The effect of genome-wide association scan quality control on imputation outcome for common variants.

0.47 MB·English

by  Southam, Lorraine

EuropeanJournalofHumanGenetics(2011)19,610–614
&2011MacmillanPublishersLimitedAllrightsreserved1018-4813/11
www.nature.com/ejhg
SHORT REPORT
The effect of genome-wide association scan quality
control on imputation outcome for common variants
Lorraine Southam1, Kalliope Panoutsopoulou2, N William Rayner3,4, Kay Chapman1, Caroline Durrant3,
Teresa Ferreira3, Nigel Arden5,6, Andrew Carr1, Panos Deloukas2, Michael Doherty7, John Loughlin8,
Andrew McCaskie8,9, William ER Ollier10, Stuart Ralston11, Timothy D Spector12, Ana M Valdes12,
Gillian AWallis13, J Mark Wilkinson14,15, the arcOGEN consortium, Jonathan Marchini16 and Eleftheria Zeggini*,2
Imputationisanextremelyvaluabletoolinconductingandsynthesisinggenome-wideassociation studies(GWASs).Directly
typedSNPqualitycontrol(QC)isthoughttoaffectimputationquality.Itis, therefore,commonpractise tousequality-controlled
(QCed)dataasaninputforimputinggenotypes.ThisstudyaimstodeterminetheeffectofcommonlyappliedQCstepson
imputationoutcomes.WeperformedseveraliterationsofimputingSNPsacrosschromosome22inadatasetconsisting of
3177sampleswithIllumina610k(Illumina,SanDiego,CA,USA)GWASdata,applyingdifferentQCstepseachtime.Theimputed
genotypeswerecomparedwiththedirectlytypedgenotypes.Inaddition,weinvestigatedthecorrelationbetweenalternativelyQCed
data.Wealsoappliedaseriesofpost-imputationQCstepsbalancingeliminationofpoorlyimputedSNPsandinformationloss.We
foundthatthedifferencebetweentheunQCeddataandthefullyQCeddataonimputationoutcomewasminimal.Ourstudyshows
thatimputationofcommonvariantsisgenerallyveryaccurateandrobusttoGWASQC,whichisnotamajorfactoraffecting
imputationoutcome.Aminorityofcommon-frequencySNPswithparticularpropertiescannotbeaccuratelyimputedregardlessof
QCstringency.Thesefindingsmaynotgeneralisetotheimputationoflowfrequencyandrarevariants.
EuropeanJournalofHumanGenetics(2011)19,610–614;doi:10.1038/ejhg.2010.242;publishedonline26January2011
Keywords: genome-wideassociationstudy;imputation;qualitycontrol;singlenucleotidepolymorphism
INTRODUCTION thatcanaffecttheanalysisresults.QCstepsaretypicallyappliedatthe
Genome-wide association scans (GWASs) have proven to be a sample-andSNP-specificlevel.Sample-levelQCincludesfilteringout
successful strategy for detecting common variants exerting modest sampleswithlowcallrates,evidencefordifferentethnicorigin,high
effects on complex disease risk. Currently available commercial heterozygosity, relatedness/duplication, gender discrepancies and
platforms focus on common variants and capture the majority genotyping batch effects. SNP-level QC includes filtering out SNPs
of HapMap1 SNPs with minor allele frequency (MAF) 40.05 in with low call rates and deviation from Hardy–Weinberg equilibrium
Europeanpopulations.2Severallarge-scaleconsortiahavebeenformed (HWE) at pre-determined thresholds. It is generally believed that
in order to carry out GWAS meta-analyses for various phenotypes, datasetsshouldbestringentlyqualitycontrolled(QCed)atthemarker
withsuccessfuloutcome(eg,Zegginietal,3Prokopenkoetal,4Franke level before applying imputation approaches. For this reason, lower
et al,5 Barret et al6 and Soranzo et al7). To enable the combination MAF SNPs tend to also be excluded, as their accuracy can be
ofdataacrossstudiescarriedoutondifferentplatforms,andtoenable hampered by poor clustering properties and incorrect automated
in silico fine mapping of association signals, imputation approaches genotype calling (at least with currently widely used algorithms).
were proposed a few years ago8 as a means of statistically inferring Even though such weight is placed on pre-imputation SNP QC,
genotypes at untyped loci using a reference set, for example, the theeffectsofapplyingdifferentcriteriaandthresholdstothestarting
HapMap(B2500000SNPs). datasethavenotbeeninvestigatedthusfar.Inthisreport,weevaluate
Animportantaspectofany GWASanalysisistheimplementation the effect of GWAS QC on imputation outcome, and find that
of a series of rigorous quality control (QC) steps before testing for imputationworksverywellforcommonvariantsirrespectiveofQC,
association.TheseQCprocedureshelpguardagainstgenotypingerror, andthataminorityofsomecommon-frequencySNPswithparticular
population stratification, sample duplication and other confounders propertiescannotbeaccuratelyimputedregardlessofQCstringency.
