Schork, AJ et al. (2013) All SNPs are not created equal: genome-wide association studies reveal a

14 Pages·2013·0.86 MB·English

nn

Schork, A. J. et al. (2013) All SNPs are not created equal: genome-wide
association studies reveal a consistent pattern of enrichment among
functionally annotated SNPs. PLoS Genetics, 9 (4). e1003449. ISSN 1553-
7390

Copyright © 2013 The Authors

http://eprints.gla.ac.uk/101165/

Deposited on: 12 January 2015

Enlighten – Research publications by members of the University of Glasgow
http://eprints.gla.ac.uk
All SNPs Are Not Created Equal: Genome-Wide
Association Studies Reveal a Consistent Pattern of
Enrichment among Functionally Annotated SNPs
Andrew J. Schork1,2,3, Wesley K. Thompson4, Phillip Pham5,6, Ali Torkamani5,6,7, J. Cooper Roddey3,
Patrick F. Sullivan8, John R. Kelsoe4, Michael C. O’Donovan9, Helena Furberg10,
The Tobacco and Genetics Consortium", The Bipolar Disorder Psychiatric Genomics Consortium",
The Schizophrenia Psychiatric Genomics Consortium", Nicholas J. Schork5,6,7, Ole A. Andreassen4,11,12,
Anders M. Dale3,13,14,15*
1CognitiveSciencesGraduateProgram,UniversityofCaliforniaSanDiego,LaJolla,California,UnitedStatesofAmerica,2CenterforHumanDevelopment,Universityof
CaliforniaSanDiego,LaJolla,California,UnitedStatesofAmerica,3MultimodalImagingLaboratory,UniversityofCaliforniaSanDiego,LaJolla,California,UnitedStatesof
America,4DepartmentofPsychiatry,UniversityofCaliforniaSanDiego,LaJolla,California,UnitedStatesofAmerica,5ScrippsHealth,LaJolla,California,UnitedStatesof
America,6TheScripps TranslationalScienceInstitute,The ScrippsResearchInstitute,LaJolla, California, United StatesofAmerica, 7DepartmentofMolecular and
ExperimentalMedicine,TheScrippsResearchInstitute,LaJolla,California,UnitedStatesofAmerica,8DepartmentofGenetics,UniversityofNorthCarolina,ChapelHill,
NorthCarolina,UnitedStatesofAmerica,9MRCCentreforNeuropsychiatricGeneticsandGenomics,SchoolofMedicine,CardiffUniversity,Cardiff,UnitedKingdom,
10Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America, 11Institute of Clinical
Medicine,UniversityofOslo,Oslo,Norway,12DivisionofMentalHealthandAddiction,OsloUniversityHospital,Oslo,Norway,13DepartmentofCognitiveSciences,
UniversityofCaliforniaSanDiego,LaJolla,California,UnitedStatesofAmerica,14DepartmentofRadiology,UniversityofCaliforniaSanDiego,LaJolla,California,United
StatesofAmerica,15DepartmentofNeurosciences,UniversityofCaliforniaSanDiego,LaJolla,California,UnitedStatesofAmerica
Abstract
Recentresultsindicatethatgenome-wideassociationstudies(GWAS)havethepotentialtoexplainmuchoftheheritability
ofcommoncomplexphenotypes,butmethodsarelackingtoreliablyidentifytheremainingassociatedsinglenucleotide
polymorphisms (SNPs).WeappliedstratifiedFalse Discovery Rate(sFDR) methodstoleveragegenicenrichmentinGWAS
summary statistics data to uncover new loci likely to replicate in independent samples. Specifically, we use linkage
disequilibrium-weighted annotations for each SNP in combination with nominal p-values to estimate the True Discovery
Rate (TDR=12FDR) for strata determined by different genic categories. We show a consistent pattern of enrichment of
polygenic effects in specific annotation categories across diverse phenotypes, with the greatest enrichment for SNPs
tagging regulatory and coding genic elements, little enrichment in introns,and negative enrichment forintergenic SNPs.
Stratified enrichment directly leads to increased TDR for a given p-value, mirrored by increased replication rates in
independent samples. We show this in independent Crohn’s disease GWAS, where we find a hundredfold variation in
replication rate across genic categories. Applying a well-established sFDR methodology we demonstrate the utility of
stratification forimproving power ofGWASin complexphenotypes, withincreased rejection rates from 20%inheightto
300%inschizophreniawithtraditionalFDRandsFDRbothfixedat0.05.Ouranalysesdemonstrateaninherentstratification
among GWAS SNPs with important conceptual implications that can be leveraged by statistical methods to improve the
discovery ofloci.
Citation:SchorkAJ,ThompsonWK,PhamP,TorkamaniA,RoddeyJC,etal.(2013)AllSNPsAreNotCreatedEqual:Genome-WideAssociationStudiesReveala
ConsistentPatternofEnrichmentamongFunctionallyAnnotatedSNPs.PLoSGenet9(4):e1003449.doi:10.1371/journal.pgen.1003449
Editor:GregGibson,GeorgiaInstituteofTechnology,UnitedStatesofAmerica
ReceivedAugust28,2012;AcceptedFebruary28,2013;PublishedApril25,2013
Copyright: (cid:2)2013 Schork et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalauthorandsourcearecredited.
Funding:AJSwassupportedbyNIHgrantsRC2DA029475andR01HD061414andbytheRobertJ.GlushkoandPamelaSamuelsonGraduateFellowship.WKT
wassupportedbyNIAgrantSR01AG022381-09.PP,AT,andNJSweresupportedinpartbyNIH/NCRRgrantnumberUL1RR025774.OAAwassupportedbythe
Research Council of Norway (183782/V50) and the South East Norway Health Authority (2010-074). AMD was supported by NIH grants RC2DA029475,
R01EB000790,R01AG031224,R01AG022381,P50MH081755,P50NS022343,andU54NS056883.Thefundershadnoroleinstudydesign,datacollectionand
analysis,decisiontopublish,orpreparationofthemanuscript.
CompetingInterests:Theauthorshavedeclaredthatnocompetinginterestsexist.
*E-mail:[email protected]
"MembershipsoftheconsortiaareprovidedintheAcknowledgments.
Introduction nucleotide polymorphisms (SNPs) [3], even when considered in
aggregate,theseSNPsexplainsmallportionsofthetraitheritability
Complextraitsaregenerallyinfluencedbymanygeneswithsmall [4].RecentresultsindicatethatGWAShavethepotentialtoexplain
individualeffects[1].This‘polygenic’architecturehasbeendifficult muchoftheheritabilityofcommoncomplexphenotypes[5,6],and
to characterize. While Genome-wide association studies (GWAS) more SNPs are likely to be identified in larger samples [7].
