genes associated with a particular disorder account

for a relatively large proportion of cases, such as in long QT syndrome (Skinner 212

213

JR and Members of the CSANZ Cardiovascular Genetics Working Group,

2007), genetic testing is generally expensive (due to the large number of genes

which may need to be screened) and has a comparatively low chance of

identifying a causative mutation in any one affected individual.

There may not be strong demand for mutation testing in families where ASD is

the only manifestation. Prenatal diagnosis and termination of affected

pregnancies is unlikely to be sought for a condition which is perceived by many

families to be relatively mild, and curable even in more severe cases. However,

counselling needs to take into account the high incidence of other, more severe,

cardiac malformations in dominant ASD families, and even if no genetic testing

is undertaken in pregnancy, monitoring with fetal echocardiography in at-risk

pregnancies is desirable.

8.2 Cardiac phenotypes other than CHD 8.2.1 AV conduction abnormalities and ASD The observation that some families with dominant forms of ASD have

associated AV conduction block is of great importance for the clinical

management of affected individuals and for guiding genetic testing. It is thus

essential that descriptions of new forms of inherited CHD include information

about the presence or absence of ECG abnormalities. On currently available

evidence, both TBX20 – associated ASD and dominant ASD with MGP fall into

the group of dominant ASD without conduction abnormalities. Published reports

indicate that the combination of ASD and AV block is a strong pointer to the

possibility of an NKX2-5 mutation, even in the absence of a family history. This

association is also seen with TBX5 mutations, but as discussed above, most

such families can be clinically distinguished on the basis of radial ray anomalies

and other features such as the characteristic sloping shoulders seen in HOS

(Newbury-Ecob et al., 1996). The history of family 1024 with the NKX2-5

mutation T178M (Section 3.2.2.1) suggests that a history of sudden death or

otherwise unexplained ventricular fibrillation should also be a pointer to the

possibility of an NKX2-5 mutation.

214

The progressive nature of the AV block in many affected individuals mandates

long term surveillance. This is particularly important for mutation positive family

members with no structural heart disease, who may be less inclined to attend

for long term follow-up.

8.2.2 Cardiomyopathy in association with mutations in TBX20 and NKX2-5

Similarly, cardiomyopathy may be a long term complication of mutations in

either TBX20 (section 3.4.2.2) or NKX2-5 (Schott et al., 1998; Benson et al.,

1999) (discussed in section 3.4.4). The proportion of affected individuals who

will go on to develop cardiomyopathy at some time in their lives is unknown at

present. Therefore, it is probably premature to make a firm recommendation

that individuals with mutations in either gene should have long-term screening

for cardiomyopathy. However, it is important that clinicians managing patients

with mutations in either gene should be aware of this association. It will be

important for clinical future studies of affected individuals to include evaluation

of affected individuals for cardiomyopathy.

8.3 The role of NKX2-5, GATA4 and TBX20 in multifactorial ASD The studies reported here suggest that these three genes may contribute, in

some cases at least, to the causation of the common multifactorial form of ASD.

Sequence variants which affect amino acid structure, but do not fulfill all

requirements for confident identification as pathogenic mutations, have been

observed in each of NKX2-5, GATA4 and TBX20. It is possible that some or all

of these play a role in causing CHD in the individuals in whom they are found,

or at least in modulating the severity of CHD in families where the mutation

does not segregate with CHD (such as T209I in TBX20 and E21Q in NKX2-5).

Determining whether this is correct will be challenging. A first step might be to

conduct a case control study (as has already been done for the GATA4

polymorphism S377G) to assess the frequency of such alleles in individuals

with CHD in comparison to those without CHD. The problem is that such an

approach is expensive and labour-intensive even for a common allele such as

S377G; it is much more difficult for a rare allele or collection of rare alleles,

since larger numbers of subjects and controls must be tested to generate

215

meaningful data. Of the reported sequence variants of currently uncertain

significance, the NKX2-5 change R25C might be most amenable to this

approach (see section 3.2 for detailed discussion of this variant). It has been

reported mainly in association with TOF, particularly in African-American

patients, and was present in 2/43 healthy African-American controls

(McElhinney et al., 2003). A case-control study restricted to African-American

cases and controls would be challenging but possible and may provide

evidence for (or against) pathogenicity of R25C.

This kind of study has limitations, however. At most it can demonstrate an

association with CHD. It provides no information about interactions with other

genes. Before we can claim a true understanding of the polygenic inheritance of

any disorder, we will need to know the genes involved, the variants in those

genes which influence phenotype and the nature of their interactions.

8.4 Future studies of dominant ASD genes in unselected subjects Notwithstanding the limitations discussed above, there is still merit in

conducting studies like those reported in Chapter 3, as conducted by other

investigators (Goldmuntz et al., 2001; McElhinney et al., 2003; Nemer G et al.,

2006). As each new dominant ASD gene is identified, it makes sense to test a

cohort of subjects for mutations in that gene. Even negative studies such as that

of Schulterman and colleagues (Schluterman et al., 2007) are helpful in defining

the contribution of mutations in each gene. While it is unlikely, based on studies

so far, that this approach will contribute a great deal to our knowledge of the

causes of non-familial ASD, it is realistic to expect that eventually the molecular

basis of most Mendelian forms of CHD will be able to be determined.

With this in mind, a collaboration has been established with Professor David

Brook, whose group identified mutations in MYH6 (Ching et al., 2005), and was

instrumental in identifying mutations in ACTC (Matsson H et al., 2005) in

dominant ASD. DNA from Australian subjects has been sent to Prof Brook’s lab

to be screened for mutations in MYH6, and it is likely a similar exercise will be

conducted with ACTC in future.

