2025
Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations
Zhou G, Yolou I, Xie Y, Zhao H. Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations. American Journal Of Human Genetics 2025, 112: 1923-1935. PMID: 40633541, PMCID: PMC12252582, DOI: 10.1016/j.ajhg.2025.06.010.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresAdmixed individualsNon-European populationsLocal ancestryTransferability of PRSPerformance of polygenic risk scoresAdmixed populationsCross-ancestryPolygenic risk score calculatorGenetic prediction of complex traitsGenetic predictionEffect sizePrediction of complex traitsPopulation ArchitectureUK BiobankPolygenic predictionAdmixed AmericansAncestry clustersGenetic architectureComplex traitsPRS modelRisk scoreGenetic variantsAncestryIndividualsIncorporating local ancestry information to predict genetically associated DNA methylation in admixed populations
Cheng Y, Zhou G, Li H, Zhang X, Justice A, Martinez C, Aouizerat B, Xu K, Zhao H. Incorporating local ancestry information to predict genetically associated DNA methylation in admixed populations. Briefings In Bioinformatics 2025, 26: bbaf325. PMID: 40622482, PMCID: PMC12232425, DOI: 10.1093/bib/bbaf325.Peer-Reviewed Original ResearchConceptsMethylome-wide association studiesAdmixed populationsComplex traitsLocal ancestryAssociation studiesDNA methylationAssociated with complex traitsLocal ancestry informationPopulations of European ancestryCpG methylation levelsNon-European populationsMeasurement of methylationAncestry informationCpG sitesMethylation levelsEuropean ancestryEpigenetic underpinningsCpGAncestryTraitsMethylationAmerican populationAfrican American populationDNAPopulationJointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation
Xu L, Zhou G, Jiang W, Zhang H, Dong Y, Guan L, Zhao H. JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation. Nature Communications 2025, 16: 3841. PMID: 40268942, PMCID: PMC12019179, DOI: 10.1038/s41467-025-59243-x.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenetic risk predictionUK BiobankGenome-wide association study summary statisticsAdmixed American populationsRisk predictionGenetic correlationsNon-European populationsContinental populationsAssociation studiesReal-data applicationBinary traitsTrait predictionSummary statisticsMultiple populationsAmerican populationData-adaptive approachSample sizeData applicationsAOUPopulationBiobankData scenarioTraitsA Bayesian approach to correcting the attenuation bias of regression using polygenic risk score
Zhou G, Qie X, Zhao H. A Bayesian approach to correcting the attenuation bias of regression using polygenic risk score. Genetics 2025, 229: iyaf018. PMID: 39891671, PMCID: PMC12168083, DOI: 10.1093/genetics/iyaf018.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremGenetic Risk ScoreHumansModels, GeneticMultifactorial InheritanceRegression AnalysisConceptsPolygenic risk scoresRisk scoreEstimation of regression coefficientsBayesian approachMeasurement error modelEstimation of coefficientsCoverage probabilityBayesian measurement error modelsAttenuation biasCredible intervalsCoefficient estimatesUK BiobankLogistic regressionMeasurement errorRegression coefficientsRegression modelsComplex traitsRegression analysisScoresEstimationError modelRegressionBiobankErrorCovariates
2024
HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data
Cheng Y, Cai B, Li H, Zhang X, D’Souza G, Shrestha S, Edmonds A, Meyers J, Fischl M, Kassaye S, Anastos K, Cohen M, Aouizerat B, Xu K, Zhao H. HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data. Genome Biology 2024, 25: 273. PMID: 39407252, PMCID: PMC11476968, DOI: 10.1186/s13059-024-03411-7.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremDNA MethylationHumansModels, GeneticQuantitative Trait LociSequence Analysis, DNAConceptsMethylation quantitative trait lociQuantitative trait lociTrait lociMethylation dataFunctional annotation of genetic variantsAnnotation of genetic variantsGenetic variantsBisulfite sequencing dataEffects of genetic variantsBiologically relevant cell typesDNA methylation levelsCell typesFunctional annotationSequence dataComplex traitsMethylation datasetsRelevant cell typesMeQTLsMethylation levelsMethylation regulatorsReal data analysesLociVariantsMethylationDNABenchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics
