Application of pooled genotyping to scan candidate regions for association with HDL cholesterol levels

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Application of

pooled genotyping

to scan

candidate

regions

for

association with

HDL cholesterol levels

David A.

Hinds,

1

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Se

ymour

,

2

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Ka

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3

P

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jee,

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Pa

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C

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F

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Th

ompson

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1*

1_Perlegen_Sciences,₂₀₂₁_Stierlin_{Court, Mountain}_{View, CA}_{94043, USA}

2_{Genomic and Proteomic Sciences, Pfizer}_Global_{Research and Development, Eastern}_Point_{Road, Groton, CT}_{06340, USA} 3_{Biostatistical} _{Applications, Pfizer}_Global _{Research and Development, Eastern}_Point_{Road, Groton, CT}_{06340, USA} *Correspondenceto: Tel:þ1 (0) 650 625 4504;Fax:þ1 (0) 650 625 4510;E-mail: [email protected]

Datereceived(in revised form):10th August 2004

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Association studiesareusedtoidentifygenetic determinants of complexhuman traits ofmedicalinterest.Withthelargenumber of

validatedsinglenucleotidepolymorphisms (SNPs)currentlyavailable,two limiting factorsinassociation studiesare genotyping capabilityand costs.Pooled DNA genotyping hasbeen proposed asanefficient means ofscreening SNPsforallele frequencydifferencesincase-control studiesand for prioritisingthemfor subsequentindividualgenotyping analysis.Here,we apply quantitativepooled genotyping followed by

individualgenotyping andreplication toidentifyassociations with human serumhigh-density lipoprotein (HDL)cholesterol levels.The DNA fromindividuals withlowand high HDL cholesterol levels was pooledseparately, eachpool wasamplified by polymerase chain reactionin triplicate and each amplifiedproduct was separatelyhybridisedtoa high-density oligonucleotide array.Allele frequencydifferencesbetween

case and controlgroups withlowand high HDL cholesterol levels were estimated for 7,283SNPsdistributed across 71candidate gene

regions spanning atotal of17.1 megabases.Anovel methodwasdevelopedto take advantageof independentlyderived haplotypemap

information toimprovethepooled estimates of allele frequencydifferences.Asubset of SNPs withthelargestestimated allele frequency

differencesbetween lowand high HDL cholesterolgroups waschosenforindividualgenotyping in thestudy population, as wellasina

separate replication population.FourSNPsinasingle haplotype block within the cholesterylester transfer protein (CETP)gene interval weresignificantlyassociatedwith HDL cholesterol levelsinbothpopulations.Our studyisamongthe first todemonstratethe application of

pooled genotyping followed byconfirmation with individualgenotypingtoidentifygenetic determinants of a complex trait.

Keywords:association study, HDL cholesterol, CETP, SNPs,pooled genotyping, haplotypes

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Association studiesarewidely viewed as oneofthemost promisingmethodsforidentifyingthe genetic determinants of human phenotypictraits ofmedicalinterest,such ascommon diseasesand individual responses to the drugs used to treat those diseases.1 _{Therefore, a considerable amount of} _research

hasbeenfocusedondevelopingmethodologies thatefficiently screencandidate generegions or whole genomesfor associ-ations of complex phenotypes with geneticmarkers,such as singlenucleotidepolymorphisms (SNPs).Themethodology relies on having a set of commongenetic markersat a suffi-cientlydense coverage across the genome,suchthateither the causal variantitselforamarkerin linkage disequilibrium with the causal variant willbetested in the association study.Thus, tobe comprehensive and reproducible, awhole genomescan

study requires the assay of hundreds of thousands of densely spaced SNPmarkersinalargenumber ofsamples. There isa considerable body of experimental2–6 _and _theoretical7–9

work that suggests genotyping ofpools consisting of DNA from manyindividuals isaviable alternativeto individual genotyping.Pooled assays replacemany measurements of individual samples with a few measurements of a pooled sample — with a corresponding reductionin cost,time and labour. Here, we describeoneof the first large-scale SNP association studies in which this methodology hasbeen applied and validated.

Human population studieshaveshown that serumhigh density lipoprotein (HDL)cholesterolconcentrationsare inverselycorrelatedwiththe development ofpremature cor-onary heartdisease.10_{In this report,}_{we describe a}_two-stage

study toidentifygeneticmarkersassociatedwith HDL

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cholesterol levels.First,weuse apooled genotypingscreen to identifySNPs likely tohavelarge frequencydifferences between lowand high HDL cholesterolgroups.Starting from 7,283SNPsdistributed across 71candidateregions,weusethe pooled datato selectabout 300SNPs withthestrongest evi-dence forassociation.Wethenindividuallygenotypethese SNPs,toconfirm theirallele frequencydifferencesin the low and high HDL cholesterolindividualsin thestudygroup.We confirmassociationsidentified in thestudy populationby individuallygenotyping SNPs withsignificantallele frequency differencesinareplicatepopulation.

We also describe anovel method for using independently derived haplotypemap data toimprovethe power of an association studybasedon pooled genotyping.Using genotype data fromaseparateset of ethnically diverse individuals,we determine haplotype blocksand sets of common haplotype patterns that together accountfor most of the variationina givengenomic interval.From pooled genotype data,we esti-mate frequencydifferencesfor these common patterns betweencase and controlgroups.Thesepatterndifferences enableus to makemore accurate estimates ofthe individual SNP frequencydifferences that exploit redundanciesin the haplotypemap,thereby reducing experimentalerror in the individualSNPmeasurements.

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Inanindependent,previouslydescribedstudy, a genome-wide SNP collection was obtainedusing high-density oligonu-cleotide array-based resequencing.11_Briefly,_{we generated}

somatic cellhybridsbyfusinglymphoblastcell linesfrom the Coriell Polymorphism DiscoveryResource12_{with a hamster}

cell linetoform between 20and 50haploid somatic cell hybridsforeach human chromosome.DNAwasisolated and amplified by long-rangepolymerase chain reaction (PCR), and the PCR products were fragmented,labelled and hybri-dised toaseries of SNP discovery arrays.These arrays were designedsuch that each baseofthereference sequencewas queried byeight 25-mer probes.We identified SNPsfrom the resulting fluorescence intensity datausing apattern recog-nitionalgorithm.

Weused a dynamic programming algorithm13 _{to partition}

these haploid SNP discoverydata intohaplotype blocks.SNPs havingminorallele frequencies of at least 10 per cent in the SNP discoverydatawere included in themap.Werequired all blocks to satisfy the condition that at least80 percent ofthe haploid samplescould be assignedtocommonhaplotype patternshaving greater than 10 percentfrequency.Fora block havingNcommon haplotypepatterns,we also required at leastN21 patterns to havetagging SNPs that distinguished eachof thosepatternsfromall of the others.