1NuffieldDepartmentofOrthopaedics,RheumatologyandMusculoskeletalSciences,UniversityofOxford,Oxford,UK;2WellcomeTrustSangerInstitute,Hinxton,UK;3Wellcome
TrustCentreforHumanGenetics,UniversityofOxford,Oxford,UK;4OxfordCentreforDiabetes,EndocrinologyandMetabolism,UniversityofOxford,Oxford,UK;5NIHR
BiomedicalResearchUnit,UniversityofOxford,Oxford,UK;6MRCEpidemiologyResourceCentre,UniversityofSouthampton,Southampton,UK;7AcademicRheumatology,
UniversityofNottingham,Nottingham,UK;8InstituteofCellularMedicine,MusculoskeletalResearchGroup,NewcastleUniversity,NewcastleuponTyne,UK;9TheNewcastle
upon Tyne Hospitals NHS Trust Foundation Trust, The Freeman Hospital, Newcastle upon Tyne, UK; 10Centre for Integrated Genomic Medical Research, University of
Manchester, Manchester, UK; 11Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK; 12Department of Twin Research and Genetic
Epidemiology,King’sCollegeLondon,London,UK;13WellcomeTrustCentreforCellMatrixResearch,UniversityofManchester,Manchester,UK;14AcademicUnitofBone
Metabolism,DepartmentofHumanMetabolism,UniversityofSheffield,Sheffield,UK;15SheffieldNIHRBoneBiomedicalResearchUnit,CentreforBiomedicalResearch,
NorthernGeneralHospital,Sheffield,UK;16DepartmentofStatistics,UniversityofOxford,Oxford,UK
*Correspondence:DrEZeggini,WellcomeTrustSangerInstitute,Hinxton,CambridgeCB101HH,UK.Tel:[phone];Fax:[phone];
E-mail:[email protected]
Received18August2010;revised23November2010;accepted25November2010;publishedonline26January2011
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MATERIALSANDMETHODS Wecomparedtwoalternativemethodsforpost-imputationQCfiltering,first,
WeusedanempiricalGWASdatasettoassesstheeffectofQConimputation theIMPUTE-infoscore,whichisassociatedwiththeimputedallelefrequency
outcome.Wefocusedonchromosome22,(n¼9038directlytypedSNPs)from estimate which ranges from 1, indicating high confidence, to 0 suggesting
3177 osteoarthritis (OA) cases from the United Kingdom, typed on the decreasedconfidence,andsecond,thefreq-add-proper-infoscoreprovidedby
Illumina 610k quad chip (Illumina) as part of the arcOGEN consortium SNPTEST, a relative statistical score ranging from 0 to 1, representing no
GWAS (manuscript submitted). Chromosome 22 is representative of the information to complete information, respectively. The SNPTEST freq-add-
genome in terms of the proportion of directly typed to imputed SNPs. proper-infoscorehasbeenshowntobehighlycorrelatedwiththeIMPUTE-info
All samples included in our analysis had passed standard sample level QC scoreundertheadditivemodel.10Inbothscenarios,wealsofilteredoutSNPs
(based on call rate, heterozygosity, relatedness, ethnicity and gender discre- withMAFo5%.Figure1illustratestheeffectsofalteringpost-imputationQC
pancies).WeimputedgenotypesatvariantsonthebasisofHapMapphaseII filters on the QCed data. On the basis of these results, we chose to use the
release 22 CEUdata(n¼33815 SNPs onchr22) using IMPUTEv1 (https:// IMPUTE-info score with a filtering threshold o0.8 and MAF o5%, which
mathgen.stats.ox.ac.uk/impute/impute.html).8 effectivelyeliminatedB79%ofthesignificantSNPswhileretainingB85%of
Weperformedeachimputationinduplicate,withandwithouttheIMPUTEv1 the nonsignificant ones (SNPTEST freq-add-proper-info o0.9 and MAF 5%
predict genotyped SNPs flag, which resulted in one set of imputed data wouldberoughlyequivalenttothiseliminatingB73%ofthesignificantSNPs
containing the original genotypes and in the other imputed genotypes. while retaining B89% of the nonsignificant ones). We applied this post-
ToassesstheeffectofvaryinglevelsofQC,wecarriedoutseveralroundsof imputationfiltertoeachofourdatasetsandcomparedtheresults.Welooked
imputation,usingdifferentlyQCedOASNPdataasthestartingpoint. at the unQCed and QCed datasets first, as synopsised in Table 1. For each
Initially,weimputedonthebasisofnoSNP-levelQC,includingalldirectly scenario,weexaminedfrequencydifferencesbetweenthedirectlytypedandthe
typedSNPs,regardlessofMAF,callrateandHWE.Wealsoimputedonthe imputedgenotypesasdescribedabove.Inaddition,wecomparedtheimputed
basisofonlythoseSNPsthatpassedstringentQCthresholds(callrate495% genotypesatimputedSNPsonlyfortheunQCedandthefullyQCed(QCed
forSNPswithaMAFZ5%andcallrate499%forSNPswithaMAFo5%, datawithallpoorlyclusteredmarkersremoved)strategies.
