Application of principal component analysis to study topography of hypoxic–ischemic brain injury

ORIGINAL ARTICLE

Application of principal component analysis to

pharmacogenomic studies in Canada

H Visscher1 CJD Ross1M-P Dube2 AMK Brown23MS Phillips23 BC Carleton4

and MR Hayden1

1Centre for Molecular Medicine and TherapeuticsChild and Family Research Institute Departmentof Medical Genetics University of BritishColumbia Vancouver British Columbia Canada2Montreal Heart Institute Research Centre andUniversite de Montreal Montreal QuebecCanada 3Montreal Heart Institute and GenomeQuebec Pharmacogenomics Centre MontrealQuebec Canada and 4Pharmaceutical Outcomesand Policy Innovations Programme Departmentof Paediatrics and Faculty of PharmaceuticalSciences University of British ColumbiaVancouver British Columbia Canada

CorrespondenceDr MR Hayden Centre for Molecular Medicineand Therapeutics Child and Family ResearchInstitute University of British Columbia 950West 28th Avenue Vancouver BC CanadaV5Z 4H4E-mail mrhcmmtubcca

Received 8 January 2009 revised 26 June2009 accepted 30 June 2009 publishedonline 4 August 2009

Ethnicity can confound results in pharmacogenomic studies Allele frequenciesof loci that influence drug metabolism can vary substantially betweendifferent ethnicities and underlying ancestral genetic differences can leadto spurious findings in pharmacogenomic association studies We evaluatedthe application of principal component analysis (PCA) in a pharmacoge-nomic study in Canada to detect and correct for genetic ancestry differencesusing genotype data from 2094 loci in 220 key drug biotransformationgenes Using 89 Coriell worldwide reference samples we observed a strongcorrelation between principal component values and geographic origin Wefurther applied PCA to accurately infer the genetic ancestry in our ethnicallydiverse Canadian cohort of 524 patients from the GATC study of severeadverse drug reactions We show that PCA can be successfully applied inpharmacogenomic studies using a limited set of markers to detect underlyingdifferences in genetic ancestry thereby maximizing power and minimizingfalse-positive findingsThe Pharmacogenomics Journal (2009) 9 362ndash372 doi101038tpj200936published online 4 August 2009

Keywords ethnicity principal components analysis association study

Introduction

Individuals manifest considerable variability in their response to drug treatmentinfluenced by differences in gender age environment as well as geneticdeterminants The importance of ethnicity with regards to drug responseincluding efficacy and risk of toxicity has been long recognized1 Geographicand ethnic differences in the frequency of allele variants in genes that influencedrug response such as drug-metabolizing enzymes transporters and drug targetshave been studied since the 1950s and provide a mechanistic basis for at leastpart of the differences in drug response between populations2ndash4 In additionthere can be ancestry-specific variants associated with serious adverse drugreactions (ADRs) that occur in a specific ethnicity An important example is theHLA-B1502 allele and carbamazepine-induced StevensndashJohnson syndromewhich has only been found in the Asian populations5

Recently with advances in technology and availability of data from theHapMap project there have been many publications of large genome-wideassociation studies for several common diseases (see ref 6 for a current list)Furthermore the first two genome-wide association studies that explore geneticvariability of drug response have now been published78 Most of these studiesare performed using a homogeneous study cohort with cases and controls ofsimilar geographic ethnic or racial descent and individuals from other originsare excluded910 The reason is that underlying differences in allele frequencies

The Pharmacogenomics Journal (2009) 9 362ndash372amp 2009 Nature Publishing Group All rights reserved 1470-269X09 $3200

wwwnaturecomtpj

between individuals of different genetic ancestry calledpopulation stratification can cause systematic differencesin allele frequencies between cases and controls and leadto false-positive associations11 This confounding biascan occur when differences in phenotypic frequencies (egdisease or drug toxicity) exist between different geneticsubpopulations included in a study Genetic markers thathappen to have a high allele frequency in a subpopulationwith a high phenotype frequency which would also beoverrepresented in cases of a study could lead to spuriousassociations1112 Combining different ethnic (sub) popula-tions in one study can also mask true effects leading to falsenegatives especially if the populations are ethnicallydistant13 It is therefore necessary to detect and correct forthese ancestral genetic differences

In our national study of severe ADRs in children(genotype-specific approaches to therapy in children orGATC) we are collecting samples from many surveillancesites across Canada14 Canada is an ethnically diversecountry with visible minorities representing 16 of thetotal population and almost half the population in largemetropolitan areas such as Toronto and Vancouver15 Manymarriages and common-law unions in Canada are inter-ethnic unions In Vancouver for example up to 85 ofall unions are between people of different ethnicities15

