Muhammad Asif - Pakistan Research Repository

Genomic Analysis for Quality Traits in Cotton (Gossypium hirsutum L.) by DNA Fingerprinting Technology

By

Muhammad Asif

Reg. No. 2000-bzb-28

A thesis submitted to BZU in fulfillment of requirements for the award of Degree of Doctor of Philosophy

(Botany)

Plant Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad

&

Institute of Pure and Applied Biology Bahauddin Zakariya University (BZU), Multan

2010

In the name of Allah Almighty, the most Affectionate, the most Merciful.

O! The children of Aadam!

Indeed, We have (Allah has) blessed you a dress with which you may cover your shameful parts

and that it be for beauty to you. And this is dress of piety, which is the best.

This is one of Allah’s prescriptions that they would remember. (Al-Quran; Para 8, Alaraf: 26)

ii

Certificate

The Controller of Examinations Bahauddin Zakariya University (BZU) Multan We, the supervisory committee, certify that the contents and form of the

dissertation, entitled, “Genomic Analysis for Quality Traits in Cotton (Gossypium

hirsutum L.) by DNA Fingerprinting Technology”, prepared and submitted by Mr.

Muhammad Asif have been found satisfactory and recommend that it be processed for

evaluation by external examiners for the award of PhD degree.

Supervisory Committee: Supervisor: Dr Javed Iqbal Mirza

Director, Institute of Pure and Applied Biology Professor of Botany, BZU, Multan

Supervisor: Dr Yusuf Zafar, TI Director Agri. and Biotech. PAEC HQs, Islamabad

iii

Acknowledgements

ACKNOWLEDGEMENTS

All praises to Allah Almighty, the most Affectionate, Who created the universe and countless salutation be upon His beloved and last Prophet Hazrat Muhammad (peace be upon him), who declared it to be an obligatory duty of every Muslim to seek and acquire knowledge. I am highly indebted and express my gratitude to Allah, the most Beneficent, and the most Merciful, Who granted me the ability to undertake this research work and complete the dissertation.

It is pleasure for me to acknowledge many people, who advised, assisted and supported during the whole research work and without whom this manuscript could not been completed. I would like to extend my sincere thanks to my worthy supervisor Dr Javed Iqbal Mirza, Ex-Director, Institute for Pure and Applied Biology, BZU, whose able supervision, guidance, stimulating suggestions and encouragement helped me all through my research and for writing this thesis.

I offer special thanks to a dynamic personality and my supervisor Dr Yusuf Zafar, TI, Director, Agriculture and Biotechnology, PAEC HQs, Islamabad, for his excellent guidance, invaluable advice, kindness and support in carrying out this research work. I am highly appreciative to Dr Z M Khalid, Director NIBGE and Dr Shahid Mansoor, Head Agricultural Biotechnology Division (ABD), NIBGE, for providing best working environment for completion of my degree. I am very thankful to Dr Saeed Ahmad Malik, Director, Institute for Pure and Applied Biology, BZU, for his timely support and guidance.

Especially, I am highly grateful to Dr Mehboob-ur-Rahman (SS), Group Leader, Plant Genomics and Molecular Breeding Group, PBD, NIBGE, for his valuable guidance, suggestions, constructive comments and encouragement throughout this research work of cotton genomics. I am thankful to all my Lab fellows and field staff for their timely and technical support. I must say thanks to all my colleagues and friends especially Dr Armughan Shehzad, Dr Kausar Shah, Dr Muhammad Babar and Dr Muhammad Ismail, who have been constantly propelling me to complete this important task.

Thanks to Director, Cotton Research Institute (CRI), AARI, Faisalabad, for providing some cotton genotypes and performing initial fiber analysis at Fiber Tech. Lab of CRI. Great appreciation is due to Chairman, Fiber Tech. Dept., Univ. of Agri. Faisalabad (UAF), for conducting fiber analysis of large populations. I greatly acknowledge the funding support for this research work from Ministry of Science and Technology (MOST), Pakistan through its project entitled, “Functional genomics for quality traits in crop plants” and International Atomic Energy Agency (IAEA) through 12396/R Research Contract entitled, “Marker assisted selection for fiber quality improvement in mutation breeding programme of cotton”. I greatly appreciate all the members of my family for their endless cooperation, patience, tolerance and selfless sacrifice during the course of this study. It is, by the virtue of their prayers and moral support that I have been able to accomplish this thesis research work.

Muhammad Asif

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Dedication

Dedicated to

my parents and family

v

Table of Contents

TABLE OF CONTENTS Contents Page Acknowledgements iv Table of Contents vi List of Tables ix List of Figures x List of Abbreviations xi Summary xii CHAPTER 1 INTRODUCTION 1-15 1.1 Cotton 1 1.2 Gossypium: Evolution, Polyploidization and Domestication 2 1.3 Cotton Fiber 4 1.3.1 Cotton fiber development 5 1.3.2 Cotton fiber transcriptome 6 1.3.3 Cotton fiber quality traits 7 1.3.3.1 Fiber length 8 1.3.3.2 Fiber strength 10 1.3.3.3 Fiber fineness and micronaire 11 1.3.3.4 Fiber color 12 1.4 Genomic Tools for Cotton Improvement 13 1.5 Objectives 15 CHAPTER 2 REVIEW OF LITERATURE 16-29 2.1 DNA Markers in Cotton 16 2.1.1 Cotton genome mapping and fiber QTL analysis 17 CHAPTER 3 MATERIALS and METHODS 30-47 3.1 Screening of Cotton Varieties/ Genotypes for Quality Traits 30 3.1.1 Statistical analysis 31 3.2 Development of Segregating Population For Genetic Mapping 31 3.2.1 Statistical analysis 36 3.3 DNA Markers Analysis 36 3.3.1 DNA isolation and quantification 36 3.3.1.1 DNA extraction 36 3.3.1.2 DNA quantification 37 3.3.2 RAPD analysis 38

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Table of Contents

3.3.2.1 PCR amplification 38 3.3.2.2 Agarose gel electrophoresis 39 3.3.2.3 RAPD data analysis 39 3.3.3 Microsatellite/ simple sequence repeat (SSR) analysis 41 3.3.3.1 PCR amplification 41 3.3.3.2 Polyacrylamide gel electrophoresis (PAGE) 42 3.3.3.3 MetaPhor agarose (ultra high resolution) gel electrophoresis 45 3.3.3.4 SSR data analysis 46 3.3.4 Linkage map construction and QTL analysis 46 CHAPTER 4 RESULTS 48-84 4.1 Screening of Cotton Varieties/Genotypes for Fiber Quality Traits 48 4.1.1 Mean performance of cotton varieties for fiber traits 48 4.1.2 Analysis of variance for fiber traits 51 4.1.3 Correlation among fiber traits in cotton genotypes 51 4.2 Parental Differences and Population for Genetic Mapping 53 4.2.1 Selection of two contrasting cotton parents for mapping population 53 4.2.2 F2:3 (FH-631S x FH-883) population for mapping fiber QTLs 53 4.2.3 Correlation among fiber traits in F2:3 population 57 4.3 DNA Markers Analysis for Cotton Fiber Quality Traits 59 4.3.1 RAPD analysis for fiber traits 59 4.3.2 SSR analysis for fiber traits 64 4.4 Genetic Linkage Map of Cotton 71 4.5 QTL Analysis for Cotton Fiber Quality Traits 76 4.5.1 QTL for fiber length (FL) 76 4.5.2 QTL for micronaire (Mic) 79 4.5.3 QTL for fiber strength (FS) 79 4.5.4 QTL for fiber length uniformity (FU) 79 4.5.5 QTL for short fiber index (SFI) 80 4.5.6 QTL for fiber elongation (FE) 80 4.5.7 QTL for fiber color (reflectance and yellowness) 80 CHAPTER 5 DISCUSSION 85-111 5.1 Performance of Cotton Varieties/ Genotypes for Fiber Traits 85 5.2 Performance of Parents and F2:3 Mapping Population 86 5.3 Polymorphism between Parents and in F2:3 Population 88 5.4 Genetic Linkage Map of Cotton 92 5.4.1 DNA markers and linkage map 92

vii

Table of Contents

5.4.2 Linkage groups and chromosomal assignment 96 5.4.3 Segregation distortion 96 5.5 QTL Analysis for Cotton Fiber Quality Traits 99 5.5.1 QTL detection methods and fiber traits 99 5.5.2 QTLs for fiber traits 102 5.5.3 Cotton genome and distribution of fiber QTLs 105 5.6 Future Challenges and Prospects of Genomic Analysis of Cotton 107 5.7 Conclusions 109 LITERATURE CITED 112-125 APPENDICES 126-147

viii

List of Tables

LIST OF TABLES No. Title Page 1.1 Standards for cotton fiber quality traits. 9 2.1 Summary of linkage mapping and fiber QTL analysis studies in cotton 28 3.1 Cotton (G. hirsutum) varieties/ genotypes, their parentage and breeding

center. 34

3.2 Analysis of variance (ANOVA) for fiber traits from 19 cotton genotypes 35 3.3 RAPD and SSR primers to screen FH-883 and FH-631S cotton parents. 40 4.1 Fiber characteristics of 19 cotton varieties/ genotypes. 49 4.2 Correlation coefficients among three fiber quality traits in 19 cotton

varieties/ genotypes. 52

4.3 Statistical analysis of cotton fiber quality traits in (FH-631S x FH-883) F2:3 population.

55

4.4 Correlation coefficients among cotton fiber traits in (FH-631S x FH-883) F2:3 population.

58

4.5 Screening FH-883 and FH-631S cotton parents with DNA markers. 60 4.6 Polymorphic primers and bands amplified in cotton parents (FH-883

and FH-631S) and their F2:3 population. 69

4.7 Allele frequency and polymorphism information content (PIC) of DNA markers amplified in cotton parents (FH-883 and FH-631S) and F2:3 population.

70

4.8 Molecular markers distribution and assignment of linkage groups to cotton chromosomes using anchored RAPD and SSR loci.

74

4.9 Linkage groups and markers segregation in (FH-631S x FH-883) F2:3 cotton population.

75

4.10 Single marker analysis of QTLs for cotton fiber quality traits. 81 4.11 Interval mapping of QTLs for cotton fiber quality traits. 82 4.12 Composite interval mapping of QTLs for cotton fiber quality traits. 83 4.13 QTLs for cotton fiber quality traits with SMA, IM and CIM at LOD>2. 84 5.1 QTLs for cotton fiber quality traits 104

ix

List of Figures

LIST OF FIGURES No. Title Page4.1 Mean performances of 19 cotton varieties/ genotypes for fiber

length, micronaire and fiber strength. 50

4.2 Fiber quality traits of two cotton parents and their F1 (FH-631S x FH-883).

54

4.3 Frequency distribution for eight fiber quality traits in (FH-883 x FH-631S) F2:3 population.

56

4.4 Amplification profile of cotton parents and F1 with polymorphic RAPD primers.

61

4.5 Amplification profile of (FH-631S x FH-883) F2:3 population with RAPD primer OPD-07.

62

4.6 Amplification profile of (FH-631S x FH-883) F2:3 population with RAPD primer OPI-13.

62

4.7 Amplification profile of (FH-631S x FH-883) F2:3 population with RAPD primer OPM-07.

63

4.8 Amplification profile of (FH-631S x FH-883) F2:3 population with RAPD primer OPU-01.

63

4.9 Amplification profile of cotton parents and F1 with polymorphic SSR primers.

66

4.10 Amplification profile of (FH-631S x FH-883) F2:3 population with SSR primer BNL-3279.

67

4.11 Amplification profile of (FH-631S x FH-883) F2:3 population with SSR primer JESPR-152.

67

4.12 Amplification profile of (FH-631S x FH-883) F2:3 population with SSR primer MGHES-06.

68

4.13 Amplification profile of (FH-631S x FH-883) F2:3 population with SSR primer MGHES-17.

68

4.14 Genetic linkage map constructed with Mapmaker using (FH-631S x FH-883) F2:3 intraspecific cotton (G. hirsutum) population.

73

4.15a Comparison of QTLs and their positions (cM) in linkage group 1 (LG1) for cotton fiber quality traits using SMA, IM and CIM.

77

4.15b Comparison of QTLs and their positions (cM) in linkage group 2 (LG2) for cotton fiber quality traits using SMA, IM and CIM.

78

x

List of Abbreviations

LIST OF ABBREVIATIONS

BNL Brookhaven National Laboratory

CIM Composite Interval Mapping

CLCuD Cotton Leaf Curl Disease

cM centi Morgan

CM Cotton Microsatellites

CMD Cotton Microsatellites Database

ESTs Expressed Sequence Tags

FE Fiber Elongation

FF Fiber Fineness

FL Fiber Length

FS Fiber Strength

IM Interval Mapping

JESPR Jenkins, El-Zik, Saha, Pepper and Reddy

LG Linkage Group

LOD Log-likelihood of Odds ratios

FU Fiber length Uniformity

MAS Marker assisted selection

MGHES Mississippi G hirsutum EST SSR

Mic Micronaire

PIC Polymorphism Information Content

PVE Phenotypic Variance Explained

QTLs Quantitative Trait Loci

RAPD Random Amplified Polymorphic DNA

RFLP Restriction Fragment Length Polymorphism

SFI Short Fiber Index

SMA Single Marker Analysis

SSR Simple Sequence Repeats

xi

Summary

SUMMARY Nineteen cotton (G. hirsutum) varieties/genotypes were screened for fiber length (FL),

micronaire (Mic) and fiber strength (FS). There were considerable variations in fiber

traits among these cotton varieties. Highly significant negative correlation was found

between FL and Mic, while highly significant positive correlation was observed

between FL and FS. Mic was significantly and negatively correlated with FS. On the

basis of fiber traits two contrasting cotton genotypes FH-883 and FH-631S were

selected and their F2:3 population was developed. Extensive variation was observed in

117 F2:3 lines for eight fiber related traits. For some traits transgressive segregation

was also observed except for FS and short fiber index (SFI). All traits distributed

normally with some skewness and hence frequency distribution of fiber traits revealed

genetic variation consistent with multigenic inheritance. There was significant positive

association of FL with FS and fiber elongation (FE), Mic with FS and fiber length

uniformity (FU), and FS with FE. Negatively significant correlation was found for FL

with FU and SFI, Mic with FE and fiber color reflectance (Rd), while similar

association was observed for SFI with FS and FE.

Five hundred and twenty RAPDs and 435 SSRs were surveyed on FH-883 and

FH-631S. Total fragments amplified with RAPD assay were 2683 with an average of

5.3 bands per primer. There were 498 monomorphic and eight (1.6%) polymorphic

RAPD primers with 10 polymorphic loci. Mean polymorphism information content

(PIC) of RAPD loci was 0.44. Among 435 SSRs, 350 were structural and 85 were

EST-SSRs. Total bands amplified were 750 with an average of 1.8 bands per SSR.

xii

Summary

Monomorphic SSR primers were 401, while 2% primers with 12 SSR loci were

polymorphic. Mean PIC of polymorphic SSR loci was 0.46.

Most of cotton genetic maps have been developed by using interspecific

population, however, linkage maps developed by intraspecific G. hirsutum population

provide a better understanding of cotton crop. Therefore, in the present study linkage

map was constructed with Mapmaker using molecular markers data of 117 F2:3 cotton

lines. Twenty loci out of 22 RAPD and SSR markers were grouped into four linkage

groups (LGs). The genetic map with Kosambi function spanned 230.2 cM with 5% of

the cotton genome coverage. The average genetic distance was 11.5 cM between two

adjacent loci. Number of markers placed on these linkage groups ranged from three to

eight. LG1 was putatively assigned to long arm of chromosome 20 in D sub-genome,

while LG2, LG3 and LG4 were putatively assigned to chromosome number 10, 18 and

15 respectively. The genetic distances between mapped loci on chromosome 10 in the

A subgenome was larger than those in homoeologous chromosome 20 in the D

subgenome. Out of 20 linked markers, 30% loci segregated according to Mendelian

fashion, while 14 markers showed segregation distortion.

QTLs analysis for fiber traits was conducted with WinQTLCart analyzing the

phenotypic and genotypic data of 117 F2:3 lines, using single marker analysis (SMA),

interval mapping (IM) and composite interval mapping (CIM) at LOD > 2. SMA

detected eight QTLs, 15 QTLs were identified with IM, while 10 QTLs were found

with CIM analysis. Collectively, 16 putative QTLs related to eight cotton fiber quality

traits were detected and 12 of the QTLs were commonly found with any two of the

xiii

Summary

three detection methods (SMA, IM, CIM), while four QTLs were identified only with

IM or CIM.

Two putative QTLs were associated with fiber length and 28.1% was their

phenotypic variance explained (PVE), while three putative QTLs detected for

micronaire explained 27.3% phenotypic variation. Three QTLs were significantly

associated with fiber strength (47.1% PVE). For fiber length uniformity, one putative

QTL of 11.6% PVE was found both with IM and CIM. Two QTLs for short fiber

index that accounted for 24% phenotypic variance and one putative QTL for fiber

elongation at LOD 2.28 were detected. For fiber color four QTLs (three for Rd and

one for +b) were found with 34.2% phenotypic variance.

A range of small to moderately high accumulative proportions of the trait

phenotypic variance in our study suggested quantitative inheritance for fiber quality.

The colocalization of the QTLs for fiber traits was mostly in accordance with the

observed phenotypic correlations. The colocalization of QTLs might be indicative of

linkage among different genes. Collectively, nine putative QTLs for fiber traits were

found on A-subgenome, while seven putative QTLs were on D-subgenome of cotton

suggesting a significant role of A subgenome in fiber development. The QTLs

detected in both the A and D subgenomes suggest that fiber-related traits result from

gene expression and interaction between homoeologous A and D subgenomes. The

combination of correlations, molecular marker and QTLs information will help cotton

breeders to understand and dissect important traits and to develop new genomic tools

for multidirectional selection and marker aided introgression of desired fiber QTLs.

xiv

Chapter 1 Introduction

CHAPTER 1

INTRODUCTION

1.1 Cotton

The word “cotton” is derived from the Arabic word “al qatan” (Chaudhry and

Guitchounts, 2003) used to describe fine textile. Cotton (Gossypium spp.) is the

leading fiber crop worldwide with the production of 24.86 million metric tons in 2006

(Anonymous, 2006a) that makes possible world commerce of raw cotton of about $20

billion annually (Rong et al., 2005). It has been utilized for the benefits of mankind

since ancient times (Fryxell, 1992). The production, marketing, consumption, and

trade of cotton-based products generate revenues in excess of $100 billion annually in

the US alone, making cotton the number one value-added crop (Wilkins and Arpat,

2005). It is grown commercially in the temperate and tropical regions of more than 80

countries, including the United States, China, Pakistan, India, Central and South

America, the Middle East, and Australia (Fryxell, 1979; Smith, 1999).

Cotton is the main cash crop in Pakistan, the basis of the national textile industry and a

major source of foreign exchange sharing 60% of the total export and hence

contributes substantially to the national economy. Pakistan has preeminent position as

the fourth largest producer of cotton, the third largest exporter of raw cotton and a

leading exporter of yarn in the world. During 2005-06 cotton area was 3.1 million

hectares and the production was 13.02 million bales in Pakistan (Anonymous, 2006b).

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1.2 Gossypium: Evolution, Polyploidization and Domestication

Pakistan a place of origin of many important food crops like bread wheat, peas,

mustard, rape and other oilseeds, also gave to the world the original diploid cotton

(Afzal and Ali, 1983). Archaeologists have discovered about 8000 years old threads of

cotton fiber in Blochistan, Pakistan (Moulherat et al., 2002). G. stocksii, one of the

two wild Gossypium species of Asian origin, occurs in the arid hinterland of Karachi.

Another important wild diploid cotton species G. herbaceum (race persicum) grows as

small shrubs with few branches in the Baluchistan area of Pakistan (Afzal and Ali,

1983). G. arboreum is not known to occur as a wild species, but it originated in the

area of present day Pakistan and most of the biodiversity of this species is available in

this country. Pakistan is the home of the Old World cultivated cottons, and both G.

herbaceum and G. arboreum have been grown in this area since pre-history (Afzal and

Ali, 1983). Most of the cotton commercially grown in Pakistan is G. hirsutum, also

called American cotton, while G. arboreum (desi cotton) is on less area (3%)

(Anonymous, 2005b).

Cotton is a member of order Malvales, family Malvaceae, tribe Gossypieae and genus

Gossypium. The genus Gossypium comprises about 45 diploid and 5 tetraploid species

indigenous to arid and semiarid regions of Africa, Central and South America, Indo-

Pak subcontinent, Arabia, Australia, the Galapagos, and Hawaii (Fryxell, 1979, 1992)

with distribution to various continents except Europe. Based on observations of

chromosome pairing, these species are assigned to nine cytological groups or

genomes: eight diploid genomes (A, B, C, D, E, F, G and K; 2n = 2x = 26) and one

tetraploid genome (AD; 2n = 4x = 52) (Beasley, 1940; Phillips and Strickland, 1966;

Edwards and Mirza, 1979; Fryxell, 1979; Endrizzi et al., 1984; Endrizzi et al., 1985;

2


Stewart, 1994; Percival et al., 1999). All diploid Gossypium have the same basic

chromosome number (n = 13), their haploid genome sizes vary 1 to 3.5 Gb (Wendel et

al., 2002; Ulloa, et al., 2007), variation largely explicable in the best-studied cases (A

vs. D) by different amount of dispersed repetitive DNA (Zhao et al., 1998a, b). Among

diploids only A genome species produce spinnable fiber, whereas species with only a

D genome is worthless in terms of fiber production (Applequist et al., 2001).

However, there exist genes related to fiber development in both the A-subgenome and

the D-subgenome in tetraploid cotton species.

There are four cultivated species: two New World tetraploid species, G. hirsutum L. [n

= 2x = 26, (AD1)] and G. barbadense L. [n = 2x = 26, (AD2)], and two Old World

diploid species, G. herbaceum L. (n = x = 13, A1) and G. arboreum L. (n = x = 13,

A2) (Brubaker et al., 1999). The most extensively cultivated cotton species are

allotetraploid G. hirsutum (Upland or American cotton), which accounts for about

90% of fiber production with high yield and wide adaptation, and G. barbadense

(American Pima cotton or “Egyptian” cotton), which is characterized by its superior

quality fiber (extra long, strong and fine fiber). Two cultivated diploid species are only

cultivated in very small acreage in South Asia (China, India, and Pakistan) (Zhang et

al., 2005a). Both these allotetraploids originated in the New World from interspecific

hybridization between species closely related to G. herbaceum (A1) or G. arboreum

(A2) and an American diploid, G. raimondii L. (D5) or G. gossypioides (Ulbrich)

Standley (D6) (Beasley, 1940). Generally G. herbaceum (A genome) and G. raimondii

(D genome) are regarded as the closest extant relatives of progenitors of At and Dt

subgenomes of allotetraploid cotton (Brubaker et al., 1994; Wendel et al., 1995; Zhao

et al., 1998a, b; Liu et al., 2001; Wendel and Cronn, 2003).

3


The A and D genome progenitors are thought to have diverged from a common

ancestor 6–11 million years ago (MYA) and have been reunited through hybridization

and polyploidization in a common tetraploid nucleus 1.1–1.9 MYA (Wendel, 1989;

Wendel and Albert, 1992; Wendel et al., 1995; Senchina et al., 2003; Wendel and

Cronn, 2003), which gave rise to a disomic polyploid consisting of five extant

allotetraploid species (Percival et al., 1999). Polyploid formation presumably involved

transoceanic migration of the Old World maternal A-genome ancestor by saltwater

dispersal, a mechanism that may contribute to the natural distribution of several

members of the cotton tribe with comose seeds (Fryxell, 1979). Polyploidization was

followed by radiation and divergence with five distinct tetraploid species now

indigenous to Central America (G. hirsutum), Western South America (G.

barbadense), North Eastern Brazil (G. mustelinum Miers ex Watt), the Hawaiian

Islands (G. tomentosum Nuttall ex Seemann), and the Galapagos Islands (G. darwinii

Watt) (Fryxell, 1979; Zhang et al., 2005a).

1.3 Cotton Fiber

Cotton fibers are single cells that terminally differentiate from trichome primordia

located in the epidermis (protoderm, the outermost cell layer) of the ovule (Rong et al.,

2005; Wilkins and Arpat, 2005). Cotton is a cellulosic fiber about 96% pure. As the

ovule enlarges, successive layers of the cellulose are laid down in the helical pattern

by the protoplast. As the fiber matures, the protoplast dies and the cell wall, which is

virtually pure cellulose, collapses inward to form convoluted ribbon. The flattening

and convolution of the dried cell wall promotes adhesion when the fibers are twisted

together in yarn bundles during spinning (Kohel and Lewis, 1984; Lee, 1984). Fibers

on the cottonseed can be separated into two groups according to length. The outer

4


layer, or lint, is composed of long fibers separated from the seed during ginning. The

inner layers or linters, sometimes called fuzz, are composed of short fibers that remain

attached to the seed after ginning. The lint fibers are used in spinning cotton yarn. The

linters or fuzz fibers are used in making rayon and various cellulose products

(Poehlman and Borthakur, 1969; Poehlman, 1987). The seeds of all 50 Gossypium

species have epidermal hairs, and in a few species may contribute to saltwater

dispersal (Fryxell, 1979).

1.3.1 Cotton fiber development

Development of fiber from its point of initiation begins on the day of

blooming, right on through to its full maturity, resulting in fibers that exceed one inch

in length. Single-celled fibers are phenomenal biological model systems to study

molecular events that control fiber morphogenesis, and in turn, govern important

agronomic properties (Wilkins et al., 2005).

Fiber growth and morphogenesis are grouped in four major developmental stages: I)

differentiation, II) expansion, primary cell wall (PCW) synthesis and elongation, III)

secondary cell wall (SCW) synthesis, and IV) maturity (Basra and Malik, 1984;

Wilkins and Jernstedt, 1999; Wilkins and Arpat, 2005; Wilkins et al., 2005). The

formation of fiber initials, observed as balloon-like protrusions on the ovule surface at

anthesis [0 days post-anthesis (dpa)], signals entry into the phase of rapid cell

expansion, in which approximately 30% of the primordia undergo morphogenesis,

producing approximately 20,000 fibers/ovule (Berlin, 1986). Fiber elongation occurs

over a period of approximately 21 days, with exaggerated growth rates (in excess of 2

mm a day) reaching peak levels at approximately 10–12 dpa. At approximately 15

dpa, cotton fibers enter a ‘‘transition’’ phase that signals the developmental switch

5


from PCW to SCW synthesis. Although it has been speculated that programmed cell

death plays a role in fiber maturity, virtually nothing is known about the latter stages

of fiber development as molecular studies are stymied by the inability to isolate RNA

from fibers much past 25 dpa.

1.3.2 Cotton fiber transcriptome

The genetic complexity of the fiber transcriptome is currently estimated in

cultivated cottons to consist of approximately 18000 genes in diploid species and

36000 genes in allotetraploid species (Wilkins and Arpat, 2005). The high genetic

complexity of the fiber transcriptome in both diploid and tetraploid species accounts

for a significant proportion (45–50%) of all the genes in the cotton genome. However,

despite the approximately 1.5 myr of evolution following the polyploidization event,

polyploidy has not been accompanied by rapid genome change, as the genome

organization and gene sequences of orthologous loci from the A and D genomes of

diploid and tetraploid cotton species are highly conserved (Senchina et al., 2003; Rong

et al., 2004). Moreover, spatial and temporal expression patterns have been

evolutionarily conserved (Cedroni et al., 2003). Thus, fiber gene function is highly

conserved in the genomes of wild and cultivated species, as well as diploid and

tetraploid species, despite millions of years of evolutionary history. The phenotypic

variation in fiber properties therefore is more likely one of quantitative differences in

gene expression as opposed to differences in the genotype at the DNA level.

Now the challenge for the breeders is to get all the genes they want in the right place

at the right time, since the genes expressed in fibers represent approximately 50% of

the entire cotton genome. There are particular genes that are specifically expressed in

only one stage or another. For instance, genes that might be involved only in

6


secondary cell wall synthesis represent about 12 percent of the transcriptome, while

primary cell wall genes represent about 14 % of the fiber transcriptome. However, the

vast majority of fiber genes are expressed in more than one stage, and therefore

function more generally than stage-specific fiber genes.

There is still a tremendous challenge ahead, in that more than 45 % of the genes are of

unknown function, or are novel genes that are not found in Arabidopsis thaliana and

may therefore represent cotton-specific genes. An example that shows the limitations

of trying to extrapolate gene function based solely on a model system like A. thaliana.

Alpha-tubulin genes expressed in the A. thaliana and cotton fiber are six and 29

respectively. There are a lot of genes that must be performing some function specific

to cotton fiber development, and not all of which are likely to perform redundant

functions as evidenced by spatial and temporal expression patterns. The challenge is to

find out what these genes do, and how they can be used in molecular approaches for

molecular breeding or genetic modification (Wilkins et al., 2005).

1.3.3 Cotton fiber quality traits

Cotton fiber quality is defined by physical properties that relate to its

spinnability into yarn and contribute to textile performance and quality (Chee et al.,

2005a). The most important of these properties (Table 1.1) are those associated with

the length, strength and fineness/ micronaire of the fiber (Poehlman and Sleper, 1995),

other components of cotton fiber quality include length uniformity index, color as

reflectance (Rd) and yellowness (+b). The naturally wide variations in fiber quality, in

combination with differences in end-use requirements, result in significant variability

in the value of cotton lint to the processor. Therefore, a system of premiums and

discounts has been established to denote a specified base quality. In general, cotton

7


fiber value increases as bulk-averaged fibers increase in whiteness (Rd), length,

strength, and micronaire; and discounts are made for both low and high mike. Ideal

fiber-quality specifications favored by processors traditionally have been summarized,

“as white as snow, as long as wool, as strong as steel and as fine as silk.” These

specifications are extremely difficult but important to incorporate into a breeding

program or to set as goals for cotton producers (Bradow and Davidonis, 2000).