[2] have successfully identified thousands of trait-associated single However,therearefew methodsavailableforidentifyingmore of
PLOSGenetics | www.plosgenetics.org 1 April2013 | Volume 9 | Issue 4 | e1003449
DifferentialEnrichmentamongSNPCategories
relativetointronicandintergenicSNPswhichwillbereflectedin
Author Summary
an increased estimated TDR and empirically confirmed through
Modern genome-wide association studies (GWAS) have improved replication rate acrossindependent samples.
failed to identify large portions of the genetic basis of TheassociationsignalofaSNPtestedinGWASisasurrogate
common, complex traits. Recent work suggested this for, or ‘tags,’ the potential effects of many other variants. Thus,
could be because many genetic variants, each with any of a number of ‘tagged’ variants could underlie the observed
individually small effects, compose their genetic architec- association signal. Focusing on the tag SNPs only, without
ture,limitingthepowerofGWAS.Moreover,thesevariants
systematically capturing the underlying causal variants within a
appearmoreabundantlyinandneargenes.Usinggenome
‘tagged’linkageblock,limitsthefunctionalinferencesthatcanbe
annotations,summarystatisticsfromseveralofthelargest
drawn from GWAS. By incorporating the correlation between
GWAS,andestablishedstatisticalmethodsforquantifying
SNPs(linkagedisequilibrium;LD)weexpectastrongerandmore
distributions of test statistics, we show a consistency
consistent differentiation of enrichment among genic annotation
across studies. Namely, we show that, across all assessed
categories. In the current study, we use an LD-weighted scoring
traits,theteststatisticsresultingfromSNPsthatarerelated
algorithmthatallowsquantificationofthepropertiesofmulti-locus
to the 59 UTR of genes show the largest abundance of
associations, while SNPs related to exons and the 39UTR LD structure implicitly captured by each tag SNP to our
are also enriched. SNPs related to introns are only enrichment analysis. These categories can be leveraged to create
moderately enriched, and intergenic SNPs show a deple- strata forestablished sFDRapproaches.
tion of associations relative to the average SNP. This We employ a model free strategy to identify enriched strata
enrichment corresponds directly to increased replication among phenotypes based on GWAS summary statistics. We first
across independent samples and can be incorporated a calculatetherelativeenrichmentindifferentgenicelements,using
priori into statistical methods to improve discovery and the category-specific empirical cumulative distribution function
prediction. Our results contribute to on-going debates (cdf) of the nominal p-values after controlling for estimated
aboutthefunctionalnatureofthegeneticarchitectureof genomicinflation.Foreachnominalp-valuethresholdanestimate
complextraitsandpointtoavenuesforleveragingexisting of the category-specific TDR=12FDR is obtained from these
GWAS data for discovery in future GWA and sequencing
empiricalcdfs.Thisanalysisisimplementedonsummaryp-values
studies.
fromtenpublishedGWASmeta-analysesstudying14phenotypes.
Wethenusethesub-studyGWASinCrohn’sdiseasetotestifthe
theSNPslikelytobeassociatedwithphenotypeswithoutincreasing estimatedincreasedTDRtranslatestoimprovedreplicationrates,
thesamplesize,asrecognizedbyrecentEuropeanandUScallsfor showing that for a given replication rate the nominal p-value
newstatisticalgeneticsmethods.Thecrucialissueisthatformost thresholdis100timeslargerforthemostenrichedgeniccategory
complextraitsalargenumberofSNPshavetoosmallaneffectto comparedtotheintergeniccategory.Finally,usinganestablished
passstandardGWASsignificancethresholdsgivencurrentsample sFDRframeworkwedemonstratetheutilityofleveragingenriched
sizes. We present results suggesting new analytical approachesfor categoriesforimprovingpowertodetect SNPslikelytoreplicate,
GWAS will uncover more of the polygenic effects in complex i.e.,toreject morenull hypotheses fora fixedFDR.
disordersandtraits.WehypothesizethatallSNPsinaGWASare
notexchangeable,butcomefrompre-determinablecategorieswith Results
differentdistributionsofeffects.Thisimpliesthatsomecategoriesof
SNPs are enriched, i.e. are more likely to be associated with a LD-Based Enrichment of Genic Elements in Height
phenotypethan others. Thisinformation canbeused tocalculate Under multiple testing paradigms such as GWAS, quantitative
thecategory-specificTrueDiscoveryRate(TDR),ortheexpected estimates of likely true associations can be estimated from the
proportion of correctly rejected null hypotheses [8]. SNPs from distributionsofsummarystatistics[17,18].Acommonmethodfor
enriched SNP categories will have an increased TDR for a given visualizingtheenrichmentofstatisticalassociationrelativetothat
effect size, or equivalently, for a given nominal p-value. Stratified expectedundertheglobalnullhypothesisisthroughQ-Qplotsof
False Discovery Rate (sFDR) methods [9] provide an established thenominalp-valuesresultingfromGWAS.Undertheglobalnull
framework for demonstrating the utility of using enriched genic hypothesis the theoretical distribution is uniform on the interval
categoriestoincreasepowertodiscoverSNPslikelytoreplicatein [0,1]. Thus, the usual Q-Q curve has as the y-coordinate the
independentsamples.PreviousworkhasappliedsFDRandrelated nominalp-value,denotedby‘‘p’’,andthex-coordinatethevalue
methodstoGWASdatastratifiedbycandidateregionsdetermined oftheempiricalcdfatp,whichwedenoteby‘‘q’’.Asiscommon
through prior linkage analysis and/or candidate gene studies in GWAS, we instead plot 2log p against the 2log q to
10 10
[10–12]andspecificbiologicalpathwaysrelatedtodiseaseetiology emphasize tail probabilities of the theoretical and empirical
[13].Othershaveconsideredstratificationbygenomeannotations distributions. In such plots, enrichment results in a leftward shift
in linkage analysis [14] and Bayesian association analyses [15], intheQ-Qcurve,correspondingtoalargerfractionofSNPswith
demonstratingtheutilityofthisapproachforimprovingpowerand nominal2log p-valuegreaterthanorequaltoagiventhreshold
10
FDRbaseddiscoverywherereliable,pre-determinablestrataexist. (see Material andMethods).