To maximize the statistical power in our pharmacogenomicassociation study we need to include as many samples aspossible while minimizing possible false-positive associationsdue to population stratification It is vital to control for thegenetic ancestry of each sample However commonly usedethnic labels are often insufficient and inaccurate proxies ofgenetic ancestry especially in populations with extensiveadmixture16 and some individuals do not know or wronglyassume their ethnicity It is therefore imperative to applyother methods to determine genetic ancestry

Several methods have been developed to detect andcorrect for population stratification in genetic associationstudies and to estimate genetic ancestry17 In manypharmacogenomics studies to date ancestry informativemarkers (AIMs) or neutral markers have been used to detectgenetic ancestry differences and to assign individuals todifferent populations using a model-based clusteringmethod1819 However this method can be computationallyintensive when run on thousands of markers for manyindividuals at once and the assignment of individuals toancestry clusters is limited by an a priori assumption of thenumber of clusters20

Genomic control is an alternative adjustment methodthat relies on a quantitative estimate of the degree ofpopulation stratification at reference single nucleotidepolymorphisms (SNPs) used to adjust for any stratificationthat might be present in the tested SNPs The method relieson the use of unselected random SNPs because AIMscould artificially inflate the population structure estimateHowever this method is conservative as the same correctionfactor is used for all investigated markers12

Principal component analysis (PCA) is an alternativemethod to detect and correct for population stratification

Principal component analysis was first applied to geneticdata more than 30 years ago21 but it was not until morerecently that a solid statistical basis was provided2022

Principal component analysis is a mathematical methodthat reduces complex multidimensional data (in this casegenotype data) to a smaller number of dimensions bycalculating the main axes or lsquoprincipal componentsrsquo (PCs)of variation These components are orthogonal vectors thatcapture the maximum variability present in the data Thefirst component explains the most variation in the dataand each subsequent component accounts for anothersmaller part of the variability When applied to genotypedata these axes of variation have been shown to have astriking relationship with geographic origin For examplePCA of genotype data from hundreds of thousands of lociin 1387 individuals from across Europe shows how a two-dimensional genetic map based on the first and second PCsclosely mirrors the geographic map of Europe and can beused to accurately estimate ancestral origin of samples23

The PCA method is simple to use and is an efficient methodeven with large datasets Compared with other clusteringmethods PCA does not assume a predefined number ofexpected ancestry clusters20 and it has the advantage ofbeing valid when used directly with the SNPs genotyped ina study provided that those are present at a sufficiently highnumber24

To explore the benefits of PCA in pharmacogenomic studiesin Canada we applied PCA to genotype data from 2094 loci in220 key drug biotransformation genes to detect and correct forgenetic ancestry differences Using Coriell reference sampleswe observed a strong correlation between PC values andgeographic origin In addition we were able to infer thegenetic ancestry of samples from the GATC study with highaccuracy and to estimate the genetic ancestry of samples ofunknown origin This shows that PCA can successfully beapplied in pharmacogenomics to correct for populationstratification using a limited set of markers

Results

We genotyped 89 Coriell worldwide reference samplesusing a customized pharmacogenomics SNP panel whichwas designed to capture the genetic variation of 220 keydrug biotransformation genes (see Material and methodssection and Supplemental Table 1) These samples werechosen to reflect as much genetic variation as possible acrossthe world We then genotyped 524 patient samples from ourGATC study (see Supplemental Table 2 for detailed patientclinical characteristics) To identify underlying geneticancestry differences we performed PCA on the genotypedata from both Coriell reference samples and GATC samplesThe first two principal components (PC1 and PC2) representthe main axes of variation within this data and explained462 and 346 of variation respectively We created scatterplots of these components to visualize these data

Initially we plotted PC1 and PC2 of the Coriell referencesamples to assess the worldwide pattern of genetic variation

Use of principal component analysis in pharmacogenomicsH Visscher et al

363

The Pharmacogenomics Journal

for this pharmacogenomics panel (Figure 1) As expectedthe cluster pattern resembled a geographic map of theworld with the three continents Europe Asia and Africaeach on different points of the lsquotrianglersquo consistent withother reports2526 The first PC distinguished betweenEuropeans and East-Asians with samples from the Indiansubcontinent at intermediate values

The second component (PC2) distinguished betweenAfricans and non-Africans with North Africans clusteredbetween sub-Saharan Africans and Europeans Within theAfrican cluster there was more variability which reflects thegreater genetic diversity in samples of African ethnicity27

Next we plotted PCs of GATC samples for which the self-reported geographic origins of all four grandparents werefrom the same continental cluster (Europe Asia or Africa) orIndia (Figure 2) Of these samples almost all of theindividuals fell within or close to their expected clusterwith one notable exception one of the East-Asian samplesfell very close to the Indian cluster This individual was of

Singaporean origin and was therefore labeled as East-AsianHowever according to the census data 89 of residents inSingapore are ethnic Indians28