1.3.3.1 Fiber length

One of the most important aspects of cotton fiber quality is fiber length,

including average length of fibers, length uniformity of the bulk fibers and content of

short fibers. Fiber or staple length is the normal length of a typical portion of the fibers

of a cotton sample. Fiber length is directly related to yarn fineness, strength, and

spinning efficiency (Moore, 1996). Longer fibers can be processed at greater

efficiencies, and produce finer and stronger yarns by allowing fibers to twist around

each other more times, while shorter fibers require increased twisting during spinning,

causing low-strength, poor-quality yarns (Perkins et al., 1984; Chee et al., 2005b).

Low fiber length uniformity and high short fiber content are associated with increased

manufacturing waste and decreased spinning efficiency during yarn processing. As a

result of the demand for improved fiber quality, the cotton marketing system imposes

premiums and discounts for fiber length and other related qualities (Jost, 2002).

Fiber elongation measures the degree of extensibility or elasticity of the fibers before a

break occurs (May, 2002). Increases in elongation are associated with improved yarn

strength (May, 2002). Therefore, fibers with good elongation generally cause less

costly disruption in the spinning process, and the resulting yarn can endure more

vigorous mechanical handling during fabric manufacturing. Span length (distance

8


Table 1.1 Standards for cotton fiber quality traits. Fiber quality traits Scales

Fiber length

2.5% span length

(mm)

(inches)

Short

<21

Under 13/16

Medium

22 - 25

13/16 to 1

M. long

26 - 28

1-1/32 to

1-3/32

Long

29 - 34

1-1/8 to

1-5/16

Extra long

>34

Fiber fineness

(Mic= µg/inch)

V. fine

<3

Fine

3 - 3.9

Ave

4 - 4.9

Coarse

5 - 5.9

V coarse

>5.9

Fiber strength

Pressley zero-gage

(tppsi)

HVI 1/8 inch-gage

(gram/ tex)

V weak

76 & below

21 & below

Weak

77 – 83

22 - 24

Ave

84 – 90

25 - 27

Strong

91 – 97

28 - 30

V strong

>97

31 & above

mm: millimeter, M: medium, Mic: micronaire, µg/inch: micro-gram per inch, tppsi: thousand pound per square inch, V: very, Ave: average. Source: Anonymous, 1992; Bradow and Davidonis, 2000; Ahmad and Ahmad, 2001.

9


spanned by a specific percentage of fibers in the test beard) is usually reported as 2.5

and 50%. The uniformity ratio is the ratio between the two span lengths expressed as a

percentage of the longer length. Fiber lengths on seeds can be determined by hand

stapling while fibers are still attached to seed (Gipson and Joham, 1969; Munro, 1987)

or after ginning by photoelectric measurement with high-volume instrumentation

(HVI) (Munro, 1987; Behery, 1993; Moore, 1996). Traditionally, staple lengths are

reported to the nearest 32nd of an inch or to the nearest millimeter. Short fiber content

is the percentage of fiber less than 12.7 mm (half an inch).

Due to the inherent variability in cotton fiber, there is no absolute value for fiber

length within a genotype or within a test sample (Behery, 1993). Even on a single

seed, fiber lengths vary significantly because longer fibers occur at chalazal

(cupshaped, lower) end of seed and shorter fibers are found at micropylar (pointed)

end. Variations in fiber length attributable to genotype and fiber location on the seed

are modulated by factors in the micro and macro environment (Bradow et al., 1997).

Environmental changes during floral anthesis may limit fiber initiation or retard the

onset of fiber elongation. Suboptimal environmental conditions may decrease the rate

of elongation or shorten the elongation period so that the genotypic potential for fiber

length is not fully realized (Hearn, 1976; Bradow and Davidonis, 2000).

1.3.3.2 Fiber strength

Fiber strength is important because the inherent breaking strength of individual

cotton fibers is considered to be the most important factor in determining the strength

of the yarn spun from those fibers (Moore, 1996). Fiber strength is related to average

length of the cellulose molecules deposited inside the cotton fiber, hence longer the

cellulose chains, stronger the fiber. The HVI systems are also used to measure the

10


breaking strength of the same fiber bundles (beards) formed during length

measurement. HVI bundle-strength is measured in grams-force tex-1 while in Pressley

zero-gage it is reported as thousand pound per square inch (tppsi) when relative

humidity of testing room is adequately controlled. Fiber or tensile strength is also

measured in kN m kg-1, where one Newton = 9.81 kg-force (Meredith et al., 1996).

1.3.3.3 Fiber fineness and micronaire

Cotton fibers of some varieties feel soft and silky, while fibers from other

varieties feel coarse and harsh. The difference in the way they feel is determined by

the fineness, which is associated with diameter of the fiber and with the thickness of

the fiber wall. When the fibers fail to develop an average amount of inner wall they

are said to be “immature”. A mature fiber is a fiber in which two times the cell wall

thickness equals or exceeds the diameter of fiber cell lumen, the space enclosed by the

fiber cell walls (Ramey, 1982). Fiber fineness is an important component of fiber

quality because of its direct impact on processing performance and the quality of end

product. Finer mature fibers can be spun into yarns with more fibers per cross-section,

resulting in not only stronger and better quality yarns but also less time in the spinning

process (Ramey, 1982; Steadman, 1997; Bradow and Davidonis, 2000).

Micronaire (Mic) has been the most widely used method of determining fiber

diameter. However, Mic reading is a measure of resistance to airflow of a constant

weight of fibers and can be confounded by the degree of development of the fiber

lumen (Steadman, 1997) that is related to fiber maturity. Therefore, while lower Mic

cotton usually indicates the finer fibers that are sought by textile mills, could also

result from immature fiber that can cause neps and dye defects because fiber maturity

has been associated with dye uptake variability in finished yarn and fabric (Smith,

11


1991; Bradow et al., 1996, 1997; Bradow and Bauer, 1997). Although other, more

direct methods for measuring fiber diameter are now available, such as fineness

measurement through HVI and the relatively new advanced fiber information system

(AFIS), MIC is still widely utilized in combination with other fiber properties in the

textile industry to blend sets of cotton bales in order to promote consistency of

performance in the yarn-manufacturing process (May, 2002; Draye et al., 2005).

Another measure of fineness and spinability of cotton fiber is the count of the yarn.

The count is the number of hanks of yarn which weighs one pound, while a hank

consists of 840 yards of yarn (Poehlman and Borthakur, 1969).

1.3.3.4 Fiber color

Raw fiber stock color measurements are used in controlling the color of

manufactured gray, bleached, or dyed yarns and fabrics (Nickerson and Newton, 1958;

Xu et al., 1998a, b). Among the components of cotton grade, fiber color is most

directly linked to growth environment. Color measurements also are correlated with

overall fiber quality so that bright (reflective, high Rd), creamy-white fibers are more

mature and of higher quality than the dull, gray or yellowish fibers associated with

field weathering and generally lower the fiber quality (Perkins et al., 1984). Although

Upland cotton fibers are naturally white to creamy-white, pre-harvest exposure to

weathering and microbial action can cause fibers to darken and to lose brightness

(Perkins et al., 1984; Allen et al., 1995). Premature termination of fiber maturation by

applications of growth regulators, frost, or drought characteristically increases the

saturation of the yellow (+b) fiber-color component. Other conditions, including insect

damage and foreign matter contamination, also modify fiber color (Moore, 1996; Xu

et al., 1998a, b). In the HVI classing system, color is quantified as the degrees of

12


reflectance (Rd) and yellowness (+b), two of the three tri-stimulus color scales of the

Nickerson-Hunter colorimeter (Thomasson and Taylor, 1995; Xu et al., 1998a, b).

1.4 Genomic Tools for Cotton Improvement

For the improvement of agronomically and economically important traits, plant

breeding generally recombines traits present in different parental lines of cultivated

and wild species. Conventional breeding programmes reach this goal by generating an

F1 hybrid and F2 segregating population and then screening the phenotypes of pooled

or individual plants for presence of desirable traits, which is followed by a process of

repeated backcrossing, selfing and testing. During this process breeder depends on

accurate screening methods and availability of lines with clear-cut phenotypic

characters, which is time consuming and difficult to achieve with classical methods

(Beckmann and Soller, 1986). Use of molecular markers facilitate these breeding

processes, since it can provide means of detecting and resolving complications and

accelerate the generation of new varieties and allow association of phenotypic traits

with genomic loci (Jiang et al., 2000). Ideal molecular markers are stable, abundant

and detectable in plant tissues regardless of growth, differentiation and defense status.

These properties make molecular markers indispensable for crop improvement

(Winter and Kahl, 1995).

A number of DNA fingerprinting techniques are available for detection of

polymorphism (Semagn et al., 2006). Restriction fragment length polymorphisms

(RFLPs) are very reliable markers in linkage analysis and crop breeding however, time

consuming, expensive and require large amount of DNA for restriction and

hybridization analysis (Paterson et al., 1993). Most of the DNA marker assays use

13


polymerase chain reaction (PCR), among them are random amplified polymorphic

DNA (RAPD), microsatellites or simple sequence repeats (SSR), amplified fragment

length polymorphism (AFLP) and single nucleotide polymorphisms (SNPs). RAPD is

much faster and cheaper than RFLP analysis and uses only minute amounts of DNA

(Williams et al., 1990). Microsatellites are typically the repeat unit of 1-6 nucleotides

and SSR analysis is performed by using pairs of specific primers flanking tandem

arrays of microsatellite repeats. SSR markers are codominant and extremely

polymorphic (Liu et al., 2000a, b). AFLP is robust and reliable for DNA fingerprinting

of different genomes because it combines the use of restriction enzymes and PCR

amplification (Vos et al., 1995). AFLP system is technically intricate and expensive to

set up, but it detects a large number of loci (up to 100). SNPs are the single base

substitutions or small insertions and deletions (Indel) in homologous genomic regions.

SNPs are more frequent and codominant in nature (Lindblad et al., 2000).

Recent developments of molecular techniques and application of molecular markers

have brought a new dimension into the traditional area of plant breeding. Molecular

markers not only allow the easy and reliable identification of breeding lines, hybrids

and cultivars (Bastia et al., 2001; Asif et al., 2005, 2006; Tabbasam et al., 2006) but

also facilitate the monitoring of introgression, mapping of QTLs (Jiang et al., 2000),

marker assisted selection (MAS) (Ribaut and Hoisington, 1998; Zhang et al., 2003)

and estimation of genetic diversity (Mukhtar et al., 2002; Rahman et al., 2002, 2008).

High-density genetic linkage maps (Guo et al., 2007; He et al., 2007) established using

molecular markers, for economically important crops provide a basis for MAS of

agronomically useful traits, for pyramiding of resistance genes and the isolation of

important genes by map-based cloning strategies (Ribaut and Hoisington, 1998).

14


In the past, most of the cotton genetic maps have been developed using interspecific

populations, which are of little significance in cotton breeding programs. Limited

efforts were made to develop maps based on intraspecific population. The genetic

linkage maps developed by intraspecific G. hirsutum population provide a better

understanding of cotton crop by possibly generating a core of markers with more

practical application in marker-assisted selection than those developed in interspecific

populations (Reinisch et al., 1994; Yu et al., 1998; Ulloa and Meredith, 2000).

Therefore, main aim of the present study was to construct a genetic linkage map using

an intraspecific population and identify QTLs controlling fiber quality traits through

their association with RAPDs and SSRs that would provide a basis for MAS breeding.

1.5 Objectives

The objectives of the present study were: 1) Screening of cotton genotypes/ varieties

for fiber quality traits (fiber length, fiber strength and micronaire); 2) Development of

segregating population of selected cotton parents for genetic mapping; 3) Screening of

selected cotton parents and their segregating population using DNA markers (RAPD

and SSR); 4) Construction of cotton genetic linkage map; 5) Identification and

mapping of fiber QTLs.

15

Chapter 2 Review of Literature

CHAPTER 2

REVIEW OF LITERATURE

2.1 DNA Markers in Cotton

Main goals of cotton breeding worldwide are the genetic enhancement of yield and

fiber quality. Improvement in fiber quality is required to keep pace with rapid changes

taking place in the technology of textile manufacturing procedure (Shen et al., 2005).

Key fiber quality traits, such as fiber length, strength and fineness/ micronaire are

controlled by quantitative trait loci (QTLs) (Jiang et al., 2000; Ulloa and Meredith,

2000; Paterson et al., 2003; Mei et al., 2004; Ulloa et al., 2005). Breeders have been

improving both qualitative and quantitative traits by conventional breeding based on

phenotypic evaluation and selection, which are time and resource consuming.

Molecular markers offer efficient tools for dissecting QTLs affecting traits with

complex genetic inheritance, and facilitate marker-assisted selection (MAS) and map-

based cloning (Park et al., 2005). Many types of DNA markers, including RFLP,

RAPD, AFLP and SSR have been developed for cotton genome research (Reinisch et

al., 1994; Jiang et al., 1998, 2000; Shappley et al., 1998a, b; Ulloa and Meredith 2000;

Reddy et al., 2001; Ulloa et al., 2002; Zhang et al., 2002; Lacape et al., 2003; Mei et

al., 2004; Nguyen et al., 2004; Rong et al., 2004).

16


Genetic information of a crop genome is usually presented in framework of a genetic

linkage map. Such maps are useful to locate or tag genes of interest, to facilitate MAS,

and to enable map-based cloning. Addition of new DNA markers to cotton linkage

map will enhance our understanding of its genetics and also improve breeding

efficiency, especially for fiber quality traits. Several types of DNA markers have been

successfully used for genetic mapping in cotton (Table 2.1). However, to construct a

saturated genetic map that would expedite genetic improvement in cotton, more

molecular markers are needed (Han et al., 2006). To date, several genetic maps of

cotton have been constructed using different molecular markers and various inter and

intraspecific mapping populations (Reinisch et al., 1994; Ulloa et al., 2002; Lacape et

al., 2003; Rong et al., 2004; Park et al., 2005; Han et al., 2006; Shen et al., 2007).

2.1.1 Cotton genome mapping and fiber QTL analysis

Reinisch et al., (1994) constructed a first detailed RFLP map to investigate

chromosome organization and evolution in cotton, a disomic polyploid. This map was

developed from an interspecific cross between G. hirsutum acc. Palmeri and G.

barbadense acc. K 101 using 57 F2 individuals with 563 DNA probes. A total of 705

RFLP loci were sorted into 41 linkage groups, covering 4,675 cM of the cotton

genome. The tetraploid cotton has 26 gametic chromosomes therefore, at least 15 gaps

existed in the map and hence overall size of the cotton genome would be 5125 cM.

Fourteen chromosomes were associated with linkage groups by using monosomic

interspecific substitution lines. A and D subgenomes showed similar recombinational

17


length, suggesting that repetitive DNA in larger A subgenome is recombinationally

inert. They suggested that cotton genome contains about 400-kb DNA per cM, hence

feasible for map-based gene cloning.

Shappley et al., (1996) characterized two F 2 populations of G. hirsutum from crosses

of HS46 × MARCABUCAG8US-1-88 (MAR) line and HS46 × PD5363 using RFLPs.

Seventy-three and six probe-enzyme combinations resulted in 42 and 11 informative

polymorphic fragments, respectively. A total of 53 polymorphic fragments and 32

polymorphic loci, representing five linkage groups, were identified between the two

populations. Shapply et al., (1998b) analyzed 96 F2.F3 bulked sampled plots of Upland

cotton, from a cross of HS46 and MAR with 129 probe/enzyme combinations

resulting in 138 RFLP loci. There were 120 loci arranged into 31 linkage groups,

covering 865 cM of the cotton genome. Two to ten loci were per linkage group, while

18 loci were not linked. Map distances between loci were 0.0 to 45.7 cM with an

average of 7.0 cM.

Reddy et al., (1997) developed AFLP markers for marker-assisted cotton breeding.

They used 94 F2 plants of an interspecific cross between G. hirsutum acc. TM-1 and

G. barbadense acc. 3-79. They adopted both automated and manual methods. In

fluorescent labeled primers, the amplified products were mixed with fluorescent-

labeled internal standard DNA marker and analyzed on the ABI373 automated DNA

sequencer. Using the 64 available AFLP primer combinations 490 markers were

18


generated, scored, and analyzed with Mapmaker 3.0 and an AFLP linkage map was

generated from this data.

Jiang et al., (1998) used a detailed RFLP map to determine chromosomal locations and

subgenomic distributions of QTLs segregating in 271 F2 progeny of a cross between

G. hirsutum cv. CAMD-E × G. barbadense cv. Sea Island Seaberry. Linkage map

included 261 RFLPs in 27 linkage groups with 3,767 cM distance and average spacing

of 14.4 cM. Sixteen markers were unlinked, suggesting an overall map length of

4,000 cM. Fourteen fiber QTLs were identified at LOD 3.0 and most of them were

located on D subgenome. QTLs on D-subgenome would elucidate that domestication

and breeding of tetraploid cottons have resulted in fiber yield and quality levels

superior to those achieved by parallel improvement of A genome diploids.

A trispecific F2 mapping population in cotton was developed in Arkansas (Khan et al.,

1998). Ninety F2 plants were derived from a cross between G. hirsutum and synthetic

tetraploid cotton made of two diploid species G. arboreum (A2 genome) and G.

trilobum (D8 genome). Severe chromosomal rearrangement and strong divergence of

the three genomes (A2, D8 and AD1) caused difficulties in genome mapping. Using

AFLP and RAPD analysis, a linkage map was developed with 51 linkage groups and

6,663 cM distance of cotton genome.

Yu et al., (1998) constructed a framework cotton map consisting primarily of RAPD

and RFLP markers, with some SSRs using 171 F2 individuals of TM-1 x 3-79. The

19


219 loci were assembled into 40 linkage groups with coverage of 3,855 cM. Linkage

groups were assigned to chromosomes by use of diploid and aneuploid cotton lines.

They detected 10 fiber related QTLs.

Ulloa and Meredith (2000) developed an RFLP genetic linkage map of G. hirsutum

from 119 F2:3 progeny of MD5678ne × Prema and used it for QTL analysis. Linkage

map comprised of 81 loci in 17 linkage groups with an average distance between

markers of 8.7 cM, covering 700.7 cM, or approximately 15% of recombinational

length of cotton genome. Twenty-six QTLs were detected on nine linkage groups and

explained 3.4 to 44.6% of the trait variation. Among them two QTLs were detected for

lint yield and three for lint percentage, explaining 5 to 20% of the variation in each

trait. Three QTLs for fiber strength and two QTLs for 2.5% fiber span length were

detected. As expected, the Prema parent contributed QTLs for low yield of fibers that

were long, strong, and fine while QTLs from MD5678ne imparted high yield of short,

coarse, and weak fibers. QTL positions on linkage groups suggested that genes

conferring fiber quality might cluster on same chromosome(s).

Kohel et al., (2001) performed RFLP analysis of 171 F2 individuals from a cross

between G. hirsutum acc. TM-1 and G. barbadense acc. 3-79. A total of 355 DNA

markers (216 RFLPs and 139 RAPDs) were assembled into 50 linkage groups,

covering 4766 cM. The DNA markers and the linkage map were used to identify 13

QTLs responsible for fiber properties in barbadense parent.

20


Guo et al., (2002) analyzed a double/ haploid interspecific population for mapping

with high level polymorphic SSR and RAPD markers. Linkage groups were associated

with their corresponding chromosomes with monosomes and telosomes in genetic

background of G. hirsutum and determined their attributive subgenomes by analysis of

markers distribution on G. herbaceum (A-subgenome) and G. raimondii (D-

subgenome). Among 624 RAPD and SSR loci detected, 489 loci were mapped into 42

linkage groups, covering 3312.2 cM of allotetraploid cotton.

Karaca et al., (2002) compared Ligon lintless (Li1) mutant (a monogenic, dominant

cotton mutant with extremely reduced fiber length) and TM-1 to reveal fiber initiation

differences between them. Thirty-eight SSR loci polymorphic between TM-1 and Li1

were used for mapping in an F population. Among them 22 loci were located on eight

linkage groups with 218.3 cM distance. Using monosomic and monotelodisomic

plants, two SSR loci (MP4030 and MP673) from Li1 were located on chromosome 22.

2

Ulloa et al., (2002) constructed an RFLP genetic linkage joinmap from four different

mapping populations of G. hirsutum. Genetic maps of two of four populations were

previously reported (Shappley et al., 1998b; Ulloa and Meredith, 2000). The third

genetic map was constructed from 199 bulk-sampled plots of an F2.3 (HQ95-62 x

MD51ne) population. Fourth genetic map was developed from 155 bulk-sampled plots

of an F2.3 (119-5 sub-okra2 x MD51ne) population. This joinmap comprised of 284

loci mapped to 47 linkage groups with average distance between markers of 5.3 cM,

covering 1,502.6 cM or 31% of cotton genome.

21


Zhang et al., (2002) used 58 haploid and double haploid plants of interspecific cross

between G. hirsutum and G. barbadense by means of Vsg, a virescently marked

semigamous line of Sea Island cotton, and some target haploids were successfully

doubled with colchicine. They utilized the monosomic and telodisomic lines of G.

hirsutum in background of TM-1 for chromosome association. With RAPD and SSR

analysis, 489 loci were assembled into 43 linkage groups covering 3314.5 cM.

Lacape et al., (2003) evaluated interspecific (G. hirsutum × G. barbadense) 75 BC1

plants with 1014 markers. The map consisted of 888 loci, including 465 AFLPs, 229

SSRs, 192 RFLPs, and 2 morphological markers, ordered in 37 linkage groups

spanning 4400 cM. Loci were not evenly distributed over linkage groups and 18 of the

26 long groups had a single dense region. They proposed a partially revised list of 13

pairs of homeologous chromosomes of tetraploid cotton genome.

Zhang et al., (2003) used G. anomalum introgression line, 7235 with good fiber traits

and screened 1840 RAPD primers. Using bulked segregation analysis (BSA)

(Michelmore et al., 1991) of F2 and F3 populations of 7235 and TM-1, three SSRs and

six RAPDs were mapped into one linkage group, linked to two fiber strength QTLs.

One major QTL mapped to chromosome 10 was associated with eight markers and

explained 30% of the phenotypic variation. Later on among these RAPD markers, two

were converted to reliable SCAR markers (Guo et al., 2003) linked to QTL for MAS.

Only SCAR4311920 marker detected polymorphism between TM-1 and 7235, whereas

SCAR7571365 showed monomorphism.

22


Baogong (2004) found 151 polymorphic AFLP markers, among them 53 markers were

assigned to individual chromosomes or chromosome arms by using a set of aneuploid

cotton genetic stock. QTL analysis of cotton yield and yield components was

conducted on an intraspecific F2:3 population. A previously developed linkage map

was used based on same population covering 1733.2 cM (37.7%) cotton genome

(4700 cM). Nine and seven QTLs were detected by interval mapping (IM) and

composite interval mapping (CIM) methods, respectively and four of which were

detected by both methods.

Mei et al., (2004) analyzed an interspecific F2 cotton population for genetic mapping.

A total of 392 genetic loci, including 333 AFLPs, 47 SSRs and 12 RFLPs, were

mapped in 42 linkage groups, which spanned 3,287 cM with 70% of genome

coverage. Using chromosomal aneuploid interspecific hybrids and a set of 29 RFLP

and SSR framework markers, they assigned 19 linkage groups involving 223 loci to 12

chromosomes. Seven QTLs were detected for six fiber-related traits; five of these were

distributed among A-subgenome chromosomes.

Nguyen et al., (2004) developed and positioned SSR markers on the genetic map of

tetraploid cotton. The map was of 1,160 loci and 5,519 cM, with an average distance

between two loci of 4.8 cM. Rong et al., (2004) constructed genetic maps for diploid

(D) and tetraploid (AtDt) Gossypium genomes composed of STS that fostered

structural, functional, and evolutionary genomic studies in cotton. The maps included

23


2584 loci at 1.72 cM (600 kb) intervals based on 2007 probes (AtDt) and 763 loci at

1.96 cM (500 kb) intervals detected by 662 probes (D).

Dongre and Parkhi (2005) identified hybrid cotton H-6 and its parents G.Cot.10 (male)

and G.Cot.100 (female). Out of 20 RAPD and 44 SSR primers; OPA 11, JESPR-2 and

JESPR-17 were found to be useful in differentiating parents and hybrid. Lacape et al.,

(2005) described the QTL analysis of 11 fiber properties measured on three

phenotypic data sets. Three interspecific populations studied were the 1st (BC1) and

2nd (BC2 and BC2S1) backcross generations. Eighty QTLs were detected, of which 50

surpassed the permutation-based LOD thresholds (3.2-5.7).

Lin et al., (2005) genotyped 69 F2 interspecific individuals with 749 polymorphic loci

(205 SSRs, 107 RAPDs and 437 sequence-related amplified polymorphisms or

SRAPs). A total of 566 loci were assembled into 41 linkage groups with at least three

loci in each group. Twenty-eight linkage groups were assigned to corresponding

chromosomes by SSR markers with known chromosome locations. The map covered

5141.8 cM with a mean interlocus space of 9.08 cM and detected 13 fiber QTLs.

Park et al., (2005) developed a cotton genetic map with EST based SSRs and complex

sequence repeat (CSR) markers using 183 RILs from an interspecific cross of TM-1

and Pima 3-79. From a total of 1232 EST-derived SSR (MUSS) and CSR (MUCS)

primer-pairs, 1019 (83%) were successfully amplified. The resulting genetic map

consisted of 193 loci mapped to 19 chromosomes and 11 LG, spanning 1277 cM with

24


27% genome coverage. Preliminary QTL analysis suggested that chromosomes 2, 3,

15, and 18 might harbor genes for fiber traits.

Shen et al., (2005) constructed three linkage maps with SSRs to tag fiber QTLs using

three elite fiber lines of Upland cotton as parents. Thirty-eight QTLs were detected for

fiber traits, in which 11 QTLs were for fiber length, 10 for fiber strength, 9 for

micronaire and 8 for fiber elongation. Among them, 15 stable QTLs (39.47%) could

be found in both F2 and F2:3 segregating populations. At least 3 identical QTLs could

be identified in two populations.

Song et al., (2005) used double haploid (DH) and BC1 populations from the same

cross between TM-1 (G. hirsutum) and Hai7124 (G. barbadense) for construction of

genetic maps. The BC1 map included 440 SSRs and 2 morphological markers in 34

linkage groups with an average interlocus distance of 9.8 cM and covered 4331.2 cM

or 78.7% of cotton genome. The haploid map comprised 444 SSRs in 40 linkage

groups with an average interlocus distance of 7.35 cM, covering 3262.9 cM or 60.0%

of cotton genome. Linkage groups in both maps were also assigned to chromosomes.

Ulloa et al., (2005) assigned chromosome to 15 linkage groups of RFLP joinmap

developed from four different intraspecific G. hirsutum populations. Overall results

revealed presence of 63 QTLs on five chromosomes of A subgenome and 29 QTLs on

three chromosomes of D subgenome. Cotton chromosomes would have islands of high

and low meiotic recombination like some other eukaryotic organisms. Fiber QTLs in

25


certain regions were located between two markers with an average of less than one cM

(0.4 - 0.6 Mb) and possibly represent targets for map-based cloning.

Zhang et al., (2005b) made a genetic map with 70 loci (55 SSR, 12 AFLP and 3

morphological loci) using 117 intraspecific F2 cotton plants. Map comprised of 20

linkage groups, covering 525 cM with an average distance of 7.5 cM between two

markers and 11.8% of cotton genome coverage. This map was used to identify QTLs

affecting lint percentage and fiber quality traits in 117 F2:3 family lines. Sixteen QTLs

were identified in six linkage groups. Several fiber related QTLs were detected within

the same chromosome region, suggesting that genes controlling fiber traits may be

linked or the result of pleiotropy. Zhang et al., (2005c) surveyed 88 SSR primers (177

loci) for genotyping 24 elite cotton cultivars. Six SSRs were correlated with fiber yield

and quality, providing impetus to validate the marker-trait associations.

Frelichowski et al., (2006) utilized an interspecific RIL population to map 433 SSR

loci (MUSB-derived markers from BAC-end sequences) in 46 linkage groups with a

genetic distance of 2,126.3 cM covering 45% of cotton genome. The linkage groups

were assigned to 23 of the 26 chromosomes. This was the first genetic map in which

the linkage groups A01 and A02/D03 were assigned to specific chromosomes.

Han et al., (2006) integrated 123 EST-SSRs into a backbone cotton map, based on

previously used interspecific BC1 mapping population [(TM-1 x Hai7124) TM-1].

EST-SSR markers were distributed over 20 chromosomes and 6 linkage groups in the

26


map. Earlier 111 EST-SSRs were integrated into this backbone map including 511

SSR loci. (Han et al., 2004).

Wang et al., (2006a) assigned previously unassigned six linkage groups A01, A02,

A03, D02, D03, and D08 to chromosomes 13, 8, 11, 21, 24, and 19, respectively. They

established 13 homeologous chromosome pairs and proposed a new chromosome

nomenclature for tetraploid cotton.