It has been suggested that variation in and around genes The stratified Q-Q plot for height (Figure 1) shows a clear
harbors more polygenic effects [6,16]. However, the particular variation in enrichment across genic annotation categories. The
geneelements(i.e.,intron,exon,UTRs)containingthesevariants separationbetweenthecurvesfordifferentcategoriesisenhanced
and the distribution of effect sizes in GWAS have been left to when using LD-weighted genic annotation categories in compar-
extrapolationandspeculation.Further,SNPsinandaroundgenes ison to non LD-weighted positional categories (Figure S3). The
have been shown to explain more variation [6] and replicate at parallelshapeofthesecurvesislikelycausedbythesignificantbut
higher rates [16] than intergenic SNPs. These studies, however, imperfect correlation among categories due to the non-exclusive
did not parse genic regions down to specific genic elements. We nature of theannotation scoring(Figure S2).
here hypothesize that SNPs inregulatory andcoding elements of Anearlierdeparturefromthenullline(leftwardshift)suggestsa
proteincodinggeneswillshowanenrichmentofpolygeniceffects greaterproportionoftrueassociations,foragivennominalp-value.
PLOSGenetics | www.plosgenetics.org 2 April2013 | Volume 9 | Issue 4 | e1003449
DifferentialEnrichmentamongSNPCategories
pattern is consistent for all phenotypes considered (data not
shown). Given the log scale of the Q-Q plots, 90% of SNPs fall
between0and1and99%fallbetween0and2onthehorizontal
axis,andthusitisclearthatamajorityofenrichedSNPshavep-
values that donot reachgenome-wide significance.
We computed significance values for the curves for each
annotationcategoryrelativetothoseforintergenicSNPs,usinga
two-sample Kolmogorov-Smirnov Test. The enrichment for
height was highly significant for all categories when compared
withtheintergeniccategory,withallp-valueslessthan2.2610216.
Nearly every genic category was also significantly enriched for
each otherphenotype (TableS5).
While the pattern of enrichment isconsistent, the shape of the
curves variesacrossphenotypes. In particular,thepoint at which
the curves deviate from the expected null line occurs earliest for
height, followed by SCZ, and finally CPD (Figure 2A–2C),
consistent with different proportions of SNPs that are likely
associated with each trait (i.e., different levels of ‘polygenicity’).
These findings are consistent with results obtained using an
established mixture-modeling framework [17] (Text S1 and
Figures S8,S9,S10,S17,S18,S19).
Intergenic Genomic Control
Figure 1. Stratified Q-Q plot for height shows enrichment by The relative absence of enrichment in intergenic SNPs as we
annotation categories using Linkage-Disequilibrium (LD)- have defined them, suggests minimal inflation due to polygenic
weighted scores. Genic annotation categories were: 1) 10,000 to effectsandamorerobustestimateoftheglobalnull.Thisfactcan
1,001basepairsupstream(10kUp);2)1,000to1basepairupstream be exploited for better estimation of variance inflation due to
(1k Up); 3) 59 untranslated region (59UTR); 4) Exon; 5) Intron; 6) 39
stratification[20]thatisminimallyconfoundedbytruepolygenic
untranslatedregion(39UTR);7)1to1,000basepairsdownstream(1k
effects [19]. We confined the estimation of the genomic inflation
Down);8)1,001to10,000basepairsdownstream(10kDown).Q-Qplot
of heightwithnon-LDweightedcategoryscoresare showninFigure factor[20],lGC,toonlyintergenicSNPs(TableS4)andadjusted
S3. summarystatisticsforallphenotypesaccordingtothis‘‘intergenic
doi:10.1371/journal.pgen.1003449.g001 inflation control’’ procedure. The stratified Q-Q plots for height
withandwithoutintergenicinflationcontrolareshowninFigure
Thedivergenceofthecurvesfordifferentcategoriesimpliesthatthe S4.
proportionofnon-nulleffectsvariesconsiderablyamongannotation
categoriesofgenicelements.Forexample,theproportionofSNPsin Category-Specific True Discovery Rate
the59UTRcategoryreachingasignificancelevelof2log (p).10is SincespecifictagSNPcategoriesaresignificantlymorelikelyto
10
roughly10timesgreaterthanforallSNPsand50–100timesgreater beassociatedwithcommonphenotypes,whileintergeniconesare
thanforintergenicSNPs. less likely, all tag SNPs should not be treated as exchangeable.
Variationinenrichmentacrossdiversegeniccategoriesisexpected
Polygenic Enrichment across Diverse Phenotypes tobeassociatedwithcorrespondingvariationinTDRforagiven
Recently Yang et al [19] demonstrated that an abundance of nominal p-value threshold. A conservative estimate of the TDR
low p-values beyond what is expected under null hypotheses in foreachnominalp-valueisequivalentto12(p/q)asplottedonthe
GWAS, but not necessarily reaching stringent multiple compar- Q-Q plots (see Online Methods). This relationship is shown for
ison thresholds, often attributed to ‘spurious inflation,’ is also height, SCZ and CPD (Figure 2D–2E). Similar category-specific
consistentwithanenrichmentoftrue‘polygenic’effects[19].The TDRplotswerecalculatedforeachofthe14phenotypes(datanot
prevalence of enrichment below the established genome-wide shown). For a given TDR the corresponding estimated nominal
significance threshold of p,561028 (2log (p).7.3;) in height p-value threshold varies with a factor of 100 from the most
10
(Figure 2A) is consistent with their hypotheses and strongly enrichedgeniccategorytotheintergeniccategory,andthepattern
suggests that current GWAS do not capture all of the additive isconsistentacrossphenotypes.SinceTDRistheoreticallyrelated
‘tagged variance’ inthis phenotype. Importantly, this enrichment topredictedreplicationrate,itisexpectedthatforagivenp-value
varies acrossgenicannotation categories. threshold the replication rate will be higher for SNPs in genic
The enrichment patterns among annotation categories are categories with high TDR. The high estimates of TDR at
consistent across phenotypes, including schizophrenia (SCZ) and significance levels below genome-wide significance is consistent
tobacco smoking (cigarettes per day, CPD; Figure 2B–2C). The with recent work in Schizophrenia that demonstrates a high
stratified Q-Q plots for height, SCZ and CPD each demonstrate proportion of likely true associations at reduced thresholds, but
the largest enrichment for tag SNPs in LD with 59UTR, and withouttheneededpowertoreachgenome-widesignificance[21].