216

8.5 Mapping genes affecting prevalence of PFO in inbred laboratory mice The studies reported in chapters 5 and 6 reveal considerable complexity in the

relationships between phenotypic features of the atrial septal wall and PFO. The

QTL mapping study identified 7 QTL with significant and 6 with suggestive

evidence of linkage. The challenge now is to identify the underlying genetic

bases for these QTL. This will not be a simple task. The object of the search

may prove to be an amino-acid altering sequence change in a gene, but could

also be a variation in the regulatory regions of a gene (including remote

elements such as enhancers) or even a change in an RNA with regulatory

functions. The first step is to narrow down the currently very large regions of

interest. Two complementary approaches to this are being undertaken.

The first technique involves the use of the AIL. The laborious first and second

stages of this project, breeding of the resource and dissection of >1000

[129T2/SvEms x QSi5] F14 mice, have been completed. While there is some

evidence of genetic drift, with the marked rise in prevalence of PFO in F14

compared with the F2 mice, it is likely that many of the QTL detected in the F2

mapping study will still be detectable in the F14 mice. It is possible that one or

more of the QTL will resolve into multiple QTL of smaller effect, as was seen in

the study by Iraqi and colleagues of trypanosomiasis resistance loci in mice

(Iraqi et al., 2000).

The intention at the start of the AIL project was to do targeted genotyping, with

the use of densely spaced markers selected to span the 1-lod dropoff regions

for a number of the QTL, with the number of loci studied and density of markers

determined largely by budget constraints. However, the rapid development of

microarray technology and the falling cost of microarray studies raises the

possibility of using a high density SNP microarray approach to genotyping the

AIL. This would make it possible to investigate all of the identified QTL again

simultaneously, and also to conduct a new genome-wide screen for additional

QTL not detected in the F2 study. The very large amount of data generated by

microarrays would be a challenge, but computational resources for mapping are

also developing rapidly.

217

The proposed second approach to fine mapping involves the use of the publicly

available mouse Hapmap data, as described in section 5.3. Dr Peter Thomson

wrote software for this purpose, based on the single marker mapping approach

(SMM) and conducted a preliminary analysis on data from 8 of the Hapmap

strains. The results were unexpected. As an example, Figure 7.1 shows the

results for FOW on chromosome 1, compared with the chromosome 1 QTL

data from chapter 6. The upper figure shows the result of Dr Thomson’s

analysis. The y axis is –log10 (p) i.e. if p = 0.001, -log10(p) = 3. The blue and

red lines represent 5% and 1% false discovery rates (FDR), respectively. In

other words, for each peak above the red line, there is a 1% chance that it

represents false discovery of an association, adjusted for the very large number

of individual tests.

This forest of peaks is typical of the analysis, with many chromosomes showing

such results. There are numerous peaks within the region defined by the F2

mapping study, but there are also many peaks outside this region. This is an

embarassment of riches – with so many highly significant associations, not

restricted to the regions of interest, it is impossible to know which peaks merit

further investigation, and which (if any) are responsible for the originally

observed QTL. While it may seem implausible that analysis based on only 8

strains could produce so many significant associations, it should be borne in

mind that not all of these significant associations are likely to be independent of

one another. Also, a comparison involving 8 strains involves 28 pairwise

comparisons, making this a powerful technique.

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100 110 120

Map distance in Mb

-log10(p)

LOD

Map distance in centiMorgans

Figure 8.1: Mapping results for chromosome 1. A. Haplotype analysis for FOW by

Dr Peter Thomson. Blue line represents 5% FDR, red line represents 1% FDR B. F2

QTL mapping results. Closed triangles indicate FOW

218

219

Phenotyping additional strains is probably worth doing, and this is planned. It is

not certain whether this may add extra significant peaks, or will simplify matters

by increasing the significance of a subset of peaks, making them targets for

further study. There are obvious attractions to integration of the AIL and

haplotype-based approaches. If the original QTL can be substantially narrowed

by analysis of the AIL, relatively few peaks from the haplotype-based analysis

will lie within each revised region of interest. Each of them will be a candidate

for further investigation. The path from that point will depend on the number and

nature of SNPs of interest . If they lie in or near genes which appear likely

candidates for influencing heart development, a possible approach would be to

study the expression of those genes in the hearts of embryos from strains with

extreme atrial septal phenotypes. If there are no obvious candidates, an

alternate approach might be to examine any region of synteny in the human,

possibly by doing an association study in subjects with CHD and controls.

If we are ever to answer the question “why does my child have this problem” –

or the equally important question “what is the risk that my future child will have

this problem” with any clarity, it will be necessary to identify common alleles of

at least moderate effect. Rare alleles of small individual effect will present a

considerable challenge of interpretation, and their interaction is likely to be so

complex as to defy attempts to analyse their significance to an affected

individual or to a couple who are seeking genetic advice. For CHD, it remains to

be seen whether such alleles exist.

8.6 Significance of findings The findings of the studies described here support the idea that numerous

genes and regulatory elements can contribute to abnormal atrial septal

morphogenesis. Mendelian ASD is highly genetically heterogeneous. The

studies of NKX2-5 and GATA4 add to our knowledge of the phenotypes

associated with NKX2-5 mutation and GATA4 deletion, and help to define the

contribution of these genes to ASD in general. The finding that TBX20

mutations cause both CHD (including ASD, VSD and valvular abnormalities)

220

and cardiomyopathy adds to the roll of ASD genes and provides insights into

the function of TBX20.

The studies of atrial septal anatomy in inbred strains of laboratory mice reveal a

complex relationship between FVL, FOW, CRW and PFO, confirming and

building on the key findings of Biben and colleagues(Biben et al., 2000). The

QTL mapping study identified 13 QTL relevant to aspects of atrial septal

development and directly or indirectly to risk of PFO, a model of ASD. This

study appears to be the first QTL study of CHD, and was made possible by

exploiting the existence of endophenotypes associated with a binary trait.

Despite the large number of QTL identified, the majority of the genetic variation

in the strains studied has yet to be explained. There is a great deal more

complexity yet to be uncovered, and a long way to go before these early

insights are developed into anything approaching full understanding.