He et al., (2007) used 834 SSRs, 437 SRAPs, 107 RAPDs and 16 retrotransposon

microsatellite amplified polymorphism (REMAP) to assay 69 interspecific F2 cotton

plants. Linkage map consisted of 1,029 loci in 26 linkage groups, which spanned

5472.3 cM with interlocus distance of 5.32 cM. Corresponding 69 F2:3 families were

scored for eight phenotypes. Fifty two QTLs were detected for fiber and lint yield.

Shen et al., (2007) constructed a genetic linkage map consisting of 156 SSR loci

covering 1,024.4 cM using a series of intraspecific cotton RILs. Phenotypic data were

collected for 5 fiber quality and 6 yield traits, and they found 25 major QTLs (LOD ≥

3.0) and 28 putative QTLs (2.0 < LOD < 3.0) for fiber quality and yield components

in two or four environments independently. The most important chromosome D8 in

the present study was densely populated with markers and QTLs, in which 36 SSR

loci and 9 QTLs for 8 traits were detected within a chromosomal region of 72.7 cM.

27


Table 2.1 Summary of linkage mapping and fiber QTL analysis studies in cotton

Investigators Pop. (plants/ lines) Mapped loci Linkage groups

cM Fiber QTLs

Reinisch et al., 1994 F2 (57) intersp. G. hir. x G. bar.

705 RFLP 41 4675 -

Shappley et al., 1996; 1998a, b

F2.F3 (96) intrasp. G. hir.

120 RFLP 31 865 21

Jiang et al., 1998 F2 (271) intersp. G. hir. x G. bar.

261 RFLP 27 3767 3

Khan et al., 1998 trispecific F2 (90), G. hir., G. arboreum, G. trilobum

RAPD, AFLP 51 6663 -

Yu et al., 1998 F2 (171) intersp. G. hir. x G. bar.

219 RAPD 40 3855 10

Ulloa and Meredith, 2000

F2:3 (119), intrasp. G. hir.

81 RFLP 17 701 12

Kohel et al., 2001 F2 (171) intersp. G. hir. x G. bar

355 (216 RFLPs, 139 RAPDs)

50 4766 7

Guo et al., 2002 DH intersp. 489 (RAPD, SSR) 42 3312 - Karaca et al., 2002 F2, intrasp. G. hir. x 22 SSR 8 218 - Ulloa et al., 2002; 2005

4 F2:3, intrasp. G. hir.

284 RFLP 47 1503 92

Zhang et al., 2002 DH (58) intersp. G. hir. x G. bar.

489 (RAPD, SSR) 43 3315 -

Lacape et al., 2003 BC1 (75) intersp. G. hir. x G. bar.

888 (192 RFLP, 465 AFLP, 229 SSR)

37 4400 -

Zhang et al., 2003 F2, F3 intrasp. G. hir. 9 (6 RAPD, 3 SSR)

1 15.6 2

Baogong, 2004 F2:3, intrasp. 151 AFLP 26 1733 9 Han et al., 2004, 2006

BC1 (140) intersp. G. hir. x G. bar.

907 SSRs 26 5060 -

Mei et al., 2004 F2 (120) intersp. G. hir. x G. bar.

392 (12 RFLP, 333 AFLPs, 47 SSRs)

42 3287 5

Nguyen et al., 2004 BC1 (75) intersp. G. hir. x G. bar.

1160 SSR 30 5519 -

Rong et al., 2004 F2 (57) intersp. G. hir. x G. bar.

3347 STS 26 4448 -

Lacape et al., 2005 3 BCs (200, BC1, BC2, BC2S1) intersp.

1306 (SSR, AFLP)

26 5597 64

Lin et al., 2005 F2 (69) intersp. 566 (SRAP, RAPD, SSR)

41 5142 13

Park et al., 2005 RILs (183) intersp. G. hir. x G. bar.

193 (SSRs, CSR) 30 1277 8

28


Shen et al., 2005 3 F2s (163, 169, 142) intrasp.

86 SSR 56 SSR 73 SSR

21 17 22

667 558 588

39

Song et al., 2005 DH and BC1 intersp. G. hir. x G. bar.

DH, 444 SSR BC1, 440 SSR

40 34

32634331

-

Zhang et al., 2005b F2, F2:3 (117) intrasp. G. hir.

70 (55 SSR, 12 AFLP, 3morph)

20 525 16

Frelichowski et al., 2006

RILs intersp. 433 SSR 46 2126 20

He et al., 2007 F2, F2:3 (69) intersp. 1029 (RAPD, SRAP, SSR, REMAP)

26 5472 52

Shen et al., 2007 RILs (258) intrasp. G. hir.

156 SSR 31 1024 27

29

Chapter 3 Materials and Methods

CHAPTER 3

MATERIALS AND METHODS

Present research work was conducted at Plant Genomics and Molecular Breeding Lab,

Plant Biotechnology Division, National Institute for Biotechnology and Genetic

Engineering (NIBGE), Faisalabad, and Department of Botany, Institute of Pure and

Applied Biology, Bahauddin Zakariya University (BZU), Multan. In this study,

RAPDs and SSRs were used to identify and map QTLs for cotton fiber quality traits.

3.1 Screening of Cotton Varieties/ Genotypes for Quality Traits

Nineteen cotton varieties/genotypes collected from different cotton research institutes

(Table 3.1), were screened at NIBGE cotton field for three main quality traits

including fiber length, fiber strength and micronaire (Asif et al., 2008a). These

genotypes/varieties were sown with 30 cm plant to plant distance and 75 cm row to

row distance in three repeats using randomized complete block design (RCBD) during

2001 normal cotton growing season (May through December). After harvesting, seed

cotton was ginned at NIBGE farm and the fiber of these cotton genotypes were

analyzed from Fiber Technology Lab, Cotton Research Institute (CRI), AARI,

Faisalabad. Fiber length was measured in mm, fiber strength in tppsi and fiber fineness

as micronaire reading.

30


3.1.1 Statistical analysis

Analysis of variance (ANOVA) (Steel and Torrie, 1980) was applied to data of

three fiber traits obtained from 19 cotton varieties/ genotypes. Correlations were also

determined between each of fiber length, fiber strength and micronaire. ANOVA

(Table 3.2) and correlation analysis were performed with MSTAT-C programme. On

the basis of these screening results cotton parents with contrasting quality traits were

selected for hybridization.

3.2 Development of Segregating Population for Genetic Mapping

Two cotton (G. hirsutum) parents FH-883 (30 mm fiber length) and FH-631S (23 mm

fiber length) contrasting for fiber quality traits were selected on the basis of their field

performance. To develop a population for mapping QTLs related to cotton fiber

quality traits, these two parents were crossed at NIBGE fields during 2002 normal

cotton season. F1 was grown in green house and selfed to get F2 seed. F2 segregating

population was developed in 2003. Next season, sowing was done in single rows of

each F2 individual plant to obtain an F2:3 population. Seed cotton was harvested from

117 F2:3 cotton lines for further analysis of quality traits.

3.2.1 Statistical analysis

Fiber analysis of 117 cotton lines of F2:3 (FH631S x FH-883) were performed with

High Volume Instrument (HVI) from Fiber Technology Department, UAF, for the

31


following eight fiber quality traits. After fiber analysis through HVI, correlations were

also determined between each of these fiber traits with MSTAT-C programme.

i. Fiber length (FL):

The distance spanned in millimeter (mm) or inches by a specific percentage of

fibers in a test beard when tested by the fibrograph, taking the amount reading at the

starting point of scanning as 100%. The 2.5% span length is the length crossed by only

2.5% of the beard fibers, while 50% span length is the length spanned by 50% of

fibers in a beard.

ii. Fiber fineness/ micronaire (FF/ Mic):

Micronaire is the relative measure of fiber fineness and maturity. Fiber

fineness is expressed as linear density or weight per unit length, while maturity refers

to the degree of development or thickening of the fiber cell wall relative to the

perimeter or effective diameter of the fibers. Micronaire was indirectly determined

according to the airflow principle. A compressed mass of coarse fibers permitted more

airflow (higher micronaire value), while less airflow was an indication of finer fibers.

iii. Fiber strength (FS):

The maximum force required to break the specimen of fibers, expressed as

thousand pounds per square inch (tppsi) in zero gage or gram per tex (g/tex) in 1/8

32


inch gage. Tex unit is equal to weight in grams of 1000 meters fiber, hence g/tex is the

force in grams to break a bundle of fibers one tex in size.

iv. Fiber length uniformity (FU):

The ratio between two span lengths as a percentage of longer length that is

50% span length divided by 2.5% span length and expressed as percentage.

v. Short fiber index (SFI):

Short fiber content was taken as the percentage of fibers that had the length

less than 12.7 mm (half an inch).

vi. Fiber elongation (FE):

It is increase in length or deformation as a result of stretching cotton fiber,

which measures degree of extensibility or elasticity of the fibers before a break occurs.

Elongation was measured as percentage increase over the unstretched original length.

vii. Fiber color (Reflectance or Rd and Yellowness or +b):

Fiber color is expressed practically with two indices; Rd is the degree

reflectance or whiteness of the sample, while +b is the degree of yellowness. Rd and

+b were measured by cotton colorimeter in HVI.

33


Table 3.1 Cotton (G. hirsutum) varieties/ genotypes, their parentage and breeding centers.

S. No. Varieties/ genotypes Parentage Breeding center

1. B-557 268-F x (45-F x LSS) CRI, Faisalabad

2. BH-36 BS-1 x TX Bonham76C CRS, Bahawalpur

3. BH-118 BS-48 x 829-4190, 829-4190= (TX 339 x ST-7A) x (ST-7A x AET-5)

CRS, Bahawalpur

4. CIM-443 CIM-109 x LRA-5166 CCRI, Multan

5. CIM-448 492/87(W 1104 x S-12) x CP15/2 CCRI, Multan

6. CIM-473 CIM-402 x LRA-5166 CCRI, Multan

7. CIM-707 CIM-243 x 738/697 CCRI, Multan

8. CIM-1100 (W-1104 x S12) x CP-15/2 CCRI, Multan

9. FH-87 AC-134 x paymaster CRI, Faisalabad

10. FH-631S Express x Lankart-57 CRI, Faisalabad

11. FH-634 (B557 x B574) x Cedix CRI, Faisalabad

12. FH-883 Selection from SLS, (SLS = AlbarA637 x Acala 4-42 AlbAcala)

CRI, Faisalabad

13. FH-900 (FH-672 x AET-5) x B557 x LRA-5166 CRI, Faisalabad

14. FH-901 CIM-240 x CIM-448 CRI, Faisalabad

15. Karishma N-86 x W 83-29 MEX NIAB, Faisalabad

16. MNH-93 149F x (MS-39 x Mex12) CRS, Multan

17. MNH-552 MS-48 x LRA-5166 CRS, Multan

18. NIAB-78 DPL-16 x AC-134 NIAB, Faisalabad

19. NIBGE-1 Karishma x LRA-5166 NIBGE, Faisalabad CCRI: Central Cotton Research Institute, CRI: Cotton Research Institute, CRS: Cotton Research Station, NIAB: Nuclear Institute for Agriculture and Biology, NIBGE: National Institute for Biotechnology and Genetic Engineering.

34


Table 3.2 Analysis of variance (ANOVA) for fiber traits from 19 cotton genotypes.

SOV Degree of freedom (df) Sum of squares (SS) Mean squares (MS) Fcal

Replications (r) r-1 3-1=2 SSr= (∑r2/r) - CF† MSr= SSr/df MSr / MSe

Genotypes (g) g-1 19-1=18 SSg= (∑g2/g) - CF MSg= SSg/df MSg / MSe

Error (e) (r-1)(g-1) 2 x 18=36 SSe= SStotal – (SSr+ SSg) MSe= SSe/df

Total rg-1 (3x19)-1=56 SStotal= ∑x2- CF

†Correction factor (CF)= (grand total)2/ rg

35


3.3 DNA Markers Analysis

DNA fingerprinting techniques (RAPD and SSR) were applied to find DNA markers/

QTLs linked to fiber quality traits by using two contrasting cotton parents (FH-883

and FH-631S) and their F2:3 population.

3.3.1 DNA isolation and quantification

3.3.1.1 DNA extraction

DNA was extracted from a few young leaves of the selected cotton parents (FH-

883 and FH-631S), their F1s and F2:3 population by CTAB method proposed by

Murray and Thompson (1980).

1. Young leaves were ground to a very fine powder in liquid nitrogen and

transferred to 50 ml Falcon tube.

2. The 15 ml of hot (65oC) 2x CTAB was added and incubated for 30-45 minutes

at 65°C with occasional swirling.

3. Equal volume of chloroform: isoamyl alcohol (24:1) was added and mixed.

4. The 50 ml tubes were spun at 4000 rpm for 10 min.

5. The supernatant was collected into another 50 ml tube.

6. Nucleic acid was precipitated with 0.6 volume of isopropanol.

7. Nucleic acid was pelleted at 4000 rpm for 5 minutes and supernatant was

discarded.

36


8. Pellet was washed with 70% ethanol.

9. The pellet was air dried and resuspended in 0.5 ml 0.1x TE buffer.

10. The suspension was transferred to 1.5 ml eppendorf tube and incubated at

37°C for 1 hour after adding 7 µl of RNase enzyme.

11. Equal volume of chloroform: isoamyl alcohol (24:1) was added and mixed.

12. Centrifuged for 10 min at 13000 rpm and supernatant was transferred to new

eppendorf.

13. 1/10th 3M NaCl was added to the supernatant and mixed gently.

14. DNA was precipitated with chilled absolute ethanol (2 volumes) and spinned at

13000 rpm for 10 minutes.

15. Discarded the supernatant and washed the pellet with 70% ethanol.

16. Air dried the DNA pellet and resuspended it in 0.1x TE buffer.

3.3.1.2 DNA quantification

DNA concentration was measured with DyNAQuant 200 Fluorometer. Two

types of assay solutions were used low range assay solution-A and high range assay

solution-B having Hoechst 33258 dye that gives fluorescence when binds to DNA.

The quantity of DNA was also confirmed by comparing with Quantification

Standards, Phage λ DNA (GibcoBRL) on 0.8% agarose gel. Quality of DNA was

checked by running 50 ng DNA on 0.8% agarose gel. The DNA samples giving smear

in the gel were rejected. Dilutions of DNAs were prepared from stocks accordingly for

RAPD and SSR analysis.

37


3.3.2 RAPD analysis

3.3.2.1 PCR amplification

RAPD analysis (William et al., 1990) was conducted using a total of 520

decamer primers for screening two cotton parents (FH-883 and FH-631S) and

subsequently screening their F1 and F2:3 population with the polymorphic primers.

These primers belonged to series OPA through OPZ, with 20 primers in each series

(Table 3.3) (Operon Technologies, Inc. USA). Total volume of each PCR reaction

mixture was 25 µl consisting of following reagents:

PCR Reagents Volume

Deionized distilled water 8.8 µl

10x PCR buffer 2.5 µl (Fermentas)

2.5 mM dNTPs 4.0 µl (Fermentas)

25 mM MgCl2 3.0 µl (Fermentas)

0.025% gelatin 2.5 µl (Sigma)

5 unit/µl Taq DNA Polymerase 0.2 µl (Fermentas)

15 ng/µl Primer 2.0 µl (Operon Tech.)

15 ng/µl template DNA 2.0 µl

In each PCR one reaction was run with out DNA, as a negative control. RAPD PCR

amplification was performed in Thermal Cycler (eppendorf mastercycler gradient,

Germany) using the following PCR conditions:

Initial DNA denaturation at 94oC for 5 minutes (1 cycle)

Denaturation at 94oC for 1 minute

Primer annealing at 36oC for 1 minute (40 cycles)

Extension at 72oC for 2 minutes

Final extension step at 72oC for 10 minutes (1 cycle)

38


Soaking at 20oC

3.3.2.2 Agarose gel electrophoresis

RAPD products were analyzed by electrophoresis on 1.2% agarose gel in 0.5x

TBE buffer and detected by ethidium bromide (10 mg/ml) staining (Sambrook and

Russel, 2001). Before loading PCR products in the gel, one drop of 6x loading dye

was added to the reaction mixture. Ten µl of the reaction mixture was loaded on the

gel submerged in TBE buffer. Samples were electrophoresed for approximately two

hours at 80 volts. After electrophoresis, the amplified products were viewed under

ultra violet (UV) transilluminator and photographed using the Stratagene Eagle Eye

still video system or Kodak (1D 3.5) imaging system.

3.3.2.3 RAPD data analysis

Good quality photographs were used to read the amplification profiles. All

visible and unambiguously scorable fragments amplified by RAPD primers were

scored. The primers that amplified DNA fragment(s), which were repeatedly present

in one parent and absent in the other were scored as polymorphic primers. The

polymorphic primers were used to amplify the DNA of 117 F2:3 lines and data was

collected about the polymorphic fragments/bands among the F2:3 population. Banding

pattern of parents was also compared with F1s for hybridity confirmation (Asif et al.,

2009). The polymorphism information content (PIC) of each RAPD locus was also

calculated using the following equation (Anderson et al., 1993):

(pij is the frequency of the jth allele for locus i)

39


Finally, RAPD data was analyzed along with SSR data for linkage map construction

and fiber QTL analysis.

Table 3.3 RAPD and SSR primers for screening FH-883 and FH-631S cotton parents.

Assay Series Primers Annealing temperature

RAPD (ºC)

Operon OPA to OPZ (1 – 20 primers in each series) 36 SSR

BNL BNL-169, BNL-252, BNL-256, BNL-448, BNL-686, BNL-786, BNL-840, BNL-1053, BNL-1059, BNL-1064, BNL-1317, BNL-1350, BNL-1414, BNL-1434, BNL-1440, BNL-1597, BNL-1665, BNL-1679, BNL-1721, BNL-2496, BNL-2544, BNL-2553, BNL-2572, BNL-2590, BNL-2634, BNL-2895, BNL-2960, BNL-3008, BNL-3034, BNL-3065, BNL-3084, BNL-3090, BNL-3103, BNL-3147, BNL-3255, BNL-3279, BNL-3359, BNL-3383, BNL-3408, BNL-3441, BNL-3442, BNL-3449, BNL-3452, BNL-3479, BNL-3482, BNL-3556, BNL-3558, BNL-3563, BNL-3599, BNL-3627, BNL-3646, BNL-3649, BNL-3792, BNL-3816, BNL-3888, BNL-3895, BNL-3902, BNL-3955, BNL-3971 and BNL-3995

50 – 60

CM CM-25, CM-27, CM-29, CM-30, CM-32, CM-42, CM-43, CM-45, CM-50, CM-56, CM-60, CM-63, CM-65, CM-66, CM-67, CM-68, CM-76, CM-160, CM-161 and CM-162

50 – 61

JESPR JESPR-01 to JESPR-270 56 – 63 MGHES MGHES-01 to MGHES-78, MGHES-1B, MGHES-

11B, MGHES-30B, MGHES-38B, MGHES-43B, MGHES-44B and MGHES-65B

55

RAPD: random amplified Polymorphic DNA, SSR: simple sequence repeats, OP: Operon, BNL: Brookhaven National Laboratory, CM: Cotton Microsatellites, JESPR: after the names of principle investigators – Jenkins, El-Zik, Saha, Pepper and Reddy; MGHES: Mississippi G. hirsutum EST SSR. Source of SSR primers: Cotton marker database-CMD (www.cottonmarker.org), please see Appendices for primer sequences.

40


3.3.3 Microsatellite/ simple sequence repeat (SSR) analysis

3.3.3.1 PCR amplification

For microsatellite analysis 446 SSR primers (including 84 EST-SSRs) were

surveyed to screen two cotton parents (FH-883 and FH-631) and subsequently

screening their F1 and F2:3 population with polymorphic SSR/ EST-SSR primers.

These primers belonged to the series of BNL, CM, JESPR and MGHES (EST based

SSR primers) (Table 3.3). The sequences of these primers were obtained from

publically available cotton microsatellite data (CMD) (Blenda et al., 2006) and

synthesized from GeneLink, USA. Conditions for PCR amplification with respect to

annealing temperature were different for different SSR primers/series. Following

concentrations of PCR reagents for 20 µl volume were used.

Reagents Concentrations Volume

Template DNA (30 ng) 2.0 µl

dNTPs (2.5 mM) 6.4 µl

Buffer (10x) 2.0 µl

MgCl2 (25 mM) 1.6 µl

SSR Primer (F+R) (30 ng/µl) 2.0 µl

Taq DNA polymerase (5 U/µl) 0.2 µl

Double distilled H2O 5.8 µl

SSR PCR amplification was performed in eppendorf mastercycler gradient using the

following PCR conditions:

Initial DNA denaturation at 94oC for 5 minutes (1 cycle)

Denaturation at 94oC for 30 seconds

Annealing at 55- 65°C (variable) for 30 sec. (35 cycles)

41


Extension at 72oC for 1 minute

Final extension step at 72oC for 4 minutes (1 cycle)

Soaking at 20oC

3.3.3.2 Polyacrylamide gel electrophoresis (PAGE)

a) Glass plates preparation: Both the glass plates (long and short) were meticulously

washed with detergent, rinsed thoroughly with tap water and then with deionized

water to remove detergent residues because detergent microfilms left on the glass

plates may result in a high (brown-colored) background upon staining the gel. Then

plates were cleaned with 95% ethanol and wiped out with a tissue paper. Short glass

plate was treated with a fresh binding solution (3 µl of Bind Silane, 1 ml of 95%

ethanol, 0.5% glacial acetic acid) to chemically crosslink gel to plate. It prevents gel

tearing during staining. Plate was completely covered with binding solution using a

fine tissue paper. After 5 minutes, plate was gently wiped thrice using fine tissue paper

saturated with 95% ethanol in one direction and then perpendicular to the first

direction. It was to remove excess binding solution to avoid from contaminating long

glass plate. Long glass plate was treated with SigmaCote® using a tissue saturated

with it and after 10 minutes, excess SigmaCote® was removed by wiping plate with a

fine tissue. Excess SigmaCote® may cause inhibition of staining. The treated surfaces

were not allowed to come into contact with one another. Both the plates were clumped

after placing spacers inside (0.4 mm thick spacer).

b) Polyacrylamide gel preparation: Freshly prepared 6% polyacrylamide gel (20:1,

acrylamide: bisacrylamide) was used to resolve products of some SSR primers

42


amplified between two parents and in F2:3 population. The gels were cast at least 90

minutes before use. Following ingredients were used to prepare 1 liter solution of gel:

Reagents Quantity

Acrylamide 60 gm

Bisacrylamide 3 gm

Urea 420 gm

Buffer (10x TBE) 100 ml

TEMED and Ammonium persulfate were added just before pouring the gel (50 ml).

10% ammonium per sulfate (APS) 250 µl

Tetra Methylethylenediamine (TEMED) 50 µl

After addition of APS and TEMED, immediately and carefully poured the gel. It was

poured from one side with a constant flow to avoid formation of bubbles and if formed

then gently tapped the plate to remove bubbles. Inserted the squaretooth comb into the

gel and to polymerize between the plates. Leftover gel solution in a beaker was used

as a polymerization control.

c) Vertical gel electrophoresis: After polymerization, plates along with gel were

placed in a vertical electrophoreses apparatus. Thoroughly flushed wells of gel with a

syringe filled with 1x TBE buffer. Electrophoresis was performed with the vertical gel

electrophoresis system (Protean II Xi Cell, BIO-RAD), using 1x TBE as running

43


buffer at 200 V. Loading dye (1 µl) was loaded into the wells and gel was pre-run for

30 minutes or until 50˚C temperature achieved before loading the SSR products. After

pre-run the wells were completely flushed with a 20 cc syringe to remove urea

precipitate or pieces of gel. SSR products were denatured by heating at 94˚C for 2

minutes and immediately chill on ice. Sequencing dye (2 µl) was added to 3 µl of each

PCR product and was loaded along with DNA size standards (50 bp) in designated

lanes. The gel was run at 200 V until xylene cyanol (slower dye) was two-thirds down

the length of gel. In a 6% PAGE, bromophenol blue migrates at about 25 bp and

xylene cyanol moves at about 105 bp size Then disassembled the apparatus and

unclamped the glass plates and removed the spacers gently. Two plates were separated

carefully by using a plastic wedge. Gel, still attached to short plate was ready for

staining was stained with silver nitrate.

d) Silver staining: Gel was placed in a shallow plastic tray and was covered with 10%

glacial acetic acid (fixative solution) and agitated for 20 minutes or until tracking dyes

were no longer visible and rinsed 3 times (2 minute each) in deionized water. Then the

gel was immersed in staining solution (1 g silver nitrate dissolved in 1 litres of

deionized water and 1.5 ml of 37% formaldehyde was added just before use) and

agitated for 30 minutes. The gel was washed with deionized water for 5-10 seconds

(longer rinses result in weak or no signal) and was immediately placed in 500 ml of

cold developing solution (30 g sodium carbonate dissolved in 1 litres of deionized

water, 1.5 ml of formaldehyde (37%) and 200 µl of 1% sodium thiosulphate were

added just before use). The gel was agitated until the bands started to appear, the

44


solution was immediately poured off and remaining 500 ml of the developing solution

was added and agitated for 2-3 minutes or until the gel was developed completely.

Development of gel was terminated by adding fixative solution directly to the

developing solution and shaking for 2-3 minutes (longer incubations fade the stain).

Rinsed the gel twice for 2 minutes each in ultrapure water before gel imaging. Used

gel was removed from plate by soaking the short plate in 10% NaOH.

3.3.3.3 MetaPhor agarose (ultra high-resolution agarose) electrophoresis

MetaPhor agarose (Cambrex Corporation, USA), an intermediate melting

temperature agarose (75°C), provides twice the resolution capabilities of the finest

sieving agarose products. MetaPhor agarose gels (2% to 4%) made in either TAE or

TBE and stained with ethidium bromide, approximate the resolution of

polyacrylamide gels (4% to 8% PAGE) and these gels are ideal for resolving SSRs

(Asif et al., 2008b). Initially, we run a few acrylamide gels, but on availability of

Metaphor, further SSR analysis was conducted with metaphor agarose gels. For the

gel preparation, metaphor agarose was slowly added to the chilled 1x TBE buffer with

fast swirling. It was soaked in that cold buffer for about 10-15 minutes before heating

it in the microwave (to prevent it from foaming and not to over boil the gel). Equal

amount (2%) of simple agarose was added for easier handling of the metaphor agarose

gel. Once the molten gel was solidified after being poured into a cast, it was kept at

4°C for 20 minutes before use to obtain a good resolution. After adding 3 µl 6x

loading dye to the PCR product, 10 µl of it was loaded on the metaphor gel submerged

in chilled TBE buffer and electrophoresed for about 6-8 two hours at 80 volts. After

45


electrophoresis, the resolved SSR products were visualized under UV light and

photographed using the Stratagene Eagle Eye still video system or Kodak (1D 3.5)

imaging system.

3.3.3.4 SSR data analysis

After gel electrophoresis good quality gel photographs were used to score the

all visible and unambiguously scorable fragments amplified by SSR/ EST-SSR

primers. The primers that produced polymorphic fragments between the two parents

were used to survey the DNA of F2:3 lines and data was collected about the

polymorphic fragments/bands in the F2:3 population. Like RAPDs, the PIC value of

each SSR locus was also calculated using the equation developed by Anderson et al.,

(1993). Finally, SSR data was analyzed along with RAPD data for linkage map

construction and analysis of fiber related QTLs.

3.3.4 Linkage map construction and QTL analysis

RAPD and SSR polymorphic bands in F2:3 population were scored as present (1) or

absent (0) and then these data codes were transformed to A, B, C, D and H genotype

codes. The following coding scheme was used for genotypes.

A homozygote for the allele from parent a of that locus

B homozygote for the allele from parent b of that locus

H heterozygote carrying both alleles a and b

C not a homozygote for allele a (either bb or ab genotype)

D not a homozygote for allele b (either aa or ab genotype)

- missing data for the individual at that locus

46


Linkage map was constructed using Mapmaker/Exp 3.0 software (Lander et al., 1987).

Map units were computed by applying the Kosambi function (Kosambi, 1944).

Linkage groups were identified at a minimum LOD 3.0 and a maximum distance of

37.2 cM. Linkage groups were putatively assigned to cotton chromosomes using

already known anchored RAPD and SSR loci (Zhang et al., 2003; Mei et al., 2004;

Han et al., 2006). To find fiber related QTLs, WinQTLCart 2.5 (Wang et al., 2006b)

was used and QTL analysis was performed with single marker analysis (SMA),

interval mapping (IM) and composite interval mapping (CIM).

47

Chapter 4 Results

CHAPTER 4

RESULTS

4.1 Screening of Cotton Varieties/ Genotypes for Fiber Quality Traits

Nineteen cotton varieties/genotypes were screened for fiber length (FL), micronaire

(Mic) and fiber strength (FS). Table 4.1 shows the characteristics of fiber related traits

of these cotton varieties/ genotypes.

4.1.1 Mean performance of cotton varieties for fiber traits

Variations in fiber traits were observed among 19 cotton varieties (Fig. 4.1).

Fiber length ranged from 23 to 30 mm with mean value of 27.6 mm. FH-883 had the

longest FL (30 mm0, while FH-631S had shortest FL (23 mm). Majority of the cotton

fell between 27-28 mm fiber. Similarly, Mic was variable with average micronaire

reading of 4.75. Three varieties namely CIM-707, CIM-1100 and FH-883 produced

fine fiber (4.4 Mic) and fiber of FH-631S was coarse with 5.5 Mic, while most of the

varieties had 4.8 Mic. Differences in fiber strength were also observed among cotton

genotypes ranging from weak (80 tppsi) to very strong (99 tppsi) fiber. FH-631S

produced weak fiber with 80 tppsi strength and the fiber of NIBGE-1 was very strong

with 99 tppsi strength, while the mean FS of 19 cotton varieties was 94.2 tppsi.