In large studies participants may not know their ancestryor simply list themselves as lsquoCanadianrsquo For example theCanadian census data list more than 30 of the totalpopulation responded as having lsquoCanadianrsquo origins15 Of allGATC samples parents of 107 individuals identified theirfamily origin as from Canada When we plotted the PCs ofthese samples most individuals fell within the Europeancluster (Figure 3) which is not surprising given the largenumber of descendants from immigrants from Europe inCanada15

Next PCA was used to estimate the genetic ancestry ofindividuals from other geographic or ethnic origins thatcould not be easily placed in one of the earlier clusters(Figure 4andashe) For each of these groups only a small numberof samples were available in our cohort Of the sixindividuals of Caribbean origin four showed a genetic

Figure 1 Scatter plot of principal component axis one (PC1) and axis two (PC2) based on genotype data of Coriell reference samples shows thepattern of genetic structure to resemble the worldwide geographic map Individual data points are colored similarly by continental or ethnic origin

(see legend)


364


structure similar to sub-Saharan Africans and African-Americans and are most likely of Afro-Caribbean descent(Figure 4a) Two others had values close to the Indianand European clusters again not unexpected given thehistory of immigration of Europeans and Indians to theCaribbean The Canadian First Nations samples showedintermediate values between the European and East-Asianclusters (Figure 4b) The native inhabitants of the Americasare thought to have originated from East-Asia29 so onemight have expected values closer to East-Asians Middle-Eastern individuals clustered near the border between theEuropean and North-African clusters which is consistentwith what has been shown before2630 (Figure 4c) TwoLatin American individuals clustered between the threecontinental clusters which likely reflects the admixture ofEuropeans Africans and Native Americans over the last fivecenturies in South and Central America (Figure 4d)

Interestingly three individuals originating from the FijianIslands in the South Pacific clustered within the Indianancestry cluster Several studies have shown that peopleoriginating from the South Pacific or Oceania are geneticallyclosest to East-Asians3132 However demographic informa-tion shows that 371 of Fijians are of Indian descent33

because of immigration in colonial times so these threesamples are likely of Indian descent

Plotting PC1 and PC2 for individuals of mixed origin showedthat the majority of these individuals had intermediate valuesbetween their two continental clusters of origin (Figure 5a)In some cases there was a clear genetic dose effect Forexample the three individuals with two grandparents fromEurope and two from the Caribbean clustered directly betweenthe African and European clusters whereas one individual withone grandparent from the Caribbean and three from Europefell closer to the European continental cluster

Figure 2 Scatter plot of PC1 and PC2 of GATC samples with self-reported geographic origin of all four grandparents from same continental cluster

(Europe Asia or Africa) or India For reference the Coriell samples are also plotted in light gray Almost all individuals have values within or close totheir expected clusters One exception is one individual labeled as Asian with origins from Singapore that falls close to the Indian cluster (see arrow)

This individual could well be a descendant from one of the many ethnic Indians in Singapore


365


The PCA results of individuals of unknown or unreportedgeographic origin showed that the majority of these sampleswere similar to individuals of European ancestry (Figure 5b)with some being similar to Asians and Africans Theindividuals with intermediate values between the Europeanand Asian continental clusters could be of mixed or FirstNations origin however with the current data it is notpossible to distinguish them

Discussion

Ethnicity has an important function in pharmacogenomicsbecause allele frequencies of genetic variants with signifi-cant effects on the biotransformation of drugs can varyconsiderably between different ethnicities Mixing popula-tions of different ancestries in an association study can leadto spurious associations Therefore it is critically importantto determine genetic ancestry of samples to correct for these

differences in a pharmacogenomic association study Wedescribe here the implementation of PCA to easily andreliably ascertain the genetic ancestry of study samplesusing a limited set of pharmacogenomic markers to assesspopulation stratification within a Canadian study cohortand to correct for these ancestry differences therebymaximizing the number of samples and power whileminimizing false-positive associations

When studies are conducted in ethnically diverse cohortswith mixed and sometimes unknown ancestry such asthe GATC pharmacogenomics study of ADRs in childrendetermining underlying genetic ancestry can be difficultbecause self-reported ethnic or racial labels are often insuffi-cient proxies for genetic ancestry or may be undisclosedor unknown by the study participant Methods thatdetermine genetic ancestry using patient genotype datareflect differences in allele frequencies significantly moreaccurately41634 Preferably these methods should be fastand easy to use even on large datasets without the need for

Figure 3 Scatter plot of PC1 and PC2 of GATC samples with self-reported geographic origin from Canada PCA shows that the genetic structure of

most of these individuals is similar to Europeans


366


Figure 4 Scatter plots of PC1 and PC2 showing the genetic structure of GATC samples with origins from other geographic regions (a) Caribbean

Four individuals have a genetic structure similar to sub-Saharan Africans whereas the two others are closer to India and Europe (b) First Nations The

individuals with First Nations origin show intermediate values between Europe and Asia (c) Middle-Eastern PC values of individuals with Middle-Eastern origin are on the border of Europe and North Africa (d) Latin American The two individuals from Latin America have PC values between the

three continental clusters (e) Fijian Three individuals with origins from the Fijian Islands show genetic structure similar to Indians