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APPENDIX 1: ASD and Marcus Gunn Phenomenon – Linkage Results

Tables A1.1a to A1.22 show the names and map locations of the markers used

in this study, together with the 2-point LOD scores obtained for each marker at

� = 0. Analysis was also done at � = 0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3 and

0.4 (data not shown) but no loci had substantially higher LOD scores at � > 0

than at � =0. Two LOD scores are shown for each marker. The first (“LOD

score all”) is the score obtained using all available phenotype data. Because

this was inconclusive, the genome scan was re-run with all unaffected

individuals coded as “unknown”, apart from the unaffected spouses II:5, II:8 and

III:9. The results are in the columns headed “LOD score affected”. The rationale

for this was that if one of the individuals who was phenotypically unaffected was

in fact heterozygous for the disease-causing mutation, this would manifest in

the mapping results as a recombinant, lowering the LOD score. Although the

maximum obtainable LOD score using only affected individuals would be lower,

this problem would be avoided. Genetic distances from the Généthon linkage

map (Gyapay et al., 1994) are given in cM from the p telomere.

Table A1.1a: Chromosome 1

Marker Genetic map position

LOD score all

LOD score affected

D1S468 0 -0.63 -1.7

D1S214 9.4 -4.58 -2.07

D1S450 17.9 -7.22 -4.59

D1S2667 21.9 -4.14 -1.9

D1S2697 34.9 -5.89 -2.37

D1S199 42.7 -8.42 -4.89

D1S234 52.2 -2.44 -0.59

D1S255 62.5 -5.55 -3.7

D1S2797 70 -5.5 -3.65

D1S2890 81.4 -6.5 -3.65

D1S230 92 -8.93 -7.4

245

246


LOD score all

LOD score affected

D1S2841 104 -9.32 -7.41

D1S207 112 -8.9 -7.4

D1S2868 126.3 -1.72 -2.13

D1S206 135.6 -4.08 -1.98

D1S2726 144.6 -2.38 -2.23

D1S252 150.6 -7.67 -4.34

D1S498 156.2 -1.2 -1.25

D1S484 169.3 -3.82 -1.77

D1S2878 178.3 2.03 -1.23

D1S196 183.3 -6.71 -4.33

D1S218 192.2 -3.83 -4.25

D1S238 203.1 -8.02 -4.59

D1S413 211.6 -3.25 -1.3

D1S249 222.2 -2.27 -1.6

D1S425 233.6 -0.5 -1.49

D1S213 243.8 -2.67 -4.25

D1S2800 252.8 -6.63 -4.66

D1S2785 266.8 -6.09 -5.19

D1S2842 275.9 -2.19 -2.39

D1S2836 287.2 -6.07 -5.26

Table A1.1b: Chromosome 1 – additional markers


LOD score all LOD score affected

D1S679 170.8 -1.99 -1.75

D1S2768 172.9 -2.41 -2.63

D1S844 175.0 -7.28 -6.01

D1S382 177.9 -4.72 -3.23

D1S2762 179.1 -3.87 -2.98

247

Table A1.2 Chromosome 2


LOD score all

LOD score affected

D2S319 6 -3.50 -2.5

D2S2211 15.2 -4.30 -2.21

D2S162 21.9 -4.15 -1.79

D2S168 27.7 -8.16 -4.53

D2S305 41.1 -0.64 -1.68

D2S165 48.2 -6.64 -1.6

D2S367 56 -3.06 -1.64

D2S2259 65.1 0.37 0

D2S391 71.5 -3.28 -0.81

D2S337 82.9 1.28 1.5

D2S2368 87.7 -1.43 -1.41

D2S286 95.9 -1.79 -0.64

D2S2333 106.4 -0.33 -0.94

D2S2216 113.7 -7.78 -4.87

D2S160 126.1 -6.05 -4.63

D2S347 136.8 -3.20 -2.39

D2S112 147.9 -4.33 -4

D2S151 158 -9.60 -1.23

D2S142 169.6 -0.41 -4.39

D2S2330 178.8 -4.76 -1.42

D2S335 185.8 -2.83 -3.7

D2S364 196.7 -5.30 -2.37

D2S117 205.3 -3.40 -4.96

D2S325 215.5 -7.51 -10.29

D2S2382 222.6 -11.56 -4.48

D2S126 233 -3.96 -8.65

D2S396 246.4 -12.24 -7.42

D2S206 252.6 -10.98 -4.98

D2S338 263.1 -5.34 -7.42

D2S125 270.2 -11.59 -4.36

248



LOD score all

LOD score affected

D3S1297 2.5 -1.89 -1.42

D3S1304 15.8 -4.14 -1.95

D3S1263 29.6 -2.10 -1.51