48

Chapter 4 Results

Table 4.1 Fiber characteristics of 19 cotton varieties/ genotypes. S. No. Varieties/

genotypes Fiber length or FL (mm)

Micronaire or Mic (µg/inch)

Fiber strength or FS (tppsi)

1. B-557 26.8 (M long) 4.8 (Average) 91.5 (Strong) 2. BH-36 27.6 (M long) 4.5 (Average) 88.6 (Average) 3. BH-118 27.0 (M long) 5.0 (Coarse) 95.5 (Strong) 4. CIM-443 27.0 (M long) 4.8 (Average) 97.0 (Strong) 5. CIM-448 28.0 (M long) 4.6 (Average) 94.5 (Strong) 6. CIM-473 29.0 (Long) 4.5 (Average) 95.0 (Strong) 7. CIM-707 29.6 (Long) 4.4 (Average) 97.0 (Strong) 8. CIM-1100 28.8 (M long) 4.4 (Average) 94.3 (Strong) 9. FH-87 28.0 (M long) 4.6 (Average) 95.5 (Strong) 10. FH-631S 23.0 (Medium) 5.5 (Coarse) 80.0 (Weak) 11. FH-634 28.5 (M long) 4.5 (Average) 95.4 (Strong) 12. FH-883 30.0 (Long) 4.4 (Average) 97.0 (Strong) 13. FH-900 26.5 (M long) 4.8 (Average) 93.0 (Strong) 14. FH-901 27.5 (M long) 5.2 (Coarse) 94.0 (Strong) 15. Karishma 26.5 (M long) 4.8 (Average) 97.5 (V strong) 16. MNH-93 27.8 (M long) 4.8 (Average) 95.0 (Strong) 17. MNH-552 27.2 (M long) 5.0 (Coarse) 97.0 (Strong) 18. NIAB-78 27.0 (M long) 4.9 (Average) 92.0 (Strong) 19. NIBGE-1 28.0 (M long) 4.8 (Average) 99.0 (V strong)

All cotton varieties Mean ± SE 27.6 ± 0.34 4.75 ± 0.07 94.2 ± 0.96 Minimum 23.0 4.4 80.0 Maximum 30.0 5.5 99.0 SD 1.49 0.29 4.20 % CV 5.4 6.13 4.5 Mean squares 6.66** 0.25** 53.0**

M: medium, V: very, SD: standard deviation, CV: coefficient of variability. ** Significant at 0.01 probability (P) level.

49

Chapter 4 Results

Fig. 4.1 Mean performances of 19 cotton varieties/ genotypes for fiber length, micronaire (Mic) and fiber strength. 1= B-557, 2= BH-36, 3= BH-118, 4= CIM-443, 5= CIM-448, 6= CIM-473, 7= CIM-707, 8= CIM-1100, 9= FH-87, 10= FH-631S, 11= FH-634, 12= FH-883, 13= FH-900, 14= FH-901, 15= Karishma, 16= MNH-93, 17= MNH-552, 18= NIAB-78, 19= NIBGE-1.

50

Chapter 4 Results

4.1.2 Analysis of variance for fiber traits

There were considerable differences (P < 0.01) among 19 cotton cultivars/

genotypes for FL, Mic and FS when analysis of variance (ANOVA) was conducted for

these fiber traits (Table 4.1). Although some variation in these traits existed among the

three repeats, but these differences were non significant. Coefficient of variability

(CV) was also calculated for these quality traits (Table 4.1). CV was 5.4%, 6.13% and

4.5% for FL, Mic and FS, respectively (Asif et al., 2008a).

4.1.3 Correlation among fiber traits in cotton genotypes

The correlation coefficients among FL, Mic and FS in 19 cotton varieties/

genotypes have been shown in Table 4.2. Highly significant negative correlation was

found between FL and Mic (r = -0.850), while highly significant positive correlation

was observed between FL and FS (r = 0.712). Mic was significantly and negatively

correlated with FS (r = -0.499).

51

Chapter 4 Results

Table 4.2 Correlation coefficients$ among three fiber quality traits in 19 cotton v

arieties/ genotypes.

Traits Fiber length (FL) micronaire (Mic) Fiber strength (FS)

FL 1

Mic -0.850 0.000 1

FS 0.712 0.001

-0.499 0.029

1

$ Correlation coefficients on above (bold), while correspondent probability below (italic).

52

Chapter 4 Results

4.2 Parental Differences and Population for Genetic Mapping

4.2.1 Selection of two contrasting cotton parents for mapping population

Two cotton genotypes FH-883 and FH-631S exhibited most contrasting FL

(Table 4.1) with mean of 30 and 23 mm, respectively. Similarly Mic was most

variable between FH-883 and FH-631S with Mic value of 4.4 and 5.5, respectively.

Moreover, FH-631S produced weakest fiber (80 tppsi) while FH-883 was third among

19 cotton genotypes to produce strong fiber (97 tppsi). Hence, FH-883 and FH-631S

were selected as parents to develop a mapping population. Major fiber traits of these

two parents and their F1 were also compared (Fig. 4.2).

4.2.2 F2:3 (FH-631S x FH-883) population for mapping fiber QTLs

An intraspecific F2:3 mapping population was derived from a cross between

two contrasting cotton (G. hirsutum) parents (FH-631S and FH-883). Based upon the

family means, 117 F2:3 lines showed extensive variations for eight fiber related traits

(Table 4.3). Among F2:3 families, range of fiber quality traits was 21.2 to 29.6 mm for

FL, 3.6 to 6.2 reading for Mic, 14.5 to 28.9 g/tex for FS, 42.8 to 56.6 % for FU, 7 to

31.6 % for SFI, 4.9 to 8.2 % for FE, 47.8 to 70.1 value for Rd and 4.9 to 12.4 value for

+b (please see Table 4.3 for trait abbreviations). Among these fiber traits,

transgressive segregation was also observed for some traits except for FS and SFI. All

traits distributed normally with some skewness and hence frequency distribution of

fiber traits revealed genetic variation consistent with multigenic inheritance (Fig. 4.3).

53

Chapter 4 Results

Fig. 4.2 Fiber quality traits of two cotton parents and their F1 (FH-631S x FH-883). A) Fiber Length (FL), B) Micronaire (Mic), C) Fiber Strength (FS).

54

Chapter 4 Results

Table 4.3 Statistical analysis of cotton fiber quality traits in (FH-631S x FH-883) F2:3 population. Fiber Traits Abbrev Mean ± SE Min Max SD Kur Skew

Fiber length (mm) FL 25.89 ± 0.18 21.20 29.60 1.99 -0.42 -0.29

Micronaire (µg/inch) Mic 04.70 ± 0.05 3.60 6.20 0.54 -0.09 0.52

Fiber strength (g/tex) FS 20.02 ± 0.31 14.50 28.90 3.38 -0.20 0.60

Fiber length uniformity (%) FU 48.77 ± 0.24 42.80 56.60 2.63 0.28 0.42

Short fiber index (%) SFI 16.05 ± 0.49 7.00 31.60 5.31 -0.42 0.46

Fiber elongation (%) FE 06.37 ± 0.06 4.90 8.20 0.67 -0.55 0.12

Reflectance (fiber color) Rd 59.71 ± 0.42 47.80 70.10 4.60 -0.35 -0.27

Yellowness (fiber color) +b 07.95 ± 0.14 4.90 12.40 1.51 0.48 0.63

Abbrev: abbreviation, SE: standard error, Min: minimum value, Max: maximum value, SD: standard deviation, Kur: kurtosis, Skew: skewness.

55

Chapter 4 Results

05

10152025

21.2 22.9 24.6 26.2 27.9 29.6LEN

Freq

1

05

10152025

3.6 4.1 4.6 5.2 5.7 6.2

MIC

Freq

2

05

10152025

14.5 17.4 20.3 23.1 26.0 28.9

STR

Freq

3

05

101520253035

42.8 45.6 48.3 51.1 53.8 56.6

UNF

Freq

4

0

5

10

15

20

25

7.0 11.9 16.8 21.8 26.7 31.6

SFI

Freq

5

0

5

10

15

20

25

4.9 5.56 6.22 6.88 7.54 8.2

ELG

Freq

6 8

0

5

10

15

20

25

30

4.9 6.4 7.9 9.4 10.9 12.4

B

Freq

0

5

10

15

20

25

30

47.8 52.3 56.7 61.2 65.6 70.1

RD

Freq

7 Fig. 4.3 Frequency distribution for eight fiber quality traits in (FH-883 x FH-631S) F2:3 population. 1) LEN: fiber length, 2) MIC: micronaire, 3) STR: fiber strength, 4) UNF: fiber length uniformity, 5) SFI: short fiber index, 6) ELG: fiber elongation, 7) RD: fiber color (Rd= reflectance), 8) B: fiber color (+b= yellowness). Freq: Frequency (number of individuals), * Please see Table 4.3 for trait units.

56

Chapter 4 Results

4.2.3 Correlation among fiber traits in F2:3 population

Correlation analysis of fiber traits in F2:3 population revealed that there was

significant positive association of FL with FS and FE, Mic with FS and FU, and FS

with FE (Table 4.4). The highest positive correlation was between FL and FS (r =

0.495). Negatively significant correlation was found for FL with FU and SFI, Mic

with FE and Rd, while similar association was observed for SFI with FS and FE.

However, correlations for other traits either positive or negative were non significant.

57

Chapter 4 Results

Table 4.4 Correlation coefficients among cotton fiber traits in (FH-631S x FH-83) F2:3 population. 8 Traits$ FL Mic FS FU SFI FE Rd

Mic -0.068

FS 0.495** 0.284**

FU -0.286** 0.276** 0.040

SFI -0.809** 0.029 -0.518** 0.040

FE 0.273** -0.220* 0.237** -0.150 -0.271** Rd 0.109 -0.215* 0.058 -0.128 -0.123 0.084

+b -0.093 -0.047 -0.048 0.158 0.056 0.054 -0.074 *, **: Correlation significant at 0.05 and 0.01 probability levels, respectively. $ Please see Table 4.3 for trait abbreviations.

58

Chapter 4 Results

4.3 DNA Markers Analysis for Cotton Fiber Quality Traits

Random amplified polymorphic DNA (RAPD) and microsatellites/ simple sequence

repeats (SSRs) were surveyed on parents to find polymorphic DNA markers.

4.3.1 RAPD analysis for fiber traits

Five hundred and twenty RAPD primers were surveyed to screen FH-883 and

FH-631S (Table 4.5). Among these, 506 primers produced scoreable bands while rest

of the primers were poorly amplified or did not produced any band. Total bands

amplified were 2683 with an average of 5.3 bands per primer, while band range was 1-

13 with maximum number of bands produced by OPU-10.

There were 498 monomorphic and eight (1.6%) polymorphic RAPD primers.

Polymorphic primers were OPD-07, OPI-13, OPJ-10, OPM-07, OPQ-13, OPR-09,

OPU-01 and OPV-01 (Table 4.6). However, polymorphic fragments were 10 with size

range of 650 - 925 bp, because primers OPJ-10 and OPM-07 produced two

polymorphic fragments each (Fig. 4.4). RAPD loci were designated by writing size of

product after the primer name as OPD07_700, OPI13_900, OPJ10_740, OPJ10_750,

OPM07_800, OPM07_925, OPQ13_650, OPR09_875, OPU01_850 and OPV01_650.

59

Chapter 4 Results

T able 4.5 Screening FH-883 and FH-631S cotton parents with DNA markers. Assay Series Total

primers Primers

amp Not amp

Mono prim

Poly prim

Total bands

Ave bands

Bandrange

RAPD

OPA to

OPZ

520 506

(97%)

14

(3%)

498

(98.4%)

8

(1.6%)

2683 5.3 1 - 13

SSR

Structural BNL 60 56 4 55 1 103 1.8 1 - 5

CM 20 18 2 17 1 35 1.9 1 - 3

JESPR 270 255 15 253 2 459 1.8 1 - 6

Functional MGHES 85 80 5 76 4 153 1.9 1 - 4

All SSRs 435 409

(94%)

26

(6%)

401

(98%)

8

(2%)

750 1.8 1 - 6

Amp: amplified, prim: Primers, Mono: monomorphic, Ploy: polymorphic, Ave: average. RAPD: random amplified Polymorphic DNA, SSR: simple sequence repeats, OP: Operon, BNL: Brookhaven National Laboratory, CM: Cotton Microsatellites, JESPR: after the names of principle investigators – Jenkins, El-Zik, Saha, Pepper and Reddy; MGHES: Mississippi G. hirsutum EST SSR.

60

Chapter 4 Results

M M

Panel A

P1 F1 P2

OPD-07

P1 F1 P2

OPI-13 M

P1 F1 P2

OPJ-10

P1 F1 P2

M O PM-07

OPR-09M

P1 F1 P2

M OPU-01

P1 F1 P2

M M

P1 F1 P2

OPV-01

Panel B

P1 F1 P2

OPQ-13

Fig. 4.4 Amplification profile of cotton parents and F1 with polymorphic RAPD primers.

Panel A. Amplification profile with OPD-07, OPI-13, OPJ-10 and OPM-07. M: marker (DNA ladder), P1: parent one (FH-883), P2: parent two (FH-631S), F1: first generation hybrid. Panel B. Amplification profile with OPQ-13, OPR-09, OPU-01 and OPV-01. M: marker (DNA ladder), P1: parent one (FH-883), P2: parent two (FH-631S), F1: first generation hybrid.

61

Chapter 4 Results

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 4.5 Amplification profile of (FH-631S x FH-883) F2:3 population with RAPD primer OPD-07. M: marker (DNA ladder), 1 to 15: fifteen F2:3 cotton lines.

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 4.6 Amplification profile of (FH-631S x FH-883) F2:3 population with RAPD primer OPI-13. M: marker (DNA ladder), 1 to 15: fifteen F2:3 cotton lines.

62

Chapter 4 Results

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 4.7 Amplification profile of (FH-631S x FH-883) F2:3 population with RAPD primer OPM-07. M: marker (DNA ladder), 1 to 15: fifteen F2:3 cotton lines.

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 4.8 Amplification profile of (FH-631S x FH-883) F2:3 population with RAPD primer OPU-01. M: marker (DNA ladder), 1 to 15: fifteen F2:3 cotton lines.

63

Chapter 4 Results

Subsequently, F1 and 117 lines of F2:3 (FH-631S x FH-883) population were screened

with polymorphic RAPD markers (Figs. 4.5, 4.6, 4.7, 4.8). Banding pattern of F1

confirmed the parentage (Asif et al., 2009). The polymorphism information content

(PIC) was calculated to estimate the informativeness of each polymorphic RAPD

locus in F2:3 population. PIC value varied from 0.22 (OPU01_850) to 0.50

(OPJ10_740 and OPJ10_750) with mean of 0.44 (Table 4.7).

4.3.2 SSR analysis for fiber traits

Four hundred and thirty five SSRs were surveyed on FH-883 and FH-631S

(Table 4.5). Among these, 350 were structural and 85 were functional (EST based)

SSRs. Primers that amplified the scoreable products were 409, while 26 primers gave

poor amplification or did not produced any fragment. Total bands amplified were 750

with an average of 1.8 bands per SSR primer pair. Six bands were produced by each of

JESPR-152 and JESPR-165 that was the maximum number of bands produced by any

SSR primer.

Monomorphic primers were 401, while eight (2%) were polymorphic. Polymorphic

primers were BNL-3279, CM-43, JESPR-152, JESPR-153, MGHES-06, MGHES-17,

MGHES-24 and MGHES-73 (Table 4.6). These eight polymorphic primers amplified

25 bands; among them 20 bands or 12 SSR loci were polymorphic. Since primer pairs

JESPR-152, JESPR-153 and MGHES-73 produced three, two and two loci,

respectively; therefore, polymorphic SSR loci were 12 (Fig. 4.9) with product size

range of 85 - 420 bp. SSR loci were named by writing size of first allele after the

64

Chapter 4 Results

primer name as BNL3279_140, CM43_85, JESPR152_110, JESPR152_290,

JESPR152_410, JESPR153_110, JESPR153_160, MGHES06_95, MGHES17_220,

MGHES24_230, MGHES73_220 and MGHES73_250. These SSRs were surveyed on

F1 117 lines of F2:3 (FH-631S x FH-883) population (Figs. 4.10, 4.11, 4.12, 4.13).

Amplification profile of F1 was in compliance with the parentage. The PIC was also

calculated to measure the informativeness of each polymorphic SSR locus in F2:3

population and the value of PIC ranged from 0.31 (MGHES17_220) to 0.50 (three loci

of JESPR_152) with an average of 0.46 (Table 4.7).

65

Chapter 4 Results

CM-43

P1 F1 P2

BNL-3279

P1 F1 P2

Panel

JESPR-152

P1 F1 P2 P1 F1 P2

JESPR-153

500bp

300bp

100bp

80bp

M

MGHES-06

P1 F1 P2

Panel B

500bp

80bp

100bp

300bp

M MGHES-17

P1 F1 P2

MGHES-24

P1 F1 P2 P1 F1 P2

MGHES-73

Fig. 4.9 Amplification profile of cotton parents and F1 with polymorphic SSR primers.

Panel A. Amplification profile with BNL-3279, CM-43, JESPR-152 and JESPR-153. M: marker (DNA ladder), P1: parent one (FH-883), P2: parent two (FH-631S), F1: first generation hybrid.

Panel B. Amplification profile with MGHES-06, MGHES-17, MGHES-24 and MGHES-73. M: marker (DNA ladder), P1: parent one (FH-883), P2: parent two (FH-631S), F1: first generation hybrid.

66

Chapter 4 Results

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 4.10 Amplification profile of (FH-631S x FH-883) F2:3 population with SSR primer BNL-3279. M: marker (DNA ladder), 1 to 15: fifteen F2:3 cotton lines.

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 4.11 Amplification profile of (FH-631S x FH-883) F2:3 population with SSR primer JESPR-152. M: marker (DNA ladder), 1 to 15: fifteen F2:3 cotton lines.

67

Chapter 4 Results

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 4.12 Amplification profile of (FH-631S x FH-883) F2:3 population with SSR primer MGHES-06. M: marker (DNA ladder), 1 to 15: fifteen F2:3 cotton lines.

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 4.13 Amplification profile of (FH-631S x FH-883) F2:3 population with SSR primer MGHES-17. M: marker (DNA ladder), 1 to 15: fifteen F2:3 cotton lines.

68

Chapter 4 Results

Table 4.6 Polymorphic primers and bands amplified in cotton parents (FH-883 nd FH-631S) and their F2:3 population. a

S. No.

Poly primers

Primer sequences (5’– 3’) Total bands

Poly bands

Ann temp

RAPD (ºC)

1. OPD-07 TTGGCACGGG 4 1 36

2. OPI-13 CTGGGGCTGA 4 1 36

3. OPJ-10 AAGCCCGAGG 7 2 36

4. OPM-07 CCGTGACTCA 8 2 36

5. OPQ-13 GGAGTGGACA 4 1 36

6. OPR-09 TGAGCACGAG 9 1 36

7. OPU-01 ACGGACGTCA 8 1 36

8. OPV-01 TGACGCATGG 6 1 36

SSR

9. BNL-3279 F=CATGTCCAATGGATGTGTCA

R=GGGCCACTTAAAGGCATTCT

2 1 50

10. CM-43 F= GCGCAGATATTATTATCACAGC

R=TATATAAATTTGCATCAGTTGGC

3 2 60

11. JESPR-152 F=GATGCACCAGATCCTTTTATTAG

R=GGTACATCGGAATCACAGTG

6 6 60

12. JESPR-153 F=GATTACCTTCATAGGCCACTG

R=GAAAACATGAGCATCCTGTG

4 4 60

13. MGHES-06 F=TCGCTTGACTTTCCATTTCC

R=AACCCTCGGGATTATCGTCT

2 2 55

14. MGHES-17 F=AACCCTTCTTTTCCCCCTTT

R=TCTTCACCGATGCCATTGTA

2 1 55

15. MGHES-24 F=CGCAACAACTGATGCAACTC

R=AACCGATACCTCCGCTTCTT

2 1 55

16. MGHES-73 F=CCCGATATCCTTAGCCTTTT

R=AGTCGGAGGTGATGGTTAGG

5 3 55

Poly: polymorphic, Ann temp: annealing temperature

69

Chapter 4 Results

Table 4.7 Allele frequency and polymorphism information content (PIC) of DNA arkers amplified in cotton parents (FH-883 and FH-631S) and F2:3 population. m

Product size (bp) S. No.

Polymorphic DNA markers

Poly bands FH-883 FH-631S

Allele freq PIC

RAPD loci

1. OPD07_700 1 - 700 0.64, 0.36 0.46

2. OPI13_900 1 900 - 0.65, 0.35 0.45

3. OPJ10_740 1 - 740 0.45, 0.55 0.50

4. OPJ10_750 1 750 - 0.55, 0.45 0.50

5. OPM07_800 1 - 800 0.71, 0.29 0.41

6. OPM07_925 1 925 - 0.36, 0.64 0.46

7. OPQ13_650 1 650 - 0.63, 0.37 0.47

8. OPR09_875 1 875 - 0.31, 0.69 0.42

9. OPU01_850 1 850 - 0.13, 0.87 0.22

10. OPV01_650 1 - 650 0.40, 0.60 0.48

SSR loci

11. BNL3279_140 1 140 - 0.56, 0.44 0.49

12. CM43_85 2 85 95 0.33, 0.67 0.44

13. JESPR152_110 2 110 130 0.48, 0.52 0.50

14. JESPR152_290 2 290 310 0.46, 0.54 0.50

15. JESPR152_410 2 410 420 0.46, 0.54 0.50

16. JESPR153_110 2 110 120 0.36, 0.64 0.46

17. JESPR153_160 2 160 170 0.39, 0.61 0.47

18. MGHES06_95 2 95 100 0.65, 0.35 0.45

19. MGHES17_220 1 - 220 0.81, 0.19 0.31

20. MGHES24_230 1 230 - 0.36, 0.64 0.46

21. MGHES73_220 1 - 220 0.39, 0.61 0.48

22. MGHES73_250 2 250 260 0.42, 0.58 0.49

FH-883: long staple cotton parent (30 mm), FH-631S: short staple cotton parent (23 mm). poly: polymorphic, freq: frequency.

70

Chapter 4 Results

4.4 Genetic Linkage Map of Cotton

Linkage map was constructed with Mapmaker (version 3.0) using molecular markers

data of 117 F2:3 lines. Twenty polymorphic loci of both RAPD and SSR were mapped

into four linkage groups (LGs) (Fig 4.14), while two markers CM43_85 and

OPV01_650 could not be assembled in a linkage group. The resulting genetic map

spanned 230.2 cM covering 5% of the cotton genome. The average genetic distance

was 11.5 cM between two adjacent markers. Number of markers placed on these

linkage groups ranged from three to eight. Distribution of DNA markers on the LGs is

given in Tables 4.8 and 4.9. Linkage groups were putatively assigned to specific

chromosomes of cotton using already known anchored and informative loci (Table

4.8). LG1 was assigned to long arm of chromosome 20 in D sub-genome, while LG2,

LG3 and LG4 were assigned to chromosome number 10, 18 and 15, respectively.

Eight markers (six RAPD and two SSR loci) were assembled in LG1 spanning 105.4

cM distance. Markers in LG1 were OPJ10_740, OPR09_875, MGHES24_230,

OPU01_850, OPM07_925, OPQ13_650, OPI13_900 and BNL3279_140. LG2 had

length of 87.8 cM with two SSR and three RAPD loci (MGHES06_95, OPD07_700,

OPJ10_750, MGHES17_220 and OPM07_800). LG3 had four SSR loci and overall

genetic distance among JESPR153_110, JESPR153_160, MGHES73_220 and

MGHES73_250 was 32.4 cM. LG4 was the smallest one (4.6 cM) with three SSR loci,

namely JESPR152_110, JESPR152_290 and JESPR152_410.

71

Chapter 4 Results

All RAPD markers exhibited dominant banding pattern like parent one or the other.

Nine SSR markers behaved as co-dominant loci, while three SSRs (BNL3279_140,

MGHES17_220 and MGHES24_230) were dominant in nature. Deviation of

segregation from expected Mendelian ratios was observed for different DNA markers.

Out of 20 linked markers, six (30%) loci segregated according to Mendelian fashion,

while 14 markers showed segregation distortion (Table 4.9).

72

Chapter 4 Results

OPJ10_740OPR09_875MGHES24_230OPU01_850OPM07_925OPQ13_650OPI13_900BNL3279_140

LGLG1 (Chr 20)

1 0.0 11.0 26.8 38.2 50.3 68.5 88.7

105.4

LG2LG2 (Chr 10)

OPM07_800 MGHES17_220 OPJ10_750

MGHES06_95 OPD07_700

0.0 20.3 37.5

64.7

87.8 LG3 LG3 (Chr 18)

MGHES73_250 MGHES73_220

JESPR153_110 JESPR153_160

32.4 31.1

0.0 1.5

LG4LG4 (Chr 15)

JESPR152_410

JESPR152_110 JESPR152_290

0.0 1.3

4.6

Fig. 4.14 Genetic linkage map constructed with Mapmaker using (FH-631S x FH-883) F2:3 intraspecific cotton (G. hirsutum) population. Marker positions are in centiMorgan (cM) with Kosambi function at min LOD 3 and max distance 37.2. The informative and framework loci that were already anchored to specific chromosomes of cotton are in boxes.

73

Chapter 4 Results

Table 4.8 Molecular markers distribution and putative assignment of linkage roups to cotton chromosomes using anchored RAPD and SSR loci. g

Linkage group

Anchored loci Chr Markers

in LG Loci

% ageLength (cM) Reference Chr (Wang

et al., 2006a LG1 BNL3279 20 8 42 105.4 Mei et al., 2004 D10

LG2 OPM07 10 5 26 87.8 Zhang et al., 2003 A10

LG3 JESPR153 A01/18 4 21 32.4 Han et al., 2006 A13/ D13

LG4 JESPR152 15 3 11 04.6 Han et al., 2006 D1

LG: linkage group, Chr: chromosome

74

Chapter 4 Results

Table 4.9 Linkage groups and markers segregation in (FH-631S x FH-883) F2:3 cotton population.

S. No.

Linkage group (LG)

Marker No. in LG Markers

Marker type Chi2

1. 1 1 OPJ10_740 A- 2.70

2. 1 2 OPR09_875 A- 2.22

3. 1 3 MGHES24_230 A- 0.13

4. 1 4 OPU01_850 A- 28.27*

5. 1 5 OPM07_925 A- 0.13

6. 1 6 OPQ13_650 A- 30.93*

7. 1 7 OPI13_900 A- 36.61*

8. 1 8 BNL3279_140 A- 17.00*

9. 2 1 MGHES06_95 Co 22.61*

10. 2 2 OPD07_700 a- 34.22*

11. 2 3 OPJ10_750 a- 13.64*

12. 2 4 MGHES17_220 a- 93.80*

13. 2 5 OPM07_800 a- 55.82*

14. 3 1 JESPR153_110 Co 32.97*

15. 3 2 JESPR153_160 Co 31.12*

16. 3 3 MGHES73_220 Co 46.12*

17. 3 4 MGHES73_250 Co 43.11*

18. 4 1 JESPR152_110 Co 0.71

19. 4 2 JESPR152_290 Co 2.90

20. 4 3 JESPR152_410 Co 20.11*

21. unlinked 1 CM43_85 Co -

22. unlinked 2 OPV01_650 a- -

* Chi2 significant at 0.01 probability level. Co: co-dominant banding pattern, A-: dominant loci with banding pattern like parent one (FH-883), a-: dominant loci with banding pattern like parent two (FH-631S).

75

Chapter 4 Results

4.5 QTL Analysis for Cotton Fiber Quality Traits

Analysis of fiber related QTLs was conducted with WinQTLCart (version 2.5) using

phenotypic and genotypic data of 117 F2:3 lines. QTLs for fiber traits were identified

using single marker analysis (SMA), interval mapping (IM) and composite interval

mapping (CIM) at LOD > 2 (Figs. 4.15a, 4.15b). All QTLs detected were on LG1 and

LG2. Comparative position of QTLs, their LOD, additive effect and phenotypic

variance explained (PVE) have been presented in Tables 4.10, 4.11 and 4.12. Single

marker analysis detected eight QTLs, 15 QTLs were identified with IM while 10

QTLs were found with CIM analysis. Collectively 16 putative QTLs were detected, of

which 12 were commonly found with at least any two QTL detection methods, while

four QTLs were identified only with IM or CIM (Table 4.13).

4.5.1 QTL for fiber length (FL)

Two QTLs for FL were identified with SMA. QTL L1s was found on LG1,

while L2s on LG2. Positions of L1s and L2s were 0.01 cM and 37.9 cM at LOD 2.52

and 2.25, respectively. Similarly two QTLs L1i and L2i were detected for FL with IM

at LOD 2.59 and 2.76, respectively. The PVE were 11.5 and 16.6% for L1i and L2i,

respectively. In CIM analysis one QTL (L1c) for fiber length was found with PVE

11.5% at 2.6 LOD.