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Thestudy population wasderived froma cohort of individuals (self-reported Caucasian)from the ACCESSstudy,14 _which

was madeup ofmales,postmenopausalfemalesand premeno-pausalfemales thateitherhad,or were at risk for, cardiovascular disease.Whole blood from subjects participating in this study was obtained inaccordancewiththe Declaration of Helsinki(2000) ofthe World MedicalAssociation, inaddition toappropriate informed consentdocumentationdefiningthe studydesignand providing anassessment of the risksand benefits associatedwithstudy participation. Individuals with high and lowHDL cholesterol levels wereselected as thetop and bottom 15 percent of the continuousHDL distribution fromeach group,resulting in the followingsamples:166high HDL($54.9 mg/dl)and182 lowHDL(#36.1 mg/dl) males; 140 high HDL($64.0 mg/dl)and142 lowHDL

(#47.3 mg/dl) postmenopausalfemales; and17high HDL ($67.4mg/dl)and 24 lowHDL (#42.2 mg/dl) premeno-pausalfemales.HDL cholesterol was measured infasting samplesfrom four preclinical visits, all DNAsamples were collected atbaseline(ie withoutdrugtreatment).In this population,the interactionbetweenage and HDL did not warrantanadjustment forage in the selection of casesand controls.

Thereplicatepopulationconsistedof 83 lowHDL and 78 high HDLsamplesfrom postmenopausal women (self-reported Caucasians).Thesesamples representedthe 25 percent tails fromboth ends of the continuousHDL distribution of anindependentcohort from an osteoporosis study (the cohort was not selectedon the basis oftheirHDL cholesterol levels or othercardiovascular risk factors),withthe high HDL cut-off at 62 mg/dl,the lowHDL cut-off at 42 mg/dland ameanage of 54.4 years.

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We constructed fourDNApoolsforestimation of SNP allele frequencydifferencesbetween the lowand high HDL cholesterol groups.Fiveof the 671 samples of the study population were excluded from pooled genotyping dueto insufficientamount of DNAorfailednormalisation. After removal ofthesesamples,therewere345lowHDLsamples and 321high HDL samples remaining.ThelowHDL cholesterol samples wererandomly split into two subgroups and usedtoconstruct poolA(consistingof173 individuals) and poolB (consistingof172individuals).Likewise,the high HDL cholesterol samples wererandomly splitinto two subgroupsand used toconstruct pool C(consisting of 161 individuals)andpoolD (consisting of 160individuals).

Genomic DNAwasextracted from whole blood usingthe PureGene DNA isolation system (Gentra) per manufacturer’s protocol. DNAsamples werequantified using a PicoGreen assaykit (MolecularProbes)and SpectraFluorPlusTecan plate readeraccordingto themanufacturers’instructions, and then

qHENRY STEWART PUBLICATIONS1473–9542.HUMAN GENOMICS.VOL1.NO6.421–434 NOVEMBER2004

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dilutedto astandard concentration using a Packard Multi-Probe Robot. Equimolaraliquots of DNAweretransferred into oneof four pool tubes using a Packard MultiProberobot. Eachpool was then requantified by PicoGreenassayand the poolsdiluted to 20 ng/mlfor use asa PCRtemplate.

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Weselected71genetargetsbasedonavariety of criteria.One gene,the cholesterylester transfer protein (CETP), had been previously shown tobe associatedwith HDL cholesterol10,15,16

andserved asa positive control.Theremaining70candidate genes (Table1; see also supplementaryTable1; supplementary tableshave been posted at:www.perlegen.com/newsroom/ supplemental/human_genomics/10_04/index.htm) were eitherknown or suspectedtobe involved in lipidmetabolism. We didnotincludesome genes previously shown tobe associatedwith HDL cholesterol levelsbecauseourgoal was to identify novelassociations;forexample, we didnot include hepatictriglyceridelipase(LIPC),lipoprotein lipase(LPL), low-density lipoproteincholesterol receptor (LDLR) or ATP-binding cassettetransporterA1 (ABCA1).17_{For the} ₇₀

candidate genes,weselected SNPs within the genomic DNA sequencesencodingthetranscripts, as wellas80kilobases (kb) upstreamand downstream of eachtranscript.We examined a larger region,spanning1.5megabases (Mb) upstreamand downstream ofCETP.The targeted17.1Mbof DNA sequence included 50 partialand 180completetranscriptsin addition to the 71 selected candidates, basedon the National CenterforBiotechnology InformationBuild30 (see sup-plementary Table2).We identified7,283 SNPmarkersin theseregions, atanaverage density ofone SNP every 2.3kb. Ofthese,112 were in transcribedsequences of the71 candi-date genes,180 were in the transcribed sequences of the 230 non-candidate genesin the intervalsexamined and 72 rep-resented aminoacid changes (supplementaryTable2).More

than50 percent ofthe17.1Mb iscovered byinter-SNP intervals of10kbor lessandmorethan80 percentisininter-SNP intervals ofless than50kb.The71 selected intervalscontain 955 haplotype blocks, having anaverageof about sixcommon SNPsandthree commonhaplotypepatterns perblock.

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High-density oligonucleotide arrays were designedso that each SNPwould be interrogated by80 25-mer oligonucleo-tide probes synthesised ona glass substrate.These 80features consistedof four sets of20features, correspondingto reference and alternate allelesforforward andreversestrands. Aset of 20featuresconsistedof fivesets of four probes,withoffsets of 22,21,0,þ1andþ2 basesbetween the centreof the 25-mer probe and the SNPposition.Foreachoffset,wetiled featuresforeachof four nucleotides substituted for the centre position ofthe 25-mer probe,thusat eachoffset we hadone perfect match feature and threemismatchprobesfor the corresponding SNP allele(Figure1).

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For pooled genotyping,7.25ng genomic DNA(pooled samples) wasamplifiedusinglong-range PCRreactions, pooled,labelled, hybridisedtohigh-densityarrays,stained and detected asdescribed.11_{The four}_DNA_{pools (low}_HDL

poolsA and B and high HDL poolsC and D) were each amplified byPCRusing1,222 long-rangeprimer pairsin three replicates.The12 sets of PCRproducts were hybridisedto separate chips.

The fluorescence intensities ofthereference and alternate perfect-match features on an array were correlatedwiththe concentration ofthe corresponding SNP allele in the DNAsample.Ourestimates of allele frequency,p^,were computed from ratios oftrimmedmeans of intensities of the

Table1. Seventycandidate genesanalysed in the association study.