Hu X, Cai M, Xiao J, Wan X, Wang Z, Zhao H, Yang C. Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics. American Journal Of Human Genetics 2024, 111: 1717-1735. PMID: 39059387, PMCID: PMC11339627, DOI: 10.1016/j.ajhg.2024.06.016.Peer-Reviewed Original ResearchConceptsMendelian randomizationMR methodsGenome-wide association study summary statisticsMendelian randomization methodsCausal inferenceInvalid instrumental variablesType I error controlInstrumental variablesCausal effect estimationIV assumptionsEffect estimatesSummary statisticsTwo-sampleGenetic variantsTrait pairsConfoundingGenetic datasetsGenetic dataConfounding scenariosRandomization methodInferencePractical guidanceSimulated datasetsGuidelinesStudyLDER-GE estimates phenotypic variance component of gene–environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information
Dong Z, Jiang W, Li H, DeWan A, Zhao H. LDER-GE estimates phenotypic variance component of gene–environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information. Briefings In Bioinformatics 2024, 25: bbae335. PMID: 38980374, PMCID: PMC11232466, DOI: 10.1093/bib/bbae335.Peer-Reviewed Original ResearchConceptsHuman complex traitsComplex traitsGene-environment interactionsGene-environmentLinkage disequilibriumPhenotypic variance componentsPhenotypic varianceProportion of phenotypic varianceSummary statisticsEuropean ancestry subjectsUK Biobank dataAssociation summary statisticsComplete linkage disequilibriumControlled type I error ratesLD informationLD matrixVariance componentsBiobank dataType I error rateEuropean ancestrySample size increaseGenetic effectsTraitsE-I pairsSimulation study
2022
Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation
Song S, Jiang W, Zhang Y, Hou L, Zhao H. Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation. American Journal Of Human Genetics 2022, 109: 802-811. PMID: 35421325, PMCID: PMC9118121, DOI: 10.1016/j.ajhg.2022.03.013.Peer-Reviewed Original ResearchConceptsLinkage disequilibrium score regressionComplex traitsSingle nucleotide polymorphismsSNP heritabilityGenome-wide association studiesDisequilibrium score regressionHigh-throughput technologiesHeritable phenotypesAssociation studiesGenetic studiesCryptic relatednessLD informationScore regressionHeritabilityGenetic contributionHeritability estimationPopulation stratificationDisease mechanismsTraitsLD matrixOnly summary statisticsUK BiobankPolygenicitySummary statisticsRelatedness
2019
A statistical framework for cross-tissue transcriptome-wide association analysis
Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 2019, 51: 568-576. PMID: 30804563, PMCID: PMC6788740, DOI: 10.1038/s41588-019-0345-7.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association analysisAssociation analysisGene-trait associationsGene expression dataGene expression levelsGenetic architectureComplex traitsMore genesGene expressionSingle tissueExpression dataAssociation resultsExpression levelsPowerful approachImputation modelHuman tissuesImputation accuracyGenotypesStatistical frameworkTissueGenesKey componentTraitsPowerful metricExpression
2017
Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction
Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genetics 2017, 13: e1006836. PMID: 28598966, PMCID: PMC5482506, DOI: 10.1371/journal.pgen.1006836.Peer-Reviewed Original Research
2015
A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data
Lu Q, Hu Y, Sun J, Cheng Y, Cheung KH, Zhao H. A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data. Scientific Reports 2015, 5: 10576. PMID: 26015273, PMCID: PMC4444969, DOI: 10.1038/srep10576.Peer-Reviewed Original ResearchConceptsHuman genomeFunctional regionsStatistical frameworkAnnotation dataFunctional annotation dataWhole-genome annotationNon-coding regionsGenomic conservationHigh-throughput experimentsENCODE projectExperimental annotationsGenomeUnsupervised statistical learningFunctional potentialHuman geneticsStatistical learningComputational predictionsIntegrated analysisAnnotationAnnotation methodDiverse typesPowerful toolGeneticsMajor goalWeb server
2013
Guilt by rewiring: gene prioritization through network rewiring in Genome Wide Association Studies