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Chapter 4 Results

Fig. 4.15a Comparison of QTLs and their positions (cM) in linkage group 1 (LG1) for cotton fiber quality traits using SMA, IM and CIM. SMA: single marker analysis, IM: interval mapping, CIM: composite interval mapping, L: fiber length, M: micronaire, S: fiber strength, U: fiber length uniformity, Si: short fiber index, E: fiber elongation, R: fiber color (reflectance= Rd), B: fiber color (yellowness= +b). Please refer Tables 4.10 to 4.12 for LOD and phenotypic variance explained (PVE).

77

Chapter 4 Results

Fig. 4.15b Comparison of QTLs and their positions (cM) in linkage group 2 (LG2) for cotton fiber quality traits using SMA, IM and CIM. SMA: single marker analysis, IM: interval mapping, CIM: composite interval mapping, L: fiber length, M: micronaire, S: fiber strength, U: fiber length uniformity, Si: short fiber index, E: fiber elongation, R: fiber color (reflectance= Rd), B: fiber color (yellowness= +b). Please refer Tables 4.10 to 4.12 for LOD and phenotypic variance explained (PVE).

78

Chapter 4 Results

4.5.2 QTL for micronaire (Mic)

SMA identified one QTL (F1s) for Mic with position at 88.8 cM on LG1 and at LOD

2.47. Two QTLs designated as F1ia and F1ib were found again on LG1, with interval

mapping. Their PVE were 9.6 and 7.4% at LOD 2.21 and 2.66, respectively. With

CIM, two QTLs namely F1c and F2c were found associated with Mic. F1c and F2c

were located on LG1 and LG2 with PVE of 6.3 and 10.3%, and at LOD 2.46 and 2.17,

respectively.

4.5.3 QTL for fiber strength (FS)

S1s and S2s QTLs were associated with fiber strength, which were detected

with SMA at LOD 2.88 and 2.37, respectively. Position of S1s was at 0.01 cM on

LG1, while S2s was at 21.3 cM on LG2. Three QTLs for FS, one on LG1 and two on

LG2 were identified with IM. These were named as S1i, S2ia and S2ib. Their LOD

scores were 4.05, 2.75 and 3.16, while PVE were 16.5, 12.9 and 17.7%, respectively.

Similar to S1i, CIM found one QTL (S1c) for FS at LOD 4.03 and 16.5% PVE.

4.5.4 QTL for fiber length uniformity (FU)

QTL was not detected for FU with SMA, however, one QTL (U2i) was found

on LG2 with IM. Its PVE was 11.6% while LOD score was 2.06. Similarly, CIM

identified one QTL (U2c) that was identical to U2i in %PVE and LOD.

79

Chapter 4 Results

4.5.5 QTL for short fiber index (SFI)

With SMA two QTLs namely Si1s and Si2s for SFI were mapped on LG1 and

LG2, respectively. Their respective peak positions were 0.01 and 37.9 cM with LOD

2.3 and 2.41. IM analysis also revealed two QTLs (Si1i and Si2i) at about same

positions. The PVE of Si1i and Si2i were 9.4 and 14.6% at LOD 2.42 and 3.28.

However, with CIM analysis found only one QTL (Si2c) for short fiber content on

LG2 with LOD score 3.28 and PVE 14.6%.

4.5.6 QTL for fiber elongation (FE)

QTL was not found for FE with SMA, however, one QTL (E2i) was found on

LG2 with IM. Its position and LOD score were 86.7 cM and 2.28, respectively. With

CIM analysis one QTL (E2c) for FE was identified that was about similar to E2i in

position and LOD score.

4.5.7 QTL for fiber color (reflectance and yellowness)

One QTL for reflectance (Rd) was selected with SMA at 2.19 LOD and 21.3

cM distance on LG2. Three QTLs, one (R1i) on LG1 and two (R2ia and R2ib) on LG2

were identified for Rd with IM. LOD score of R1i was 2.21 with PVE 12.1%. For

R2ia and R2ib LOD was 2.25 and 2.12, while their PVE were 12.5 and 9.6%,

respectively. Similarly, CIM revealed two QTLs for Rd on LG2 identical to two QTLs

found with IM. For yellowness (+b), one QTL was detected with IM, which was

identical in position (81.3 cM) and LOD (2.15) to QTL found with CIM analysis.

80

Chapter 4 Results

Table 4.10 Single marker analysis of QTLs for cotton fiber quality traits. Position (cM) Trait$ QTL LG

(Chr) Distance Peak Locus LOD

FL L1s 1 (20) 0 – 3.8 0.01 OPJ10_740 2.52

L2s 2 (10) 29.6 – 40.9 37.9 OPJ10_750 2.25

Mic M1s 1 (20) 84.6 – 92.1 88.8 OPI13_900 2.47

FS S1s 1 (20) 0 – 5.4 0.01 OPJ10_740 2.88

S2s 2 (10) 13.3 – 40.4 21.3 OPD07_700 2.37

SFI Si1s 1 (20) 0 – 2 0.01 OPJ10_740 2.30

Si2s 2 (10) 25.8 – 42.5 37.9 OPJ10_750 2.41

Rd R2s 2 (10) 17.9 – 23.3 21.3 OPD07_700 2.19 $ Please see Table 4.3 for trait abbreviations. LG: linkage group, Chr: chromosome.

81

Chapter 4 Results

T able 4.11 Interval mapping of QTLs for cotton fiber quality traits.

Trait$ QTL Marker interval

LG (Chr)

Position (cM) LOD Additive

effect %PVE

FL L1i OPJ10_740- OPR09_875

1 (20) 00.01 2.59 0.78 11.5

L2i OPD07_700-

OPJ10_750 2 (10) 32.31 2.76 1.01 16.6

Mic M1ia OPJ10_740- OPR09_875

1 (20) 04.01 2.21 0.20 09.6

M1ib OPQ13_650- OPI13_900

1 (20) 88.49 2.66 -0.19 7.40

FS S1i OPJ10_740- OPR09_875

1 (20) 00.01 4.05 1.60 16.5

S2ia MGHES06_95- OPD07_700

2 (10) 18.01 2.75 1.51 12.9

S2ib OPD07_700- OPJ10_750

2 (10) 28.31 3.16 1.77 17.7

FU U2i MGHES06_95- OPD07_700

2 (10) 06.01 2.06 1.10 11.6

SFI Si1i OPJ10_740- OPR09_875

1 (20) 00.01 2.42 -1.89 09.4

Si2i MGHES06_95-OPD07_700-OPJ10_750

2 (10) 32.31 3.28 -2.53 14.6

FE E2i MGHES17_220-OPM07_800

2 (10) 86.70 2.28 - -

Rd R1i OPR09_875- MGHES24_230

1 (20) 17.60 2.21 -1.97 12.1

R2ia MGHES06_95- OPD07_700

2 (10) 14.01 2.25 -2.03 12.5

R2ib OPD07_700- OPJ10_750

2 (10) 22.31 2.12 -1.79 09.6

+b B1i OPQ13_650- OPI13_900

1 (20) 81.30 2.15 - -

LG: linkage group, Chr: chromosome, PVE: phenotypic variance explained. $ Please see Table 4.3 for trait abbreviations.

82

Chapter 4 Results

Table 4.12 Composite interval mapping of QTLs for cotton fiber quality traits.

Trait$ QTL Marker interval LG (Chr)

Position (cM) LOD Additive

effect %PVE

FL L1c OPJ10_740-

OPR09_875

1 (20) 00.01 2.60 0.78 11.5

Mic M1c OPQ13_650-

OPI13_900

1 (20) 88.49 2.46 -0.18 06.3

M2c MGHES06_95-

OPD07_700

2 (10) 08.01 2.17 0.21 10.3

FS S1c OPJ10_740-

OPR09_875

1 (20) 00.01 4.03 1.60 16.5

FU U2c MGHES06_95-

OPD07_700

2 (10) 06.01 2.06 1.11 11.6

SFI Si2c OPD07_700-

OPJ10_750

2 (10) 30.31 3.28 -2.53 14.6

FE E2c MGHES17_220-

OPM07_800

2 (10) 87.00 2.30 - -

Rd R2ca MGHES06_95-

OPD07_700

2 (10) 14.01 2.25 -2.03 12.5

R2cb OPD07_700-

OPJ10_750

2 (10) 22.31 2.12 -1.79 9.6

+b B1c OPQ13_650-

OPI13_900

1 (20) 80.50 2.14 - -

LG: linkage group, Chr: chromosome, PVE: phenotypic variance explained. $ Please see Table 4.3 for trait abbreviations.

83

Chapter 4 Results

Table 4.13 QTLs for cotton fiber quality traits with SMA, IM and CIM at LOD > 2. Fiber trait$

SMA (S) LG1 LG2 Total

IM (I)

LG1 LG2 Total

CIM (C)

LG1 LG2 Total

Fiber QTLs Common QTLs Unique QTLs Total

LG1 LG2 LG1 LG2

% PVE cumulative

FL 1

1 2 1 1 2 1 - 1 1 (S, I, C)

1 (S, I) - -

2 28.1

Mic 1

- 1 2 - 2 1 1 2 1 (S, I, C) -

1 (I)

1 (C)

3 27.3

FS 1 1 2 1 2 3 1 - 1 1(S, I, C)

1 (S, I) -

1 (I)

3 47.1

FU - - - - 1 1 - 1 1-

1 (I, C) - -

1 11.6

SFI 1 1 2 1 1 2 - 1 1 1(S, I)

1 (S, I, C) - -

2 24

FE - - - - 1 1 - 1 1-

1 (I, C) - -

1 -

Rd - 1 1 1 2 3 - 2 2-

2 (S, I, C)

1 (I) -

3 34.2

+b - - - 1 - 1 1 - 1 1(I, C) - - -

1 -

Total 4 4 8 7 8 15 4 6 10 5 7 2 2 16 SMA: single marker analysis, IM: interval mapping, CIM: composite interval mapping, LG: linkage group, PVE: phenotypic variance explained. $ Please see Table 4.3 for trait abbreviations.

84

Chapter 5 Discussion

CHAPTER 5

DISCUSSION

5.1 Performance of Cotton Varieties/ Genotypes for Fiber Traits

Variation existed among 19 Upland cotton varieties/ genotypes tested for three fiber

traits in the present research work (Table 4.1, Fig. 4.1, Asif et al., 2008a). Maximum

differences were observed for fiber strength followed by fiber length, while minimum

variation was accounted for micronaire. Variation among unapproved germplasm lines

was more as compared to approved varieties. Despite of the variations, most of the

varieties had uniformity in a sense of trait standards as they could be grouped with

medium long fiber length, average Mic and strong fiber. It was due to the fact that

breeders developed varieties to meet certain fiber standards and requirements (Bayles

et al., 2005). The selected cotton line FH-883 had the maximum fiber length (30 mm)

among the tested genotypes, however, it is also possible that some one can find lines

with even longer fiber length after screening different germplasm. In future studies,

more genotypes and preferably wild accessions (Cheatham et al., 2003) would be

included in multilocation field trials for germplasm screening and evaluation, which

will also reveal genotypes x environment interactions (Paterson et al., 2003).

85


Fiber length, micronaire reading and strength were closely and significantly correlated

such that cultivars with longer fiber had stronger fiber and lower micronaire reading.

Also, varieties having stronger fiber were with lower micronaire reading (Table 4.2).

Similar findings were reported by Zhang et al., (2005c) while evaluating field

performance of commercial cotton cultivars. Correlations between fiber traits would

be employed for successful cotton breeding programs. Development of cotton varieties

with improved fiber traits has been very difficult due to the quantitative inheritance of

these traits, which can be mitigated using new genomic tools like molecular markers

(Jauhar, 2006).

5.2 Performance of Parents and F2:3 Mapping Population

On the basis of fiber properties, we selected two cotton (G. hirsutum) parents FH-883

and FH-631S to develop mapping population. Considerable variation in fiber traits of

these parents and their F1 was present (Fig 4.2). Statistical analysis for quality traits of

F2:3 (FH-631S x FH-883) population depicted extensive variations for eight fiber traits

(Table 4.3). Other studies also reported such variations in intraspecific (Ulloa and

Meredith, 2000) and interspecific (Paterson et al., 2003) mapping populations.

In the present study, fiber data of F2:3 population revealed that a transgressive

segregation occurred for some traits except for FS and SFI, which suggested that

neither parent carried all positive or negative alleles. Yadav et al., (1997) also reported

transgressive segregation for root morphology traits in rice. Unexpected sources of

variation may also be attributed to epistasis. Favorable or unfavorable alleles may be

86


present in a parental line but not expressed in its genetic background. When crossed to

another individual, however, epistatic interaction may affect expression of the alleles

(Veldboom et al., 1994).

All fiber traits distributed normally with some skewness, therefore frequency

distribution (Fig. 4.3) of these traits showed genetic variation that supported a model

for multigenic inheritance for quality traits (Landen and Thompson, 1990; Paterson et

al., 1991; Ulloa and Meredith, 2000; Mei et al., 2004). We observed typical ranges for

fiber traits, one of them was in lint color for reflectance and yellowness. Discolored

cotton would be generally associated with weathering, storage and high moisture

content (Curley et al., 1990). Production systems that minimize exposure to open lint

prior to harvest typically improve color grade (Nichols et al., 2004).

In correlation analysis of F2:3 population, a number of fiber traits were positively and

some were negatively associated with each other (Table 4.4). These correlations would

be useful in developing selection criteria for improving and pyramiding fiber quality

traits. Associations observed for FL with Mic, FL with FS, FL with FE, and FS with

FU in our study, are in agreement with findings of other researchers (Mei et al., 2004;

Lacape et al., 2005). In the present study, it was observed that Mic and FS were

negatively associated in genotypes, while these traits were positively correlated in F2:3

population. Correlations among fiber traits in cotton cultivars are affected by selection

during breeding process, however, correlations among cotton fiber traits in a

population of a cross are due to genetic linkage. Therefore, correlations among fiber

traits in cotton cultivars are different from the correlations of fiber traits in a

87


population from a cross. Furthermore, association between/ among some of the quality

traits may vary among segregating populations depending upon the parental choice

(Chang and Li, 1981; MeKenzie and Rutger, 1983). Multiple traits can be correlated

due to linkage, pleiotropy or the correlated traits may be components of a more

complex variable. Two components of bundle fiber strength are fiber length and

perimeter (Meredith, 1992). Fine fiber (small perimeter) results in more fibers per

bundle, which can confer greater fiber strength. Additionally, longer fibers promote

fiber-to-fiber contact, tending to increase fiber strength (Ulloa and Meredith, 2000).

5.3 Polymorphism between Parents and in F2:3 Population

Recent developments in molecular genetics offer plant breeders a rapid and precise

alternative approach to conventional selection schemes for improving cultivars for

yield, quality, adaptability, pest and disease resistance. Molecular markers are

important tools for generating genetic linkage maps and have provided a significant

increase in genetic knowledge of many cultivated plant species. Molecular markers are

the promising genetic tools with the potential to enhance selection efficiency in cotton

(Meredith, 1995; Ulloa and Meredith, 2000). In the present study, to overcome the

limitations of conventional breeding we used DNA fingerprinting techniques to find

molecular markers/ QTLs that would be utilized for the improvement of cotton fiber

quality traits through marker-assisted selection. RAPDs and microsatellites were

successfully employed to find polymorphic DNA markers between two G. hirsutum

parents, FH-883 and FH-631S, and which were surveyed on F2:3 population.

88


The level of polymorphism between these two cotton parents was 1.6% for RAPDs

and 2% for SSRs (Table 4.5), which was less than the expected. Brondani et al.,

(2001) also found low polymorphism than expected. The polymorphism level detected

in this study was lower than that observed in barley (11%) (Becker et al., 1995), 28%

in rice (Mackill et al., 1996), 50% in sugar beet (Schondelmaier et al., 1996) and

11.2% in cotton (Akash, 2003). Mei et al., (2004) found 7% polymorphic level of

AFLPs within Gossypium species, which was relatively low, compared to that between

species (15%). Using SSRs Rungis et al., (2005) reported 4.5% polymorphism either

among or within G. hirsutum cultivars, while Shen et al., (2007) detected 7.9%

polymorphism between G. hirsutum parents. Genetic diversity in modern Upland

cotton cultivars is thought to be narrow, thus limiting the genetic advance. A number

of studies have suggested that cultivated Upland cotton germplasm possesses a

relatively low level of genetic diversity and polymorphism could be as low as 1 to 3%

(Zhang, 2005a).

We found that microsatellites were more polymorphic than RAPD markers. Similarly

Dayanandan et al., (1998) estimated that information content per locus was higher in

SSRs than in RAPDs. Moreover among SSRs (Table 4.5), we detected that functional

genomics markers (EST-SSRs) were more polymorphic (5%) than structural genomics

SSRs (1.2%). This would be due to some variation between parents for fiber traits.

Several investigators have suggested the higher frequency of SSR polymorphisms

within G. hirsutum and among Gossypium species (Reddy et al., 2001; Qureshi et al.,

2004; Nguyen et al., 2004). Reddy et al., (2001) observed 21% polymorphism within

89


G. hirsutum and 49% polymorphism between G. hirsutum and G. barbadense using

genomic SSRs. Similarly, Nguyen et al., (2004) estimated 56% polymorphism

between G. hirsutum and G. barbadense. Qureshi et al., (2004) reported 26%

polymorphism in EST-SSRs within G. hirsutum while 52% between G. hirsutum and

G. barbadense using 84 EST-SSRs. Han et al., (2006) identified a 23% polymorphic

rate between TM-1 and Hai7124, and TM-1 and 3–79, while in their previous study

18.2% of 544 G. arboreum derived EST-SSRs were polymorphic and segregated in

interspecific BC1 cotton mapping population [(TM-1xHai7124)xTM-1] (Han et al.,

2004). Such discrepancy in polymorphic rates would be due to different plant

materials, different number of ESTs from the different tissues or number of EST-SSR

primers used (Han et al., 2006).

More than 70% of the flowering plants, including many important crops (like cotton,

wheat, oat, canola and peanut) are polyploid (Stebbins, 1971; Masterson, 1994; Leitch

and Bennett, 1997). Polyploidy may have some advantages because duplicated

genomes provide additional genetic material for functional divergence and adaptive

evolution. Moreover, diploidization of polyploids also gave rise to the fixation of

homoeologous genes or hybrid vigor. However, diploidization and self-fertilization in

many polyploids result in homozygosity for loci within each genome, a process called

as an evolutionarily dead end or genetic “bottleneck” (Stebbins, 1940, 1950, 1971). As

a result, genetic variation is extremely limited in polyploid species such as G.

hirsutum, especially among elite cultivated types (Mei et al., 2004).

90


Another possible reason for lack of high polymorphism between cotton parents in our

study is the narrow genetic base of cotton varieties (Multani and Lyon, 1995; Ulloa et

al., 1999; Gutierrez et al., 2002), especially in Pakistan where genetic relatedness

among cotton genotypes was estimated up to 93.41 and 94.90% (Iqbal et al., 1997;

Rahman et al., 2002). Moreover, for the present study, FH-631S and FH-883 were

obtained from Cotton Research Institute (CRI), AARI, Faisalabad. These cotton

genotypes were from the different genetic background (Table 3.1), but possibly

developed using unified breeding approach for specific requirements at the same

breeding center. Although FH-631S and FH-883 were dissimilar for fiber traits and

therefore, we found some important fiber related QTLs in their population, but these

varieties were also similar in many morphological and agronomic traits, which might

have resulted in low polymorphism at DNA level between them.

DNA markers have been used for hybrid and parentage verification in cotton (Mei et

al., 2004) and other plants (Benedetti et al., 2000; Asif et al., 2007). We employed

polymorphic RAPD and SSR loci for the hybridity determination (Fig. 4.4, 4.9).

Codominant markers successfully verified the parentage of F1 and hence confirmed the

efficiency of molecular markers for verification of hybridity and purity identification.

Polymorphism information contents (PIC) have been estimated in animals (Mateescu

et al., 2005) and plants (Cordeiro et al., 2002). In the present study, we calculated the

PIC value to estimate informativeness of each polymorphic RAPD loci in F2:3

population, which varied from 0.22 to 0.50 with mean of 0.44, while PIC value of SSR

loci ranged from 0.31 to 0.50 with average of 0.46 (Table 4.7). This revealed that SSR

91


loci are comparatively more informative than RAPDs, which is in agreement with

earlier findings (Dayanandan et al., 1998; Bertini1 et al., 2006). Liu et al., (2000b)

found that in wild G. hirsutum accessions maximum PIC value was 0.82 with a mean

value of 0.31. Maximum PIC values in present experiment and that of found by

Bertini1 et al., (2006) were lower than those found by Liu et al., (2000b). This might

be due to the fact that they used wild accessions instead of cultivars from cotton

breeding programs.

5.4 Genetic Linkage Map of Cotton

5.4.1 DNA markers and linkage map

The majority of the genetic maps in cotton have been developed through

interspecific hybridization, however, the linkage maps developed by intraspecific G.

hirsutum population provide a better understanding of cotton crop by possibly

generating a core of markers with more practical application than those developed in

interspecific populations (Reinisch et al., 1994; Yu et al., 1998; Ulloa and Meredith,

2000). For better understanding of the Upland cotton genome, we generated a genetic

linkage map using molecular markers data of 117 intraspecific lines of F2:3 (FH-631S x

FH-883) population with Mapmaker/Exp. Twenty RAPD and SSR loci were mapped

into four linkage groups (Fig 4.14), while two markers remained unlinked. The

markers placed on these linkage groups ranged from three to eight. The total distance

of genetic map with Haldane function was 274.1 cM with 6% of the cotton genome

coverage and mean distance between two adjacent loci was 13.7 cM. While the

92


resulting genetic map with Kosambi function spanned 230.2 cM with 5% genome

coverage and the average genetic distance was 11.5 cM between two adjacent loci.

Genome coverage estimates were based on 4660 cM minimum map distance of the

cotton genome calculated by Stelly (1993) and Reinisch et al., (1994).

Haldane’s mapping function works for situations with absence of crossover

interference (Haldane, 1919), while Kosambi (1944) considered crossover interference

as a function of recombination fraction. However, experimental evidence has been

found to support that crossover interference exists and crossovers occur non-randomly

in genomes (Muller, 1916; Liu, 1998). Therefore, we used Kosambi mapping function

for final construction of linkage map and further QTL analysis in our study.

Shapply et al., (1998b) analyzed 96 F2:3 bulked sampled plots of Upland cotton and

identified 31 linkage groups containing 120 RFLP loci, which covered 865 cM, or an

estimated 18.6% of the cotton genome. The linkage groups ranged from 2 to 10 loci

each, while ranged in distance from 0.5 to 107 cM with average distance of 7 cM.

Ulloa and Meredith (2000) constructed a genetic linkage map from 119 intraspecific

bulk-sampled plots of an F2.3 population. They used JoinMap and Mapmaker/Exp to

generate linkage map and these two programs linked the same number of loci in

linkage groups, except for two RFLP loci. However, some discrepancy was observed

in map distances within the linkage groups, which was probably due to markers

exhibiting distorted segregation because of the rationale used in these programs to

assign markers and their ordering to linkage groups. Based on the JoinMap, 81 loci

93


were mapped in 17 linkage groups with at least two markers per group. Total map

distance was 700.7 cM (15% cotton genome coverage) with an average distance of 8.7

cM between the two markers.

Akash (2003) assigned 143 AFLP markers to 13 major and 15 minor linkage groups

using 138 intraspecific F2:3 Upland cotton lines. Ulloa et al., (2005) mapped 284 RFLP

loci to 47 linkage groups that covered 31% of recombinational length of the cotton

genome with an average distance between two markers of 5.2 cM using four

intraspecific (G. hirsutum) populations. Using RILs developed from an F2 population

of an Upland cotton, Shen et al., (2007) constructed a genetic linkage map consisting

of 156 SSR loci in 31 linkage groups and covering 1,024.4 cM with about 23% cotton

genome coverage.

A genetic map containing 392 loci (333 AFLPs, 47 SSRs, and 12 RFLPs) in 42

linkage groups was developed from an F2 interspecific cotton population (94 F2

plants). This map spanned 3,287 cM and covered about 70% of the cotton genome

with 8.4 cM average genetic distance between adjacent loci (Mei et al., 2004).

Generally, cotton genetic maps constructed from interspecific populations (Reinisch et

al., 1994; Jiang et al., 1998; Kohel et al., 2001; Saranga et al., 2001; Zhang et al.,

2002; Paterson et al., 2003; Han et al., 2004; Mei et al., 2004; Nguyen et al., 2004;

Rong et al., 2004; Lacape et al., 2005; Park et al., 2005, Song et al., 2005;

Frelichowski et al., 2006; Han et al., 2006; Hua et al., 2007) have more cotton genome

coverage as compared to maps developed from intraspecific populations (Shappley et

94


al., 1998a, b; Ulloa and Meredith, 2000; Akash, 2003; Zhang et al., 2003; Shen et al.,

2005, 2006, 2007; Ulloa et al., 2005).

It was suggested that increasing population size would most likely reduce the number

of linkage groups by identifying key recombinants and filling gaps (Kesseli et al.,

1994; Keim et al., 1997). Small linkage groups may occur when a less number of

markers are used to cover a big genome like cotton. Less coverage of genome was

possibly due to low polymorphism at DNA level between parents in the present study.

Brondani et al., (2001) also found less polymorphism than expected and also

suggested the use of a diverse and larger segregating population to detect more

recombination events between the co-segregating markers (Brondani et al., 2001). Use

of novel germplasm and interspecific reference mapping populations would also

increase the chances of finding more polymorphism for comparative mapping.

To improve the map density, further use of additional SSRs (Temnykh et al., 2001)

will be useful to increase the number of mapped loci on all chromosomes and to detect

precise location of QTLs. Therefore, adding more DNA markers, especially SNPs,

SSRs and AFLPs will saturate the map. Such a dense map would be helpful for gene

tagging and identification of tightly linked markers/QTL that could be used in marker

assisted selection in crosses designed to use alleles from exotic accessions or cultivars

to develop elite cotton breeding lines (Wu et al., 2007).

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5.4.2 Linkage groups and chromosomal assignment

In the present study, four linkage groups were putatively assigned to four

chromosomes of cotton using already known anchored and informative loci (Zhang et

al., 2003; Mei et al., 2004; Han et al., 2006; Wang et al., 2006a). LG1 was assigned to

chromosome 20, LG2 to chromosome 10, LG3 to chromosome 18 and LG4 to

chromosome 15. Similarly Mei et al., (2004) assigned linkage groups to specific

chromosomes using already anchored a set of 17 SSR and 12 RFLP loci (Reinisch et

al., 1994; Liu et al., 2000a). After aligning groups with common SSR loci, Shen et al.,

(2007) assigned linkage groups to the subgenomes and chromosomes based on two

backbone linkage maps (Han et al., 2004; Song et al., 2005) and two other maps

developed by Lacape et al., (2003) and Rong et al., (2004).

Allotetraploid cotton has 26 pairs of chromosomes, 13 large from A subgenome and

13 small from D subgenome. In the present study, we found that the genetic distances

between mapped loci on chromosome 10 of the A subgenome was larger than those in

homoeologous chromosome 20 of the D subgenome (Fig. 4.14). It would result due to

the occurrence of large amount of repetitive and heterochromatin DNA in the A-

subgenome chromosomes (Kimber, 1961; Geever et al., 1989; Zhao et al., 1998b; Mei

et al., 2004).

5.4.3 Segregation distortion

In the present mapping experiment, 14 out 20 linked markers showed

segregation distortion, deviating from Mendelian ratios (Table 4.9). In another study

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of Upland cotton, a total of 88 (44%) of 200 markers showed segregation patterns

skewed from the expected 3:1 ratio (Akash, 2003), while Shen et al., (2007) observed

segregation distortion with 95 (52.49%) of the 181 SSR markers using G. hirsutum

RILs. Segregation distortion has been reported in a wide range of plant species

(Jenczewski et al., 1997). Segregation distortion may occur due to the presence of

lethal genes or fragment complexes (overlapping fragments consisting of identically

sized fragments) (Hansen et al., 1999; Nikaido et al., 1999). It could also be related to

different sizes of the parent genomes or to distorting factors, such as self-

incompatability alleles (Bert et al., 1999). Distortion and high proportion of RFLP

markers in an intraspecific cotton population presumably resulted from polyploidy of

cotton (Ulloa and Meredith, 2000).

Polyploidy and chromosomal rearrangement may affect interpretation of segregation

data and linkage inferences (Doerge and Craig, 2000; Livingstone et al., 2000).

Linkage analysis would be complicated by extensive recombination per homologous

chromosome and the genetic redundancy associated with polyploidy (Reinisch et al.,

1994). Some chromosomes may contain recombination hotspots, resulting in large

genetic distances, whereas others contain a large portion of heterochromatin associated

with low recombination frequencies. Segregation distortion and linkage drag may also

affect locus behavior among different interspecific hybrid populations (Mei et al.,

2004). Interspecific hybrids would result in a wide range of phenotypic segregation

and fertility. F1 hybrids may become semi-sterile, and one-third of the plants in the F2

and subsequent generations would not produce any bolls or seeds. Segregation

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distortion may result from competition among gametes or from abortion of gametes or

zygote (Harushima et al., 1996). It may arise from genetic, physiological and

environmental causes, while the relative contribution of these factors may differ in

specific populations (Xu et al., 1997).