367


Figure 5 (a) Scatter plot of PC1 and PC2 of GATC samples with mixed origins The majority has PC values between the two continental orgeographic clusters they are originating from There seems to be a genetic dose effect for example three individuals with two European and two

Caribbean grandparents fall on the border of the African cluster (open arrow) whereas one individual with three European and one Caribbean

grandparent falls closer to the European cluster (closed arrow) (b) Scatter plot of PC1 and PC2 of GATC with unknown geographic origin On the

basis of PC values most samples seem to be of European origin


368


genotyping additional AIMs Unlike some other methodsPCA of genotype data is fast and easy can be used on largedatasets and does not require any additional genotyping20

Other groups have shown that PCA can accurately detectunderlying differences in genetic ancestry and estimatean individualsrsquo ancestral origin with high accuracy2023

We show here that PCA using a limited set of importantpharmacogenomic markers in an ethnically diverse cohortcan reliably detect underlying differences in genetic ancestry

We have applied PCA to detect differences in geneticancestry using 2094 SNPs in 220 key drug biotransformationgenes in a varied cohort of samples from the GATC studyin patients with geographic origins from around the worldInitially worldwide Coriell reference samples of predeter-mined ancestry were used to assess worldwide geneticvariation using PCA and then the PCs of GATC sampleswere plotted We found that the first two PCs were highlyinformative of genetic ancestry and strongly correlated withthe known geographic origin of samples We were also ableto determine the genetic ancestry of samples with mixed orunknown origins Principal component analysis could alsodetect outliers such as an individual from Singapore thatotherwise may have been misclassified Furthermore PCA isa powerful approach to classify groups of patients that donot belong to one of the three continental groups The PCvalues could be used to stratify samples into differentgenetically similar groups for analysis However PCs arecontinuous axis of variation20 and therefore in most casesit will be more appropriate to use them as continuouscovariates in regression analyses This strategy will increasethe power of a study because all participants can be analyzedtogether while at the same time correct for populationstratification

Our ability to discriminate between samples from differ-ent ethnic or geographic origins and to detect furtherpopulation stratification will likely increase when moreSNPs are investigated in our pharmacogenomics panel22

Using PCA on hundreds of thousands of markers severalgroups were able to show detailed underlying geneticdifferences with high correlation between the genetic andgeographic map in Europe2335

Increasing the number of samples might further increasethe ability to detect additional population structure Cur-rently samples with genetic ancestry from Europe wererelatively overrepresented in our cohort compared withthe number of samples from Asian and especially Africanancestry Genetic diversity in certain regions such as Africaor India is extensive2736 and including more samples fromthese origins and from other geographic locations will likelyfurther improve this method

Increasing the number of SNPs or including more sampleshowever will not likely change the overall worldwide mapwhen plotting PC1 and PC2 but investigating plots ofsubsequent PCs might show additional geographic structurein subpopulations2526 In this study visual inspection ofplots of subsequent components (PC3ndashPC10) did not showfurther discrimination possible between subpopulations(data not shown)

To test the robustness of PCA and to estimate theminimum number of SNPs needed in our SNP panel todetect population structure we performed PCA using sets ofdifferent numbers of randomly selected SNPs Plotting thefirst two components using 1000 or 500 randomly selectedSNPs shows a similar cluster pattern as using the full dataset(Supplemental Figure 1a and b) Using less SNPs (250 100or even as low as 50) still shows some evidence of thecontinental clusters (Supplemental Figure 1cndashe) howeverthe clusters are close together and there is substantialvariability within clusters The plots suggest that at least250ndash500 SNPs are needed to discriminate clearly betweenclusters

The pharmacogenomic SNP genotyping panel consistedof different classes of SNPs (ie non-synonymous synon-ymous intronic etc) To evaluate whether the results onlydepended on a particular class we applied PCA to thedataset using only intronic (nfrac14972) or only non-synon-ymous (nfrac14 332) SNPs Again plotting the first two compo-nents using either group of SNPs shows a similar clusterpattern as using the full dataset (Supplemental Figure 2)This suggests that the observed structure does not dependon a particular class of SNP as long as the dataset issufficiently large Removing the AIMs which were includedin the design of the SNP panel from the full dataset also didnot change the results (data not shown)

Principal component analysis to detect population struc-ture works well if the genetic markers are independentHowever in extreme cases of linkage disequilibrium (LD)the results can be difficult to interpret Components can becorrelated with genotype patterns in large blocks in whichall markers are in LD and distort the eigenvalue structure ifnot corrected for22 In this study many genes have beendensely genotyped with varying patterns of LD To assesswhether LD between markers might influence our resultswe removed a marker from every pair of markers that werein tight LD so that only unlinked markers were included inthe genotype dataset (nfrac14778) We then applied PCA to thisreduced dataset and plotted the first two components (PC1and PC2) of the Coriell reference samples (SupplementalFigure 3) The cluster pattern showed in this analysis is verysimilar to the initial PCA using the full set of markers whichsuggests that LD between markers in our dataset did notinfluence our results which is consistent with what hasbeen shown earlier25