ABCB6 ABCC3 ABHD1 ACACA ACACB ACAT1 ACAT2

ADAM28 ADAMTS4 AR ATP6V1E1 B3GALT2 BPI CACNG5

CEL CLN2 CRP CTSC EPHB6 ESR2 ESRRB

FPR1 G6PC GHSR GJA4 GLRA1 GPRC5B HAT

HDL-CBP HNF4A KCNJ9 LIPE LIPG LOC51275 MBTPS1

MBTPS2 MMP3 MTP NDRG1 NR0B2 NR1H2 NR1H3

NR1H4 NR1I3 NR2F1 PCOLCE2 PLA2G2A PLTP PON1

PON2 PPARA PPARD PTPRF PTPRH RORA RORB

RPS6KC1 SAA1 SAA2 SAA4 SCAP SCD SERPINE1

SPTLC1 SPTLC2 SSTR1 TCF1 TRHR TSHR UPS3

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perfect-match featuresafter subtracting a measureof back-ground computed from trimmedmeans of intensities of mismatch features

^

p¼_ð_I_~ I~PM;Re f 2~IMM

PM;Re f 2I~MMÞþðI~PM;Alt2I~MMÞ

where:

~

IMM ¼1₄ðI~MM;Re f;FwdþI~MM;Re f;RevþI~MM;Alt;FwdþI~MM;Alt;RevÞ

~

IPM;Re f ¼1₂ðI~PM;Re f;Fwdþ~IPM;Re f;RevÞ

~

IPM;Alt¼1₂ðI~PM;Alt;Fwdþ~IPM;Alt;RevÞ

TheI~terms denotetrimmedmean intensitiesforaset of featuresdenoted by thesubscript.Thetrimmedmeans are arithmetic means ofthe intensity measurementsafter discard-ingthe highestandlowest 25percent ofvalues.Incases where thisdidnot yield aninteger number ofterms,onemoreterm wasincluded and the smallestand thelargest terms received halfweight. Eachset of 20featurescontributed five perfect-matchmeasurementsfor one allele,oneperfect-match measurementfor the otherallele at offset 0, and14mismatch measurements.Thus, forexample,thereweresix perfect-match featuresfor thereference alleleon the forward strand, and:

~

IPM;Re f;Fwd¼1₃%1₂IPM;Re f;Fwd;2þIPM;Re f;Fwd;3 þIPM;Re f;Fwd;4þ1₂IPM;Re f;Fwd;5&

wherethenumericsubscriptsdenotepositionsin thelist ofsix sorted intensities.

Two quality control metrics wereusedtoassess the reliability ofthe intensitiesfora SNP inanarray scan.The first metric,‘conformance’,measuredthe presenceof specific target DNA for that SNP.Thesecond metric,signal to

background ratio,measuredtherelative amounts ofspecific and non-specific binding.Cut-offs were appliedtoboth metrics, and SNP featuresets that didnot passeither metric were discarded from furtheranalysis.

Conformancewascomputed independentlyforboth reference and alternate allele featuresets, and amaximum taken ofthe two values. The conformance foraparticular allelewasdefined as the fraction of featuresetsfor whichthe perfect-match featurewasbrighter thanall threemismatch features.In the 80-feature SNPtiling, each allele hadten such sets of fourfeatures. SNPmeasurementshaving conformance

,0.9 were discarded from furtherevaluations.

Thesignal tobackgroundratio wascalculated from inten-sity measurementsforboth alleles, for theperfect-matchversus mismatch features, as:

signal¼qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiI~_{PM;Re f}2 þI~_PM;Alt2

background¼qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi~I_{MM;Re f}2 þI~_MM;Alt2

Thetrimmedmeanintensitiesfor perfect-match and mis-match featuresets wereobtained asdescribed above.SNP measurementshavingsignal/background,1.5were discarded fromfurtherevaluations.

Foreach SNP, weobtained atotal of12allele frequency estimates,p^;as three independent measurementsforeachof the fourDNApools.Estimated allele frequency differences,

Dp^;between lowand high HDL groups were determined from averages ofthe replicatesforeachpool:

D^p¼1

2ð^pAþ^pBÞ212ðp^Cþp^DÞ

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In order to limit thenumber of SNPs requiringsubsequent genotyping inindividual samples,we developed ananalysis method that usedour independentlyderived haplotype

Figure1.Genotypingsinglenucleotidepolymorphisms (SNPs) using high-density oligonucleotide arrays.Each SNP is queried by

80 25-mer oligonucleotides synthesisedona glass substrate.Theten oligonucleotides shownareperfect-matchprobesfor the

reference(R)and alternate(A)allelesat fiveoffsets on the forwardstrandsequencerelativeto the SNP(22,21,0,þ1,þ2).

Not shownare additional mismatchprobes wherethemiddlepositions oftheprobes shownarereplaced by thethree alternate

nucleotides, and anequivalent set of probesfor thereversestrand.

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mapinformation to refine estimates of SNP allele frequency differencesbetween pooled DNAsamplesincase-control studies. The method exploits the fact that withina haplotype block,most ofthevariationin SNP allele frequenciescanbe accounted forby variationin the frequencies of arelatively small set of commonhaplotypepatterns— defined as patterns presentata frequency of at least 10 percentin the ethnically diversepopulation used forSNP discovery.Withina block, the sum of differencesin thesepatternfrequenciesbetween twogroups should be approximately 0,to the extent that those patterns in the haplotypemap accurately represent the total genetic diversity ofthat interval (Figure2).

The method uses linear regression todeterminethese underlying haplotypepatternfrequencydifferences, givenaset of estimated SNP allele frequencydifferencesfora haplotype block.Our method forhaplotypemap construction guaran-tees that inevery block,there are at leastenough SNPs to determinethe frequencies ofthe commonhaplotypepatterns. MostSNPsare inblocks that containadditional redundant SNPs,so ifmeasurementerrorsareuncorrelated,regression shouldyield estimates thataremore accuratethan theoriginal SNPmeasurements.From the fittedpatterndifferences,more accurate estimates ofthetrue allele frequencydifferencesfor individualSNPscan thenbe determined.

LetDp^ibe the estimated frequencydifferenceof the

‘reference’allelesforSNPiwithina haplotype block, and let Dfj bethe (unknown)frequencydifferenceof common

haplotypepatternj[1. . .N:Our model proposes that:

Dp^i¼

XN

j¼1

mijDfjþ1

wheremijisa coefficient that takesavalueofþ0.5 ifthe allele

at positioniin patternjmatches thereference allele and20.5 if it matches the alternate allele for that SNP.Thereasonfor the 0.5 factor is that the frequencydifference foranallele wouldotherwise be double countedwhendifferencesfor the completeset ofpatternsare evaluated.We further requirethat thepatternfrequencydifferences must sum to 0; thisconstraint canbe folded into the previousequationbyeliminating DfN

and definingrij;mij2miN to obtain:

Dp^i¼

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rijDfjþ1

Solvingthese equationsgivenDp^iandrij isalinear regression

problem.Standardregression statistics (R2_and_the_P_{value for}

anFtest)canbeusedtojudgethequality ofthe fit ofthe SNP datato the haplotypepattern information.Deviationsfrom a perfect fit canarise both from experimentalerrorsand inac-curaciesin the haplotypemodel.Ininstances wherethequality ofthe fit to the haplotypemap isgood, the fitted allele fre-quencydifferences should havelower variancethan theraw data forindividualSNPsbecausetheyincorporate information about the expected correlations betweenSNPs.

