Rare‐Variant Associaon Tesng for Exome Data · Kardia_Sequence Kernel Association Test Author:...
Transcript of Rare‐Variant Associaon Tesng for Exome Data · Kardia_Sequence Kernel Association Test Author:...
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Rare‐VariantAssocia/onTes/ngforExomeData
TheSequenceKernelAssocia/onTest
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SequenceKernelAssocia/onTest(SKAT)
• Gene‐level(orSNPset)analysisapproach• Testsanassocia/onbetweenSNPsetsandcon/nuousordiscretephenotypes
• BypassestheproblemofdifferenttaggingSNPsbeingassociatedwithoutcomesofinterestacrossethnicgroups
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SKATMainEffectsModel
• Yi=outcomeforsubjecti• α0=interceptterm• Xi=vectorofnon‐gene/ccovariates• Gi=vectorofgenotypes• εi=errorterm;followsanydistribu/onwithmean0andvarianceσ2
• Assumeeachβj,j=1,…,p,followsanarbitrarydistribu/onwithmean0andvariancewjτ– Wheretheweights(wj)arespecifiedbytheuser
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SKATbasics• Tes/ng
H0:β=0isequivalenttotes/ngH0:τ=0
• Thescoretestforvariancecomponentinthecorrespondingmixedmodelisoftheform:
Qρ=(1–ρ)Qs+ρQB
– whereρistheparameteroftheunifiedtest,QSisateststa/s/cofSKAT,andQBisascoreteststa/s/cofweightedburdentest
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Kernel
• Therearepre‐specified6typesofkernels:– "linear“– "linear.weighted"– "IBS“– "IBS.weighted“– "quadra/c"– "2wayIX"
• Youcanuseoneofthemoryoucangiveyourownkernelmatrixasaparameter.
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DefaultKernel
• Thedefaultkernelistheweightedlinearkernel
• Thekernelmatrixfortheweightedlinearkernelis K=GWWG
– WhereGisthenxpmatrixofgenotypedataandWisthepxpdiagonalmatrixoftheweightscorrespondingtoeachvariant.
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QSta/s/c
Qρ=(1–ρ)Qs+ρQB
• TheQsta/s/chasamixtureofchi‐squareddistribu/onunderthenullhypothesesthatcanbeevaluatedexplicitlyandusedasareferencedistribu/ontocomputethep‐values.
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Weights
• ThematrixWisadiagonalmatrixthatcontainstheweightsofthepvariants
• Goodchoicesofweightscanimprovepower
• Weightsarepre‐specified
• Ifweightjislarge,thenthatvariantmakesalargecontribu/ontotheQsta/s/c
• Upweigh/ngacausalvariant(whichisexpectedtohavealargeeffect)canimprovethepower
• Wedon’tknowwhichvariantsarecausalandthuswedon’talwaysknowwhichweightstouse
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Weights
• SKATauthorssuggestusing
• BetaPDF:
WhereBdenotestheBetafunc/on,alphaandbeta(inourweightequa/on,alpha‐1andalpha‐2)areshapeparametersandthefunc/onisevaluatedwhenx=MAFj
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Results
• TheQsta/s/candassociatedp‐valuewilltellusiftheSNPsetisassociatedwiththeoutcome.
• H0:τ=0,assesseswhetherthereisanyvarianceintheSNPset(Bjs)fromthemeanof0inany(+/‐)direc/on
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Example
• Theassocia/onbetweenSNPsintheChr9p21regionandtheGeneExpression
• Predictor:SNPsintheChr9p21region(297SNPs)• Outcome:geneexpressionofthegenesacrossthewholegenome
• SingleSNPAssocia/onTest– Geneexpression=singleSNP+random(family)(297tests)
• SequenceKernel(SNPset)associa/on(SKAT)– Geneexpression(anerfamiliaradjustment)=AllSNPs(1test)
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SingleSNPAssocia/onAnalysisBetweenCDKN2BASandSNPsintheChr9p21
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SingleSNPAssocia/onAnalysisBetweenCDKN2BASandSNPsintheChr9p21
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CDKN2BAS(SKATp=0.429)SNP MAF B_SNP_P_CDKN2BAS
rs7865618 0.446317 8.58E‐06
rs634537 0.44901 8.93E‐06
rs2157719 0.495097 9.34E‐06
rs613312 0.439054 1.07E‐05
rs615552 0.453232 1.09E‐05
rs543830 0.438996 1.11E‐05
rs599452 0.438958 1.13E‐05
rs564398 0.440362 1.28E‐05
rs679038 0.439493 1.51E‐05
rs944801 0.459316 1.52E‐05
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Specifyingweights
• Beta(1,25)– Upregulaterarevariantsanddownregulatecommonvariants
• Beta(1,1)– Equalweightstoallvariants
• Beta(0.5,0.5)– Madsen & Browning weight
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Betadistribu/ons
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ApplyingDifferentWeighttoCDKN2BAS
Beta(1,25) Beta(1,1) Beta(0.5,0.5)
SKATpvalue 0.429 0.0020 0.0021
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Examplep‐valuesAdjus/ngWeightChangesResultsDrama/cally:
TopResultswithWeightbeta(0.5,0.5)
Transcript NPvalue
beta_1_25Pvalue
_beta_1_1Pvalue_
beta_0.5_0.5
ENST00000301908 801 0.036968216 0.000178972 0.000130954
ENST00000370551 801 0.684927084 0.000178319 0.000217497
ENST00000412318 801 0.112609677 0.000302452 0.00026976
ENST00000497037 801 0.241666486 0.000345966 0.00034696
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Conclusion
• ChoosingappropriateweightisveryimportantinSKAT
• Beta(1,25)givesverylisleweight,ifany,tothecommonvariants
• Beta(1,1)hasverylislepowerpickingupsignalsfromrarevariants
• Beta(0.5,0.5)canpickupsignalsfrombothcommonandrarevariants,butsuffersfromlowerpower.
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NextSteps
• We’vebeenworkingwithShawnLeetotesthisSKATprogramsfor:– Gene‐environmentkernels(currentlyunweighted)– Gene‐genekernels(currentlyunweighted)– Meta‐analysissubrou/nes(Meta‐SKAT)
– Modifica/onsforfamilydata