D3S2338 35.6 -7.37 -4.96

D3S1266 47.6 -6.22 -5

D3S1277 57.6 -9.57 -5.29

D3S1289 70.6 -8.31 -5.41

D3S1300 79 -6.92 -4.89

D3S1285 90.8 -1.49 -1.93

D3S1566 97.9 -6.23 -5.19

D3S3681 108.9 -11.34 -7.65

D3S1271 117.9 -5.69 -2.24

D3S1278 128.9 -4.51 -1.9

D3S1267 137.1 -10.77 -7.95

D3S1292 146.1 -7.97 -7.31

D3S1569 158.4 -7.04 -4.91

D3S1279 168 -7.64 -5.71

D3S1614 177.9 -3.36 -4.64

D3S1565 190.2 -9.94 -5.41

D3S1262 203.7 -9.10 -6.51

D3S3686 212.7 1.52 1.03

D3S1580 219.7 -2.39 -1.94

D3S1601 229.7 -2.29 -1.9

249



LOD score all

LOD score affected

D4S412 3.7 -4.82 -4.5

D4S2935 12.7 -2.13 -1.19

D4S403 25.7 -6.20 -4.25

D4S419 35 -3.20 -2.5

D4S391 47 -6.99 -4.47

D4S405 61 -5.31 -1.8

D4S1592 75 -5.58 -1.38

D4S392 85 -9.55 -5.07

D4S2964 95 -4.04 -1.72

D4S1534 101 -4.13 -1.72

D4S414 106.2 -5.78 -5.37

D4S1572 113.3 -2.24 -2.07

D4S406 123.7 -3.81 -2.37

D4S402 130.4 -12.23 -8.33

D4S1575 137.5 -6.48 -4.78

D4S424 151.1 -7.18 -4.76

D4S413 161.9 -11.59 -10.4

D4S1597 174 -4.50 -4.55

D4S1539 185 -5.85 -3.1

D4S415 189 -9.83 -7.4

D4S1535 191.4 -5.76 -1.9

D4S426 205.6 -2.03 -1.9

250



LOD score all

LOD score affected

D5S1981 0 -8.25 -5.43

D5S406 11 -4.45 -2.26

D5S630 18.6 -9.45 -5.75

D5S416 27.6 -3.55 -2.89

D5S419 40.3 -11.88 -8.19

D5S426 53.2 -6.60 -4.96

D5S418 60.4 -10.06 -8.35

D5S407 66.3 -8.29 -5.29

D5S647 76.5 -7.57 -4.94

D5S424 82.7 -9.53 -4.89

D5S641 92.7 -7.89 -7.38

D5S428 95.7 -5.88 -4.66

D5S644 106.1 -10.43 -7.83

D5S433 114.7 -11.51 -8.03

D5S2027 122.7 -5.92 -5.46

D5S471 132.7 -10.88 -8.05

D5S2115 144.7 -6.35 -5.36

D5S436 155.7 -7.82 -4.91

D5S410 164.4 -3.84 -2.2

D5S422 172.4 -5.16 -2.92

D5S400 185.5 -5.85 -5.01

D5S408 204.5 -5.68 -2.02

251




D6S1574 8.7 -6.93 -4.48

D6S309 13.7 -5.58 -4.78

D6S470 18.7 -3.25 -1.78

D6S289 31.2 -14.30 -8.33

D6S422 37.4 -5.31 -2.19

D6S276 45.3 -0.61 0.3

D6S1610 52.3 -5.11 -2.8

D6S257 76.1 -5.02 -3.7

D6S460 87.1 -2.92 -1.93

D6S462 97.1 -1.94 -1.67

D6S434 106.4 -2.07 -0.96

D6S287 118.1 -4.87 -2.5

D6S262 127.8 -6.03 -4.48

D6S292 133 -5.54 -1.80

D6S308 142.1 -3.27 -1.21

D6S441 150.7 -6.15 -4.68

D6S1581 163 -5.22 -2.37

D6S264 173 -6.80 -4.89

D6S446 183 -8.94 -1.42

D6S281 196 -8.15 -1.9




D7S531 4.8 -6.40 -1.41

D7S517 8.7 -7.59 -1.6

D7S513 17.4 -8.40 -5.24

D7S507 29.1 -8.95 -5.24

252



D7S493 37.4 -3.14 -2.75

D7S516 45.2 -5.17 -2.12

D7S484 58.4 -3.10 -2.75

D7S510 63.5 -6.84 -2.73

D7S519 75.9 -3.96 -1.6

D7S502 87.9 -5.58 -1.9

D7S669 91.9 -6.15 -1.6

D7S630 99.9 -7.06 -1.6

D7S657 106.9 -8.81 -4.89

D7S515 116.5 -2.76 0.8

D7S486 129.7 -4.86 -2.12

D7S530 139.9 -5.93 -2.02

D7S640 143.4 -5.22 -5.54

D7S684 154.6 -6.83 -5.27

D7S661 163.6 -6.36 -5.48

D7S636 171.6 -8.37 -7.32

D7S798 179.6 -9.07 -5.87

D7S2465 189.6 -9.75 -7.94




D8S264 0.7 -3.94 -1.11

D8S277 8.7 -2.01 -2.09

D8S550 16.7 -4.04 -1.37

D8S549 27.7 -2.83 -1.90

D8S258 37.7 -1.58 0.80

D8S1771 47.7 -4.88 -2.31

253



D8S505 57.7 -4.32 -2.20

D8S285 68.2 0.87 1.20

D8S260 77.5 -3.33 -1.42

D8S270 101.6 -2.23 -1.65

D8S1784 112.6 -3.47 -1.68

D8S514 125.6 -10.07 -1.30

D8S284 139.1 -10.08 -1.30

D8S272 151.5 -7.35 -1.30




D9S288 8.8 -6.40 -4.78

D9S286 18.1 -6.51 -5.42

D9S285 29.2 -2.69 -4.03

D9S157 33.1 -2.67 -4.03

D9S171 42.2 0.70 0.8

D9S161 49.3 -5.52 -5.21

D9S1817 56.3 -9.88 -4.89

D9S273 63.5 -4.21 -3.1

D9S175 68 -9.38 -7.93