Asimilar method could beusedto estimate haplotype patternfrequenciesineachpooled sample,with a constraint that the frequencies of common patternsaddup to 1.We choseto work in thespaceof allele frequencydifferencesfor several reasons. The frequencydifferencesarethe quantities we areultimatelyinterested in, and it seemedmost parsimo-nious toevaluate a fit for these differencesdirectly,rather than performingseparate fits onfrequenciesineachpool

Figure2.Haplotype block-fitting analysis.Starting fromestimated allele frequencydifferencesforeach individual singlenucleotide

polymorphism (SNP)from pooled genotyping,weuselinear regression to solve forfrequencydifferences oftheunderlying common

haplotypepatterns.In thisexample,weshowa hypotheticalhaplotype block consistingofsixSNPsandthree commonhaplotypes.

Measured frequencydifferencesareshownfor the haplotypetagging allelesforeach SNP,which are alsoindicated byboxesin the haplotypepatterns.In thisexample,we are estimatingtwofreeparametersfrom sixSNPmeasurements,sincethethreepatterndiffer -encesare constrainedto sum to 0.Thus,thesepatterndifferences should havelower variancethan the individualSNPmeasurements.

From thepatterndifferences,we are abletoestimatethetrue allele frequencydifferencesforeach SNPmore accurately.

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and thencombiningtheseto obtaindifferences. Also,the quality of a fit on absolute frequencies would besensitiveto the presenceof rare haplotypes notincluded in themodel, even under thenullhypothesis ofno pool differences.Our constraint onfrequencydifferences summingto 0 onlyimplies that theproportion ofrare haplotypesincase and control pools is similar. Finally, duetoexperimentaldifferencesinSNP hybridisationcharacteristics,we havemore confidence in our ability todetect pooldifferences than to obtain unbiased estimates of absolute allele frequencies.

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Forindividualgenotyping by high-density oligonucleotide arrays,samples were amplified by short-rangemultiplexPCR, labelled, hybridisedto the arrays,stained and detected as described.18

The individualgenotypesforanSNPwere determined by clusteringmeasurementsfrom multiplescansin the two-dimensional space defined by reference and alternate perfect-matchtrimmedmeanintensities. Trimmedmeanintensities were computed asdescribed above.Weused a K-means algorithm toassignp^ measurements toclusters representing distinctdiploid genotypes. Insteadof estimatingthe back-ground intensity termI~MM from asinglescan,we determined

an optimal value foreach SNPthat minimisedthevariance in

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pwithin the assigned genotype clusters.The K-meansand background optimisation steps were iterateduntilcluster membership and background estimatesconverged.To deter-minethe appropriatenumber of genotype clusters,werepeated the analysisfor one, twoandthree clustersandselectedthe most likely solution, consideringlikelihoods ofthe data andthe cluster parameters.The datalikelihoodwasdeterminedusing a normal mixturemodelfor the distribution ofp^ aroundthe cluster means.Themodel likelihoodwascalculatedusing a priordistribution of expected cluster positions (ie homozygous reference allelenear^p¼1:0;heterozygotenear^p¼0:5 and homozygousalternate allelenearp^¼0:0).

Forindividualgenotyping by template-directed dye-terminator incorporation with fluorescence-polarisation detection (FP-TDI),19_{samples were amplified by}_PCR,

primerextension was performedusing AcycloPrime FP SNP detectionkit (PerkinElmerLife Sciences) and changesin fluorescencepolarisation weremeasuredusing Analyst HT (LJL Biosystems)asdescribed.16

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Inanassociation study,systematic differencesinancestry betweencase and controlgroupscan producelargenumbers of statistically significantbut spurious associations.20,21_We

examinedthe348 individuals withlowHDL levels and the 329individuals with high HDLlevelsin thestudy population

toensurethat they were adequately matchedprior to con-structing DNApools.We individuallygenotyped thesamples for 300SNPs thatare genetically unlinked and uniformly spaced across the genome, asdescribed previously.18

Inx2 _tests_for_{association with}_{the HDL cholesterol}

phenotype,weobserved a smallexcess of moderatepvalues. For 280 SNPsgiving high-qualitygenotype data, 43 had

p,0:1 versus 28 expected.A sensitive global testfor popu-lation structure based on the sum ofx2 _statistics22_was

significantðp,0:001Þ;however, apermutationanalysis ofthe genotype data indicatedthat the expected increase in variance of allele frequency measurementsdueto stratification of this magnitude was less than 1 percent.We alsoanalysedthe genotype data for population structureusing the structure

program.23_The_structure_{program uses}_a_model-based

cluster-ingmethod foridentifyingsubpopulations suchthat,withina cluster, all markersare inHardy–Weinberg and linkage equilibrium. Thisanalysisdidnot show convincing evidence for morethan onesubpopulation. In runs with between two and five assumed clusters,most samples were assignedsimilar admixtureproportionsineachpredictedsubpopulation; for twoclusters,75 percent ofsamples were givenadmixture proportionsbetween 0.4 and0.6. Basedon theseresults, and the limited accuracy ofpooled genotyping assays,we judged that thelowand high HDL cholesterolgroups were ade-quately matched.

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Foreach SNP,we estimated anallele frequencydifference,Dp^, between the lowHDL cholesteroland high HDL cholesterol pools.Wethenexcluded asmall proportion of the pooled data dueto spurious experimentalerrors,such as saturated features or inconsistenthybridisation patterns.We also excluded SNPs where all threemeasurementsforany oneof the four pools failed and SNPs wherethestandard error of

Dp^ exceeded 0.025.Of the 7,283 SNPs tiledon the array, 6,611 (91 percent) passed all of these dataquality filters.