HWEexact P40.0001, MAF40.01andremovingallSNPswithGCorTA
alleles;Table1).Althoughimputationbiasescanoccurduetopoorclustering RESULTS
ofSNPswithmiscalledgenotypesinthestartingdataset,clusterplotcheckingis Table 1 summarises the number of SNPs with significantly (Po10(cid:2)6)
not feasible at the genome-wide scale and therefore, it is not implemented differentallelefrequenciesbetweenthedirectlytypedandimputeddata
instandardGWASQC. in the same set of individuals for each of the different QC sets.
We evaluated the accuracy of imputed genotypes by comparing allele Correlation plots and R2 values for the comparisons of the QCed and
frequenciesatthesameSNPbetweenimputedandtrue,directly typeddata.
unQCeddatasetsarepresentedinFigure2.Thedifferencebetweenthe
ForeachQC-imputationiteration,weperformedanallelefrequencycompar-
unQCed (R2¼0.993) and QCed data (R2¼0.994) was minimal. After
ison between the actual directly typed and imputed SNPs. Under perfect
post-imputationfilteringtherewere77SNPswithsignificantlydifferent
imputation,wewouldexpecttoseealignmentwiththenullhypothesisofno
(imputed v. directly typed) allele frequencies in the unQCed data
association.WeusedSNPTEST(http://www.stats.ox.ac.uk/~marchini/software/
gwas/snptest.html)9 to investigate differences between directly typed and compared with 67 significant SNPs in the QCed data. In an attempt
imputedgenotypesatthesamevariantswithinthesamesamples,takinginto toimproveimputationforthesmallsubsetofpoorlyimputedSNPsin
accountthedistributionofgenotypeprobabilitiesforeachindividual.Forthe theQCeddata,weexcludedallSNPswithMAFo5%and,subsequently,
purposesofourcomparison,weusedthoseSNPsthatweredirectlygenotyped also SNPs with MAFo10%. We found that eliminating these lower
in OA cases and also present in the HapMap reference samples. Table 1 MAF SNPs before imputation had little effect overall. The R2 for the
summarisesthenumberoftheseSNPsforeachQCthreshold. post-imputation QC filtered comparison with the QCed data was
Whencomparingdirectlytypedwithimputedallelefrequenciesatthesame
virtually identical both when excluding all SNPs with MAFo5%
variantinthesameindividuals,wearbitrarilyconsideredPo10(cid:2)6assignifi- (R2¼0.994)andwhenexcludingallSNPswithMAFo10%(R2¼0.991).
cantly different. We calculated the correlation between imputed and directly
Given this apparent minimal influence of input data QC on
typed MAF, using the expected counts to allow for genotype-associated
imputation outcome, we investigated further the small set of SNPs
probabilities. We alsoapplieda series of post-imputation QCsteps in order
toeliminateunreliably imputedSNPs,aiming tofilteroutasmanyofthese showing significant allele frequency differences for the presence
SNPs as possible while retaining a good proportion of nonsignificant SNPs. of a common characteristic that could conceivably be used as a
Table1 SummaryofQCstepsandrelatedSNPnumberbreakdown
Post-imputation Post-imputation
unfilteredSNPs QCfilteredSNPsa
DirectlytypedSNPsalso
Pre-imputeQCthresholdapplied presentinHapMap NS S NS S
None(‘unQCed’dataset) 8064b 7689 375 6498 77
TypicalGWASQC(‘QCed’dataset)c 7910 7585 325 6446 67
Asaboveplus14significantSNPsremovedwithpoorclusterplotsd 7896 7592 304 6449 61
Asaboveplus36additionalSNPsremovedwithpoorclusterplotse 7860 7557 303 6419 58
TypicalGWASQCplusMAFo5%c 7554 7269 285 6434 65
TypicalGWASQCplusMAFo10%c 6544 6287 257 5569 53
Abbreviations:GWAS,genome-wideassociationstudy;MAF,minorallelefrequency;NS,notsignificant;QC,qualitycontrol;QCed,qualitycontrolled;S,significant.