exonic variation, showingnearly tenfold increasesin termsofthe
proportionofp-valuesexpectedbelowagiventhresholdunderthe Quantification of Enrichment
null hypothesis. SNPs that tag intergenic regions show nearly While the TDR provides a quantification of enrichment for a
tenfold depletions in comparison to all tag SNPs, although not given nominal p-value threshold (equivalently, SNP z-score
when compared to the expected null. SNPs tagging intronic threshold), we also provide a single number quantification of
variation show minimal enrichment over all tag SNPs, despite enrichment for each LD-weighted annotation category within
making up the largest proportion of genic SNPs (Table S3). The each phenotype, computed as the sample mean (z2)21. The
PLOSGenetics | www.plosgenetics.org 3 April2013 | Volume 9 | Issue 4 | e1003449
DifferentialEnrichmentamongSNPCategories
Figure 2. Stratified Q-Q plots and true discovery rates show consistency of enrichment. Upper panel: Stratified Q-Q plots illustrating
consistentenrichmentof genicannotationcategories acrossdiversephenotypes: (A)Height,(B) Schizophrenia(SCZ),and (C) Cigarettesper Day
(CPD).Allfiguresarecorrectedforinflationusingintergenicinflationcontrol.Onlynominalp-valuesbelowthestandardgenome-widesignificance
threshold(p,561028)areshown.Lowerpanel:StratifiedTrueDiscoveryRate(TDR)plotsillustratingtheincreaseinTDRassociatedwithincreased
enrichmentin(D)Height,(E)SCZand(F)CPD.Genicannotationcategorieswere:59untranslatedregion(59UTR),Exon,Intron,39untranslatedregion
(39UTR),AllSNPs,inadditiontoIntergenic.
doi:10.1371/journal.pgen.1003449.g002
samplemean,takenoverallSNPsinagivencategory,providesan thatisinconsistentwiththiseffectdrivingourenrichmentfindings
estimate of the variance due to null and non-null SNPs; by (Table S9 and Figure S6). To further address the possibility that
subtractingoneweobtainaconservativeestimateofthevariance some of the differential enrichment of categories could be due to
ineffectsizesattributabletonon-nullSNPsalone.BothTDRand category-specific genomic inflation from the above factors, we
mean (z2)21 are justified based on a standard mixture model performed null-GWAS simulations based on genotypes from the
formulation (see Text S1). These enrichment scores, normalized 1000 Genome Project. The results suggest that such effects are
by the maximum value across categories within each phenotype, non-existent ornegligible (TableS11).
are presented in Figure 3. The 59UTR annotation category was
themost enrichedcategory across all fourteen phenotypes (Table Replication Rate
S6).Additionally,theexoncategoryisconsistentlymoreenriched To further address the possibility that the observed pattern of
than theintroncategory. differential enrichment results from spurious (i.e., non-generaliz-
Categories where each SNP tags more reference SNPs on able) associations due to category-specific confounding effects or
averageorrepresentsalargertotalamountofLDcouldspuriously statistical modeling errors, we also studied the empirical replica-
appearenriched.Wedonotecategoricaldifferencesinthenumber tion rate across independent sub-studies for one phenotype (CD)
ofSNPsandtotalsummedLDcapturedbyeachSNP(TablesS7 where the required sub-study summary statistics were available.
and S8) but multiple regression analyses show the effect of these Figure 4A shows the estimated TDR curves for different
variables is negligible and independent categorical effects persist annotation categories in CD, with a similar pattern as that
(Table S10) despite the significant correlation among categories describedforinheight,SCZandCPD,above.TDRisanestimate
(Figure S2). Likewise, systematic deviations in minor allele of the expected replication rate for a sufficiently large replication
frequency(MAF)acrosscategoriescouldbiasannotationcategory sample.WehypothesizedthatstratawithhigherTDRforagiven
effects as MAF acts multiplicatively with effect size to explain nominalp-valuewouldalsoshowhigherempiricalreplicationrate.
variance. We found minimal categorical stratification for MAF Figure4Bshowstheempiricalcumulativereplicationrateplotsas
PLOSGenetics | www.plosgenetics.org 4 April2013 | Volume 9 | Issue 4 | e1003449
DifferentialEnrichmentamongSNPCategories
Figure3.Categoricalenrichmentforsevendiversephenotypes.Therelativepatternofenrichment,asmeasuredbythemean(z-score221)
afterintergenicinflationcontrol,ofLD-weightedgenicannotationcategoriesremainconsistent.ResultsforallphenotypesareshowninFigureS5,
TableS6.
doi:10.1371/journal.pgen.1003449.g003
a function of nominal p-value for the same categories as for the oflikelynon-nullSNPsinGWASbytakingadvantageofthenon-
stratified TDR plot in Figure 4A. Consistent with the category- exchangeability of SNPs demonstrated by our enrichment
specific TDR pattern, we found that the nominal p-value analyses.Otherformulationsofstrataandcontinuedinvestigations
corresponding to a wide range of replication rates was 100 times
higher forintergenic relative tothemostenrichedgenic category
(59UTR). Similarly, SNPs from genic annotation categories
showing the greatest enrichments replicated at higher rates, up
tofivetimeshigherthanintergenicfor59UTRSNPs,independent
ofp-valuethresholds.Theincreaseinreplicationratewasfoundto
be greatest for SNPs that do not meet genome-wide significance,
suggesting that adjusting p-value thresholds according to the
estimated category-specific TDR could greatly improve the
discovery of replicatingSNP associations.
Increased Power Using Stratified False Discovery Rate
In order to demonstrate the utility of the enriched category
information for improved discovery, we leveraged an established
method for computing stratified False Discovery Rates [9]. The
sFDRmethodimprovesthepoweroftraditionalmethodsforFDR
control [22] by taking advantage of pre-defined, differentially
enrichedstrataamongmultiplehypothesistestingp-values.Here,
we define an increase in power from using stratified (vs.
unstratified) methods as a decreased Non-Discovery Rate (NDR)
foragivenlevelofFDRcontrola,whereNDRistheproportion
of false negatives among all non-null tests [23]. Specifically, the
ratio of 1-NDR from stratified FDR control to 1-NDR from
unstratified FDR control captures the relative power of the two
approaches.Thisratiocanbeshowntobeequivalenttotheratio
ofthenumberofSNPsrejectedbysFDRtothenumberrejected
by unstratified FDR fora commonlevela.
For each phenotype we divided the SNPs into independent
strata according to its predicted tagged variance (z2). Tagged
variance was predicted using on a linear model with regression
weights for each annotation category trained using the height
GWAS summary statistics. The enrichment of these strata is Figure 4. Independent study replication confirms enrichment
presentedinFigureS11.InFigure5(andTableS12)weshowan in Crohn’s disease. (A). Stratified True Discovery Rate (TDR) plots
increase in the number of discovered SNPs. For example, for illustrating the increase in TDR associatedwith increasedenrichment.
(B)Cumulativereplicationplotshowingtheaveragerateofreplication
a=.05theincreasedproportionofdeclarednon-nullSNPsusing
(p,.05) within sub-studies for a given p-value threshold shows
sFDR ranges from 20% in height to 300% in schizophrenia.
enrichedcategoriesreplicateatahigherrateinindependentsamples.