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Ofthe6,611SNPsfor whichweobtained good pooled genotyping data, 4,387 SNPs were included in the haplotype map.Table2 shows the results ofthe haplotype block fitting analysisfor these SNPs; the resultsforallblocks,thesubset of blocks thatare informative(thosethatcontain redundantSNP information)andthesubset ofthesethathadp,0:05 inanF

testforagreement oftheD^pwiththe haplotypemodelfor that block areshown.Good fits shouldonlybepossible forblocks thathaverealallele frequencydifferencesbetween thelowand high HDL cholesterol pools, eitherdueto sampling variation orassociation withthephenotype.Thus,wewould expect mostblocks tohavepoorpvalues, duetoalackofsignificant allele frequencydifferences. Infact,morethan40 percent of the 4,387SNPsare inblocks with good agreementbetween

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Dp^ and the haplotypemodel, and thesetend tobethelarger blocks.Uninformative blocks oftencontainjust oneor two SNPsand whilethey represent alarge fraction of allblocks, they representa muchsmaller proportion of SNPsand base pairscovered.Here, informative blocks represented 53 per cent of all blocks, butincluded 86 percent of SNPsin the haplotypemapand about 75percent of the DNA sequence.

Analysis of variance allows us todetermine how muchof the variationinSNP allele frequencies observed between the DNApoolsisconsistent withthe haplotypemap and how much is residual variationduetoexperimentalerrorsin theDp^

measurements,the contribution ofrarepatterns not rep-resented in the haplotypemapand errorsin the haplotype map.We can measurethe effectiveness ofthe algorithmby the extent to whichthe fraction ofvariance explained by the fitted haplotypepatternsexceeds the fraction of degrees of freedom used in the fits.In thisanalysis (Table2),we foundthatabout 77 percent of all the variance in the datawasconsistent with the modelbased oncommonhaplotypes. Basedon the number of freeparametersin the haplotypemodel,wewould have expected only42 percent of thevariance tobe accounted forbychance.Werepeated thisanalysisafter per-muting the individualDp^ measurements.Here,the haplotype mapexplainedonly 43 percent ofthe variance and only 5percent of SNPs were inblockshavingp,0:05 inanFtest. Thisanalysis shows that the agreementbetween the haplotype modeland the originalDp^ data could notarise bychance.

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Selecting the SNP markers that arethemost likely tohave large allele frequencydifferencesbasedon the pooled array data isdifficult.Theset of SNPshavingthelargestabsoluteDp^

isdominated byasubset ofmeasurements with veryhigh experimental variance. At-testisalso inadequate, because it favoursSNPs withlowexperimental variance, eveniftheDp^is too small tobe of biologicalinterestand is probablydueto samplingvariation.The experimental variance is poorly determined from the limitednumber of datapointsavailable. DuetodifferencesinSNP calibrationin ourgenotyping assay, ourability toestimate absolute allele frequencies, and hence samplingvariance, is similarly limited.Basedondata from experiments withpools of knowncomposition,we foundthat the strategy of excluding data forSNPs withveryhigh stan-dard errors, and then selecting SNPs withthelargestDp^, performed as well or better than testsbased on variance esti-matesforeach SNP(datanot shown).

Atotal of 312SNPmarkers were chosenfor individual genotyping basedon the capacity of asmallhigh-density oligonucleotide array. Basedon the pooled allele frequency data,weselected 284 SNPsexpectedto havelarge allele fre-quencydifferences. Halfofthe 284 SNPs were chosen to be ‘haplotype conforming’— belongingtoinformative haplo-type blocks that had good fits withp,0:05 —whilethe otherhalfwere chosen tobe‘non-conforming’SNPs selected from theremainderbasedonly on pooled estimates ofDp^.We ranked1,934 conforming SNPsby the smaller oftheir actual and fittedDp^values, andselectedthetop 142SNPs yielding a cut-off of jDp^j.0:037:For4,677 non-conforming SNPs, ranking byabsoluteDp^andselectingthetop 142 yielded a cut-offofjD^pj.0:048:Weselected a higher proportion of conforming SNPsfor individualgenotyping becausetheir consistency withthe haplotypemap provided additional evidence forallele frequencydifferencesat thosepositions. We did includenon-conforming SNPs, however,soas not to overlooksignals that werenotinblocks,orfor whichthe fit to

Table2. Haplotype block-fittingresultsand analysis ofvariance.

Allblocks Informativea _p_<_0.0₅b

Haplotype blocks 955 507 172

SNPs passingqualityfilters 4387 3758 1934

SNPscontributingtofits 4171 3578 1833

Fitted degrees of freedom 1736 1143 442

Residualdegrees of freedom 2435 2435 1391

%degrees of freedom used 42% 32% 24%

Total sum ofsquares 2.241 1.947 1.258

Fittedsum ofsquares 1.725 1.431 1.002

Residual sum ofsquares 0.516 0.516 0.256

% variance explained 77% 73% 80%

a_B_lo_ck_s_ha_v_i_n_g_r_ed_un_da_nt_i_n_f_orm_a_t_i_on_{, ie a}_{t l}_ea_st_a_{s m}_a_ny_SNP_m_ea_sur_e_m_e_nts_a_s_c_ommon_ha_plotyp_e_p_a_tt_e_rns. b_I_n_f_orm_a_t_i_v_{e b}_lo_ck_s_f_{or w}_{hich a}_n_F_t_e_{st on t}_{he fi}_{t o}_f_t_{he SNP da}_t_a_{to t}_{he ha}_plotyp_e_stru_c_tur_{e had}_p_,_0.0₅_.

SNPs,singlenucleotidepolymorphisms.

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the haplotypemap was poorfor other reasons. Anadditional 28 SNPs thatdidnot meet these criteriawereselected because they were either incandidatelociof interest orhad been independentlygenotyped in thesamepopulation using flu-orescencepolarisation. They wereused toassess the accuracy of ourhigh-density array-based individualgenotyping.

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Atotal of 832DNAsamplesin the studyand replicate populations were individually genotyped for the312 selected SNPS.Three quality-controlfilters were appliedto the indi-vidualgenotype data.We first requiredthat SNPshave an unambiguous genotype callinat least80 percent ofthe 832 DNAsamplesassayed.Secondly,werequiredthat both SNP allelesbesegregating in the population (ie have at least two identifiable genotype clusters).Finally,werequiredthat the SNP allelesbe inHardy–Weinberg equilibriumðp.0:001Þ:

We found that large deviationsfrom Hardy–Weinberg equi-librium were generallyassociatedwithsystematic hybridisation artefacts.Of the312 SNPs,284(91 percent) passed all three data quality filters.

Toestimatethe quality of the individualgenotypescalled usingthe high-density oligonucleotide array platform,we comparedourgenotype calls withthoseobtainedusing FP-TDI for 19SNPsin three generegions (CETP, endothelial lipase [LIPG] and liver receptoralpha [LXRa]).The call rate

(the fraction of assigned genotypes out ofpotentialgenotypes) for the array platformisabove98percent,very similar to that generatedusing FP-TDI usingthe same DNA samples (sup-plementaryTable3).Ofthe genotypescalled byboth methods,the concordance (the fraction of SNPsassigned genotypesbybothmethods that were inagreement)between the oligonucleotide arrayand FP-TDI methodologiesis greater than 99 percent.