aFilteringisbasedonremovalofSNPswithanIMPUTE-infoscoreofo0.8andMAFo5%.
bTherewere8082SNPsintheunQCeddata,ofwhich18weremonomorphicinthearcOGENcasesbutpolymorphicinHapMap;theseSNPswereremovedbyIMPUTE.
cTypicalGWASQCwasMAFr5%withcallrateo95%andMAFo5%withcallrateo99%,Hardy–WeinbergequilibriumPo1(cid:3)10(cid:2)4,andexclusionofGCandATalleleSNPsandMAFo1%
SNPs,appliedasanadditionalpost-associationanalysisandpre-imputationQCstep.
dSignificantSNPswithpoorclusterplotsremoved.
eThoseSNPsflankingthesignificantSNPswithpoorclusterplotsremoved.
arcOGENdataforchromosome22detailingthedifferentpre-imputationQCsteps.AbreakdownoftheSNPnumberforeachQCthresholdisindicatedbothwithandwithoutthepost-imputationQC.
NS,PZ1(cid:3)10(cid:2)6;significantSNPs,Po1(cid:3)10(cid:2)6.
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8000 8000
7000 7000
6000 6000
Ps Ps
N 5000 N 5000
S S
mber of 43000000 mber of 43000000
Nu Nu
2000 2000
1000 1000
0 0
none 0.5 0.6 0.7 0.8 0.9 0.95 0.5 0.6 0.7 0.8 0.9 0.95
IMPUTE info QC threshold Freq-add-proper-info QC threshold
SNPs filtered out SNPs p≥1x10-6
SNPs remaining SNPs p<1x10-6
100
90
80
s 70
P
N
S 60
of Impute info non-significant SNPs removed
e 50 Impute info significant SNPs removed
g Freq_add_proper_info non-significant SNPs removed
a
nt 40 Freq_add_proper_info significant SNPs removed
e
c
er 30
P
20
10
0
0 0.5 0.6 0.7 0.8 0.9 0.95
Impute info or freq_add_proper_info score
Figure1 (a)ImputationresultsfortheQCeddataindicatingthetotalnumberofSNPsfilteredfordifferentQCthresholdsusingtheIMPUTE-infoandfreq-
add-proper-info scores. The SNPs remaining after the filter (red bar) have been subdivided into SNPs that are significant (green bar) and not significant
(yellow bar). (b) The same data as percentage of significant and nonsignificant SNPs removed for each threshold. Both methods of filtering appear to be
equivalent,butthefreq-add-proper-infoisshiftedtotherightforthesamenumericalthreshold;wechosetheIMPUTE-infoo0.8forfurtheranalysis(similar
toafreq-add-proper-infoo0.9).
post-imputation filter. To rule out poor genotyping as the cause of Differencesinregion-specificrecombinationratesmayaccountfor
these significant differences, we examined all cluster plots for the the few remaining significant SNPs, as variants in areas of especially
unfiltered significant SNPs (Po1(cid:3)10(cid:2)6, n¼325). In all, 14 poorly high recombination rate may be more challenging to impute
clustered SNPs were removed and the data were re-imputed. After accurately regardless of QC. To investigate this, we first examined
post-imputation QC, threeadditional SNPs were not significantand the QCed unfiltered data and found that when the data were
six were less significant. We then inspected the cluster plots for dichotomisedintothosemarkerswithlower(o1cm/Mb)andhigher
10SNPsoneithersideofthe61SNPsremainingsignificantlydifferent (Z1cM/Mb) recombination rates, there were more significant
to rule out poor imputation due to flanking SNP poor clustering SNPs present in the higher recombination rate group compared
properties. We examined the cluster plots for 1008 SNPs and found withthelowerrecombinationgroup(P¼1.85(cid:3)10(cid:2)27,averagerecom-
that 36 of these were poor; these resided in the proximity of 35 of binationratesof12.8and3.04,respectively).Whenweexaminedthe
the significant SNPs. We subsequently removed these SNPs and QCed data post-imputation QC, this difference disappeared
re-imputed. We found that following post-imputation QC filtering, (P¼0.526).Thisclearlyindicatesthatapplicationofthepost-imputa-
only3ofthe61SNPswerenolongersignificant,andtheR2remained tion QC filter successfully identifies the majority of significant SNPs
the same as for the QCed data (R2¼0.994) for the post-imputation with high recombination rates. Therefore, to include recombination
QCfiltered data. Whenwe repeated comparisons using IMPUTEv2 rate as an extra filter would not be prudent, for example,
withtheHapMap3(CEU,releaseno.2February2009)anddatafrom using the QCed post-imputation QC filtered data and applying a
the 1000 genomes project (Pilot 1 genotypes released March 2010; further filter using a recombination rate threshold of 41cM/Mb
phased haplotypes released June 2010) as the reference panels, we would eliminate 2075 SNPs, only 24 of which are significantly
observedqualitativelysimilarresults. different.