Several other factors such as inter-assay variability orcryptic relatedness can potentially lead to evidence forstructure in genotyping data and be mistaken for populationstratification203738 However inter-assay variability for ourgenotyping panel was low with a concordance in genotyp-ing results for replicates of greater than 999 (nfrac14132) Wealso assessed cryptic relatedness or hidden distant kinshipby calculating the average identity by state but found nocryptic related individuals These factors will therefore nothave influenced the results substantially

We have shown that PCA applied to a limited set ofpharmacogenomic data can be used to detect importantunderlying differences in genetic ancestry Principal


369


component analysis can be used in pharmacogenomicassociation studies to maximally use available patientsamples which are often very valuable and in limitedsupply while at the same time minimizing the likelihood offinding false-positive associations

Material and methods

Populations and samplesInitially DNA samples from different reference populationswere selected and obtained from the Human Variation Panelfrom the Coriell Cell Repositories (Coriell Institute forMedical Research Camden NJ USA) 10 Northern-European9 Italian 9 Hungarian 9 Indo-Pakistani 9 Chinese 9Japanese 9 Southeast Asians 9 African-American 7 AfricansNorth of the Sahara and 9 Africans South of the Sahara

A total of 524 patient DNA samples were then analyzed aspart of the GATC project a national project established inCanada to identify novel predictive genomic markers ofsevere ADRs in children14 Patients who suffered any seriousADR were enrolled as well as drug-matched controlsPatients were treated for a variety of diseases with manydifferent drugs There were slightly more males than females(57 vs 43) No siblings or relatives were included in thisanalysis As part of this study parents were asked for thegeographic origin of the four grandparents of their child SeeSupplemental Table 2 for more details on patient character-istics Written informed consent was obtained from eachstudy participant or parent and the study was approved bythe ethics committees of all the participating universitiesand hospitals DNA was extracted from blood saliva orbuccal swabs using the QIAamp 96 DNA Blood kit (QiagenON Canada)

Genotyping

All DNA samples were genotyped for 2977 SNPs using acustomized Illumina GoldenGate SNP genotyping assay(Illumina San Diego CA USA) which was designed tocapture the genetic variation of 220 key drug biotransforma-tion genes (ie phases I and II drug-metabolism enzymesdrug transporters drug targets drug receptors transcriptionfactors ion channels and other disease-specific genes relatedto the physiological pathway of ADRs) The panel consisted of1536 tagSNPs identified using the LD Select algorithm toselect a maximally informative set of tag SNPs to assay in thecandidate genes39 The tag SNP selection was performedusing data from the International HapMap project thatincluded all four populations (CEU CHB JPT YRI) with athreshold for the LD statistic r2 of 08 and a minor allelefrequency of more than 005 Furthermore 1536 functionalSNPs were included that had been identified primarily byliterature review or from public databases that cause non-synonymous amino-acid changes or have been or could beassociated with changes in enzyme activity or functionNinety-five SNPs were both tag and functional SNP so a totalof 2977 unique SNPs were included The initial design alsoincluded 50 AIMs All SNPs were manually clustered in theBeadStudio software suite SNPs that could not be clustered

or were non-polymorphic were excluded from furtheranalyses (883 SNPs) In total 2094 SNPs were available foranalysis See Supplemental Table 1 for more details on theSNP panel Researchers who wish to obtain more informa-tion can contact the corresponding author

Data quality controlSamples with a call rate of less than 95 were not includedin the analysis The average genotyping call rate for allsamples was 993

To evaluate our genotyping platform for inter-assayvariability or batch-to-batch variation we genotyped allCoriell samples as well as a subset of patient samples inreplicate In addition a positive control sample with knowngenotypes was included in each genotyping batch Theconcordance of genotype calls between these replicategenotyped samples was greater than 999 (nfrac14132)

The average identity by state was computed for eachsubject pair as implemented in PLINK40 but no duplicates(499 identity) or (cryptic) related individuals (86ndash98identity) were found

Principal component analysis

Principal components of the SNP genotype data from theCoriell reference samples and the GATC patient sampleswere calculated using the Eigenstrat method20 implementedin HelixTree 642 (Golden Helix Bozeman MT USA) usingdefault settings Applying PCA to the genotype dataconsisting of 2094 SNPs reduced this multidimensionaldataset into a smaller number of PCs which each explainssubsequent parts of variation The first and second PCs thatexplain the largest portion of variation 462 and 346respectively were plotted using Excel (Microsoft RedmondWA USA)