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Toevaluatethe effectiveness ofthepooled genotypingstepfor estimating allele frequencydifferencesbetween the case and controlDNApools, we examined the relationshipbetween pooled allele frequencyestimatesand allele frequencies determined by individualgenotyping. Foreachofthe 284 SNPs selected from thepooled data,we have allele frequency estimatesforfour pooledsamplesand corresponding individ-ualgenotype data forall thesamples used tocomposethe pools.Whilethe numbers of datapointsand ranges of allele frequenciesforeach SNP aresmall,we can still usethese data toexaminetherelationship betweenapooledp^and anallele frequencypdetermined byindividualgenotyping.This relationship foran individualSNP is very nearly linear; however,there is substantial variationin slope and intercept betweenSNPs (Figure3).Aregression ofthep^, averagedover the fourHDLpoolsagainstanallele frequencyp, determined

Figure3.Relationshipbetween pooled allele frequencyestimatesand allele frequenciesdetermined byindividualgenotyping.The frequencyestimatesfrom pooled genotyping,p^, arelinearly relatedtoallele frequencies,p, determined byindividualgenotyping. (A)AcrossallSNPs that were individuallygenotyped,variationin slope and intercept partially obscures thestrengthofthis relation

-ship.Here,weshowp^ plotted againstpaveragedover the fourhigh-density lipoprotein poolsfor 284 SNPs. (B)Foreach SNP,we have independent measurements ofpandp^ infour pools.Weshow representative data forfourSNPshaving(dueto samplingvariation orassociation) relatively largeseparationbetween the four values ofp.

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byindividualgenotyping forall 284 SNPs, hasanR2_of_0.71.

When we examinedthe independent measurements of pand

^

pin the four poolsfor the 284 individualSNPs,themedian

R2_¼₀_:₈₅_:_{In principle,} _{we could calibrate assays}_for_each

SNPusingsamples of known allele frequency;however,this becomesimpractical when many thousands of assaysare ana-lysed.Somevariationin sensitivitycan betolerated because the pooled data areonly used asascreenfor selecting SNP candidatesfor individualgenotyping.

Toevaluatethesensitivity of SNPselectionfrom pooled genotyping,weusedx2_{tests to measure allelic association of}

each SNPwiththe HDL cholesterol phenotype.To the extent that pooled allele frequencydifferencesarepredictive,weshould see anexcess ofsmallpvaluesin testsfor the284 SNPs selected basedon thepooledresults.Given tests ofNSNPsatathreshold ofp,X;we expectðN£XÞSNPs to meet that threshold due to samplingvariationinallele frequenciesbetween thepools. Infact,wesee far moresmallpvalues than would be expected basedon 284tests (Table3).From the entire6,611SNPs we usedtochoosethe284 forindividualgenotyping,wewould expect 6,611 £ 0.01¼66 tohavep,0:01;and

6,611 £ 0.04¼284tohavep,0:04:AperfectSNPselection procedurewould have captured all ofthese.Infact,we captured 32 percent ofthe expectedtotal number of SNPsat the

p,0:04level, and62 percent ofthe expectednumberat the

p,0:01 level.Thus,thepooled assayhas sufficient sensitivity tocapture asubstantialfraction of SNPshaving even very modest allele frequencydifferencesat thelevel ofsamplingvariation. Sensitivityfor largerallele frequencydifferencesindicativeof causalassociations should be correspondinglyhigher.

Toassess the effectiveness of the haplotype fitting pro-cedure,welooked at thenumbers of‘haplotype conforming’ and‘non-conforming’SNPshavingsmallx2 _test_p_values_in

individualgenotyping (Table3).Of SNPshavingp,0:04;

about 65percentcame from the‘haplotype conforming’ category, andthisexcess was quitesignificantðp<0:001Þ:The sametrend was seenamong SNPshavingp,0:01;however, the numbers of observations were insufficient to reach sta-tistical significance.Thus, SNPs selected basedon corrobo-rating haplotype data indeed seem tobe morelikely to show largerallele frequencydifferencesinindividualgenotyping.

Tofurtherexaminethe impact ofusing haplotype block information to improve estimates of SNP allele frequency differencesbetween pooled DNAsamplesincase-control studies,we comparedthe correspondenceof estimates ofDp^to actualallele frequencydifferencesdetermined byindividual genotyping(Figure 4). Whilethe correspondenceof pooled estimates ofDp^ toactualallele frequencydifferences wasgood

ðr2_¼₀_:₈₂_Þ_;_{the correspondence}_of_{the haplotype-fitted}

esti-mates of D^pto actualallele frequency differences was sub-stantiallybetterðr2_¼₀_:₉₀_Þ_:_These_results_demonstrate_{that the}

accuracy of estimating SNP allele frequency differencesin pooled genotyping isimprovedusing haplotype block information.

SNP a

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Our two-stage experimentaldesign posed a tricky multiple testing problem.Whileweperformedtests onjust 312 indi-viduallygenotyped SNPs,thesewereselected as likely tohave large allele frequencydifferencesfrom atotal of6,611 SNPs with goodpooled genotyping dataquality.Ifour pooled assay was perfect,then wewere effectively testing all 6,611SNPs;if thepooled estimates wereuncorrelatedwith allele frequencies, then we arereally only testingthe 312SNPs selected for individualgenotyping.Basedon our captureratesforSNPs withsmallpvalues,we consider that the effectivenumber of tests wewereperforming wasasubstantialfraction of6,611.

Using a conservative Bonferroni correction, a global false-positiverateof 0.05 for 6,611SNPs testedwouldrequire

p,7:6£1026 _for_an_{association to}_be_significant.

Consi-dering only the studycohort used for pooled genotyping, thereweresix SNPs, all in the CETPgene,that met this threshold ofsignificance(Table 4).Of thesesixSNPs, four (rs711752,rs708272,rs11508026, rs7205804)had been selected basedon positiveresultsin the pooled genotyping screenandtwo (rs1800775,rs11076175)had beenincludedto testcross-technologyconcordance of genotype calling.The fourSNPs thathad beenassayed in thepooledscreen were all in the same haplotype block(Figure 5),which had a fitted

pvalueof,0.002in ourhaplotype analysis ofthepooled data, andthelargestfittedDp^ values observed in the study.The

Table3. Distribution ofx2_t_e_{st s}_c_or_e_s_i_n_SNP_{s s}_e_l_ec_t_{ed f}_or_i_n_di_v_id_u_a_l_ge_notyp_i_n_g_.