Leveraging our genic annotation categories in the sFDR Theverticalinterceptistheoverallreplicationratepercategory.
frameworkprovidesonepossibleavenueforimprovingtheoutput doi:10.1371/journal.pgen.1003449.g004
PLOSGenetics | www.plosgenetics.org 5 April2013 | Volume 9 | Issue 4 | e1003449
DifferentialEnrichmentamongSNPCategories
Figure5.Enrichmentimprovesdiscoveryusingestablishedmethods.Amongthreephenotypes,(A)Height,(B)Crohn’sDisease,(C)and
Schizophrenia,wedemonstrateanincreaseddiscoveryofSNPsatagivenFDRwhenincorporatingtheenrichedgenicannotationinformationintoan
establishedstratifiedfalsediscoveryrate(sFDR;red)framework.SNPsdeclaredsignificantbysFDRalsoreplicateatahigherrate(FigureS12).
doi:10.1371/journal.pgen.1003449.g005
into enrichment are likely to further improve the power of this impoverishedcategorieswillhaveeffectsizesthatreplicateweakly
approach. in independent samples [17] (Figure S10). Identifying SNPs with
generalizableeffectsiscrucialtoimprovingthepredictivepowerof
Discussion polygenicriskscoresthatcombineSNPeffectstopredictvariation
in complex traits and diseases in new samples [24]. Properly
Our results show a significant and consistent pattern of
assessingthegeneralizationperformanceofSNPeffectsizeswillbe
enrichment amonggenicelements,particularly the59UTR, exon
ofhighimportanceforpersonalizedmedicinebasedonpolygenic
and39UTRcategories,forassociationwithdiversecomplextraits
riskscores.
and disorders. Intergenic SNPs were depleted more than tenfold.
While knowing which SNP categories are enriched for true
This has important analytical and conceptual implications. The
associations can guide gene discovery, knowing which SNPs are
results suggest that all tag SNPs should not be treated as
unlikelytohaveaneffectisalsoimportantandcanguidecontrol
exchangeable,butratherfunctionalannotationsoftheunderlying
of spurious inflation through improved genomic inflation correc-
tagged SNPs can be leveraged in SNP discovery. Moreover, the
tion [19,20]. We show how the genic enrichment pattern can be
results point to a common functional nature of the polygenetic
used for genomic inflation control in GWAS. By estimating
architecture across diversecomplex phenotypes.
genomic control from intergenic tag SNPs, we can minimize the
GWAS have traditionally treated all SNPs as exchangeable,
contamination of inflation estimates from true polygenic associa-
implicitly assigning all SNPs equal a priori probability of
tions.Whileemergingstudieshavesuggestedthatpolygeniceffects
association. The current findings suggest that this assumption of
are detectable in GWAS data [5,6], particularly in and around
exchangeability is not valid, and that the traditional statistical
genes[6,16],andthepresenceoftheseeffectsisconsistentwitha
approaches toGWASarehighly suboptimal.Sunetal[13]have
skewed distribution of p-values [17,25] that resembles spurious
laid the groundwork for incorporating this non-exchangeability
inflation [19], differential confounds among our categories could
into Hypothesis Driven Genome-Wide Association Studies (HD-
persist. We provided a null GWAS (Table S11) and found no
GWAS), and further applications and development of such
indications of spurious enrichment due to differential MAF and
methodsislikelytoprovefruitful.Toillustratetheutilityofthese
LD structure among our categories. Further, if our findings were
approaches we used our LD-weighted tag SNP annotations,
due to spurious category-specific inflation, including differential
combined with an established method for computing stratified
population stratification, one would not expect a mirroring
False Discovery Rates, to demonstrate improved discovery of
increase in replication across independent samples (Figure 4),
SNPs in GWAS under this testing framework. Annotation
except under the extreme condition where the population
categories were chosen based on previous literature suggesting
structure of the discovery sample was mirrored in the replication
that SNPs in and near genes are likely to harbor many true
polygenic effects. We provide a proof of principle, using intuitive sample. In addition, the variable shape of the enrichment,
categories, that a priori information about SNPs, irrespective of deviating at different points along the expected line for different
phenotype,canimprovediscoveryoflikelynon-nullSNPs.Given phenotypes, is inconsistent with spurious variance inflation. It is
the wealth of information available for SNP variants, it is likely alsoofimportancetonotethatthepresenceofspuriouscategory-
that other annotation schemes will potentially yield even greater specific inflation would imply that any GWAS association within
enrichment andfurtherincrease thegainsof ourbasic approach. anenrichedcategory(i.e.,tagginganexonicorUTRSNP)should
We expect this approach will be of particular importance in be considered less reliable. Our findings are consistent with the
polygenic complex phenotypes. Only a small fraction of the presenceoftruepolygeniceffects,however,wecannotentirelyrule
heritability is explained by currently discovered variants but out contributions of potential confounding effects or alternate
converging evidence suggests much more remains buried in hypotheses. It is highly implausible, however, that they would
GWAS(i.e.,traitswithalarge‘‘missingheritability’’)[4]forthese explain away both the described enrichment and increased
traits. replication.
Moreover, the non-exchangeability of SNPs based on LD- Conceptually, our results support findings that aspects of the
weighted genic categories has important implications for the genetic architecture are consistent across phenotypes [26], and
generalizability of estimated SNP effect sizes. In particular, SNPs previoussuggestionsfrombothmodelorganisms[27]andhumans
in highly enriched categories will have effect size estimates that [6,16] that polygenic contributions are greater from variants in
replicate strongly in independent samples, whereas SNPs in and around genes. Our findings agree with emerging trends in
PLOSGenetics | www.plosgenetics.org 6 April2013 | Volume 9 | Issue 4 | e1003449
DifferentialEnrichmentamongSNPCategories
model organisms [26] and post-hoc GWAS analyses [28–30] Positional Annotation Categories
suggesting that quantitative traits are affected by a large but Bi-allelic SNP genotypes from the European reference sample
quantifiable number of polymorphisms, inconsistent with ‘infini- providedbytheNovember 2010releaseofPhase1ofthe1KGP
tesimal’ models [31], but notably polygenic. We also show wereobtainedinpre-processedformfromhttp://www.sph.umich.