D9S167 81.9 -3.05 -4.21

D9S283 93 -2.17 -1.12

D9S287 103 -4.78 -1.59

D9S1690 106.2 -8.54 -4.36

D9S1677 117.4 -5.28 -2.42

D9S1776 126.4 -7.78 -4.66

D9S1682 135 -5.44 -2.20

D9S290 143.3 -8.07 -4.96

254



D9S164 150.5 -0.77 0.50

D9S1826 162.6 -4.61 -2.01

D9S158 165.4 -0.79 0.48




D10S249 0 0.61 0.8

D10S591 11.3 -1.73 -1.14

D10S189 16.5 -0.79 1.63

D10S547 28.4 -5.8 -4.98

D10S1653 40.1 -9.66 -7.71

D10S548 48.3 -0.14 0.08

D10S197 54.3 -4.15 -2.67

D10S208 64 -7.86 -4.96

D10S196 73.3 -9.68 -7.65

D10S1652 83.9 -10.45 -7.76

D10S537 95.3 -10.46 -10.4

D10S1686 108.3 -7.09 -5.21

D10S185 119.8 -4.59 -1.9

D10S192 126.8 -4.29 -1.9

D10S597 129.8 -2.96 -1.15

D10S1693 139.2 0.4 1.03

D10S587 150.3 -8.31 -4.73

D10S217 158.7 -5.7 -1.7

D10S1651 169.8 -8.67 -4.59

D10S212 172.2 0.6 0.46

255




D11S4046 3.9 -9.9 -5.21

D11S1338 14.5 -8.7 -4.87

D11S902 22.1 -2.8 -1.51

D11S904 35.4 -0.16 -1.51

D11S935 46.9 -4.2 -2.50

D11S905 55.8 -10.5 -4.89

D11S4191 61.9 -11.3 -4.60

D11S987 68.2 -1.85 -2.09

D11S1314 75.6 -5.35 -1.90

D11S901 82.5 -11.3 -5.29

D11S937 87.1 -11.28 -5.29

D11S4175 94.4 -6.48 -2.53

D11S898 100.7 -2.15 -2.14

D11S908 110.2 -9.66 -4.36

D11S925 121.3 -6.45 -3.81

D11S4151 130.7 -3.09 -2.71

D11S1320 142.6 -8.25 -4.96

D11S968 150.6 -7.88 -7.42




D12S352 0 -5.16 -0.15

D12S99 13.8 -8.09 -0.27

D12S336 22.1 -7.34 -2.02

D12S364 31.9 -6.21 -0.84

D12S310 37.3 -2.17 0.29

256



D12S345 57.3 -9.78 -1.32

D12S85 65.6 -7.50 -2.24

D12S368 69.8 -8.02 -2.3

D12S83 78 -9.96 -2.67

D12S326 91.7 -5.17 -1.29

D12S351 101 -3.18 -2.5

D12S346 111 -6.66 -4.96

D12S78 118.5 -8.43 -7.25

D12S79 131.3 -8.31 -5.21

D12S86 140.3 -12.60 -8.19

D12S324 143.8 -7.99 -5.25

D12S1659 152.8 -3.57 -2.05

D12S1723 162.8 -5.97 -1.9




D13S175 7.4 -3.9 -1.66

D13S217 16.2 -8.06 -5.32

D13S171 24.5 -6.79 -5.68

D13S218 44.5 -6.3 -1.42

D13S263 50.5 -6.35 -2.37

D13S153 60 -7.42 -4.19

D13S156 69.3 -9.83 -8.2

D13S170 77.9 -12.5 -10.6

D13S265 85 -7.61 -5.01

D13S159 94.2 -2.61 -4.45

D13S158 102.1 -6.32 -4.36

D13S173 108.6 -6.16 -4.36

D13S1265 114.6 -4.62 -3.89

D13S285 126.6 -9.81 -8.07

257




D14S261 0 -4.73 -2.44

D14S283 7.1 -2.56 -2.89

D14S275 19.9 -6.46 -5.35

D14S70 33.2 -3.93 -2.47

D14S288 40.2 -7.29 -5.59

D14S276 47.6 -3.1 -2.89

D14S63 61.9 -7.85 -7.55

D14S258 66.8 -4.64 -2.2

D14S74 79.8 -8.47 -7.55

D14S280 96.5 0.14 0

D14S65 107.8 -8.35 -4.89

D14S985 116.8 -2.44 -2.19

D14S292 124.8 -4.71 -5.1




D15S128 6.1 -0.44 0.2

D15S1002 15.1 0.2 -1.48

D15S165 22.1 -1.25 -2.63

D15S1007 28.1 -3.64 -7.43

D15S1012 38.1 -2.72 -4.89

D15S994 43.1 -3.06 -5.21

D15S978 52.1 -3.07 -7.1

D15S117 57.8 -2.62 -4.49

D15S153 68.4 -3.8 -8.28

D15S131 75.3 -3.1 -5.54

258



D15S205 84.7 -2.02 -4.5

D15S127 92.6 -5.99 -4.28

D15S130 105.3 -2.38 -1.55

D15S120 116.9 -7.18 -2.32




D16S423 8.4 -3.89 -1.89

D16S404 16 -12.26 -8.33

D16S3075 23 -7.72 -2.2

D16S3103 32 -4.15 -1.84

D16S3046 41 -5.33 -3.64

D16S3068 50 -1.6 -1.72

D16S3136 61 -6.24 -7.32

D16S415 69 -2.48 -2.43

D16S503 77 -4.16 -4.9

D16S515 88.2 -6.89 -7.27

D16S516 98.6 -6.89 -7.19

D16S3091 109.6 -8.1 -4.91

D16S520 123.6 -11.26 -8.2

259




D17S849 0.6 -7.45 -4.59

D17S831 6.6 -1.64 -1.72

D17S938 15.6 -7.17 -4.59

D17S1852 23.6 -5.62 -1.98

D17S799 33.6 -7.68 -7.54

D17S921 38.6 -7.74 -5.18

D17S1857 45.6 -2.26 -2.09

D17S798 57.6 -7.07 -4.59