A second PCA was performed using only markers not inLD (nfrac14778) Linkage disequilibrium between all pairs ofmarkers was calculated in HelixTree A marker was removedfrom every pair of markers that were in LD so that onlyunlinked markers (r2o02) were included

Abbreviations

PCA principal component analysisGWAS genome-wide association studyADR adverse drug reactionSNP single nucleotide polymorphismGATC genotype-specific approaches to therapy in children

Acknowledgments

We thank the patients and their families for their participation inthe GATC project We acknowledge the support of the GATCactive ADR surveillance network particularly the site investiga-tors Cheri Nijssen-Jordan David Johnson Kevin Hall MichaelRieder Shinya Ito Gideon Koren Regis Vaillancourt PatElliott-Miller Jean-Francois Bussieres Denis Lebel MargaretMurray Darlene Boliver Carol Portwine site surveillance clin-icians Linda Verbeek Rick Kaczowka Shanna Chan BeckyMalkin Facundo Garcia Miho Inoue Sachi Sakaguchi Toshihiro


370


Tanaka Elaine Wong Brenda Wilson Pierre Barret Carol-anneOsborne Amy Cranston and research staff at POPI and theCMMT Anne Smith Claudette Hildebrand Lucila Castro RezaGhannadan Catherine Carter Fudan Miao Terry Pape andGraeme Honeyman The study was funded by the GenomeCanada and additional funding was also provided by theGenome British Columbia the Child amp Family Research Institute(Vancouver BC) the Canadian Institutes of Health ResearchFaculties of Pharmaceutical Sciences and Medicine University ofBritish Columbia University of Western Ontario the CanadaGene Cure Foundation the Canadian Society of Clinical Phar-macology C17 Research Network the Childhood Cancer Founda-tion Candlelighters Canada the Canadian Paediatric SocietyMerck Frosst Janssen-Ortho Illumina IBM Eli Lilly and PfizerHV is supported by a postdoctoral research fellowship award fromthe Michael Smith Foundation for Health Research and the Childand Family Research Institute

Conflict of interest

The authors declare no conflict of interest This work wasfunded as part of the peer-reviewed Genome CanadaApplied Health Research Program

References

1 Gurwitz D Motulsky AG Drug reactions enzymes and biochemicalgenetics 50 years later Pharmacogenomics 2007 8 1479ndash1484

2 Ameyaw MM Regateiro F Li T Liu X Tariq M Mobarek A et al MDR1pharmacogenetics frequency of the C3435T mutation in exon 26 issignificantly influenced by ethnicity Pharmacogenetics 2001 11217ndash221

3 McLeod HL Pritchard SC Githangrsquoa J Indalo A Ameyaw MM PowrieRH et al Ethnic differences in thiopurine methyltransferase pharmaco-genetics evidence for allele specificity in Caucasian and Kenyanindividuals Pharmacogenetics 1999 9 773ndash776

4 Wilson JF Weale ME Smith AC Gratrix F Fletcher B Thomas MG et alPopulation genetic structure of variable drug response Nat Genet 200129 265ndash269

5 Lonjou C Thomas L Borot N Ledger N de Toma C LeLouet H et alA marker for Stevens-Johnson syndrome ethnicity matters Pharmaco-genomics J 2006 6 265ndash268

6 Hindorff L Junkins H Mehta J Manolio TA Catalog of PublishedGenome-Wide Association Studies Available at wwwgenomegov26525384|

7 Cooper GM Johnson JA Langaee TY Feng H Stanaway IB Schwarz UIet al A genome-wide scan for common genetic variants with alarge influence on warfarin maintenance dose Blood 2008 1121022ndash1027

8 Link E Parish S Armitage J Bowman L Heath S Matsuda F et alSLCO1B1 variants and statin-induced myopathymdasha genomewide studyN Engl J Med 2008 359 789ndash799

9 Wellcome Trust Case Control Consortium Genome-wide associationstudy of 14 000 cases of seven common diseases and 3000 sharedcontrols Nature 2007 447 661ndash678

10 Sladek R Rocheleau G Rung J Dina C Shen L Serre D et al A genome-wide association study identifies novel risk loci for type 2 diabetesNature 2007 445 881ndash885

11 Pritchard JK Stephens M Rosenberg NA Donnelly P Associationmapping in structured populations Am J Hum Genet 2000 67170ndash181

12 Balding DJ A tutorial on statistical methods for population associationstudies Nat Rev 2006 7 781ndash791

13 Huang RS Duan S Shukla SJ Kistner EO Clark TA Chen TX et alIdentification of genetic variants contributing to cisplatin-inducedcytotoxicity by use of a genomewide approach Am J Hum Genet2007 81 427ndash437

14 Ross CJ Carleton B Warn DG Stenton SB Rassekh SR Hayden MRGenotypic approaches to therapy in children a national active surveil-lance network (GATC) to study the pharmacogenomics of severe adversedrug reactions in children Ann N Y Acad Sci 2007 1110 177ndash192