AllSNPs Conforminga _N_on_-c_on_f_orm_i_n_gb

Selected SNPs 284 142 142

Number meetingqualitycriteria 263 135 128

p,0:04c ₉₀ ₅₉ ₃₁

p,0:01c ₄₁ ₂₄ ₁₇

a_SNP_{s s}_e_l_ec_t_{ed ba}_s_ed_on_c_orro_b_or_a_t_i_n_{g e}_v_ide_n_{ce f}_{rom t}_{he ha}_plotyp_e-fi_tt_i_n_g_pro_ced_ur_e_. b_SNP_{s s}_e_l_ec_t_{ed ba}_s_ed_on_a_l_a_r_ge_D_p_^_b_{ut w}_i_t_h_{out support}_i_n_{g ha}_plotyp_{e i}_n_f_orm_a_t_i_on. c_SNP_{s m}_ee_t_i_n_g_t_hi_{s t}_h_r_e_s_h_ol_{d i}_n_a_x2_t_e_{st o}_{f a}_ll_e_l_{ic a}_sso_cia_t_i_{on w}_i_t_h_t_{he HDL}_p_he_notyp_e_.

SNPs,singlenucleotidepolymorphisms.

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nextbestSNP in the data for thestudy cohort outsidethe

CETPgene had ax2_test_p_value_of_0.0015,_which_would _not

besignificant, even using a generous threshold basedon 312independent tests.

SNP a

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To replicate anassociationin the studycohort, weonly need toconsider testsin thereplicatesamplesfor thesubset of SNPs thatgavesignificantx2 _scores_{in that}_analysis._{In the} _replicate

population, there areonlyfive SNPs with good dataquality havingp,0:01; and all are inCETP(Table 5). For the six

SNPsinCETP that showedsignificance associationin the study samples, a conservative Bonferroni correctionfora globalfalse-positivelevel of0.05wouldrequirep,8:3£1023

tocountasareplication. Of thesesix SNPs, four (rs711752,

rs708272,rs11508026,rs7205804) were alsoassociated at this significance levelin thereplicatepopulation.Two SNPs (rs1800775,rs11076175) that hadsmall pvaluesin the study populationdidnot meet significance levelsin the replicate DNAsamples.These differences werenot unexpected, given thelimitedsamplesizeofthereplicate cohort and incomplete linkage disequilibrium ofthe SNPsin thisinterval.

Figure 4.Correspondence betweenestimatedpooled allele frequencydifferencesand actualdifferencesdetermined byindividual

genotyping.For thisanalysis,we included data for single nucleotidepolymorphisms (SNPs)having call rates of at least 90 percent. (A)The correlationbetweenestimated and actualallele frequencydifferencesfor 267SNPsis 0.82. (B)For 164 SNPsinhaplotype blocks with fittedp,0:05;the correlationbetweenallele frequencydifference andthe estimate basedon the haplotype-fitting

procedure is 0.90.In theupper rightcorner of eachplotare five SNPsin the genomic intervalencodingCETP.

Table 4. SNPshavingsignificantassociation with HDL cholesterolin thestudy population. Chromosome Positiona _dbSNP _r_ef

Lb altL refH altH Dpc x2 pvalue

16 56729856 rs1800775 392 196 288 322 0.193 46.2 1.1£10211

16 56730908 rs708272 446 234 318 320 0.151 33.5 7.2£1029

16 56730831 rs711752 451 239 314 312 0.165 31.2 2.4£1028

16 56733948 rs11508026 441 227 303 289 0.167 28.6 9.1£1028

16 56739509 rs7205804 436 244 312 318 0.146 28.4 9.7£1028

16 56740998 rs11076175 535 155 563 73 20.101 28.1 1.2£1027 a_P_os_i_t_i_{on on}_Ge_n_Ba_n_k_s_e_qu_e_n_ce_NC_{_000016.}₄_.

b_C_{ounts o}_f_r_efe_r_e_n_{ce a}_n_{d a}_lt_e_rn_a_t_{e a}_ll_e_l_e_s_f_{or low}_a_n_{d high HDL ch}_ol_e_st_e_rol_g_roups. c_A_ll_e_l_{e f}_r_e_qu_e_n_c_y_diffe_r_e_n_{ce, ca}_l_c_ul_a_t_{ed a}_{s r}_efL=_ð_r_efL_þ_a_lt

LÞ2refH=ðrefHþaltHÞ: dbSNPs singlenucleotidepolymorphismdatabase;HDL, high-density lipoprotein.

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Previous studieshave identified two majorblocks of linkage disequilibriumacross the CETPlocus.4,17,24 _Of _the _{14 SNP}

markersinCETPthat we examined forassociation with HDL cholesterol levels (supplementaryTable 4),nine aremembers ofour whole-genome haplotypemap (Figure 5). Consistent withtheseother studies, in our map,thesenine SNPsare divided into twohaplotype blocks, each havingthree common haplotypepatterns.We computed D0_for _{all pairs of}_the₁₄

SNPsinCETP,using anexpectation maximisation (EM) algorithm todetermine haplotype frequencies.25,26_These

results, again,show two blocks ofvery strong disequilibrium.

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The goal ofthis study was todeterminethe effectiveness of a large-scalepooled genotypingscreen toidentifycommon variantsassociatedwith a complex trait.CETP,whichtransfers cholesteryl estersfrom the anti-atherogenic HDLto the pro-atherogenic very-low- and low-density lipoproteinfractions, playsanimportant role inHDL cholesterol metabolism and served as our positive control.CorrelationsbetweenSNPsin the genomic intervalencodingCETPand variations in the mass/activity ofthe CETPproteinand corresponding HDL levels have beenintensively studied10,16,27_{and consistently}

Figure 5.Singlenucleotidepolymorphisms (SNPs)and haplotypestructure in the Cholesterylester transfer protein (CETP)gene. (A)The CETPtranscript (GenBank accessionNM_000078)is roughly 22kilobasesand contains 16exons.Weobtained goodquality

genotype data for 14 SNPsin this region.Ofthese,ninewere commonSNPs that were included in ourhaplotypemap.In themap,

thesewere assignedto twohaplotype blocks, each havingthree commonhaplotypepatterns.The 50_b_lo_{ck i}_n_c_lu_de_{s t}_{he Ta}_q1_{B SNP,}

rs708272.28,29_(B)_We_m_ea_sur_ed_l_i_n_{kage di}_s_e_qu_i_l_ib_r_i_um_,_D0_{, f}_or_a_{ll p}_ai_{rs o}_{f SNP}_s_i_{n t}_hi_s_i_nt_e_rv_a_l_,_us_i_n_{g a}_n_e_xp_ec_t_a_t_i_{on m}_a_x_i_m_i_s_a_t_i_{on (}_EM₎ algorithm todetermine haplotypes.These data also show two strong blocks of high disequilibrium.