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a 0.5 0.5
Unfiltered Filtered
F 0.4 0.4
A F
M A
d M
Directly type 000...321 Directly typed 000...321 UnQCed data
R2 = 0.98426241 R2 = 0.99301225
0 0
0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5
Imputed MAF Imputed MAF
0.5 0.5
Unfiltered Filtered
F 0.4 F 0.4
A A
M M
d 0.3 d 0.3 QCed data
e e
p p
y y
y t 0.2 y t 0.2
ctl ctl
e e
Dir 0.1 Dir 0.1
R2 = 0.98783721 R2 = 0.99380961
0 0
0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5
Imputed MAF Imputed MAF
b 0.5
Unfiltered
a
at 0.4
d
d
e
C 0.3
Q
y
ull 0.2
e f
h
n t 0.1
F i R2 = 0.99920016
A
M 0
0 0.1 0.2 0.3 0.4 0.5
MAF in the unQCed data
Figure2 CorrelationplotsandtheassociatedR2for(a)TheunQCedandtheQCedwithandwithoutpost-imputationQCfiltering(IMPUTE-infoo0.8and
MAF o5%). (b) The imputed-only markers in the unQCed and fully QCed data (QCed data with all poorly clustered markers removed) without
post-imputationQCfiltering.
DISCUSSION signalscouldbemissed.Someoftheinaccuratelyimputedvariantswere
The imputation accuracy of common variants does not appear to be due to poor clustering properties. It is plausible that the handful of
substantially affected by GWAS QC steps. Our data demonstrate that variants that still remained inaccurately imputed could be because
there is little difference in imputation accuracy observed in unQCed ofthedifferencesinethnicitybetweenourdataandtheHapMapCEU
GWAS data when compared with QCed GWAS data. Furthermore, referencepanelfromwhichthegenotypeswerepredicted.Wehaveused
the implementation of additional QC steps (eg, filtering out variants IMPUTE,butdonotexpectourresultsandconclusionstoqualitatively
with MAFo0.05 and o0.10) does not considerably improve overall differ with different imputation methods, for example, BEAGLE and
imputationaccuracy.Missingvariantsanddirectlytypedvariantsthat MACHexhibitsimilarimputationaccuracytoIMPUTE.11Differences
failpre-imputationQCchecksareimputedandthesedataareusedfor inpopulationstructurebetweenthereferencepanelandtargetdataset
downstream analyses. Post-imputation QC successfully eliminates a can be a source of imputation inaccuracy. Imputation accuracy for
goodproportionofinaccuratelyimputedSNPs.Specifically,byapply- common SNPs may be further increased by using larger reference
ing a very stringent post-imputation QC threshold, a smaller set of panelswithdataondensersetsofvariants.OurresultsshowthatGWAS
variants with more accurately predicted genotypes remain. The QC is not of paramount importance for the imputation of common
IMPUTE-infothresholdofo0.8andMAFr5%criterionsuccessfully variants. This may be different for the imputation of low frequency
filtered out the majority of poorly imputed SNPs. However, the and rare variants based on emerging reference panels such as the
application of these strict filters in GWAS data could result in many 1000 genomes (http://www.1000genomes.org) and UK10k (http://
SNPsbeingexcludedfromthedata,andthuspotentialtrueassociation www.uk10k.org) projects. In summary, our study demonstrates that
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ThisworkislicensedundertheCreativeCommons
1 The International HapMap Consortium: The International HapMap Project. Attribution-NonCommercial-No Derivative Works
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