One more pertinent reason of high segregation distortion in our population would be

the high incidence of cotton leaf curl disease (CLCuD). High infestation of the virus

(CLCuV) could cause extensively heavy losses in cotton production (Rahman et al.,

2002; Rahman et al., 2005), especially in the populations of less diverse parents, as in

our case. One of the parent (FH-631S) was highly susceptible to CLCuD, while

second parent (FH-883) was moderately susceptible. These parents and their F1s were

also maintained under controlled conditions in green house, while segregating

populations were sown in cotton field. F2 population was highly infested with the

disease, because 732 out of 1095 F2 plants were infected at an early developmental

stage, which caused stunted growth. There was also some germination problem in F2:3

population and more than 50% of F2:3 lines were severely infected with the disease.

Due to such disease severity, many F2 plants and F2:3 lines did not produce any seed or

produced very less seed cotton, even not sufficient for fiber analysis. We included

only those plants for linkage and QTL analysis that produced at least sufficient seed

cotton for HVI analysis. All these factors would have caused high segregation

distortion. Therefore, for mapping studies in Pakistan both the cotton parents should

be very diverse in genetic background and preferably highly tolerant to CLCuD.

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5.5 QTL Analysis for Cotton Fiber Quality Traits

5.5.1 QTL detection methods and fiber traits

We conducted the QTL analysis of F2:3 population for cotton fiber traits with

WinQTLCart by performing single marker analysis (SMA), interval mapping (IM) and

composite interval mapping (CIM) at LOD > 2.0 as a threshold. QTL detection is a

statistical test of an association of a trait with a genetic locus or maker interval using

maximum likelihood (Lander and Botstein, 1989), regression (Haley and Knott, 1992),

marker regression (Kearsey and Hyne, 1994), IM (Tinker and Mather, 1995), and CIM

(Zeng, 1994). Although every method has some limitations and biasness, the results

obtained from different methods are often similar if heritability of a QTL is high

(Hyne et al., 1995).

The prime value of WinQTLCart's SMA is its quick scanning of the entire genome to

find best possible QTLs and identification of missing or incorrectly formatted data that

could affect later analysis. SMA (Weller, 1986) is based on the idea that if there is an

association between a marker genotype and trait value, it is likely that a QTL is close

to that marker locus. Single-marker analysis can be useful for a quick look at the data,

but it has been superceded by IM and CIM, because of their more precision and

accuracy in identification of QTLs.

In SMA, only one marker is used in QTL mapping but effects are underestimated and

the exact QTL position cannot be determined. Interval mapping provides a systematic

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way to scan the whole genome for evidence of QTL, basically IM is an extension of

SMA (Lander and Botstein, 1989). IM uses two observable flanking markers to

construct an interval within which to search for QTL. A map function (Haldane or

Kosambi) is used to translate from recombination frequency to genetic distances.

Then, LOD score is calculated at each increment (walking step) in the interval.

Finally, the LOD score profile is calculated for the whole genome. When a peak has

exceeded the threshold LOD value, we declare that a QTL has been found at that

location. Typically, a LOD threshold between 2 and 3 is required to ensure an overall

5% false positive error for detecting QTLs (Lander and Botstein, 1989).

When traits are complex with a low heritability, IM is intuitively attractive, while an

ideal method should consider effects from all of the QTLs. Composite interval

mapping adds background loci (20-40 cM apart) to IM (Jansen and Stam, 1994; Zeng,

1994; Basten et al., 2001). CIM fits parameters for a target QTL in one interval while

simultaneously employing partial regression coefficients for background markers to

account for variance caused by non-target QTL. CIM gives more power and precision

than simple IM because the effects of other QTL are not present as residual variance.

The power of QTL detection, defined as the probability of detecting a QTL at a given

level of statistical significance, depends on the strength of the QTL and the number of

progeny in the population (Manly and Olson, 1999). Furthermore, CIM can remove

the bias that would normally be associated with a QTL that is linked to the position

being tested.

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In the present study, among three QTL detection methods used, SMA detected eight

QTLs and 15 QTLs were found with IM, while 10 QTLs were identified with CIM

analysis (Table 4.10, 4.11, 4.12). Ulloa and Meredith (2000) performed QTL analysis

for ten agronomic and fiber traits in cotton at threshold LOD > 2.0. Mapmaker/QTL

yielded 26 QTLs, MapQTL identified 45 QTLs, and QTL Cartographer using simple

linear regression, stepwise regression, IM and CIM detected 34 QTLs. For five cotton

fiber traits, Akash (2003) used SMA, including simple and logistic regression, and

interval mapping analysis, including IM and CIM. He detected nine QTLs, using a

significant threshold of 2.0 LOD, with both IM and CIM.

Mei et al., (2004) found 28 QTLs for 11 fiber related traits with CIM using LOD 2.5

as a threshold. Lacape et al., (2005) conducted QTL analysis with SMA, IM and CIM,

and detected 80 QTLs for 11 cotton fiber properties using LOD 2.5 as a threshold. In

another study conducted by Park et al., (2005), eight potential QTLs were detected at

LOD ≥ 2.0 for five fiber traits using CIM and they also determined loci association

with fiber traits using SMA. Ulloa et al., (2005) performed QTL analyses with

Mapmaker/ QTL (Lander et al., 1987), MapQTL and QTL Cartographer (Basten et al.,

2001) at threshold LOD 2. Ninety-two QTLs for multiple fiber and yield traits, from

two G. hirsutum populations were identified on 15 linkage groups of the joinmap.

Using CIM Shen et al., (2007) found 25 major QTLs (LOD ≥ 3.0) and 28 putative

QTLs (2.0 < LOD < 3.0) for five fiber quality and six yield traits using RILs in two or

four environments.

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5.5.2 QTLs for fiber traits

In the present research work, collectively 16 putative QTLs related to eight

cotton fiber traits were detected, among them 12 were commonly found with at least

two of the three QTL detection methods (SMA, IM and CIM), while four QTLs were

identified only with IM or CIM (Table 4.13). Two putative QTLs with 28.1%

phenotypic variance explained (PVE) were associated with fiber length, while three

putative QTLs were detected for micronaire with 27.3% phenotypic variation. Three

QTLs were found significantly associated with fiber strength (47.1% PVE). For fiber

uniformity, one putative QTL of 11.6% PVE was found both with IM and CIM. Two

QTLs were mapped for short fiber index that accounted for 24% phenotypic variance.

We also found one putative QTL for fiber elongation at LOD 2.28, however, for fiber

color four QTLs (three for Rd and one for +b) were found with 34.2% phenotypic

variance. QTLs associated with cotton fiber related traits, detected in the present study

and identified by other investigators have been presented in Table 5.1 for comparison.

A range of small to moderately high accumulative proportions of the trait phenotypic

variance in our study suggested quantitative inheritance for fiber quality traits (Landen

and Thompson, 1990; Ulloa and Meredith, 2000). A major QTL can overshadow

effects of minor independently segregating QTL by increasing total phenotypic

variation, and thus genes with lesser effects might fall below the threshold for

detection (Zhang et al., 2003). The reason for detecting not many QTLs for a trait

could be due to less significant differences between parents. Detection of a QTL in

one population but not in other could also be explained as that there would be less or

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null variation between two parents for that QTL and null segregation for that QTL.

Different genetic background could affect gene expression in some populations.

Moreover, environmental effects interfere with expression of some QTLs with very

small impact, which could only be detected in replicated trials (Huang et al., 1996).

The DNA markers/ QTLs identified in the present research work can be used for

molecular breeding after validation process, which includes testing of marker

effectiveness in different environments, genetic backgrounds and populations.

The colocalization of the QTLs for fiber traits (Fig. 4.15a, 4.15b) was mostly in

accordance with the observed phenotypic correlations (Table 4.4). Of significant

interest was the coincidence of QTLs related to traits of greater economic importance,

like fiber length, strength and micronaire/ fineness, in the present study and also

reported by other investigators (Lacape et al., 2005). Mei et al., (2004) described three

QTLs associated with number of seeds, seed weight and fiber strength, clustered in the

same region of chromosome 9 that would represent one QTL with pleiotropic effects

on these traits. In another study, fiber length QTLs were colocalized with fiber

strength QTLs (Lacape et al., 2005). Several fiber related QTLs detected within the

same chromosome region, indicate that genes controlling fiber traits might be linked

or the result of pleiotropy (Zhang et al., 2005b). It was suggested that because fiber

length, strength, and fineness/ micronaire would be considered as physiologically

independent and relating to different spatial and temporal biological processes

(Wilkins and Jernstedt, 1998), the colocalization of QTLs might be more indicative of

linkage between different genes than of pleiotropy (Lacape et al., 2005).

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Table 5.1 QTLs for cotton fiber quality traits

Fiber QTLs Investigators

FL FF/ Mic

FS FU SFI FE Fiber color

Total

Jiang et al., 1998 - - 3 - - - - 3

Shappley et al., 1998a, b - 15 6 - - - - 21

Ulloa & Meredith, 2000 2 4 3 - - 3 - 12

Kohel et al., 2001 3 - 4 - - - - 7

Akash, 2003 1 2 3 2 - 1 - 9

Paterson et al., 2003 6 25 21 7 - 9 11 79

Zhang et al., 2003 - - 2 - - - - 2

Mei et al., 2004 1 1 2 - - 1 - 5

Lacape et al., 2005 15 5 12 6 - 10 16 64

Park et al., 2005 1 2 2 - - 3 - 8

Frelichowski et al., 2006 5 5 6 - - 4 - 20

Hua et al., 2007 5 8 3 - - - - 16

Shen et al., 2007 8 5 7 3 - 4 - 27

Present study 2 3 3 1 2 1 4 16

FL: fiber length, FF/ Mic: fiber fineness/ micronaire, FS: fiber strength, FU: fiber length uniformity, SFI: short fiber index, FE: fiber elongation, Fiber color: Rd or reflectance and +b or Yellowness.

104


In another study, approximately 49% of the 92 putative QTLs for agronomic and fiber

traits were placed on the two major joinmap linkage groups, indicating that cotton

chromosomes would have islands of high and low meiotic recombination, and gene-

rich regions in cotton genome. QTLs for fiber quality traits closely located in certain

genomic regions, represent possible targets for map-based cloning. It was also

suggested that identification of chromosomal location of DNA markers common to

different intra and interspecific populations would facilitate development of portable

framework markers, as well as genetic and physical mapping of the cotton genome

(Ulloa et al., 2005).

5.5.3 Cotton genome and distribution of fiber QTLs

In the present genomic analysis of cotton for quality traits, SMA detected four

QTLs on each of LG1 (chromosome 20) and LG2 (chromosome 10); with IM eight

QTLs were found on Chromosome 10 and seven on Chromosome 20, while six QTLs

were identified with CIM on Chromosome 10 and four on Chromosome 20 (Table

4.13). Seven putative QTLs on chromosome 10 and five of the QTLs on chromosome

20 were commonly found with at least two of three methods (SMA, IM, CIM), while

two QTLs on each of chromosome 10 and chromosome 20 were detected only with

IM or CIM (Table 4.13). Both of the chromosomes 10 and 20 are homeologous

chromosomes of A- and D-subgenome, respectively. Collectively, nine putative QTLs

for fiber traits were found on A-subgenome, while seven putative QTLs were on D-

subgenome of cotton. Similarly, Mei et al., (2004) detected five fiber QTLs on A-

subgenome and two QTLs on D-subgenome, while Ulloa et al., (2005) found 68%

105


fiber QTLs on A subgenome and 32% on D subgenome, suggesting a significant role

of A subgenome in fiber development. However, in another study, Jiang et al., (1998)

identified 14 fiber QTLs and most of them were located on D subgenome. QTLs on D-

subgenome would elucidate that domestication and breeding of tetraploid cottons have

resulted in fiber yield and quality levels superior to those achieved by parallel

improvement of A genome diploids.

A genome contributes to fiber production but EST-SSR markers mapped on the D-

subgenome revealed that there are also some important genes for fiber development on

D-genome (Han et al., 2006). This would explain why QTLs for fiber-related traits

were mapped in Dt genome chromosomes in tetraploid cotton, although D-genome

species do not produce spinnable fibers (Jiang et al., 1998; Kohel et al., 2001; Han et

al., 2004; Nguyen et al., 2004; Mei et al., 2004; Han et al., 2006). These results

propose the complex evolutionary relationships between A and D subgenomes.

The QTLs detected in both A and D subgenomes suggest that fiber-related traits result

from gene expression or interaction between homeologous A and D subgenomes.

Genome duplication and polyploidization may contribute to novel variation (Osborn et

al., 2003; Wendel, 2000) associated with fiber development. The combination of A

and D genomes stimulates the production of fibers superior to those produced by the

A-genome progenitor species (Applequist et al., 2001). It is possible that some genes

associated with fiber development are suppressed in the D-genome diploid but de-

repressed after the combining of the A and D genomes in allotetraploids. Moreover,

106


the expression of genes in the A-subgenome is enhanced because of interactions

between homeologous chromosomes.

5.6 Future Challenges and Prospects of Genomic Analysis of Cotton

Presence of segregation distortion, recombination hotspots and suppression regions

suggest the complexity of cotton genome, which make it challenging to identify a gene

using a map-based approach (Mei et al., 2004). A possible solution is to construct a

universal and consensus map using a set of framework markers. Several cotton genetic

maps have been developed with different molecular markers using F2 populations

(Reinisch et al., 1994; Ulloa and Meredith, 2000; Mei et al., 2004, He et al., 2007),

F2:3 populations (Ulloa et al., 2002), RILs (Park et al., 2005; Frelichowski et al., 2006;

Shen et al., 2007), doubled haploid population (Zhang et al., 2002) and back cross

populations (Han et al., 2004; Lacape et al., 2005; Han et al., 2006). There is a need to

accelerate efforts for comparative mapping of various maps from different populations

(Shen et al., 2005). The value of framework markers will be enhanced if locus

information is fully described in each map. Indeed, establishment of more framework

markers (like standard set of SSR loci) would effectively integrate several genetic

maps in cotton (Lacape et al., 2003, 2007).

To evaluate consistency of QTLs across different populations, the genetic structure of

those populations, their relationships, range of polymorphism and therefore different

genome coverage, as well as QTL x environment interactions must be taken into

107


consideration (Paterson et al., 1991; Melchinger et al., 1998; Lacape et al., 2005).

Knowledge of QTL-rich chromosome regions that are congruent between mapping

populations, generations and locations would be highly valuable from breeding

perspective. Such consistency in the detection of QTLs will increase their authenticity

and these QTL-rich chromosome regions would serve as primary targets in future

research on cotton fiber quality.

The development of near isogenic lines (NILs) differing only by the introgression of

G. barbadense alleles at a given QTL (QTL-NILs) will prove useful for studying the

effect of a single given QTL on phenotypic value of a plant harboring it. Such material

will also be useful for expression studies and map-based cloning. The identification of

precise QTLs and practice of MAS should become more feasible as more molecular

markers are developed and the map is supplemented with finely scaled increments.

However, the putative locations of the QTL do not necessarily represent physical

distances. Thus, a physical map of cotton is very more important and would be of

great worth in cloning significant QTLs (Shappley et al., 1998a; Lacape et al., 2005).

Bioinformatics analysis of ESTs generated from various cDNA libraries (Ji et al.,

2003; Arpat et al., 2004) would be a valuable genomic tool for the identification of

genes (Paterson et al., 2000; Han et al., 2006; Udall et al., 2006). Han et al., (2006)

employed BLASTX to survey 489 SSR-containing ESTs and mapped some of those

with important functions. EST-SSR mapping is one way to achieve a saturated cotton

genetic map. Cotton genome exploration would be accelerated through the valuable

108


cotton web resources like an integratedWeb database (Gingle et al., 2006) and cotton

microsatellite database (CMD) (Blenda et al., 2006). Such comprehensive resources

could solve the problem of genetic vulnerability and would be invaluable not only to

map genes or QTLs for cotton yield, fiber quality and disease resistance, but also for

integrating genetic and physical maps for better understanding of structural and

functional genome of cotton.

In addition to cotton breeding facilitated by marker-aided selection, direct introduction

of important genes through genetic engineering has offered new potentials for

improving fiber quality. Identification of fiber genes could be accomplished through

EST approaches for the development of transgenic cotton with more lint production

and better fiber quality. Adopting new genomic tools and novel marker systems such

as EST-SSRs, SNP, DNA chips and microarrays, it would be possible in future to

select the best lines for cotton breeding based on RNA expression profiles.

5.7 Conclusions

Variation was found among 19 cotton varieties tested for fiber traits, which

facilitated the selection of FH-631S and FH-883 for mapping. Fiber traits distributed

normally with some skewness in F2:3 (FH-631S x FH-883) population, exhibiting

complex and quantitative nature of quality traits. Correlations between different fiber

traits would be exploited as selection criteria for successful cotton breeding. Multiple

traits can be correlated due to linkage or pleiotropy.

109


DNA markers can be successfully employed for hybrid and parentage

verification in cotton. SSRs were more polymorphic than RAPDs and among

microsatellites EST-SSRs were more informative. Low level of polymorphism

between FH-631S and FH-883 depicted narrow genetic base.

Less coverage of genome was possibly due to low polymorphism at DNA level

between parents and additional SSRs would be used to improve map density. The total

distance of genetic map with Haldane function was more than with Kosambi function.

Although, interspecific population would be preferred for genetic mapping due to

more genome coverage, however linkage map developed using intraspecific of G.

hirsutum population provide a core of markers with more practical application (Ulloa

and Meredith, 2000).

Segregation distortion may occur due to presence of lethal genes or fragment

complexes. High segregation distortion in our population suggested that for mapping

studies in Pakistan, the cotton parents should be highly diverse in genetic background

and highly tolerant to the cotton leaf curl disease. Genetic distances between mapped

loci on chromosome 10 (A subgenome) were larger than in homeologous chromosome

20 (D subgenome). This would be due to the more repetitive DNA in A subgenome.

QTL analysis indicated that FH-883 would be a good source of positive alleles

for improving fiber quality traits in cotton. Contribution of both parents for QTL

identification depict occurrence of transgressive segregates, which would be a useful

source of new alleles for cotton. The colocalization of the QTLs for fiber traits was

mostly in accordance with the observed phenotypic correlations. More putative fiber

QTLs were found on A-subgenome than on D-subgenome suggesting a significant role

110


of A subgenome in fiber development. The QTLs on both A and D subgenomes

suggest that quality traits result from gene expression and complex evolutionary

interaction between homeologous A and D subgenomes.

The present research work delineated the ability of DNA markers to uncover

genetic variation that otherwise will remain masked in conventional breeding methods.

Although we were not able to develop a dense map with wide cotton genome coverage

and a large number of fiber QTLs, but we were successful in finding some important

fiber QTLs. The present research work would provide a basis for genome mapping in

the country, which will expedite QTL analysis and facilitate MAS in cotton breeding

for introgression of desirable genes into elite genotypes.

111

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APPENDICES

DNA extraction buffers:

2x CTAB (Hexadecyle trimethyle ammonium bromide)

2% CTAB

100 mM Tris base (M.W. 121.1, pH 8.0)

20 mM EDTA (M.W. 372.24, pH 8.0)

1.4 M NaCl (M.W. 58.44)

1% PVP (Polyvinylpyrrolidone)

1% mercapto-ethanol

0.1x TE Buffer

1.0 mM Tris base (pH 8.0)

0.1 mM EDTA (pH 8.0)

RNase stock solution

The enzyme (RNase A, 10 mg/ml) was dissolved in 10 mM Tris.Cl (pH 7.5)

and 15 mM NaCl. The solution was heated near boiling (in a water bath) for at least 15

minutes to get rid of DNases and then cooled slowly to room temperature (Sambrook

et al., 1989).

Standards/ solutions for DyNA QuantTM 200 Flourometer:

Hoechest dye (H 33258) stock solution

H 33258 dissolved in distilled water (1 mg/ml) and stored at 4°C in an amber bottle.

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10x TNE (Tris-NaCl-EDTA) buffer

12.11 g Tris 100 mM

3.72 g EDTA Na, 2H2O 10 mM

116.89 g NaCl 0.2 M

Dissolved in about 800 ml distilled water and adjusted pH to 7.4 with concentrated

HCl, then made up total volume to 1000 ml. Filtered before use and stored at 4oC.

Assay solution low range (A)

(10 to 100 ng/ml final concentration)

H 33258 stock solution 10 µl

10x TNE buffer 10 ml

Distilled filtered water 90 ml

Assay solution high range (B)

(100 to 5000 ng/ml final concentration)

H 33258 stock solution 100 µl

10x TNE buffer 10 ml

Distilled filtered water 90 ml

Assay solutions (A and B) kept at room temperature and prepared fresh every time.

Calf thymus DNA standard

For low range assay (A) 100 ng/µl

For high range assays (B) 1000 ng/µl

127

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Electrophoresis:

5x TBE (Tris Borate EDTA)

Tris base 54.0 g

Boric acid 27.5 g

0.5 M EDTA 20.0 ml

Distilled water x ml to make total volume 1000 ml

1.2% agarose gel

0.5x TBE 100 ml

Agarose 1.2 g

Ethidium bromide (10 mg/ml) 2 µl

6x loading dye

Sucrose 40 g

Bromophenol blue 0.25 g

5 mM EDTA 1 ml

10 mM Tris (pH 7.4) 1 ml

Made total volume up to 100 ml by adding distilled water.

1Kb DNA ladder

1Kb ladder (stock, 1 µg/µl) 20 µl

Loading dye (6x) 30 µl

Distilled water 400 µl

128

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50bp DNA ladder

50bp ladder (stock, 1 µg/µl) 20 µl

Loading dye (6x) 30 µl

Distilled water 400 µl

Ingredients for 1 liter solution of PAGE:

Acrylamide 60 gm

Bisacrylamide 3 gm

Urea 420 gm

Buffer (10x TBE) 100 ml

Just before pouring 100 ml gel.

10% ammonium per sulfate (APS) 500 µl

Tetra Methylethylenediamine (TEMED) 100 µl

Silver staining solutions:

Fixative solution

Glacial acetic acid 10%

Staining solution

Deionized water 2 litres

Silver nitrate 2 g

37% formaldehyde 3 ml

129

Appendices

Developing solution

Deionized water 2 litres

Sodium carbonate 60 g

37% formaldehyde 3 ml

1% sodium thiosulphate 400 µl

MetaPhor agarose:

Metaphor agarose 2 g

Agarose 2 g

Ethidium Br. (10 mg/ml) 2 µl

1x TBE 100 ml

130

Appendices

RAPD and SSR Primer Sequences (5’-3’)

RAPD primers

OPA OPC OPA-01 CAGGCCCTTC OPC-01 TTCGAGCCAG OPA-02 TGCCGAGCTG OPC-02 GTGAGGCGTC OPA-03 AGTCAGCCAC OPC-03 GGGGGTCTTT OPA-04 AATCGGGCTG OPC-04 CCGCATCTAC OPA-05 AGGGGTCTTG OPC-05 GATGACCGCC OPA-06 GGTCCCTGAC OPC-06 GAACGGACTC OPA-07 GAAACGGGTG OPC-07 GTCCCGACGA OPA-08 GTGACGTAGG OPC-08 TGGACCGGTG OPA-09 GGGTAACGCC OPC-09 CTCACCGTCC OPA-10 GTGATCGCAG OPC-10 TGTCTGGGTG OPA-11 CAATCGCCGT OPC-11 AAAGCTGCGG OPA-12 TCGGCGATAG OPC-12 TGTCATCCCC OPA-13 CAGCACCCAC OPC-13 AAGCCTCGTC OPA-14 TCTGTGCTGG OPC-14 TGCGTGCTTG OPA-15 TTCCGAACCC OPC-15 GACGGATCAG OPA-16 AGCCAGCGAA OPC-16 CACACTCCAG OPA-17 GACCGCTTGT OPC-17 TTCCCCCCAG OPA-18 AGGTGACCGT OPC-18 TGAGTGGGTG OPA-19 CAAACGTCGG OPC-19 GTTGCCAGCC OPA-20 GTTGCGATCC OPC-20 ACTTCGCCAC OPB OPD OPB-01 GTTTCGCTCC OPD-01 ACCGCGAAGG OPB-02 TGATCCCTGG OPD-02 GGACCCAACC OPB-03 CATCCCCCTG OPD-03 GTCGCCGTCA OPB-04 GGACTGGAGT OPD-04 TCTGGTGAGG OPB-05 TGCGCCCTTC OPD-05 TGAGCGGACA OPB-06 TGCTCTGCCC OPD-06 ACCTGAACGG OPB-07 GGTGACGCAG OPD-07 TTGGCACGGG OPB-08 GTCCACACGG OPD-08 GTGTGCCCCA OPB-09 TGGGGGACTC OPD-09 CTCTGGAGAC OPB-10 CTGCTGGGAC OPD-10 GGTCTACACC OPB-11 GTAGACCCGT OPD-11 AGCGCCATTG OPB-12 CCTTGACGCA OPD-12 CACCGTATCC OPB-13 TTCCCCCGCT OPD-13 GGGGTGACGA OPB-14 TCCGCTCTGG OPD-14 CTTCCCCAAG OPB-15 GGAGGGTGTT OPD-15 CATCCGTGCT OPB-16 TTTGCCCGGA OPD-16 AGGGCGTAAG OPB-17 AGGGAACGAG OPD-17 TTTCCCACGG OPB-18 CCACAGCAGT OPD-18 GAGAGCCAAC OPB-19 ACCCCCGAAG OPD-19 CTGGGGACTT OPB-20 GGACCCTTAC OPD-20 ACCCGGTCAC

131

Appendices

OPE OPG OPE-01 CCCAAGGTCC OPG-01 CTACGGAGGA OPE-02 GGTGCGGGAA OPG-02 GGCACTGAGG OPE-03 CCAGATGCAC OPG-03 GAGCCCTCCA OPE-04 GTGACATGCC OPG-04 AGCGTGTCTG OPE-05 TCAGGGAGGT OPG-05 CTGAGACGGA OPE-06 AAGACCCCTC OPG-06 GTGCCTAACC OPE-07 AGATGCAGCC OPG-07 GAACCTGCGG OPE-08 TCACCACGGT OPG-08 TCACGTCCAC OPE-09 CTTCACCCGA OPG-09 CTGACGTCAC OPE-10 CACCAGGTGA OPG-10 AGGGCCGTCT OPE-11 GAGTCTCAGG OPG-11 TGCCCGTCGT OPE-12 TTATCGCCCC OPG-12 CAGCTCACGA OPE-13 CCCGATTCGG OPG-13 CTCTCCGCCA OPE-14 TGCGGCTGAG OPG-14 GGATGAGACC OPE-15 ACGCACAACC OPG-15 ACTGGGACTC OPE-16 GGTGACTGTG OPG-16 AGCGTCCTCC OPE-17 CTACTGCCGT OPG-17 ACGACCGACA OPE-18 GGACTGCAGA OPG-18 GGCTCATGTG OPE-19 ACGGCGTATG OPG-19 GTCAGGGCAA OPE-20 AACGGTGACC OPG-20 TCTCCCTCAG OPF OPH OPF-01 ACGGATCCTG OPH-01 GGTCGGAGAA OPF-02 GAGGATCCCT OPH-02 TCGGACGTGA OPF-03 CCTGATCACC OPH-03 AGACGTCCAC OPF-04 GGTGATCAGG OPH-04 GGAAGTCGCC OPF-05 CCGAATTCCC OPH-05 AGTCGTCCCC OPF-06 GGGAATTCGG OPH-06 ACGCATCGCA OPF-07 CCGATATCCC OPH-07 CTGCATCGTG OPF-08 GGGATATCGG OPH-08 GAAACACCCC OPF-09 CCAAGCTTCC OPH-09 TGTAGCTGGG OPF-10 GGAAGCTTGG OPH-10 CCTACGTCAG OPF-11 TTGGTACCCC OPH-11 CTTCCGCAGT OPF-12 ACGGTACCAG OPH-12 ACGCGCATGT OPF-13 GGCTGCAGAA OPH-13 GACGCCACAC OPF-14 TGCTGCAGGT OPH-14 ACCAGGTTGG OPF-15 CCAGTACTCC OPH-15 AATGGCGCAG OPF-16 GGAGTACTGG OPH-16 TCTCAGCTGG OPF-17 AACCCGGGAA OPH-17 CACTCTCCTC OPF-18 TTCCCGGGTT OPH-18 GAATCGGCCA OPF-19 CCTCTAGACC OPH-19 CTGACCAGCC OPF-20 GGTCTAGAGG OPH-20 GGGAGACATC