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Ta b l e5 . Ge notyp i n g r e sults f or 1 4 CETP ge n eS NP s i nt he t e st a n d r e pl ica t e s a mpl e s. P os i t i on a dbSNP S tu d ys a mpl e s Re pl ica t e s a mpl e s O v e r a ll p v a lu e b D pp v a lu eO dd sr a t i o c D pp v a lu eO dd sr a t i o 5 67296 58 rs 4 7 8 3962 0.060 1.2 £ 10 2 2 1.3 8 0.100 2. 8 £ 10 2 2 1. 8 71 . 4 £ 10 2 3 5 6729 85 6r s1 8 0077 5 0.193 1.1 £ 10 2 11 2.26 0.10 4 1. 5 £ 10 2 1 1. 4 01 . 8 £ 10 2 11 5 6730 8 31 rs7117 5 20 .16 5 2. 4 £ 10 2 8 1. 88 0.20 85 .0 £ 10 2 4 2.21 5 .9 £ 10 2 11 5 673090 8 rs70 8 272 0.1 5 17 .2 £ 10 2 9 1.92 0.202 3.2 £ 10 2 4 2.2 5 1.2 £ 10 2 11 5 67339 48 rs11 5 0 8 026 0.167 9.0 £ 10 2 8 1. 85 0.1 8 61 .9 £ 10 2 3 2.06 7.1 £ 10 2 10 5 6739 5 09 rs720 58 0 4 0.1 4 69 .7 £ 10 2 8 1. 8 20 .1 4 2 5 .6 £ 10 2 3 1. 8 91 .9 £ 10 2 9 5 67 4 099 8 rs1107617 5 2 0.101 1.2 £ 10 2 7 0. 45 – 0.006 8 .9 £ 10 2 1 0.96 1.6 £ 10 2 6 5 67 4 3996 rs2 8 9716 2 0.060 1.6 £ 10 2 2 0.7 5– 0.039 3.9 £ 10 2 1 0. 8 11 .1 £ 10 2 2 5 67 458 0 5 rs 4 7 84 7 44 2 0.07 55 .0 £ 10 2 3 0.72 – 0.1 5 7 8 .7 £ 10 2 3 0. 5 32 . 4 £ 10 2 4 5 67 5 016 5 rs1 8 0077 4 0.009 7. 5 £ 10 2 1 1.0 4 0.102 5 . 8 £ 10 2 2 1. 5 32 . 5 £ 10 2 1 5 67 5 0712 rs 588 2 2 0.073 4 . 4 £ 10 2 3 0.72 – 0.017 8 .0 £ 10 2 1 0.9 4 7.7 £ 10 2 3 5 67 5 1939 rs1 8 00777 2 0.0 4 11 .1 £ 10 2 3 0.33 – 0.009 2.9 £ 10 2 1 1.06 4 .2 £ 10 2 3 5 67 5 22 8 2r s1 8 01706 0.029 2.0 £ 10 2 1 1.20 – 0.022 2.9 £ 10 2 1 0.73 5 .0 £ 10 2 1 5 67 5 23 8 2r s2 8 97 4 2 2 0.023 1.2 £ 10 2 1 0.76 – 0.0 5 3 4 .6 £ 10 2 2 0. 4 03 .0 £ 10 2 1

aPos

i t i on on Ge n Ba n k s e qu e n ce NC _000016. 4 . bCa l c ul a t ed f ro m a x

2te

st f or t he c om bi n ed stu d y a n d r e pl ica t e s a mpl e s. cCa l c ul a t ed a s ð r efL = a ltL Þ = ð r efH = a ltH Þ ; w he r e t he s ea r ea ll e l ec ounts a s i n Ta b l e4 . SNP s i n b ol da r e t h os e w i t h s ig n ifica nt a sso cia t i on w i t hH DL ch ol e st e ro l i n b ot h t he stu d y a n d r e pl ica t e popul a t i ons. CETP ,c h ol e st e ry l e st e rt r a ns fe rp ro t ei n dbSNP , s i n g l e nu c l e ot ide polymorp hi sm da t aba s e ; HDL, high-de ns i ty l i popr ot ei n.

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shown tobe associated.CETP isestimatedtoaccountfor

,5percent ofthe variability of HDLlevelsin the general population.17_{Here, four}_SNPs_in_CETP_had_strong_signals_and

were independentlyidentified asbeing associatedwith HDL levelsin thepooledscreen.The fact that we identifiedCETP

in this studyasbeing associatedwith HDLlevelsconfirms that pooled genotyping canbeused ingenetic association studies toidentify genes underlying complex phenotypes.

Whilewe findCETP tobereplicable and convincingly associatedwith HDL cholesterol serum levels,noneofthe70 candidate genes or 230 othergenesin the 17.1Mbof DNA screened appear to playamajor role in the geneticvariability of HDL cholesterol levelsin this population.Basedon thestrong association ofCETPwith HDLobserved in our study,we are likely tohave hadsufficient power toidentify similareffect sizesin the othercandidate genes.Recent worksuggests that there arelikely tobeseveraladditionalgenes thatcontributeto HDLphenotypicvariance and are as yet unidentified.17_We

examined SNPsdistributed across onlyabout 0.5percent of the genome, and thusitis likely that theseunidentified genes arelocated ingenomic intervals that we didnotexamine.

In ourcandidateregion study,weused a design incorpor-atingstratificationanalysis,pooled genotyping, confirmation ofpromising candidateloci by individualgenotyping and replicationinan independent cohort. We have demonstrated thatindependentlyderived haplotypemapinformationcanbe usedto improve SNPselectionfrom apooled genotyping screen.High-density oligonucleotide arrays permit the scale and efficiency required for very large-scale association studies. These experimental methodsand analysis strategiescanbe directly scaledup to whole-genome association studies.

Ack

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Wewouldliketo thank CharlesShearandthe LipitorTeamforassistance in

the clinical trials;PascualStarink, EllenJacobsand Eric BuljanforLIMs developmentandsupport;Geoff Nilsen, MichaelJenand Wade Barrettfor

designingthe high-densityarraysand assistancewith data analysis;John

Sheehanfor qualityanalysis ofthe high-densityarraydata;AlbertYee, Reed

George, Julie Marschner, Joe Karbowski and Mike Kennemerfor the DNA

normalisationandpooling;Patrick Chufor thepooled genotyping;Clariza

LaRosa, MattMorenzoni, Pei-Hua Wang, RajalPatel, Rhode Vergara, Robin

Li, Thai Lai, VincentMendoza, KarelKonvicka and Renee Stokowski for the

high-densityarrayindividualgenotyping;Maruja Lira, AmyMank-Seymour

and Jodi Richmond for the fluorescencepolarisationgenotyping;Erica Beil

-harzforassistancewithmanuscript preparation;PaulFeeneyand Michael

Swietek forassistance in sample handling;andthepatientsfordonating

samplesfor research.

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