evidence that tagged variance is proportional to genotypic edu/csg/abecasis/MACH/download/. Using Plink version 1.07
variance (Figures S6 and S7), which supports the notion that [46,47] 1KGP SNPs with a minor allele frequency less than 1%,
commonvariationexplainsanimportantpartofthevariabilityin missing in more than 5% of individuals and/or violating Hardy-
commondiseases andtraits [32]. Weinberg equilibrium (p,161026) were excluded from the
Our findings also suggest that regulatory genic elements may referencepanel.Individualsmissingmorethan10%ofgenotypes
be particularlyenrichedforpolygenic effects.Thisisinlinewith were excluded.
themoststronglyassociatedSNPsfromGWAS,whichmainlytag Each remaining 1KGP SNP was assigned a single, mutually
regulatory genic elements [3]. The 59UTR, specifically, is exclusive genic annotation category based on its genomic
important for the regulation of gene expression [33] making it
position (hg19). Genic annotation categories were: 1) 10,000 to
acompellingcandidateforplayingacausalroleincomplextrait
1,001 base pairs upstream (10 k Up); 2) 1,000 to 1 base pair
variation[1].Also,59UTRsarelessconservedevolutionarilythan upstream(1 kUp);3)59untranslatedregion(59UTR);4)exon;5)
codingregions[34],despitetheirnotedfunctionality,pointingto intron; 6) 39 untranslated region (39UTR); 7) 1 to 1,000 base
a potential source for regulatory variation thought to drive
pairs downstream (1 k Down); 8) 1,001 to 10,000 base pairs
evolutionary differences among primates [35] and other species.
downstream (10 k Down), all with reference to protein coding
We also found a stronger enrichment of SNPs tagging exons
genes only. Annotations were assigned based on the first gene
compared to introns. However, because we are considering tag
transcript listed in the UCSC known genes database [48]. In
SNPswecanonlyspeculateaboutthefunctionalconsequencesof
total9,078,4051KGPSNPswereassignedpositionalcategories.
the underlying causal variants.
All positional categories were scored 0 or 1. For further details
Exome sequencing studies have identified causal variants for
see Text S1.
Mendelian disorders by leveraging hypotheses about the genetic
architecture of these traits and thus focusing on protein changing
Linkage Disequilibrium (LD)-Weighted Scoring
variants [36]. While methods are continually improving for
For each GWAS tag SNP a pairwise correlation coefficient
predictingthefunctionalconsequencesofcodingchanges,predicting
approximation to LD (r2) was calculated for all 1KGP SNPs
regulatoryfunctionhasremainedachallenge.Futuretargetcapture
within 1,000,000 base pairs (1 Mb) of the tag SNP using Plink
methods,deepsequencingeffortsandcustomSNParraydesigns,as
version 1.07 [46,47]. LD scores were thresholded providing
wellasfunctionalpredictioneffortsincomplextraitsmayimprove
continuousvaluedestimatesfrom0.2to1.0;r2values ,0.2were
power and utility by adding focus to regulatory elements, in
setto0andeachSNPwasassignedanr2valueof1.0withitself.
particular59UTRs.Asotherpotentialannotationcategories,suchas
LD-weighted annotation scores werecomputed as the sum of r2
transcriptionfactorbindingsites,methylationtargets,conservation/
LD between the tag SNP and all 1KGP SNPs positioned in a
selection,andgeneexpressionpatterns,becomebettercharacterized
particular category. Each tag SNP was assigned to every LD-
thecurrentanalysescouldbeextendedtoincludethese.
weighted annotation category for which its annotation score
was greater than or equal to 1.0. The resulting LD-weighted
Materials and Methods
annotationcategorieswerenotmutuallyexclusivesuchthateach
Genome-Wide Association Study (GWAS) Data GWAS tag SNP could be annotated with multiple categories.
Fourteenphenotypes,bodymassindex(BMI)[37],height,waist Summary statistics describing the distribution of scores in each
tohipratio[38](WHR),Crohn’sdisease[39](CD),ulcerativecolitis category for the 2,558,411 SNPs, representing the union of all
[40] (UC), schizophrenia [41] (SCZ), bipolar disorder [42] (BD), GWASconsidered,areprovidedinTablesS2andS3.FigureS1
smoking behavior as measured by cigarettes per day [43] (CPD), providesaschematicofourscoringalgorithm.Allanalyseswere
systolicanddiastolicbloodpressure[44](SBP,DBP),andplasma repeated using a second set of LD thresholding parameters and
lipids [45] (triglycerides, TG, total cholesterol, TC, high density found to be robust (Text S1 and Figures S13, S14, S15, S16).
lipoprotein,HDL,lowdensitylipoprotein,LDL),wereconsidered.
Genome-wide association study (GWAS) results were obtained as Intergenic SNPs
summarystatistics(p-valuesorz-scores)frompublicaccesswebsites Intergenic SNPs were determined after LD-weighted scoring
(BMI, Height, WHR, TC, TG, HDL, LDL), published supple- and defined as having LD-weighted annotations scores for each
mentary material (SBP, DBP), or through collaborations with of the eight categories equal to zero. In addition they were
investigators(CD,UC,SCZ,BD).ForCD,pre-meta-analysis,sub- defined to not be in LD with any SNPs in the 1KGP reference
studyspecificp-valuesandeffectsizes(z-scores)wereobtainedfrom panellocatedwithin100,000basepairsofaproteincodinggene,
the study principal investigators. In total these studies considered within a noncoding RNA, within a transcription factor binding
more than 1.3 million phenotypic observations, but considerable sitenorwithinamicroRNAbindingsite.SNPslabeledintergenic
sampleoverlapmakesthenumberofuniqueindividualsmuchless. weredefinedtobeaspecificcollectionofnon-genicSNPschosen
Fordetails,seeTextS1andTableS1. to not represent any functional elements within the genome
(including through LD). Because of how they are defined these
GWAS Summary Statistics Processing SNPs are hypothesized to represent a collection of null
The summary statistics from the respective GWAS meta- associations. Other non-genic categories (1 k up, 10 k up, 1 k
analyses, derived according to best practices, were used as-is. No down and 10 k down) were included in our analyses to ensure
furtherprocessingwasperformed,withtheexceptionofintergenic SNPsnottoofarawayfromgenes,butnotwithinproteincoding
inflation control (described below). Results from SNPs with genes,were representedbynon-geniccategoriesandenrichment
reference SNP (rs) numbers that did not map to our 1000 due to these SNPs was not solely attributed to LD with genic
genomes project(1KGP) reference panel wereexcluded. categories.