D17S1868 72.6 -0.56 0.51

D17S787 82.6 -5.93 -2.5

D17S944 90.6 -5.72 -5.07

D17S949 101.6 -6.23 -5.26

D17S785 113.6 -7.23 -4.63

D17S784 125.6 -8.44 -4.9

D17S928 137.2 -6.34 -2.07




D18S59 0.1 -9.56 -4.47

D18S63 6.1 -6.04 -2.42

D18S452 17.1 -14.15 -8.36

D18S464 31 -4.59 -2.2

D18S53 40 -9.9 -7.49

D18S478 53.1 -5.88 -2.2

D18S1102 63.1 -9.69 -5.42

D18S474 73.1 -6.57 -4.93

D18S64 86.7 -4.37 -3.09

260



D18S68 98.7 -9.15 -7.87

D18S61 109.7 -6.87 -4.87

D18S1161 120.7 -10.13 -6.04

D18S462 127.7 -9.18 -5.89

D18S70 133.7 -8.31 -5.72




D19S209 10.8 -3.05 -5.27

D19S216 17.7 -2.62 -5.08

D19S884 23.7 -4.4 -4.96

D19S221 33.7 -7.07 -5.26

D19S226 41 -8.62 -4.98

D19S414 53.6 -11.6 -7.68

D19S220 61.5 -12.65 -7.52

D19S420 67 -11.2 -8.18

D19S902 69 -0.42 -1.51

D19S571 80 -3.60 -1.95

D19S418 90 -5.13 -2.19

D19S210 99.8 -4.57 -1.90




D20S117 2 -2.98 -1.91

D20S889 10.9 -7.26 -4.27

D20S115 22.9 -4.67 -4.56

D20S186 34.9 -5.72 -3.97

D20S112 42 -7.64 -4.9

261



D20S195 44.6 -1.16 -0.61

D20S107 51.5 -3.1 -4.32

D20S119 55.1 -0.11 0.8

D20S178 60.8 -11.2 -4.59

D20S196 68.2 -3.76 -2.6

D20S100 76.9 -5.3 -1.9

D20S173 90 -5.68 -1.8

D20S171 93.2 -5.19 -2.2




D21S1256 8.6 -3.28 -2.51

D21S1914 22.6 -5.22 -4.68

D21S263 30.6 -4.8 -2.2

D21S1252 39.3 -3.91 -2.37

D21S266 51.9 -3.13 -2.15




D22S420 0 -3.82 -4.27

D22S539 9 -1.34 -1.81

D22S315 17 -1.89 0.5

D22S280 27.7 -4.45 -2.2

D22S283 35 -8.22 -4.2

D22S423 41.9 -8.16 -4.2

D22S274 49.7 -5.21 -1.72

APPENDIX 2: Microsatellite markers used for QTL mapping

Table A2.1 List of markers with map location

MMU Marker Maplocation


1 D1Mit230 7.7 4 D4Mit101 5.5

1 D1Mit212 19.7 4 D4Mit195 16.4

1 D1Mit46 43.7 4 D4Mit303 45.9

1 D1Mit26 64.5 4 D4Mit251 66.7

1 D1Mit265 76.5

1 D1Mit206 94 5 D5Mit73 11

1 D1Mit512 113.7 5 D5Mit255 25.1

5 D5Mit24 45.9

2 D2Mit1 2.2 5 D5Mit97 64.5

2 D2Mit235 22.5 5 D5Mit287 82

2 D2Mit91 38.3

2 D2Mit58 51.4 6 D6Mit232 0.3

2 D2Mit48 73.2 6 D6Mit186 20.8

2 D2Mit517 98.5 6 D6Mit149 40.4

6 D6Mit14 63.4

3 D3Mit167 13.1

3 D3Mit86 54.6 7 D7Mit22 5.5

3 D3Mit163 66.7 7 D7Mit158 18.6

3 D3Mit147 79.4 7 D7Mit126 37.2

7 D7Mit68 45.9

262

263



8 D8Mit155 0 11 D11Mit168 75.4

8 D8Mit24 18.6

8 D8Mit177 31.7 12 D12Mit182 2.2

8 D8Mit211 50.3 12 D12Mit114 23

8 D8Mit361 63.4 12 D12Mit77 41.5

12 D12Mit8 56.8

9 D9Mit250 2.2

9 D9Mit67 13.1 13 D13Mit57 2.2

9 D9Mit75 39.3 13 D13Mit64 15.3

9 D9Mit182 53.6 13 D13Mit99 25.1

9 D9Mit322 69.9 13 D13Mit151 50.3

10 D10Mit80 2.2 14 D14Mit11 3.3

10 D10Mit20 25.1 14 D14Mit209 13.1

10 D10Mit261 42.6 14 D14Mit102 37.2

10 D10Mit163 58 14 D14Mit116 43

10 D10Mit14 69.9 14 D14Mit178 67.8

11 D11Mit62 2.2

11 D11Mit20 19.7

11 D11Mit4 36.1

11 D11Mit70 52.5

264



15 D15Mit53 7.7 18 D18Mit230 6.6

15 D15Mit58 17.5 18 D18Mit184 26.2

15 D15Mit71 35 18 D18Mit4 37.2

15 D15Mit159 49.2

15 D15Mit161 65.6 19 D19Mit68 3.3

19 D19Mit40 17.5

16 D16Mit162 7.7 19 D19Mit1 43.7

16 D16Mit38 26.2

16 D16Mit219 51.4 X DXMit55 1.1

X DXMit50 17.5

17 D17Mit113 2.2 X DXMit170 29.5

17 D17Mit66 19.7 X DXMit132 48.1

17 D17Mit219 40.4 X DXMit222 59

APPENDIX 3: Candidate genes within QTL Mouse genes located within 1- LOD drop-off of QTL identified in Chapter 6 and

annotated as “heart” (see section 6.7.5)

Table A3.1: Genes within QTL affecting FVL

Chromosome Physical location (Mb)