Hou L, Chen M, Zhang CK, Cho J, Zhao H. Guilt by rewiring: gene prioritization through network rewiring in Genome Wide Association Studies. Human Molecular Genetics 2013, 23: 2780-2790. PMID: 24381306, PMCID: PMC3990172, DOI: 10.1093/hmg/ddt668.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesWide association studyDisease-associated genesGWAS signalsNetwork rewiringAssociation studiesFunctional genomic informationGene expression networksCo-expression networkDisease-associated pathwaysExpression networksGene networksGenomic informationAssociation signalsGene prioritizationDisease genesDisease locusSusceptibility lociGenesAssociation principleRewiringDisease associationsLociMillions of candidatesDisease conditions
2011
Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies
Chen M, Cho J, Zhao H. Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies. PLOS Genetics 2011, 7: e1001353. PMID: 21490723, PMCID: PMC3072362, DOI: 10.1371/journal.pgen.1001353.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesBiological pathwaysSingle gene-based methodsMarkov random field modelGene-based methodsPrior biological knowledgeRandom field modelGWAS analysisAssociation signalsMultiple genesPathway topologyGene associationsAssociation analysisGenesBiological knowledgeField modelGenetic variantsSpecific pathwaysReal data examplePathwayStatistical inferenceConditional modes algorithmExchangeable setRegression form
2001
Multipoint Genetic Mapping with Trisomy Data
Li J, Sherman S, Lamb N, Zhao H. Multipoint Genetic Mapping with Trisomy Data. American Journal Of Human Genetics 2001, 69: 1255-1265. PMID: 11704925, PMCID: PMC1235537, DOI: 10.1086/324578.Peer-Reviewed Original ResearchConceptsExpectation-maximization algorithmMultipoint genetic mappingAmount of computationProbability distributionTrisomy dataStatistical methodsFirst approachMarkov modelSecond approachProbabilityCrossover processComputationLarge numberSetModelApproachGeneral relationshipDistributionAlgorithmNumber of markersOn Relationship Inference Using Gamete Identity by Descent Data
Zhao H, Liang F. On Relationship Inference Using Gamete Identity by Descent Data. Journal Of Computational Biology 2001, 8: 191-200. PMID: 11454305, DOI: 10.1089/106652701300312940.Peer-Reviewed Original ResearchTest of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium Test
Zhang S, Zhang K, Li J, Sun F, Zhao H. Test of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium Test. Genetic Epidemiology 2001, 21: s370-s375. PMID: 11793701, DOI: 10.1002/gepi.2001.21.s1.s370.Peer-Reviewed Original ResearchConceptsQuantitative pedigree disequilibrium testPedigree disequilibrium testQuantitative traitsTraits of interestGenetic Analysis Workshop 12Disequilibrium testGeneral pedigreesSequence dataCandidate genesGenetic markersGenetic linkageQualitative traitsLinkage disequilibriumTraitsLarge pedigreePresence of linkagePedigreeStatistical methodsFamilyNuclear familiesTests of associationGenesUnrelated nuclear familiesLinkageDisequilibriumThe Power of Transmission Disequilibrium Tests for Quantitative Traits
Li J, Wang D, Dong J, Jiang R, Zhang K, Zhang S, Zhao H, Sun F. The Power of Transmission Disequilibrium Tests for Quantitative Traits. Genetic Epidemiology 2001, 21: s632-s637. PMID: 11793752, DOI: 10.1002/gepi.2001.21.s1.s632.Peer-Reviewed Original Research
2000
Transmission/disequilibrium tests for quantitative traits.
Sun F, Flanders W, Yang Q, Zhao H. Transmission/disequilibrium tests for quantitative traits. Annals Of Human Genetics 2000, 64: 555-65. PMID: 11281218, DOI: 10.1017/s000348000000840x.Peer-Reviewed Original ResearchMultipoint Genetic Mapping with Uniparental Disomy Data
Zhao H, Li J, Robinson W. Multipoint Genetic Mapping with Uniparental Disomy Data. American Journal Of Human Genetics 2000, 67: 851-861. PMID: 10958760, PMCID: PMC1287890, DOI: 10.1086/303072.Peer-Reviewed Original ResearchLinkage disequilibrium mapping in populations of variable size using the decay of haplotype sharing and a stepwise‐mutation model
Zhang S, Zhao H. Linkage disequilibrium mapping in populations of variable size using the decay of haplotype sharing and a stepwise‐mutation model. Genetic Epidemiology 2000, 19: s99-s105. PMID: 11055377, DOI: 10.1002/1098-2272(2000)19:1+<::aid-gepi15>3.0.co;2-1.Peer-Reviewed Original Research
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