15 Statistics Canada Ethnic Origin and Visible Minorities 2006 Census(2008) Statistics Canada Demography Division Ottawa

16 Suarez-Kurtz G Vargens DD Struchiner CJ Bastos-Rodrigues LPena SD Self-reported skin color genomic ancestry and thedistribution of GST polymorphisms Pharmacogenet Genomics 200717 765ndash771

17 Barnholtz-Sloan JS McEvoy B Shriver MD Rebbeck TR Ancestryestimation and correction for population stratification in molecularepidemiologic association studies Cancer Epidemiol Biomarkers Prev2008 17 471ndash477

18 Pritchard JK Stephens M Donnelly P Inference of population structureusing multilocus genotype data Genetics 2000 155 945ndash959

19 Falush D Stephens M Pritchard JK Inference of population structureusing multilocus genotype data linked loci and correlated allelefrequencies Genetics 2003 164 1567ndash1587

20 Price AL Patterson NJ Plenge RM Weinblatt ME Shadick NA Reich DPrincipal components analysis corrects for stratification in genome-wideassociation studies Nat Genet 2006 38 904ndash909

21 Menozzi P Piazza A Cavalli-Sforza L Synthetic maps of human genefrequencies in Europeans Science (NY) 1978 201 786ndash792

22 Patterson N Price AL Reich D Population structure and eigenanalysisPLoS Genet 2006 2 e190

23 Novembre J Johnson T Bryc K Kutalik Z Boyko AR Auton A et alGenes mirror geography within Europe Nature 2008 456 98ndash101Epub 31 August 2008

24 Tian C Gregersen PK Seldin MF Accounting for ancestry populationsubstructure and genome-wide association studies Hum Mol Genet2008 17 R143ndashR150

25 Nelson MR Bryc K King KS Indap A Boyko AR Novembre J et al Thepopulation reference sample POPRES a resource for populationdisease and pharmacological genetics research Am J Hum Genet2008 83 347ndash358

26 Jakobsson M Scholz SW Scheet P Gibbs JR VanLiere JM Fung HC et alGenotype haplotype and copy-number variation in worldwide humanpopulations Nature 2008 451 998ndash1003

27 Campbell MC Tishkoff SA African genetic diversity implications forhuman demographic history modern human origins and complexdisease mapping Annu Rev Genomics Hum Genet 2008 9 403ndash433

28 Singapore Department of Statistics Population Trends 2008 Depart-ment of Statistics Singapore

29 Wang S Lewis CM Jakobsson M Ramachandran S Ray N Bedoya Get al Genetic variation and population structure in native AmericansPLoS Genet 2007 3 e185

30 Li JZ Absher DM Tang H Southwick AM Casto AM Ramachandran Set al Worldwide human relationships inferred from genome-widepatterns of variation Science (NY) 2008 319 1100ndash1104

31 Friedlaender JS Friedlaender FR Reed FA Kidd KK Kidd JR ChambersGK et al The genetic structure of Pacific Islanders PLoS Genet 20084 e19

32 Kimura R Ohashi J Matsumura Y Nakazawa M Inaoka T Ohtsuka R et alGene flow and natural selection in oceanic human populations inferredfrom genome-wide SNP typing Mol Biol Evol 2008 25 1750ndash1761

33 Fiji Islands Bureau of Statistics (2008) Census 200734 Estrela RC Ribeiro FS Carvalho RS Gregorio SP Dias-Neto E

Struchiner CJ et al Distribution of ABCB1 polymorphisms amongBrazilians impact of population admixture Pharmacogenomics 20089 267ndash276

35 Lao O Lu TT Nothnagel M Junge O Freitag-Wolf S Caliebe A et alCorrelation between genetic and geographic structure in Europe CurrBiol 2008 18 1241ndash1248

36 Indian Genome Variation Consortium Genetic landscape of the peopleof India a canvas for disease gene exploration J Genet 2008 87 3ndash20

37 Clayton DG Walker NM Smyth DJ Pask R Cooper JD Maier LMet al Population structure differential bias and genomic control ina large-scale case-control association study Nat Genet 2005 371243ndash1246

38 Voight BF Pritchard JK Confounding from cryptic relatedness in case-control association studies PLoS Genet 2005 1 e32


371


39 Carlson CS Eberle MA Rieder MJ Yi Q Kruglyak L Nickerson DASelecting a maximally informative set of single-nucleotide polymorph-isms for association analyses using linkage disequilibrium Am J HumGenet 2004 74 106ndash120

40 Purcell S Neale B Todd-Brown K Thomas L Ferreira MA Bender Det al PLINK a tool set for whole-genome association andpopulation-based linkage analyses Am J Hum Genet 2007 81559ndash575

Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website (httpwwwnaturecomtpj)