Genesymbol

Gene name

6 118.6 Ret ret proto-oncogene6 119 Cacna1c calcium channel, voltage-

dependent, L type, alpha 1C subunit

6 120 Wnk1 WNK lysine deficient protein kinase 1

6 121.7 Usp18 ubiquitin specific peptidase 18

6 123 Phc1 polyhomeotic-like 16 123.5 C3ar1 complement component 3a

receptor 1 6 126 Vwf von Willebrand factor

homolog6 127.2 Kcna5 potassium voltage-gated

channel, shaker-related subfamily, member 5

6 127.7 Fgf23 fibroblast growth factor 23 6 127.8 Ccnd2 cyclin D26 135 Etv6 ets variant gene 6 (TEL

oncogene)6 135.7 Cdkn1b cyclin-dependent kinase

inhibitor 1B (P27) 6 137.6 Mgp matrix Gla protein 6 138 Ptpro protein tyrosine

phosphatase, receptor type, O

6 138.5 Strap serine/threonine kinase receptor associated protein

6 143.4 Kcnj8 potassium inwardly-rectifying channel, subfamily J, member 8

6 145 Sox5 SRY-box containing gene 5 6 146 Kras Kirsten rat sarcoma viral

oncogene homolog 8 111 Bcar1 breast cancer anti-estrogen

resistance 1 8 120.5 Foxc2 forkhead box C2 8 121.7 Zfpm1 zinc finger protein, multitype

18 127.7 Nrp1

265

neuropilin 1

266


Genesymbol

Gene name

10 116.8 Frs2 fibroblast growth factor receptor substrate 2

10 117.5 Rap1b RAS related protein 1b 10 118 Ifng interferon gamma10 121.2 Tbk1 TANK-binding kinase 1 10 128.3 Erbb3 v-erb-b2 erythroblastic

leukemia viral oncogene homolog 3 (avian)

10 128.7 Itga7 integrin alpha 7

13 10 Chrm3 cholinergic receptor,muscarinic 3, cardiac

13 23 Hist1h1d histone 1, H1d 13 23.1 Hist1h1e histone 1, H1e 13 23.2 Hist1h1c histone 1, H1c 13 28.4 Sox4 SRY-box containing gene 4 13 29.8 Agtr1 angiotensin receptor 1 13 31.3 Foxc1 forkhead box C1 13 36.4 F13a1 coagulation factor XIII, A1

subunit 13 40.3 Tcfap2a transcription factor AP-2,

alpha13 41.9 Edn1 endothelin 1

15 32.3 Sema5a Semaphorin 5A15 34.3 Matn2 matrilin 215 37.9 Rrm2b ribonucleotide reductase

M2 B (TP53 inducible) 15 38 Edd1 E3 ubiquitin protein ligase,

HECT domain containing, 1 15 40.6 Zfpm2 zinc finger protein, multitype

215 56.7 Has2 hyaluronan synthase 2 15 62 Myc myelocytomatosis

oncogene15 73.1 Eif2c2 eukaryotic translation

initiation factor 2C, 2 15 73 Ptk2 PTK2 protein tyrosine

kinase 2 15 74.9 Cyp11b2 cytochrome P450, family

11, subfamily b, polypeptide 2

15 75 Ly6e lymphocyte antigen 6 complex, locus E

267


Genesymbol

Gene name

15 76.5 Scx scleraxis 15 76.7 Foxh1 forkhead box H1 15 77 Mb myoglobin15 80 Pdgfb platelet derived growth

factor, B polypeptide 15 80 Map3k7ip

1mitogen-activated protein kinase kinase kinase 7 interacting protein 1

15 81.3 Gpr24 G protein coupled receptor 24

15 81.7 Ep300 E1A binding protein p300 15 85.8 Ppara peroxisome proliferator

activated receptor alpha 18 3.3 Crem cAMP responsive element

modulator18 10.8 Mib1 mindbomb homolog 1

(Drosophila)18 11.1 Gata6 GATA binding protein 6 18 17 Cdh2 cadherin 218 23.7 Dtna dystrobrevin alpha18 32.4 Proc protein C18 32.6 Bin1 bridging integrator 1 18 34.5 Apc adenomatosis polyposis coli18 36.7 Hbegf heparin-binding EGF-like

growth factor 18 52.7 Lox lysyl oxidase

19 40 Rad9 RAD9 homolog (S. pombe) 19 40.8 Adrbk1 adrenergic receptor kinase,

beta 1 19 44 Rce1 RCE1 homolog, prenyl

protein peptidase (S. cerevisiae)

19 61.2 Men1 multiple endocrineneoplasia 1

19 70 Mark2 MAP/microtubule affinity-regulating kinase 2

19 15.4 Gnaq guanine nucleotide binding protein, alpha q polypeptide

19 23.5 Fxn frataxin19 27 Rfx3 regulatory factor X, 3

(influences HLA class II expression)

19 30 Prkg1 protein kinase, cGMP-dependent, type I

19 32 Pten phosphatase and tensin homolog

268


Genesymbol

Gene name

19 33.6 Fas Fas (TNF receptor superfamily member)

19 37 Cyp26a1 cytochrome P450, family 26, subfamily a, polypeptide 1

19 43.6 Chuk Chuk, conserved helix-loop-helix ubiquitous kinase

19 45 Fgf8 fibroblast growth factor 8 19 45.3 Kcnip2 Kv channel-interacting

protein 2 19 45.6 Ldb1 LIM domain binding 1 19 46 Sufu suppressor of fused

homolog (Drosophila)

Table A3.2: Genes within QTL affecting FOW


Genesymbol

Gene name

1 45.6 Col3a1 procollagen, type III, alpha 1

1 59 Cflar CASP8 and FADD-like apoptosis regulator

1 59 Casp8 caspase 81 60 Bmpr2 bone morphogenic protein

receptor, type II (serine/threonine kinase)

1 61.2 Ctla4 cytotoxic T-lymphocyte-associated protein 4

1 63 Nrp2 neuropilin 21 65 Fzd5 frizzled homolog 5

(Drosophila)1 67.1 Acadl acetyl-Coenzyme A

dehydrogenase, long-chain 1 69 Erbb4 v-erb-a erythroblastic

leukemia viral oncogene homolog 4 (avian)

1 71.9 Fn1 fibronectin 11 73.1 Igfbp2 insulin-like growth factor

binding protein 2 1 75.6 Des desmin1 78.4 Pax3 paired box gene 3 1 82.6 Irs1 insulin receptor substrate 1 1 85.9 Htr2b 5-hydroxytryptamine