132

Appendices

OPI OPK OPI-01 ACCTGGACAC OPK-01 CATTCGAGCC OPI-02 GGAGGAGAGG OPK-02 GTCTCCGCAA OPI-03 CAGAAGCCCA OPK-03 CCAGCTTAGG OPI-04 CCGCCTAGTC OPK-04 CCGCCCAAAC OPI-05 TGTTCCACGG OPK-05 TCTGTCGAGG OPI-06 AAGGCGGCAG OPK-06 CACCTTTCCC OPI-07 CAGCGACAAG OPK-07 AGCGAGCAAG OPI-08 TTTGCCCGGT OPK-08 GAACACTGGG OPI-09 TGGAGAGCAG OPK-09 CCCTACCGAC OPI-10 ACAACGCGAG OPK-10 GTGCAACGTG OPI-11 ACATGCCGTG OPK-11 AATGCCCCAG OPI-12 AGAGGGCACA OPK-12 TGGCCCTCAC OPI-13 CTGGGGCTGA OPK-13 GGTTGTACCC OPI-14 TGACGGCGGT OPK-14 CCCGCTACAC OPI-15 TCATCCGAGG OPK-15 CTCCTGCCAA OPI-16 TCTCCGCCCT OPK-16 GAGCGTCGAA OPI-17 GGTGGTGATG OPK-17 CCCAGCTGTG OPI-18 TGCCCAGCCT OPK-18 CCTAGTCGAG OPI-19 AATGCGGGAG OPK-19 CACAGGCGGA OPI-20 AAAGTGCGGG OPK-20 GTGTCGCGAG OPJ OPL OPJ-01 CCCGGCATAA OPL-01 GGCATGACCT OPJ-02 CCCGTTGGGA OPL-02 TGGGCGTCAA OPJ-03 TCTCCGCTTG OPL-03 CCAGCAGCTT OPJ-04 CCGAACACGG OPL-04 GACTGCACAC OPJ-05 CTCCATGGGG OPL-05 ACGCAGGCAC OPJ-06 TCGTTCCGCA OPL-06 GAGGGAAGAG OPJ-07 CCTCTCGACA OPL-07 AGGCGGGAAC OPJ-08 CATACCGTGG OPL-08 AGCAGGTGGA OPJ-09 TGAGCCTCAC OPL-09 TGCGAGAGTC OPJ-10 AAGCCCGAGG OPL-10 TGGGAGATGG OPJ-11 ACTCCTGCGA OPL-11 ACGATGAGCC OPJ-12 GTCCCGTGGT OPL-12 GGGCGGTACT OPJ-13 CCACACTACC OPL-13 ACCGCCTGCT OPJ-14 CACCCGGATG OPL-14 GTGACAGGCT OPJ-15 TGTAGCAGGG OPL-15 AAGAGAGGGG OPJ-16 CTGCTTAGGG OPL-16 AGGTTGCAGG OPJ-17 ACGCCAGTTC OPL-17 AGCCTGAGCC OPJ-18 TGGTCGCAGA OPL-18 ACCACCCACC OPJ-19 GGACACCACT OPL-19 GAGTGGTGAC OPJ-20 AAGCGGCCTC OPL-20 TGGTGGACCA

133

Appendices

OPM OPO OPM-01 GTTGGTGGCT OPO-01 GGCACGTAAG OPM-02 ACAACGCCTC OPO-02 ACGTAGCGTC OPM-03 GGGGGATGAG OPO-03 CTGTTGCTAC OPM-04 GGCGGTTGTC OPO-04 AAGTCCGCTC OPM-05 GGGAACGTGT OPO-05 CCCAGTCACT OPM-06 CTGGGCAACT OPO-06 CCACGGGAAG OPM-07 CCGTGACTCA OPO-07 CAGCACTGAC OPM-08 TCTGTTCCCC OPO-08 CCTCCAGTGT OPM-09 GTCTTGCGGA OPO-09 TCCCACGCAA OPM-10 TCTGGCGCAC OPO-10 TCAGAGCGCC OPM-11 GTCCACTGTG OPO-11 GACAGGAGGT OPM-12 GGGACGTTGG OPO-12 CAGTGCTGTG OPM-13 GGTGGTCAAG OPO-13 GTCAGAGTCC OPM-14 AGGGTCGTTC OPO-14 AGCATGGCTC OPM-15 GACCTACCAC OPO-15 TGGCGTCCTT OPM-16 GTAACCAGCC OPO-16 TCGGCGGTTC OPM-17 TCAGTCCGGG OPO-17 GGCTTATGCC OPM-18 CACCATCCGT OPO-18 CTCGCTATCC OPM-19 CCTTCAGGCA OPO-19 GGTGCACGTT OPM-20 AGGTCTTGGG OPO-20 ACACACGCTG OPN OPP OPN-01 CTCACGTTGG OPP-01 GTAGCACTCC OPN-02 ACCAGGGGCA OPP-02 TCGGCACGCA OPN-03 GGTACTCCCC OPP-03 CTGATACGCC OPN-04 GACCGACCCA OPP-04 GTGTCTCAGG OPN-05 ACTGAACGCC OPP-05 CCCCGGTAAC OPN-06 GAGACGCACA OPP-06 GTGGGCTGAC OPN-07 CAGCCCAGAG OPP-07 GTCCATGCCA OPN-08 ACCTCAGCTC OPP-08 ACATCGCCCA OPN-09 TGCCGGCTTG OPP-09 GTGGTCCGCA OPN-10 ACAACTGGGG OPP-10 TCCCGCCTAC OPN-11 TCGCCGCAAA OPP-11 AACGCGTCGG OPN-12 CACAGACACC OPP-12 AAGGGCGAGT OPN-13 AGCGTCACTC OPP-13 GGAGTGCCTC OPN-14 TCGTGCGGGT OPP-14 CCAGCCGAAC OPN-15 CAGCGACTGT OPP-15 GGAAGCCAAC OPN-16 AAGCGACCTG OPP-16 CCAAGCTGCC OPN-17 CATTGGGGAG OPP-17 TGACCCGCCT OPN-18 GGTGAGGTCA OPP-18 GGCTTGGCCT OPN-19 GTCCGTACTG OPP-19 GGGAAGGACA OPN-20 GGTGCTCCGT OPP-20 GACCCTAGTC

134

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OPQ OPS OPQ-01 GGGACGATGG OPS-01 CTACTGCGCT OPQ-02 TCTGTCGGTC OPS-02 CCTCTGACTG OPQ-03 GGTCACCTCA OPS-03 CAGAGGTCCC OPQ-04 AGTGCGCTGA OPS-04 CACCCCCTTG OPQ-05 CCGCGTCTTG OPS-05 TTTGGGGCCT OPQ-06 GAGCGCCTTG OPS-06 GATACCTCGG OPQ-07 CCCCGATGGT OPS-07 TCCGATGCTG OPQ-08 CTCCAGCGGA OPS-08 TTCAGGGTGG OPQ-09 GGCTAACCGA OPS-09 TCCTGGTCCC OPQ-10 TGTGCCCGAA OPS-10 ACCGTTCCAG OPQ-11 TCTCCGCAAC OPS-11 AGTCGGGTGG OPQ-12 AGTAGGGCAC OPS-12 CTGGGTGAGT OPQ-13 GGAGTGGACA OPS-13 GTCGTTCCTG OPQ-14 GGACGCTTCA OPS-14 AAAGGGGTCC OPQ-15 GGGTAACGTG OPS-15 CAGTTCACGG OPQ-16 AGTGCAGCCA OPS-16 AGGGGGTTCC OPQ-17 GAAGCCCTTG OPS-17 TGGGGACCAC OPQ-18 AGGCTGGGTG OPS-18 CTGGCGAACT OPQ-19 CCCCCTATCA OPS-19 GAGTCAGCAG OPQ-20 TCGCCCAGTC OPS-20 TCTGGACGGA OPR OPT OPR-01 TGCGGGTCCT OPT-01 GGGCCACTCA OPR-02 CACAGCTGCC OPT-02 GGAGAGACTC OPR-03 ACACAGAGGG OPT-03 TCCACTCCTG OPR-04 CCCGTAGCAC OPT-04 CACAGAGGGA OPR-05 GACCTAGTGG OPT-05 GGGTTTGGCA OPR-06 GTCTACGGCA OPT-06 CAAGGGCAGA OPR-07 ACTGGCCTGA OPT-07 GGCAGGCTGT OPR-08 CCCGTTGCCT OPT-08 AACGGCGACA OPR-09 TGAGCACGAG OPT-09 CACCCCTGAG OPR-10 CCATTCCCCA OPT-10 CCTTCGGAAG OPR-11 GTAGCCGTCT OPT-11 TTCCCCGCGA OPR-12 ACAGGTGCGT OPT-12 GGGTGTGTAG OPR-13 GGACGACAAG OPT-13 AGGACTGCCA OPR-14 CAGGATTCCC OPT-14 AATGCCGCAG OPR-15 GGACAACGAG OPT-15 GGATGCCACT OPR-16 CTCTGCGCGT OPT-16 GGTGAACGCT OPR-17 CCGTACGTAG OPT-17 CCAACGTCGT OPR-18 GGCTTTGCCA OPT-18 GATGCCAGAC OPR-19 CCTCCTCATC OPT-19 GTCCGTATGG OPR-20 ACGGCAAGGA OPT-20 GACCAATGCC

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OPU OPW OPU-01 ACGGACGTCA OPW-01 CTCAGTGTCC OPU-02 CTGAGGTCTC OPW-02 ACCCCGCCAA OPU-03 CTATGCCGAC OPW-03 GTCCGGAGTG OPU-04 ACCTTCGGAC OPW-04 CAGAAGCGGA OPU-05 TTGGCGGCCT OPW-05 GGCGGATAAG OPU-06 ACCTTTGCGG OPW-06 AGGCCCGATG OPU-07 CCTGCTCATC OPW-07 CTGGACGTCA OPU-08 GGCGAAGGTT OPW-08 GACTGCCTCT OPU-09 CCACATCGGT OPW-09 GTGACCGAGT OPU-10 ACCTCGGCAC OPW-10 TCGCATCCCT OPU-11 AGACCCAGAG OPW-11 CTGATGCGTG OPU-12 TCACCAGCCA OPW-12 TGGGCAGAAG OPU-13 GGCTGGTTCC OPW-13 CACAGCGACA OPU-14 TGGGTCCCTC OPW-14 CTGCTGAGCA OPU-15 ACGGGCCAGT OPW-15 ACACCGGAAC OPU-16 CTGCGCTGGA OPW-16 CAGCCTACCA OPU-17 ACCTGGGGAG OPW-17 GTCCTGGGTT OPU-18 GAGGTCCACA OPW-18 TTCAGGGCAC OPU-19 GTCAGTGCGG OPW-19 CAAAGCGCTC OPU-20 ACAGCCCCCA OPW-20 TGTGGCAGCA OPV OPX OPV-01 TGACGCATGG OPX-01 CTGGGCACGA OPV-02 AGTCACTCCC OPX-02 TTCCGCCACC OPV-03 CTCCCTGCAA OPX-03 TGGCGCAGTG OPV-04 CCCCTCACGA OPX-04 CCGCTACCGA OPV-05 TCCGAGAGGG OPX-05 CCTTTCCCTC OPV-06 ACGCCCAGGT OPX-06 ACGCCAGAGG OPV-07 GAAGCCAGCC OPX-07 GAGCGAGGCT OPV-08 GGACGGCGTT OPX-08 CAGGGGTGGA OPV-09 TGTACCCGTC OPX-09 GGTCTGGTTG OPV-10 GGACCTGCTG OPX-10 CCCTAGACTG OPV-11 CTCGACAGAG OPX-11 GGAGCCTCAG OPV-12 ACCCCCCACT OPX-12 TCGCCAGCCA OPV-13 ACCCCCTGAA OPX-13 ACGGGAGCAA OPV-14 AGATCCCGCC OPX-14 ACAGGTGCTG OPV-15 CAGTGCCGGT OPX-15 CAGACAAGCC OPV-16 ACACCCCACA OPX-16 CTCTGTTCGG OPV-17 ACCGGCTTGT OPX-17 GACACGGACC OPV-18 TGGTGGCGTT OPX-18 GACTAGGTGG OPV-19 GGGTGTGCAG OPX-19 TGGCAAGGCA OPV-20 CAGCATGGTC OPX-20 CCCAGCTAGA

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OPY OPZ OPY-01 GTGGCATCTC OPZ-01 TCTGTGCCAC OPY-02 CATCGCCGCA OPZ-02 CCTACGGGGA OPY-03 ACAGCCTGCT OPZ-03 CAGCACCGCA OPY-04 GGCTGCAATG OPZ-04 AGGCTGTGCT OPY-05 GGCTGCGACA OPZ-05 TCCCATGCTG OPY-06 AAGGCTCACC OPZ-06 GTGCCGTTCA OPY-07 AGAGCCGTCA OPZ-07 CCAGGAGGAC OPY-08 AGGCAGAGCA OPZ-08 GGGTGGGTAA OPY-09 AGCAGCGCAC OPZ-09 CACCCCAGTC OPY-10 CAAACGTGGG OPZ-10 CCGACAAACC OPY-11 AGACGATGGG OPZ-11 CTCAGTCGCA OPY-12 AAGCCTGCGA OPZ-12 TCAACGGGAC OPY-13 GGGTCTCGGT OPZ-13 GACTAAGCCC OPY-14 GGTCGATCTG OPZ-14 TCGGAGGTTC OPY-15 AGTCGCCCTT OPZ-15 CAGGGCTTTC OPY-16 GGGCCAATGT OPZ-16 TCCCCATCAC OPY-17 GACGTGGTGA OPZ-17 CCTTCCCACT OPY-18 GTGGAGTCAG OPZ-18 AGGGTCTGTG OPY-19 TGAGGGTCCC OPZ-19 GTGCGAGCAA OPY-20 AGCCGTGGAA OPZ-20 ACTTTGGCGG

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SSR primers

SSR Forward Primer Reverse Primer BNL169 TCACAAATAAAAGTGAAATTGCG GGCTGGTGACCATAAAAGGA BNL252 TGAAGAGCTCGTTGTTGCAC CGAAAGAGACAAGCAATGCA BNL256 TTTTGCTCCATTTTTTTGCC TTTATTAATTTCGTTTAGCTTCCG BNL448 GCAGCTTGCTTTTCTGCTTC ACGCAAGCTTGGTCAATACC BNL686 ATTTTTCCCTTGGTGGTCCT ACATGATAGAAATATAAACCAAACACG BNL786 CTTTCCACGTGTAATTTGTTGATA GATCTTAACTCTTGCTCTCTCTCTCTC BNL840 CTCGTGGAAACACCAGGAAT TCTCGCCATTAAACTGCCTT BNL1053 AGGGTCTGTCATGGTTGGAG CATGCATGCGTACGTGTGTA BNL1059 CCTTCTCTGACACTCTGCCC TGTATTCTCTTCTTTTCCTTATACTTTT BNL1064 TTTGCGGGTAATCCTATTGC TGTCTATGGGACATTTCGCA BNL1317 AAAAATCAGCCAAATTGGGA CGTCAACAATTGTCCCAAGA BNL1350 TAGGAGGAGAAGTTGGCGAA CAAGATGTGACCTTACCGCC BNL1414 AAAAACCCCTTTCCATCCAT GGGTGTCCTTCCCAAAAATT BNL1434 AAATTCAAGAATCAAAAAACAACA TTATGCCAAAGTATATGGAGTAACG BNL1440 CCGAAATATACTTGTCATCTAAACG CCCCCGGACTAATTTTTCAA BNL1597 GGGCTTTCCGATACTGAACA CCTGCAATAAGGCGTTCAAT BNL1665 CAGAACCAACATACTTTCTACGG ATGTGCAAAAACTTGATGTGG BNL1679 AATTGAGTGATACTAGCATTTCAGC AAAGGGATTTGCTGGCAGTA BNL1721 TGTCGGAATCTTAAGACCGG GCGCAGATCCTCTTACCAAA BNL2496A TCGAAATGAATTTAGATGACCA TCCTTTTTTTTGTACTTCTCTTGC BNL2544 GCCGAAACTAAAACGTCCAA TCCTTACTCACTAAGCAGCCG BNL2553 GGGTCAAAAGTGGAAAACGA GCCCACAGGAAAACAAAAAA BNL2572 GTCCTATTACTAAAATTGTTAATTTAGCC CGATGTTAAATCAATCAGGTCA BNL2590 GAAAAACCAAAAAGGAAAATCG CTCCCTCTCTCTAACCGGCT BNL2634 AACAACATTGAAAGTCGGGG CCCAGCTGCTTATTGGTTTC BNL2895 CGATTTTACTGCTTCAGACTTG TACCATCTCACGGATCCACA BNL2960 TAAGCTCTGGAGGCCAAAAA CCATTTCAATTTCAAGCATACG BNL3008 ATCTCAGATTTAAACATATAATAGAGGG TAAAATGAAGGCCATCAGGC BNL3034 AAAGGAAATGGTCATTGGCA AGTACCCGCCATTTCAAGTG BNL3065 CAAACGGGAGACCAAAAAAA CGAACTGGCGAGTTAGTGCT BNL3084 TGTTCATAAAATGAAATCCAAGC AGTGCGCGACGTAAGTAACC BNL3090 GAAATCATTGGAAGAACATATACTACA TTGCTCCGTATTTTCCAGCT BNL3103 ACTTTGAGATATTGTTATTCTACCCG TCGAACAATTACGAATCAAATG BNL3147 ATGGCTCTCTCTGAGCGTGT CGGTTCAGAGGCTTTGTTGT BNL3255 GACAGTCAAACAGAACAGATATGC TTACACGACTTGTTCCCACG BNL3279 CATGTCCAATGGATGTGTCA GGGCCACTTAAAGGCATTCT BNL3359 TTGTTGTTGGGAATGATGGA TGACCCTTCACCGACTTTCT BNL3383 GTGTTGTCATCGGCACTGAC TGCAATGGTTCAGTGGTGAT BNL3408 ATCCAAACCATTGCACCACT GTGTACGTTGAGAAGTCATCTGC BNL3441 CGTCATAAACCGTGCTTGTG GGCCACTTTAAGGCTGTCAC BNL3442 CATTAGCGGATTTGTCGTGA AACGAACAAAGCAAAGCGAT BNL3449 AAGCTGTGGCTATGATGCCT AGAGCAAAAAACAATTACAAAAGC BNL3452 TGTAACTGAGCAGCCGTACG GCCAAAGCAGAGTGAGATCC BNL3479 AGTGGGTTGGACTTTCATGC CACGGGCTTTTTTTTTTTCA BNL3482 ATTTGCCCCAGGTTTTTTTT GCAACACCTTTTCCTCCCTA

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BNL3556 CCTTTCATGACCCTGCAAAT AGATGGGGAATGGATCTGTG BNL3558 AAGCAAATCATGATGAACATACG TGCGAAGAGTAGCTCTGCTG BNL3563 AAGCATAAACTTGACACAAGCC AATGGGCAAGAAAAGGGAAC BNL3599 TTTAGCCCCAGTAACATGCC ACTGCAAGCTCTGCCCTAAA BNL3627 TATGGGCCTGTCCACCTAAG CAAAGCAACATGCACACACA BNL3646 CCCAATACGAGGAGAGCACA TCGAAAATGGGGGAGAGAG BNL3649 GCAAAAACGAGTTGACCCAT CCTGGTTTTCAAGCCTGTTC BNL3792 TTCGAGATCCCCTGTTCTGA CATATTCCAGTCAAACCAAACG BNL3816 GTTAGCCACGTGTTAGTTCTATG ATCGATCACTTGCTGGTTCC BNL3888 GCCCACTTTGCCTCTTACAG AGCTTTTCCCCTTTCACCAT BNL3895 CGCTCTTGGTCATGGATTTT GCCAAGCTCACTGGAAGAAC BNL3902 GAGTTTGGGGGCTGTGTATG GGGGTGCTTATGTCAGACGT BNL3955 AGAGATGCAATGGGATCGAC ATGTGATAATGCGGGGAATG BNL3971 CACATATTTTTGCCTCACGC TGTGGACCCAAAAAGGAAGA BNL3995 ATATTTTATTCTTTTAATAGCTTTATTCCC TTGGAAAAACCCATGGTGAT CM25 AGCAGCTCAACCGAAAC ATAACGGCTTTCTCGAT CM27 GAATTGTGTAAAAGACACTTC TTCTTGGAGAATGCTCT CM29 TTCCAAGTTCCAATTTCTTC ATCAACCACTTTGACAATGTT CM30 TGTCTTCAAACAGTGCCAAAA CTCGAACACCTGTTGTCCC CM32 AAAACGTTACGGATTATTGG ATGTATTACAACCACACATC CM42 GTCTTAAAGACTGACATGCAGC GAGTATAGGATAGGGGCTAAAC CM43 GCGCAGATATTATTATCACAGC TATATAAATTTGCATCAGTTGGC CM45 GATGCCAGTAAGTTCAGGAATG GCCAACTTATATTCGGTTCCT CM50 GCCGAAAAGGTGATCTAATTTG GATGATATCACACCCTTCAAC CM56 CAATCATAGACCGCCACATC ACATTGCTAGATATTATAAAAGAAG CM60 GAAGATCCATCTGCAGACCCAG CCAACAAAACCATAAACATGAACTC CM63 GTCTGCACTGCTCGGTTATGTGAG GCAGAAAAGTGTTTAACTTGCGA CM65 GTGTTGCTTTCAGCGATCCC TATAGAAATGTTCGCAAGAGTTG CM66 GGATACGTAGGCCTCCACATATTC GCTGCCTGCTGTTGAATGCTG CM67 GAGTTTTCTTTGGTTTTCCATTC GTTGAAACGAGAAGATGAGATTGGAGG

CM68 CTTAAAAGGGCATAAGGCCACCC GTATATCTGAAAATATGATGGAA CM76 TTAATTTTCAAAGGGCTCTTAGAAAG GTATAATGGTAGGAGAGAAGGGTTAGGG

CM160 TTTGCGTTGATTCTTATTTTTCCC GTAAACATGCTTTATCTGATAAACAG CM161 TTGACCTTTCATTTTACAATCCC GGGGGCCAAATCAAATATGACCCAAC CM162 CCATTCTCTTTCATTTTCTCC GGAGCAACGAAACTTAGCAGC JESPR1 CCCTCTTCCTCTCACCCACC GCTTGTGGTTTCTAGACACACC JESPR2 ATCACCGGCATCATCATCAT GCTCATCAAATATCTCACAATTTATC JESPR3 CCGCATTTAAGAACCCTAAC GGATTGGACAACCATTCTTC JESPR4 GACATGTGGCATAAATGACG GGCAGAGACACTTTTAACTAGAG JESPR5 GTCTCCTTCCCCTTCCTCTTCTTC CAACAACCCATGACGACGAC JESPR6 ACTAAACCCTAAACACAATATCTCC CATTATAAGGTCCCCAATGTC JESPR7 GCTGACGGAAGTGACAGGACCCT GTCTCCTTCCCCTTCCTCTTCTTC JESPR8 GGCATCTTACGGTGGAAATGAC GGTTAGGTTTGGGGTGTTACATAC JESPR9 AGTTGACGAAGAGGCAAAAGGGTC GGCTTCTCTTGCTTAGATCTGGAC JESPR10 GAGGCAATGTCGGATGTGGGC GCAAGTAGGTGGTGGCCGGAG JESPR11 CTGGACTAACCTAAACTTGACAC CCTATGTACATATGCTCTTC JESPR12 CCTAGACATCTGATTTAGCCAC GAAGAAGAAGAATCCGACAG JESPR13 GCTCTCAAATTGGCCTGTGT GGTGGAGGCATTCCTGCTAAC

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JESPR14 GGGAGGGGGTGAATAAACGGTG GGTCAGGTAAACTTGCCATAGTGGG JESPR15 CTTCTCTTGCTTAGATCTGGAC GCGAGTACTAGTAATGACTGTC JESPR16 GATGTGAGTATTTGGCACTTTGAC GCTATCTATATCCGACTCAGCCCG JESPR17 CATGTCGTAGTGGTAACTGC GCCTTGTTACTTAACTAATAGTCC JESPR18 CGGCTTCTCTTGCTTAGATCTGG CCTCTAGATTGCCCTCCTTGTGC JESPR19 GAATTCTAATGTTTATAAAAAAATA CTATATAAAGTATCAGAGAAATTC JESPR20 GGCTTCTCTTGCTTAGATCTGG CGGTACATGGCTCGAGAGAG JESPR21 GAGGGGGTGAATAAACGGTGAGG CGGCTTCTCTTGCTTAGATCTGGAC JESPR22 GGCTAATGGTGGTTGTGGATGC CCCATGAAGATTTTTCCAGGGGAG JESPR23 GACTATGGCTTAAGGTTCAG CCCATTAATGTTAATGGCAAC JESPR24 CGGCTTCTCTTGCTTAGATCTGGAC GCTGACGGAAGTGACAGGACC JESPR25 GCCCTATCCACGTTGCTGTCG CCCTCTCGGTCAAAATCAACAC JESPR26 GGTGACATCAGTGTTGTTC GTCGTTCACAGCAGCCCTATCCACG JESPR27 AGATCTGGACTAACATATATTCCG GGAACTTACCTGGAATTTAGTG JESPR28 CAACATCAACCTCGTGAACCAAT GTGAGGCCCCTTCTATTATTAGAC JESPR29 CACCGTTTCCAAGTAAGATT GGTTAATCTTAGTTGAGGTC JESPR30 CAAGCTAAGCCACTTGTATTACC GAAGAGAATAAGAATAGCCC JESPR31 CCAGTTAACTTGCCACGGTG CCCCACCAATATAGTTGTTTCAAAT JESPR33 GGATTTGCTTCCTAGTTCCCTTC GACAGATTGTAATCAATCCAACACG JESPR34 TGGTACCGGGGATTCAGTGTGCCAC ATGTGGCGCATCAGATCCGGTC JESPR35 GGTTCAGTTTCCTTTCAGCTCT TTGGGAGGAAGGAAGGAAGGAA JESPR36 AGGACTAAACTTGCCACGTTGG GATGTTGGGTTAAGAAGTGGAGTG JESPR37 GATCTGGACTAACTGTAGGGTC GTCAGCGTAATTGATTGCTTC JESPR38 GAATCTTACAGTGGAGCAAGG GGG AAA GGC TAA TGT GGT TG JESPR39 GGTTCAGTTTCCTTTCAGCTCT TTGGGAGGAAGGAAGGAAGGAAG JESPR40 CTTCTCTTGCTTAGATCTGGACT GTGTGCTTATTATATGTGAATGTAG JESPR41 GACCATTGGGAATTGCTGATACG GGGTAATCGACATAGGTAACGGG JESPR42 CGTTGCCGTCTTCGACTCCTT GTGGGTGGCTAATATGTAGTAGTCG JESPR43 CGGCTTACAACAACAACAAC GCTTCTCTTGCTTAGATCTGGAC JESPR44 CTCTTGCTTAGATCTGGACTAAC CCTGTACCAGTAGTAATAGTAGC JESPR45 GAAACTCGATCCCTCAAGATATG ATGAAATGAAAGAAAAGAAGGGAGG JESPR46 GCTGTTGACTAACACATAAATAC ATTGTAAATGTTACTGTATGATGCC JESPR47 GGG GTG AGG GAT TGG ACA ACA CCC CCT AAC TGG CCA GGT AG JESPR49 GCT GGA GAA GGA AAA GGT GG CCTTGCTCTCACTGTACCTCTG JESPR50 GTA GTC TTC TCA ACT CCA CTG TT GGTGACATCAGTGTTGTTC JESPR51 CTTGGTACCGGGGATTCAGTGTGCC CATAACATCTAGGTCAGGTTTGGGG JESPR52 GCCGTACAATCACAGATTGGGAC GCGCTTCTCATTGAGTCATCCTG JESPR53 GCCAATGGGACTATATACCGGTG CCATGTCCCACGCCAGATTG JESPR54 CTCACCTGAATCGCCCCATCTATC GCTTAATTTGGCTGGGTCTCCAC JESPR55 GTTCGAGGAGGATTGAGGTAGAGGA CCCCTTCTCTTGCTTAGATCTGGAC JESPR56 CCAGTTAGCACCAATTTAGG CCACAATAACACACTGGAATC JESPR57 CGCCCTTCTCTTGCTTAGATCTGG CTCAAGAGCAAAAGGAACTTAACTCG JESPR58 CCGCCCTTCTCTTGCTTAGATCTGG GGAGCCAATTGAGAAGTGAATCCAA JESPR59 CGCCCTTCTCTTGCTTAGATCTGGA TAGTAGAAGGATTCGGCTATGGGGG JESPR60 CGCCCTTCTCTTGCTTAGATCTGG TAGATCTGGACTACCTACGAGACCC JESPR61 CGCCCTTCTCTTGCTTAGATCTGGA CACATCCTCCTCCCTACTCCCTCC JESPR62 GAATTGAGTGGAAAAGGGGGG CCTTCTCTTGCTTAGATCTGG

