全基因組關聯研究中整合分析統計方法的比較（英文）ComparisonofIntegratedAnalyticalStatisticalMethodsinGenome-WideAssociationStudiesIntroduction:Genome-WideAssociationStudies(GWAS)haverevolutionizedthefieldofgeneticsbyidentifyinggeneticvariantsassociatedwithcomplexdiseasesandtraits.Withtheadvancementinhigh-throughputgenotypingtechnologies,GWAShavebecomefeasibleonalargescale,generatingmassiveamountsofdata.However,analyzingsuchvastdatasetsposescomputationalandstatisticalchallenges.Toaddressthesechallenges,variousintegratedanalyticalstatisticalmethodshavebeendevelopedandimplementedinGWAS.Thispaperaimstocompareandevaluatesomeofthesemethods.Methods:1.Meta-Analysis:Meta-analysisisawidely-usedmethodinGWASthatintegratesdatafrommultipleindependentstudiestoincreasestatisticalpower.Itcombineseffectsizesandstandarderrorsfromindividualstudiesandcalculatesanoveralleffectsizeestimate.Meta-analysiscanbeconductedusingfixed-effectsorrandom-effectsmodels,dependingontheassumptionsofhomogeneityacrossstudies.Theadvantagesofmeta-analysisincludegreaterpowertodetectassociationsandtheabilitytoestimatetheoveralleffectsizeaccurately.However,itassumesthattheeffectsizesareestimatedconsistentlyacrossstudies.2.PathwayAnalysis:Pathwayanalysisisanintegratedapproachthataimstoidentifybiologicalpathwaysorgenesetsthatareenrichedwithdisease-associatedvariants.ItutilizespriorbiologicalknowledgetointerpretGWASfindingsinamoremeaningfulway.Pathwayanalysisalgorithmsexaminethecollectiveeffectofmultiplevariantswithinabiologicalpathwayorgenesetanddeterminetheirsignificance.Thisapproachprovidesinsightsintotheunderlyingbiologicalmechanismsandcanprioritizegenesforfurtherfunctionalcharacterization.However,pathwayanalysisreliesheavilyontheexistingknowledgeanddatabases,whichmaybebiasedorincomplete.3.PolygenicRiskScores:Polygenicriskscores(PRS)arecalculatedbysummingtheeffectsizesofmultiplegeneticvariantsassociatedwithatraitordisease.Thesescoresrepresentthecumulativegeneticriskacrossthegenomeandcanbeusedtopredictanindividual'sgeneticpredispositionforaparticularphenotype.PRScanbeconstructedusingdifferentmethods,suchasthresholdmethodsorweightedmethods,dependingontheeffectsizeestimationandsignificancethresholds.PRShaveshownpromisingapplicationsinriskprediction,diseasestratification,andunderstandingthegeneticarchitectureofcomplextraits.However,PRSrequirelargesamplesizesandassumeanadditivegeneticmodel.4.BayesianMethods:BayesianmethodsofferaflexibleandrobustframeworkforintegratinggeneticdataandpriorknowledgeinGWAS.ThesemethodsutilizeaBayesianstatisticalframeworktoestimatemodelparametersandperformhypothesistesting.Bayesianmethodscanincorporatevariousdatatypes,suchasgenotypes,geneexpression,andepigeneticdata,toimprovetheprecisionandaccuracyofassociationanalyses.Theyalsoallowresearcherstoexplicitlymodeltheuncertaintyandpriorbeliefsintheestimationprocess.However,Bayesianmethodscanbecomputationallydemanding,andthechoiceofpriordistributionsandmodelassumptionscaninfluencetheresults.Comparison:Theintegratedanalyticalstatisticalmethodsdiscussedabovehavedifferentstrengthsandlimitations.Meta-analysisisadvantageousbecauseitcanincreasestatisticalpowerandestimatetheoveralleffectsizeaccurately,butitassumesconsistenteffectsizesacrossstudies.Pathwayanalysisprovidesinsightsintobiologicalmechanisms,butitreliesonexistingknowledge,whichmaybebiasedorincomplete.PRSofferpredictivecapabilitiesandinsightsintogeneticarchitecture,buttheyrequirelargesamplesizesandassumeanadditivegeneticmodel.Bayesianmethodsprovideaflexibleframeworkforintegrationandpreciseestimation,buttheycanbecomputationallydemandinganddependonpriorassumptions.Conclusion:Inconclusion,integratedanalyticalstatisticalmethodsplayacrucialroleinextractingmeaningfulinformationfromGWASdata.Eachmethodhasitsownmeritsandlimitations,andthechoiceofmethoddependsonthespecificresearchquestionandavailableresources.Bycomparingthesemethods,researcherscanmakeinformeddecisions,improvetheaccuracyofassociationanalyses,andgaindeeperinsightsintothegenetic