(serotonin) receptor 2B 1 93.7 Pdcd1 programmed cell death 1

269


Genesymbol

Gene name

2 117.6 Thbs1 thrombospondin 1 2 118.7 Spint1 serine protease inhibitor,

Kunitz type 1 2 118.8 Dll4 delta-like 4 (Drosophila) 2 119.3 Tyro3 TYRO3 protein tyrosine

kinase 3 2 120.5 Epb4.2 erythrocyte protein band

4.22 125 Fbn1 fibrillin 1 2 126.9 Adra2b adrenergic receptor, alpha

2b2 128 Mertk c-mer proto-oncogene

tyrosine kinase 2 130 Oxt oxytocin2 131 Adra1d adrenergic receptor, alpha

1d2 131 Slc23a2 solute carrier family 23

(nucleobase transporters), member 2

2 131.6 Bmp2 bone morphogenetic protein 2

2 136.2 Snap25 synaptosomal-associatedprotein 25

2 136.4 Mkks McKusick-Kaufmansyndrome protein

2 136.6 Jag1 jagged 1 2 143 Pcsk2 proprotein convertase

subtilisin/kexin type 2 2 152.7 Kif3b kinesin family member 3B

4 41.8 Il11ra1 interleukin 11 receptor,alpha chain 1

4 43.5 Npr2 natriuretic peptide receptor 2

4 46 Tmod1 tropomodulin 1 4 46.1 Xpa xeroderma pigmentosum,

complementation group A 4 47.1 Col15a1 procollagen, type XV 4 47.3 Tgfbr1 transforming growth factor,

beta receptor I 4 53 Abca1 ATP-binding cassette,

sub-family A (ABC1), member 1

4 55.3 Rad23b RAD23b homolog (S. cerevisiae)

4 62.5 Whrn whirlin4 65.9 Tlr4 toll-like receptor 4

270


Genesymbol

Gene name

9 31.1 Aplp2 amyloid beta (A4) precursor-like protein 2



9 32.4 Fli1 Friend leukemia integration 1

9 37.5 Esam1 endothelial cell-specific adhesion molecule

9 44.1 Cbl Casitas B-lineage lymphoma

9 45.9 Tagln transgelin9 50.6 Pts 6-pyruvoyl-tetrahydropterin

synthase9 50.7 Sdhd succinate dehydrogenase

complex, subunit D, integral membrane protein

Table A3.3: Genes within QTL affecting CRW


Genesymbol

Gene name

3 8.6 Hey1 hairy/enhancer-of-splitrelated with YRPW motif 1

3 10.2 Fabp4 fatty acid binding protein 4,adipocyte

3 14.6 E2f5 E2F transcription factor 5 3 19.3 Cp ceruloplasmin 3 19.3 Hps3 Hermansky-Pudlak

syndrome 3 homolog (human)

3 29.4 Evi1 ecotropic viral integration site 1

3 31.8 Pik3ca phosphatidylinositol 3-kinase, catalytic, alpha polypeptide

3 33.5 Fxr1h fragile X mental retardation gene 1, autosomal homolog

3 36.8 Fgf2 fibroblast growth factor 2 3 51.9 Foxo1 forkhead box O1 3 53.2 Frem2 Fras1 related extracellular

matrix protein 2

271


Genesymbol

Gene name

3 53.8 Trpc4 transient receptor potential cation channel, subfamily C, member 4

3 66.6 Shox2 short stature homeobox 2 3 79.3 Ppid peptidylprolyl isomerase D

(cyclophilin D) 3 80.8 Pdgfc platelet-derived growth

factor, C polypeptide 3 82.2 Npy2r neuropeptide Y receptor

Y2 3 84.7 Fbxw7 F-box and WD-40 domain

protein 7, archipelago homolog (Drosophila)

3 86.3 Mab21l2 mab-21-like 2 (C. elegans) 3 88.2 Lmna lamin A 3 89 Gba glucosidase, beta, acid 3 89.1 Adam15 a disintegrin and

metallopeptidase domain 15 (metargidin)

3 89.2 Shc1 src homology 2 domain-containing transforming protein C1

3 89.6 Adar adenosine deaminase, RNA-specific

3 90.3 Npr1 natriuretic peptide receptor1 3 90.3 S100a1 S100 calcium binding

protein A1 3 95 Arnt aryl hydrocarbon receptor

nuclear translocator 3 95.4 Aph1a anterior pharynx defective

1a homolog (C. elegans) 3 96 Hfe2 hemochromatosis type 2

(juvenile) (human homolog) 3 96.4 Gja5 gap junction membrane

channel protein alpha 5 3 97.5 Notch2 Notch gene homolog 2

(Drosophila)3 101 Atp1a1 ATPase, Na+/K+

transporting, alpha 1 polypeptide

3 102.5 Nras neuroblastoma ras oncogene

3 114.4 Edg1 endothelial differentiation sphingolipid G-protein-coupled receptor 1

3 114.9 Vcam1 vascular cell adhesion molecule 1

272


Genesymbol

Gene name

7 109 Sox6 SRY-box containing gene 67 120.8 Tbx6 T-box 6 7 122 Cox6a2 cytochrome c oxidase,

subunit VIa, polypeptide 2 7 124.3 Fgfr2 fibroblast growth factor

receptor 2 7 124.5 Ate1 arginine-tRNA-protein

transferase 1 7 125 Tacc2 transforming acidic coiled-

coil containing protein 2 7 128 Adam12 a disintegrin and

metallopeptidase domain 12 (meltrin alpha)

7 136.8 Ctsd cathepsin D 7 137 Ins2 insulin II 7 137.3 Th tyrosine hydroxylase 7 137.6 Kcnq1 potassium voltage-gated

channel, subfamily Q,member 1

7 139.3 Ccnd1 cyclin D1

The Genetics of Atrial Septal Defect and Patent Foramen Ovale

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