PLOSGenetics | www.plosgenetics.org 7 April2013 | Volume 9 | Issue 4 | e1003449
DifferentialEnrichmentamongSNPCategories
Stratified Q-Q Plots and Enrichment Thisisupwardlybiased,andhencep/qisconservativeestimateof
Q-Q plots compare two probability distributions. For each theFDR,and12p/qisaconservativeestimateoftheBayesTDR
phenotype, for all SNPs and for each categorical subset, 2log10 [17]. If p0 is close to one, as is likely true for most GWAS, the
nominal p-values were plotted against 2log10 empirical p-values. increase in bias from setting p0 to one in Eq. [3] is minimal.
Leftward deflections of the observed distribution from the ReferringtotheformulationoftheQ-Qplots,weseethatFDR(p)
projected null line reflect increased tail probabilities in the is equivalent to the nominal p-value under the null hypothesis
distribution of test statistics (z-scores) and consequently an over- dividedbytheempiricalquantileofthep-values.Giventhe2log10
abundanceoflowp-valuescomparedtothatexpectedbychance. transformation appliedtotheQ-Qplots, wecaneasily read off
Wequalitativelyrefertothisdeflectionas‘‘enrichment’’(Figure1
and Figure2, Figure S3). {log ðFDRðpÞÞ&log ðqÞ{log ðpÞ ð4Þ
10 10 10
We estimated the significance of the annotation enrichment
usingtwosampleKolmogorov-Smirnov(KS)Teststocomparethe demonstrating that the (conservatively) estimated FDR is directly
distribution of test statistics in each genic annotation category to related to the horizontal shift of the curves in the stratified Q-Q
the distribution of the intergenic category, for each phenotype. plotsfromtheexpectedlinex=y,withalargershiftcorresponding
SNPs were pruned randomly to approximate independence toasmallerFDR.FortheTDRplotsinFigure2,weestimatedthe
(r2,0.2)tentimesandTableS5reportsthep-valuecorresponding TDR foreach geniccategory accordingtoEq. [4].
tothemedian KS statistics fromthetencomparisons. Eq.[3]istheEmpiricalBayespointestimateoftheBayesFDR
given in Efron (2010). Using Eq. [3] to control FDR (i.e., the
Intergenic Inflation Control expected proportion of falsely rejected null hypotheses) [22] is
The empirical null distribution in GWAS is affected by global closely related to the ‘‘fixed rejection region’’ approach of Storey
varianceinflationduetofactorsincludingpopulationstratification [49,50]. Specifically, Storey [49] showed, for a given FDR a,
and cryptic relatedness [20] and deflation due to over-correction rejectingallnullhypothesessuchthatp/q,aisequivalenttothe
of test statistics for polygenic traits by standard genomic control Benjamini-Hochberg procedure and provides asymptotic control
methods [19]. We applied a control method leveraging only of the FDR to a if the true null p-values are independent and
intergenicSNPsthatarelikelydepletedfortrueassociations.Allp- uniformly distributed. Storey [49] also noted that asymptotic
values were converted into z-scores and, for each phenotype, the controlispreservedunderpositiveblockwisedependence,whereas
genomic inflation factor [20], l , was estimated for intergenic Schwartzman and Lin [51] showed that Eq. [3] is a consistent
GC
SNPs.All test statistics were dividedby l . estimator of FDR for asymptotically sparse dependence (i.e. the
GC
The inflation factor l was computed as the median z-score proportion of correlated pairs of p-values goes to zero as the
GC
squared divided by the expected median of a chi-square numberofhypothesistestsbecomeslarge).Sparsedependenceisa
distribution with one degree of freedom or all phenotypes except good description of the dependence present in GWAS data; for
CPD,wherethe.95quantilewasusedinplaceofthemedian.For example, based on a threshold of r2..05 within 1,000,000
correction statistics see Table S4. basepairs, we estimate the ratio of correlated pairs (r2..05) to
total pairs of p-values at 0.000128.
Quantification of Categorical Enrichment
Replication Rate
For each phenotype,enrichment was measured asthemean(z-
score221) for each category and normalized by the largest value For each of eight sub-studies contributing to the final meta-
perphenotype.Themean(z-score221)isaconservativeestimateof analysisintheCDreportweindependentlyadjustedz-scoresusing
the variance attributable to non-null SNPs, given a standard intergenic inflation control. For each of 70 (8 choose 4) possible
normal null distribution and a non-null distribution symmetric combinations of four-study discovery and four-study replication
sets,wecalculatedthefour-studycombineddiscoveryz-scoreand
around zero(seeText S1).
four-study combined replication z-score for each SNP as the
average z-score across the four studies, multiplied by two (the
Q-Q Plots and False Discovery Rate (FDR)
squarerootofthenumberofstudies).Fordiscoverysamplesthez-
Enrichment seen in the conditional Q-Q plots can be directly
scores were converted to two-tailed p-values, while replication
interpreted in terms of the FDR. Specifically, for a given p-value
samples were converted to one-tailed p-values preserving the
cutoff, theBayes FDR[17] isdefined as
direction of effect in the discovery sample. For each of the 70
discovery-replication pairs cumulative rates of replication were
FDRðpÞ~p0F0ðpÞ=FðpÞ, ð1Þ calculated over 1000 equally-spaced bins spanning the range of
negative log (p-values) observed in the discovery samples. The
10
wherep0istheproportionofnullSNPs,F0isthenullcdf,andFis cumulative replication rate for any bin was calculated as the
the cdf of all SNPs, both null and non-null. Under the null proportionofSNPswitha2log (discoveryp-value)greaterthan
10
hypothesis, F0 is the cdf of the uniform distribution on the unit the lower bound of the bin with a replication p-value,.05.
interval [0,1], sothat Eq. [1]reduces to Cumulative replication rates were calculated independently for
eachoftheeightgenicannotationcategoriesaswellasintergenic
FDRðpÞ~p p=FðpÞ: ð2Þ SNPsandallSNPs.Foreachcategory,thecumulativereplication
0
rateforeachbinwasaveragedacrossthe70discovery-replication
ThecdfFcanbeestimatedbytheempiricalcdfq=N /N/cdfF pairs and the results are reported in Figure 4. The vertical
p p
isthenumberofSNPswithp-valueslessthanorequaltop,andN intercept isthe overallreplication rate.