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JESPR63 CATCTTGGGTATTTTTTGAGTG GACTACCAAATGCACCATCTC JESPR64 CGCCCTTCTCTTGCTTAGATCTGGA GCAATTGAGGGGTGGGGTTGTCTG JESPR65 CCACCCAATTTAAGAAGAAATTG GGTTAGTTGTATTAGGGTCGTTG JESPR66 CTGGACTAACTATTTGGTATCCCTC GATCTGGACTACCGCTAATCAC JESPR67 GTAAAGAGCAACCTACACCTACCT CCAAGATAGTTCATACTTCCCTC JESPR68 GATATTTATTGTGTTTAACAGCAG TACTCTTATCGATGTCCTTTTCA JESPR69 CGCCCTTCTCTTGCTTAGATCTGG AAACTTTGCCGTTGATGGAGACCC JESPR70 CTGGACTAAAAGGAAGATGAGAG GAATACAGGTTCAAAGTTGATA JESPR71 CGCCCTTCTCTTGCTTAGATCTGG GCACCCTGCTCCAATCCTCTTTC JESPR72 CGCCCTTCTCTTGCTTAGATCTGG GGGCAAGCTGACGATGAGGAATG JESPR73 CCACCGAAATCGATAGAGAGCAAT CACTGTCCGACTAGGCCAATAC JESPR74 CCCTTCTCTTGCTTAGATCTGGAC GCATTATGCTTGCTAGTTCCCTGC JESPR75 CTTCTCACGTTACCATTGATTCTTC GGCTGTTCACGGACTAGCTGTA JESPR76 ACCCTTCTCTTGCTTAGATCTGGAC CAGTTGCTTCCAATGCAGCTACAG JESPR77 TCTCTTGCTTAGATCTGGACT CTTATCCTTACTTTGTGGCG JESPR78 GAAGTGCTCATAGTCCATCATAG GTCTGGCAGGACATAGAGAAG JESPR79 GGGACAAATGTAATCTTGCATCCAG CGCAGTGTCTGAATCGCCTTC JESPR80 CTCTTGCTTAGATCTGGACT ATGACTATGATTTAGCAGCG JESPR81 GTGGAATGGTTGATAAGCATGTTG GGATATAACACCAGGCACAAATAC JESPR82 GCAAAACATGGAATTTAAGTC CTAGATATTAGTTCCCGAATCAC JESPR83 CATAGGCAAGCCTTGTAGCAATC CCTCTTCTTTCACTACCACCTGC JESPR84 GACTCCCGGAGGCAATCAGAG CCAGGGCTCATACTATCGCTGC JESPR85 CCACCCAAATTTTTCATGGAGAG CCTTCCTCATGTATGACATTGATGG JESPR86 GGAGGAAGTTAGGAGCATGTCTCAG ACAGGGTAGTCACGTAACAACTGC JESPR87 GCCCTTCTCTTGCTTAGATCTGGA CAAAACGGTCGTAGCTAGGGTATG JESPR88 GTCAGCACAGTGAGGGTAAGAG GAATACTCCCTCTTCCCTCG JESPR89 CCCCAACCCACGAACATTCCA GGTGTTAACTGGATTGCTGACGTGG JESPR90 CATGGAGTTTCAATGGCGAAGAATC GGAACCGCTGATGTGGCTAGTTAAC JESPR91 GGGGTGTTGAGTAGAATGGTAG CGACATTTGCGATAAGTTGTG JESPR92 GGGACCTCTATTGAATAGCTGGAG CTCTTGGCATCATTAGTTCCTGG JESPR93 GCTTAGATCTGGACTACCCGTTG GGTCGTGGTGGTGGTCTTGC JESPR94 GCAAGACCACCACCACGACC GTCTGAATCGCCCTTCTCTTGC JESPR95 GCTTTTCTCGTAGACGTATG GCATATTTATATACCAAGTCCCTC JESPR96 CATCAGGTTCGAGATTGTCCCTCTG CGTCCTGCTGCACACTCTACTCTC JESPR97 CTTCTCTTGCTTAGATCTGGAC GAAGAGGCTTTTCTTTATGATTC JESPR98 CTTGATGAGGACCTATTTCCC CACATCTCATCTTCACACTCTC JESPR99 GCTATGCAGGCTCTGGGAAGGCTC CCCCTTCTCTTGCTTAGATCTGGAC JESPR100 CACATGGTTGACCGTACCGCCTCG GCTAGGTCCTGGAGTGCTCGGTG JESPR101 CCAAGTCAAGGTGAGTTATATG GCTCTTTGTTACTGAAATGGG JESPR102 CTTGTGAAGTCCTTTAGGGC GTTATCCATCATGGTCAAATGC JESPR103 CTATGAAACTCAAAGCCAAACTC CCAAGATTCGTTCGATCGACC JESPR104 GATGTTTAAGAATAACTATG GGAAATTTTGATACACATCCAC JESPR105 GGAAGACCAACCAAGTCAAG GGATATGATATTCCAAAGCCC JESPR106 CTAACAACTCTAACCTCTAACTG GGACTAAAAGTTGTTATTTG JESPR107 GACAATCCAGGCAGTCAGAG CATACTAATTAGCCATTCTCACCC JESPR108 CGATAGTCTCTAGCCTCAAATTC CGTAACACTAGTCGAACGAGC JESPR109 GATTACAGTATGTTCTCTGAGG CACACAATATCTCTCTTCTC

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JESPR110 GGCGAAGAGCTACCTGTGAATGGC CCAATGGGGACTCTACATGTGGC JESPR111 CTCTTGCTTAGATCTGGACTACC CTCCTTCCTCATCATCCTCTC JESPR112 ACTTCTCTTGCTTAGATCTGGAC CAATGGCTCTCTAGCTTACTTG JESPR113 CCCCCGAAGCCTTCAAGTAAGTTAC CCCTTCTCCTTGCTTAGATCTGGAC JESPR114 GATTTAAGGTCTTTGATCCG CAAGGGTTAGTAGGTGTGTATAC JESPR115 GAAACATGATTGTATGGTAATG CAGGAATCACTATTTTGGAC JESPR116 GGTCACATTCAAACTAAATGTTCC CAAATAGCCTCTTTAGCAGTGG JESPR117 CAAACATCTGGCTTTTTAACTC CTGTCTTCCAGTTTCAGAGC JESPR118 CTTTTTCTCTTTTCAACACGTG GTTGAAAGGAAGACTCCAAAC JESPR119 CTCAGGGAACTATTTGTAGTAGC GATCCACAAGAAACTGAAACTAG JESPR120 GTAACCGAATACCCCTCAACTTAAG ACAGCCGCTTTGTCGAAGAT JESPR121 CCTCAGATCAATTAACTATGATTC CGGTTGTAAAACTATACTATTTGTC JESPR122 GCTGCTGGTTTTACTTGTTGG CTATGGTGGAGGAGCAACAAC JESPR123 CTCTTGCTTAGATCTGGACTACC CTAAAACTACAGTCGAAAGGGG JESPR124 GGATATCGCTCTCCCTCT CTCTTGCTTAGATCTGGACTAAC JESPR125 CATTGTGATCAACCCCACCAAC GTTGGTGGGGATGGTCACATGC JESPR126 GCATGTGACCATCCCCACCAAC GCCAGTGTGCTGAATCGCCCTTC JESPR127 GATTTGGGTAACATTGGCTC CTGCAGTGTTGTGTTGGGTAGA JESPR128 CTGAAAAATCTGCATTTCCG CTCCTAGATTTTGCTCTCCTGTC JESPR129 CCTAACCTTTATCATCATGATC CAGTATTGAAAAGCATTTGATC JESPR130 GCCCAATTACAACATTTCCAAC GGTAGAGCCCACTTTTATGTCTC JESPR131 GGCACTACCGGTTTGTCTTTC GAGGTGAATGGATATCATGAATG JESPR132 GATCGAACAGATGGGTTAGTG GCTTAGATTGGACTAACATCTTG JESPR133 CAAGGATAAGGTTGAAGCTTC CCTCATCACCTACGGCTCTAC JESPR134 GTCAGAGTCTTCGGGTTGTC GTAACAGCAGAGAAGTCGGTG JESPR135 CAAAACCATCATCACTCTCAAG CGAGAGCCCACTAACAGAAAAG JESPR136 GCAGGAAGCATTGGTATCTC CTCTGATGTAAGAGTTGAATCAAG JESPR137 CTTCTCTTGCTTAGATCTGGAC GCAAGTGGTGATGTAATAAGTTG JESPR138 GATCAACTATCAGTCCAATTGG GTACTGCAATGAACATATATTCC JESPR139 CCTTCTCTTGCTTAGATCTGG CTTGCATGTCCTGAGAATACC JESPR140 CTTCTCTTGCTTAGATCTGGAC GGAGCAACTGTGTATGTGTG JESPR141 CTCAAGCTCTTCCCCCTTC ACTACCATGACAAGGAAGGTGG JESPR142 CTCTTGCTTAGATCTGGACTAAC GAGCAATAATGCCTTTCTTG JESPR143 CCTTCTCTTGCTTAGATCTGG GCCTCTGATAATGAAGATAACTG JESPR144 ACTCTTATTTGTGTAACTACTGTTCC CATACACATACACATACACATACAC JESPR145 CGCCCTTCTCTTGCTTAGATCTGG GCGAGCAACAATCAATTTCACCTC JESPR146 CGCCCTTCTCTTGCTTAGATCTGGA CGTTTCAGCCATCAGAATAGCTCC JESPR147 GCTTAGATCTGGACTACCGAATCCT CCAAATCAACATCCTCCATTACCC JESPR148 GCTTCTCTTGCTTAGATCTGG GTCGCTTTGTAAGTGAATGAG JESPR149 GTTCTTAAGTGAGGATTGGACG CTCATTAAGACCCTAGGTAGGC JESPR150 GCTTAGATCTGGACTAACATACG GATAATTTCATGTAAAATCCCTG JESPR151 CTGGACTAAAAACCTTAACTGG CTCGATTCTAACTCAATCACG JESPR152 GATGCACCAGATCCTTTTATTAG GGTACATCGGAATCACAGTG JESPR153 GATTACCTTCATAGGCCACTG GAAAACATGAGCATCCTGTG JESPR154 GTTCCCTCAGTTGCTCAGAAG GGAGGAGTTGGCAGAAAATAGC JESPR155 GCTTAGATCTGGACTAAAATAGCC GATTTACAGAGGAGGGAACATG JESPR156 GCCTTCAATCAATTCATACG GAAGGAGAAAGCAACGAATTAG

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JESPR157 CAAGTTCCCACCATCTTTAC CTTCTTTGACTGAAATTGCTC JESPR158 CACCATTCGGCAGCTATTTC CTGCAAACCCTAGCCTAGACG JESPR159 CAGCTGACTGCATTGGTTCAATCTC AACGAGTTGAAGAGAGTGAGGATCC JESPR160 CTTGCTTAGATCTGGACTAACC CACCGAGACATTCATATCAC JESPR161 CGGAAGGGCTGCTGATGGAG CTACCCCCATTTTTTGGATTCACC JESPR162 CGGCTTCTCTTGCTTAGATCTGG CATGTTGATCGTCAATCTGGGG JESPR163 CTCCAGTTCACTCCAAATTATC GGCACTACTACTGAGAAACAAG JESPR164 GCGCCTATTAGCCATGAACTCAAGG GACGTTGGCTCGAGTTGTTAAAGG JESPR165 CAAAACTCACCATGGGGAAAC GAATCAATGGCAGAAGTGTTGAAG JESPR166 GGCTTCTCTTGCTTAGATCTG CAAGCTTGAGTTTCGGGAAC JESPR167 CTCCCCTCTTCTCTTGTTGTC GTCAACAACACTTGAAGCAC JESPR168 GGCTTCTCTTGCTTAGATCTG GTGCTAATAGAGACCAGCTG JESPR169 CTCAGATCTAATGATTGGGTTGG GAGTAAATTGACCACTTGTTCGC JESPR170 CCCATATTTCAACGTTGACAC CTTATCCTCCAGGTTTCACC JESPR171 CTGCAAGGTGGAAACTGAACCTG CCATGCATGTATTAAATTGTGAG JESPR172 CAATCTGAAAAACCATAAGACC ACCATTGAAACACCATGTC JESPR173 GACCATTTTAGTCCCTTCATC GGAGAAACAAATAGATGTCGAAG JESPR174 CAACAGGTTCCTACAGGTCTG GATTGCTGAAATCACAGAGG JESPR175 CCCCTATTGGCTGCTGAAAG GTTTCTTTTTTTTTTCCCCTGTA JESPR176 CGGCTTCTCTTGCTTAGATCTGGAC GGGCATGATAAATGACAATCCTCC JESPR177 GCTTAATCTGGACTAACATATGC CGGTACATACAGCAAAATGC JESPR178 CCGCTGATGTGGCCAGTTAACTTGCC GATGCTTGTCCAACATGGCTTTC JESPR179 CTGACACTGTATGCTTGCAG CATATTTGGCATATCACATAGAG JESPR180 GCGTAGTACATATAGATGCCC CTTGGAGTATGTATGCTCTATTC JESPR181 CAACTTTTAGATTTGGAAATGG GAGTTGAAGCTTGACCTGTC JESPR182 CTTAGATCTGGACTAGGAGCC GGAAGTGGATTGATAATGAGG JESPR183 CATTTGTTTCACTTCAGGTCC CATGCTACCATTGCTTCTCATC JESPR184 CTTAGATCTGGACTAAACTCTTGC GTTGATGGGGATAGTTCAAGTG JESPR185 CCCAAGCTACAGAGATAACC CACACAAATTGGGTAAGAATAG JESPR186 CCGTGTTGTGAGTGGTACAGGTC AGGTTAGGTTTGGGGTGTTACATAC JESPR187 CAGGTCATGAGGAGCAGAAG GTGCTTAATTTGCAAAAAGGACC JESPR188 GGCTTCTCTTGCTTAGATCTG GGTTTATTTGATTGCTAAGTCC JESPR189 CCATAGACTTGGTTCATGACC GTTCCAGAGTCGTACAGTCG JESPR190 GCCCGCCATCTTTGAGGATCCG GGCAAAACTTGACAATTTTCTCGGC JESPR191 GGTCTAGCCTTTCGGAATTTG GAATCATTCTCGTTCTCGGAC JESPR192 GGAACCTCTACTGAATAGTCGGAG CAGGGATTTCAGCTGACTGC JESPR193 GATCTGGACTAACTATCTTCTTG GTGGTATAAGTTAGTACTTGATGG JESPR194 GAGTTTATTGAGAAAGGCTTTCC CTCAAAGTGGCTGTGCTTTG JESPR195 GATCTGGACTAAACTAGTTGATGTG GCCAATAATGGATGAAGGTTAC JESPR196 CCCTAACACCTCTCAGTTTCACAGC GGGGGCTGCTGATGGAGTTAAGACC JESPR197 CAATACCTGGAACATAGACAAATG CTTGAGGCTTGCAAAAAATG JESPR198 GCTTCTCTTGCTTAGATCTGG GTTTGAATGGCTTCACAAAG JESPR199 GGCAAAGTCCAAAGGCGGTGG CCATCAAAACAGGTGATTGTATTTGG JESPR200 CTTCTCTTGCTTAGATCTGGAC GTGATGTGACCAGTTAACTAGC JESPR201 TCGATCAGTTAGGGTTTTGG CGAATCTCAACCAGATTTCC JESPR202 CACCCGGGAAAAGCTAATGTGGTTG GCCATGAACTCAAGGTACCCATTG JESPR203 CTCTTGCTTAGATCTGGACTAAC CTGCTGAAAGAAATTGTTACCCC

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JESPR204 CTCCAGGTTCAATGGTCTG GCCATGTTGGACAAGTAGTC JESPR205 CCCAACTCTTTCCAAACTTGAG GTACATATAGATGCCCTCGTG JESPR206 CACAGTCTCCCAGAAGCTCCC TAGTTGCGTGTGTCTCCTCTTCTC JESPR207 CAGCAAAGGAACAAGAAACCAGA GTTAATGCACTAAGACTTGGAAG JESPR208 CGCAACCAAACATATACTTCACAC CCCTTTCCATCCATAGAACG JESPR209 ATTGAGAGGCATTTTGGTC AGATGACTAAAAATTGTGCC JESPR210 GCATGTTCTACAATGGTAAGCATA GAATTCTGCTTCTCTTGCTTAGAT JESPR211 CATCATTTTTCCAAGTTCCAATTTC CAAACCGTTAAGGCTCCAGC JESPR212 CCAAAGGTTTTTGTTGTTGCTC CAGATTCTGCTTCTCTTGCTTAG JESPR213 ATATGGAAACCCTAGGAGAG GACAAGAGAACTTACCCAATTAAGC JESPR214 GTAACATTGACGCGATTTATCC CCCTCGACGGATACATATGG JESPR215 CGAGAAGATGAGATTGGAGGAG CCCTTCTGAGTTTTCTTTGG JESPR216 CAAGAGAACTTACCCAATTAAGCC GGAAACCCTAGGAGAGAGAG JESPR217 GCTCTTGCTTAGATCTGGAC GGTGATTCATCCCATGAAATG JESPR218 GGGGCTAAACTTGAAAAATGACC CATGCAGCTTCCAGTTTTG JESPR219 GCATAGTTATGAATGACTCTCTCT GGGGAGTTGAAAAGAAGTATC JESPR220 CGAGGAAGAAATGAGGTTGG CTAAGAACCAACATGTGAGACC JESPR221 CTTAGGTGCTTCAGGCATGATTC CCCAACCCCTTCCTTC JESPR222 GGGCCAACATCTTGC GGGGGACATTAATGATTGG JESPR223 TGGTCCAAAGCTCAAAG CGTTACGGATTATTGGACATG JESPR224 GGGGAGCAACGAAAACTTAGC CCACCATTCTCTTTCATTTTCTCC JESPR225 GTTATGGCGAGGAATATAAC ACTCAAGTGTCCCATCTC JESPR226 GAGGCATGAATATTCAG GAGACATCAAAGTTTGCA JESPR227 CGAGAAGATGAGATTGGAGGAG GGTTTTCCATTCTCTTTCATTTTC JESPR228 CAGAACAACACCATCAACACTCTCAG GGCAAGCAAAGCAAAACTC JESPR229 CCATTCTCTTTCATTTTCTCC GTTGAAACGAGAAGATGAG JESPR230 GGGACTAAAGAAGTAATTATGCC GAAACCCTTGGCCATGAG JESPR231 GCTGGTGGGATTCTCTG CTATGAACTGCTGGCTATGG JESPR232 CAGACCACGCTATTTTTGCC CGTTGTATTATTTCCAGTGCTCG JESPR233 GAGACATCAAAGTTTGCAGC CCCAAACTATTGAACCAAC JESPR234 GCATAGTTATGAATGACTCTC CTAACTCGAATCCGTCAC JESPR235 GAGCAAGGATGAGGAACGAG CAAATTACTCAAGTGTCCCATCTC JESPR236 GACTGACATGCAGCTTCCAG GGGGCTAAACTTGAAAAATGAC JESPR237 GGCATCTCCATGTAGAAATAG TGTCAGTCCCCCATCACC JESPR238 CAGAGAGCTTAGTTAACCC GTTGATCCTTATTTTTCCCC JESPR239 CGACCTGGGATGAGATTTTC CAATAGTGAAGCCCAGTAAG JESPR240 CAGATCCCCTTTTCTTTC GAAGAAGCAAAGCGAGAG JESPR241 CTTACCCAATTGACCTATG GATTCTTCTTATCATCCCC JESPR242 CAATGCGATTTTCAAACCC GCCAGTGTGATGGATATCTGC JESPR243 GTGTGTTCTTAGGTGCTTCAG CCAAACCAAAATAATAGACATCC JESPR244 GAAGATCTTCATCATTTTTCCAAG CAGAGAGCTTAGTTAACCCA JESPR245 GAGACACCAAAGTTTGCAGC GTTTGGAGGCTGAAGGATGTC JESPR246 GGAGCTTTACGGAGAGAGTTG GAGCTCCACTCCAAAGCC JESPR247 GCTTCTTCCATTTTATTCAAG CAGCGGCAACCAAAAAG JESPR248 TCTCTCCCTTTCAAATCTC GTGAATGAAAGGTGTGGTG JESPR249 CCATTACTCTCCTCAAGTATG TCGTAGTCAATGTGGTAC JESPR250 CCAAGAAATCCACCTCATAAG GAGTGCAAGGCTATGCTATTACC

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JESPR251 CAACTAGAATGATAAGACAC CTTTAAGTACGTATGCATC JESPR252 GCTATTGTTGATCTGATCCTG GATTGATCAATCCTGTAACTC JESPR253 CAAACCACGTCTTCTCT CTTAACATCGTCGAATTTTC JESPR254 GTATTGGTTTAATAAAAAGT GGTCCAGTTTGTTTACAGAG JESPR255 GTATTGGTTTAATAAAAGGT GAACTGGTTAAACGAATG JESPR256 GTCAATGAATGCAGAAC GTATATACAAGTATAAAGTATTGG JESPR257 CAAATGATAATATAAAAGACTG GCATATACTCAATGGTATCAC JESPR258 CAAAGTTGGGATTAGAGAC GTATATACAAGTATAAAGTATTGG JESPR259 CCCTTAAATCATAAGAAAACAC GAAGGAGGATCAACTATC JESPR260 CTAGACTCATATGCCCATCTAC CAATAAATGCAAGAAGACAG JESPR261 GGTCATCCTAGGTTCTC CAATAAATGCAAGAAGACAG JESPR262 ACCATCTGTCTTTGGTTTTC CGTTTCTTTGCATCATC JESPR263 CCTTTTTATCTCATGGAAACAC GGCGCGGTACTATGAAC JESPR264 GCTTATTCCATTTTTCAAC CAACTGAAAGAGGATCAAC JESPR265 GATCAACTACAACGCAC AATTCAGTGGTCAAAACA JESPR266 GGTGACTCTAGCTCCG CCAGCAGTTTTGGTCTC JESPR267 CCCTCTAACTTTCCCCA GAAAACCCAGCATTGC JESPR268 GGTTGGAAAGGAAGGAC CATGTGTTGCCATGATATA JESPR269 GCATCGGGATGGTGTG GATCAGCCACCCAAAATTAG JESPR270 ACGCAACTCGCATATAAACAC GTAGCTTAGAATTTGAATGGC MGHES1a GACATGAGGAAGCAGTTGAAAGG GCATCACCTGAACAACATCCACC MGHES1b ACAGGGCAGCGTTTAATTTG CACCTGAACAACATCCACCA MGHES2 TCTCTCAAAATCTCAAACCCAGA GCTTAGGGCAAACCACTGAA MGHES3 TCTCTCAAAATCTCAAACCCAGA GCTTAGGGCAAACCACTGAA MGHES4 GCCGGTTCCTTTGACCAC CCCGCATCGTCATTAACTTT MGHES5 ATTTGCGGGTGGAGAAGAC TGGCGATTGAACAACAAAGA MGHES6 TCGCTTGACTTTCCATTTCC AACCCTCGGGATTATCGTCT MGHES7 CCTTCTTCAACACCAATCTCC TGCATTTCTGCTGAGTACCG MGHES8 CAAGCGATTGTTTCTCATCC CGTCATCATAAACCAACGTGC MGHES9 TCGAGAAATTTGGCTTCACC GTGTTGGATGTAGCGGGAGT MGHES10 CTGATTCCACTCTCAAAACCAC CTACTTTCCATCAGATCCCC MGHES11a CGACTCCTCGACTCGCTATT GCGCCACATACATCTCTCC MGHES11b CATCATGGCTTTCCGTTTTT CCAGGATTGGTAAACCCGTA MGHES12 GTTTCCAGGACAGAAAGGTGTC GAGTTCCCAGTTACAGAGGC MGHES13 CAGGGGAGCCATTGTTAGAA CAGGGGTCCTGTGTTTCAGT MGHES14 GAGGAGGCTGTGGTTGAAGA ATGGTGACCCTGCTTACACC MGHES15 AATCGAAGCGTTTCATCACC CGAAGATCTTGGACAGACGA MGHES16 ACCCCAATACAACCCCATTT GCAGAGAAAAGGGACAGAGG MGHES17 AACCCTTCTTTTCCCCCTTT TCTTCACCGATGCCATTGTA MGHES18 GCCATCAATTGGTGAAGCAT ATGCCTCGGTGAGAAAATTG MGHES19 CACCGATCAGATAGCAGCAG TGGCGTCTCAGAGATGAAGT MGHES20 CGAACCCTAGCTTTCAGTCG AGTCGACGGCTTCAGATTGT MGHES21 TTTTTCGGGCTATGCTTTTG GGGGTTGACATGTCCTATGC MGHES22 GGAACAGAGGCAACTGAGGA TCGAAGGCACAGAGAAGGTT MGHES23 AGCCGCATCACTTTTTGCTA TCAAAAACAGAAGCACCAAGG MGHES24 CGCAACAACTGATGCAACTC AACCGATACCTCCGCTTCTT

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Appendices

MGHES25 TGAGGAACCAAGCAAAAACC CTTGGGCAACTTCCAAGGTA MGHES26 AAGGGGAGGTTTTGTGTAAGG GACAAGAACCAGCTCCCAAA MGHES27 TAATGGCGGCTAAACCTTCA GGGGTTGCCTTCTTCTGTAG MGHES28 CCTGCAAACGCTATTGATCC CCCAGACTGGTGATGATGAA MGHES29 TTTCCATTTTCTCCTGCTTCA TCAGCTCATGGGAGCAAATA MGHES30a GAATTGAATCATTTTCTCTGCAA GGTCCACCCTTTCCTTTCTC MGHES30b GGAGAAAGGAAAGGGTGGAC GCAACACCAGAGAACACACG MGHES31 AAGTTAGCGGCTTCTTGTGG GGGTCAGAACTGGACAAGGA MGHES32 CGTCGCTTCCTTTGCTAAAC GTCGGGTTAATTGCAAATCG MGHES33 TTTTTGGGCTTTCTTTTCTCTC CCAATTACGCATGTTCAACG MGHES34 TTCCTCCCTTCCTTCTCTCC TTCCATTGTCATCGTTTCCA MGHES35 TCGAACGGCTCGTTAAATCT CAGCAAAGAGTGGTTCTCTGG MGHES36 CGACAGCGAGTGTGAAACAT GGTGGGAAAAACGCAAACT MGHES37 GAAAATCCCAATTTCCACGA TCATGATACCAATTTTGCTTCG MGHES38a TTTGTTTTTGCAGCCCTTCT GGGTCAAAGGAAGTGAACCA MGHES38b CCACCACCACTCTCCTTCTC TCTTCTGCTGGTGCAATGAC MGHES39 CACAACCCTAGCTAATCCCAAT ACGTCGCTTCTGAGATTCGT MGHES40 CGCGTTCCCAACTTATTTGT GGTGCTCCCGGATTAGATTT MGHES41 GAAGGAGGGCGAAAAACATA TTGGAGATTACGCGACCTTC MGHES42 GAAGAGCAGGTGGACCTTGA CCCCTCATTAGCATCAGAGC MGHES43a AGGACTTGTCCACGTGCTTC TTTGATTCTTTTCGGCTGCT MGHES43b AAGCGTTCACACCATGACTTC GGGAATCTCCGGGTTAAA MGHES44 ACCACTTGGGATTGGTTCAA GAGGCCACCACATATCGTTT MGHES45 TGTGTCGTTTTCTGGGACCT TATCACCGACATCTCCACCA MGHES46 CGATTTCCATTCCACACCTC GCATTGCAATCGAAACACAT MGHES47 CCCCATCAGAAGGAGTGCTA TGAATGATACCGCAGGGATT MGHES48 AAAGGGAGATTGAAGCAGCA CACCACCAAATCATCTGCAT MGHES49 GGGGTCTCACTCAAATGCTC TGGTGAGGGCTTAATCATGG MGHES50 TGCACTAAATTCACCCACCA GGGACCGAAGAAAGGAAGAA MGHES51 GCCCTTCAAACCAAACGTAA GGCTGCTTCAAAAGCATAGA MGHES52 GGAAGGAGGAGGCAGTGATA GGGAAGATGAAACCGGTAGG MGHES53 ACAAGGACCAGCAGTTTTGG TCAAGTCCAAGTACTGCAATGAA MGHES54 AGCCCTTCACTTTCCCCTTT CGTCGCCCGTACCATAAC MGHES55 CGAACCCTAGCTTTCAATCG CGGCTTCAATTGTACGGTCT MGHES56 ACCAGGACTGGGCTGAGATA GAACGTATTTCCACAAGTCTAGCA MGHES57 CCACCCAGTTTGGAAAGTCA TCTCCACTGGACTGCAACTG MGHES58 TCTCCATGTATCCACCCACA GCATCGTGAGAGAAGTGAAGG MGHES59 GCAAACCCAACCAGAGTCAT AACCACTGCTGTTGTTGCTG MGHES60 TCCATGGACCCAGAAGAAGA TCAGTCTGCAACTCTTCCACA MGHES61 CCAGTCCTTGCCTCCATTTA TCAGTCTGCAACTCTTCCACA MGHES62 TGCATCTGATCTAATTGTTGGTG TGTTCCTCACAGCAAGAGCA MGHES63 GCGGACAATTGGTGCTTAAT CCGCAAACTGACTCTAATTTTTC MGHES64 CCGTTATTCTTTCTCTATTCTACCTG ACATCTAAGCAGCCCAGCAG MGHES65a CAATGTGTGAGGGAATGCAG CCTTGAAGCAAACCTTTGGA MGHES65b TCTTCCAGGCAAAGCTCATT TAAAGACCGAACCCGTCATC

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Appendices

MGHES66 TCCTCCTCCCACTTCATCAC GACTGTGGCTGGAGGAGAAG MGHES67 GACTACCCACCCACCAAAGA CCCGAGTTTTCGCTACTGAT MGHES68 CTTCGCCCATTTCTTACCTG GAGAAGGGTCGGGAGAGATT MGHES69 AAGAGGGCTTGAAGCTGTTG TATAGGCGAAATGGGTACGG MGHES70 CCAATAGGACTTTGGGTTTGG CTTGCGAGGATCAGAAAAGC MGHES71 ATCACCACCTCCACCATCTC CTCCGATTACAGGTGGCAGT MGHES72 CCCTTCGTTGTTTCCTCAAC GGGCTCATCCTCTTCGACTT MGHES73 CCCGATATCCTTAGCCTTTT AGTCGGAGGTGATGGTTAGG MGHES74 AAACCCCCAGGGGAAAAA GGGGCCATGTTTCTGTACC MGHES75 GCATATGTCGAAGATGCTACCC AGCATCAGCAGTAGGCCAAG MGHES76 ATGGGGTCATCCTCCATTTT TTTCTCCATTTGCCTTCACC MGHES77 GCACAGGGTGAGGAATTGAG TGCACCAATATCACTTCTTTGC MGHES78 TGCTGGCAACTACCTGAGTG GATTCCAGGAACCACAATGG

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Muhammad Asif - Pakistan Research Repository

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