372


between individuals of different genetic ancestry calledpopulation stratification can cause systematic differencesin allele frequencies between cases and controls and leadto false-positive associations11 This confounding biascan occur when differences in phenotypic frequencies (egdisease or drug toxicity) exist between different geneticsubpopulations included in a study Genetic markers thathappen to have a high allele frequency in a subpopulationwith a high phenotype frequency which would also beoverrepresented in cases of a study could lead to spuriousassociations1112 Combining different ethnic (sub) popula-tions in one study can also mask true effects leading to falsenegatives especially if the populations are ethnicallydistant13 It is therefore necessary to detect and correct forthese ancestral genetic differences

In our national study of severe ADRs in children(genotype-specific approaches to therapy in children orGATC) we are collecting samples from many surveillancesites across Canada14 Canada is an ethnically diversecountry with visible minorities representing 16 of thetotal population and almost half the population in largemetropolitan areas such as Toronto and Vancouver15 Manymarriages and common-law unions in Canada are inter-ethnic unions In Vancouver for example up to 85 ofall unions are between people of different ethnicities15

To maximize the statistical power in our pharmacogenomicassociation study we need to include as many samples aspossible while minimizing possible false-positive associationsdue to population stratification It is vital to control for thegenetic ancestry of each sample However commonly usedethnic labels are often insufficient and inaccurate proxies ofgenetic ancestry especially in populations with extensiveadmixture16 and some individuals do not know or wronglyassume their ethnicity It is therefore imperative to applyother methods to determine genetic ancestry

Several methods have been developed to detect andcorrect for population stratification in genetic associationstudies and to estimate genetic ancestry17 In manypharmacogenomics studies to date ancestry informativemarkers (AIMs) or neutral markers have been used to detectgenetic ancestry differences and to assign individuals todifferent populations using a model-based clusteringmethod1819 However this method can be computationallyintensive when run on thousands of markers for manyindividuals at once and the assignment of individuals toancestry clusters is limited by an a priori assumption of thenumber of clusters20

Genomic control is an alternative adjustment methodthat relies on a quantitative estimate of the degree ofpopulation stratification at reference single nucleotidepolymorphisms (SNPs) used to adjust for any stratificationthat might be present in the tested SNPs The method relieson the use of unselected random SNPs because AIMscould artificially inflate the population structure estimateHowever this method is conservative as the same correctionfactor is used for all investigated markers12

Principal component analysis (PCA) is an alternativemethod to detect and correct for population stratification

Principal component analysis was first applied to geneticdata more than 30 years ago21 but it was not until morerecently that a solid statistical basis was provided2022

Principal component analysis is a mathematical methodthat reduces complex multidimensional data (in this casegenotype data) to a smaller number of dimensions bycalculating the main axes or lsquoprincipal componentsrsquo (PCs)of variation These components are orthogonal vectors thatcapture the maximum variability present in the data Thefirst component explains the most variation in the dataand each subsequent component accounts for anothersmaller part of the variability When applied to genotypedata these axes of variation have been shown to have astriking relationship with geographic origin For examplePCA of genotype data from hundreds of thousands of lociin 1387 individuals from across Europe shows how a two-dimensional genetic map based on the first and second PCsclosely mirrors the geographic map of Europe and can beused to accurately estimate ancestral origin of samples23

The PCA method is simple to use and is an efficient methodeven with large datasets Compared with other clusteringmethods PCA does not assume a predefined number ofexpected ancestry clusters20 and it has the advantage ofbeing valid when used directly with the SNPs genotyped ina study provided that those are present at a sufficiently highnumber24

To explore the benefits of PCA in pharmacogenomic studiesin Canada we applied PCA to genotype data from 2094 loci in220 key drug biotransformation genes to detect and correct forgenetic ancestry differences Using Coriell reference sampleswe observed a strong correlation between PC values andgeographic origin In addition we were able to infer thegenetic ancestry of samples from the GATC study with highaccuracy and to estimate the genetic ancestry of samples ofunknown origin This shows that PCA can successfully beapplied in pharmacogenomics to correct for populationstratification using a limited set of markers

Results

We genotyped 89 Coriell worldwide reference samplesusing a customized pharmacogenomics SNP panel whichwas designed to capture the genetic variation of 220 keydrug biotransformation genes (see Material and methodssection and Supplemental Table 1) These samples werechosen to reflect as much genetic variation as possible acrossthe world We then genotyped 524 patient samples from ourGATC study (see Supplemental Table 2 for detailed patientclinical characteristics) To identify underlying geneticancestry differences we performed PCA on the genotypedata from both Coriell reference samples and GATC samplesThe first two principal components (PC1 and PC2) representthe main axes of variation within this data and explained462 and 346 of variation respectively We created scatterplots of these components to visualize these data

Initially we plotted PC1 and PC2 of the Coriell referencesamples to assess the worldwide pattern of genetic variation


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Application of principal component analysis to study topography of hypoxic–ischemic brain injury

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Transcript of Application of principal component analysis to study topography of hypoxic–ischemic brain injury