is the total number of SNPs. Replacing F by q and replacing p
0
with unityinEq. [2],weget Stratified False Discovery Rates
A multiple linear regression was used to predict the tagged
FDRðpÞ&p=q, ð3Þ variance (z2) for each SNP in the height GWAS from the
PLOSGenetics | www.plosgenetics.org 8 April2013 | Volume 9 | Issue 4 | e1003449
DifferentialEnrichmentamongSNPCategories
unthresholdedLD-weightedannotationscores.Usingthecategory Q-Qplotofheightaftercorrectionforgenomicinflationusingthe
weights determined from this ‘training’ regression on the height ‘intergenic inflation control’. Note the overcorrection (grey line
GWAS,thetaggedvarianceforeachSNPwaspredictedfromits below null-hypothesis line, marked by red arrows) in the un-
annotationvectorforeachotherphenotype.Foreachphenotype, corrected Q-Q plot in Panel A is resolved in Panel B. Although
SNPs were grouped into strata according to the rank of this slight,becauseofthelogscalingoftheseplots,thisslightdeflation
predicted tagged variance. Enrichment for each stratum was (left of 1.5 on the x-axis of Panel A) occurs over a much greater
demonstrated using Q-Q plots as described above (Figure S11). proportionofthedistributionandthushasastrongereffectonthe
WenotethatforFigure5Atheheightdataservesasbothour‘test’ mean and median of the distribution than the more visually
(forcreatingstrata)and‘training’(fordetectingenrichment)data, apparent inflation in the extreme tails (right of 2 on the x-axis of
but for each other GWAS the training and test data is PanelA).Forlambdavalues,seeTableS4.Onlynominalp-values
independent. Sun et al [9] described a stratified False Discovery below the standard genome-wide significance threshold
Rate (sFDR) procedure which can result in improved statistical (p,561028) are shown.
power over traditional FDR methods [22] when a collection of (TIF)
statistical tests can be grouped into disjoint strata with different
Figure S5 Categorical enrichment for all phenotypes. The
levels of enrichment. In order to demonstrate the utility of using
mean(z-score221) for each category of SNPs per phenotype
genic annotation categories in combination with sFDR for
reveals consistent enrichment across fourteen phenotypes. BD,
increasing power, we computed the number of SNPs deemed
Bipolar Disorder; BMI, Body Mass Index; CD, Crohn’s disease;
significant at a given FDR threshold using both traditional [22]
CPD, Cigarettes per Day; DBP, Diastolic blood pressure; HDL,
and stratified FDR, where the strata were determined by the
High density lipoprotein; LDL, Low density lipoprotein; SBP,
predicted tagged variance for each SNP based on regression
systolicbloodpressure;SCZ,Schizophrenia;TC,totalCholester-
weights determined from the height GWAS summary statistics
ol; TG, triglycerides; UC, Ulcerative Colitis; WHR, Waist-hip-
(Figure 5). The increase in rejections for a common threshold a
ratio.
when using sFDR is equivalent to increased power demonstrated
(TIF)
byaratioofoneminusNon-DiscoveryRates(1-NDRs)forsFDR
toFDR greater than1 [23]. FigureS6 Mean Z2 per decile of genetic variance by category.
We also computed the average proportion of SNPs above a The figure shows the relationship between genetic variance
givenrank(i.e.top1000)thatreplicatedbasedonunadjustedand defined as a function of minor allele frequnecy (MAF) by
strata adjusted ranks (determined from the sFDR procedure) MAF6(1-MAF) and effect size (mean z-score2) per genic
acrossthe70permutationsoffourstudydiscoveryandfourstudy annotation category for height. The mean is taken at each decile
replication samples possible in the eight study CD meta-analysis ofgeneticvariance.Theredlinesarefitsfromgeneralizedadditive
GWAS (Figure S12). These results demonstrate that for a given models to the mean mean observed squared z-scores. Note the
threshold, SNPs ranked via genic category-informed sFDR clear increase in effect size with increasing MAF, which shows
replicate in higher numbers than SNPs ranked via traditional similar effect of MAF across categories. The exception is for the
FDR. intergenic category whichshows little multiplicative effect further
suggesting itharborsa majority of true null SNPs.
Supporting Information (TIF)
Figure S7 Enrichment by MAF category for height and
Figure S1 Annotation Category Scoring Schematic. For each
GWAS tag SNP in the1KGP we estimated r2 LD with all SNPs schizophrenia. The effect of minor allele frequency (MAF) is not
within 1 megabase. LD scores were thresholded at r2.0.2. For consistentacrossphenotypes.(A)ForheightmorecommonSNPs
showacontinuallylargerenrichmentthanlesscommonSNPs.(B)
eachSNPthesumofLDwitheachgenicannotationcategorywas
For Schizophrenia common SNPs show more enrichment at
recorded. SNPs were assigned to categories by thresholding
moderate to small z-scores, but for larger z-scores less common
continuousscoreswithaninclusivelowerboundof1.0.Positional
SNPs aremore enriched.
(nonLD-weighted)scoreswererecordedastheannotationforthe
(TIF)
GWAS tag SNP’slocation only.
(TIF) Figure S8 locfdr plot for all SNPs in Crohn’s disease. Mixture
modelfitsforallSNPsforCrohn’sdisease.Black:empiricalz-score
FigureS2 Correlationsamongannotationcategoriesandscores.
distribution, Purple: estimated non-null distribution, Green line:
(A) Heat map displaying the Spearman’s correlation coefficients
smooth estimate of mixture distribution (full distribution), Blue
among continuous valued LD-weighted annotation scores. (B)
line:smoothestimateofthenulldistribution.Diamonds:localfalse
Heat map displaying the Spearman’s correlation coefficients
discovery rate (LFDR) of 0.2. The estimated proportion of non-
amongthresholdedandbinarizedannotationcategoriespresented
nullSNPsvariesbycategory,asdoesthevarianceintheestimated
inQ-Qplots.Correlationsarereportedusingtheannotationsfor
non-nulleffect size.
theunion of SNPsacross allGWAS (2,558,411SNPs).
(TIF)
(TIF)
FigureS9 locfdrplotsforgenicannotationcategoriesinCrohn’s
FigureS3 EnrichmentinHeightwithoutLDweightedannota-
disease. Mixture model fits for each annotation category for
tion.Q-Qplotshowingenrichmentofgenicannotationcategories
Crohn’s disease. Black: empirical z-score distribution, Purple:
using positional scores (non LD-weighted). Enrichment patterns
estimated non-null distribution, Green line: smooth estimate of
arepresent,butlessapparentthanusingLD-weightedannotation
mixture distribution (full distribution), Blue line: smooth estimate
scores (Figure 1). No inflation correction was performed by our
of the null distribution. Diamonds: local false discovery rate
group.
(LFDR)of0.2.Theestimatedproportionofnon-nullSNPsvaries
(TIF)
by category, asdoes thevariance intheestimated non-null effect
FigureS4 HeightbeforeandafterIntergenicInflationControl. size.
(A)Q-Qplotofheightwithoutcorrectionforgenomicinflation.(B) (TIF)
PLOSGenetics | www.plosgenetics.org 9 April2013 | Volume 9 | Issue 4 | e1003449