Featured Publications
Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation
Shen J, Zhang Y, Zhu Z, Cheng Y, Cai B, Zhao Y, Zhao H. Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation. NeuroImage 2024, 297: 120739. PMID: 39009250, PMCID: PMC11367654, DOI: 10.1016/j.neuroimage.2024.120739.Peer-Reviewed Original ResearchGenetic networksComplex traitsGenetic architecture of complex traitsArchitecture of complex traitsGenome-wide association analysisGenetic correlationsGenetic architectureGenetic variationAssociation analysisGenetic basisPhenotypic similarityGenetic effectsFunctional variationRight hemisphereBrain regionsUK BiobankCortical thicknessTraitsCortical measuresCorrelation networkSignificant pairsHeritabilitySimilarity matrixBrainBrain lobesSDPRX: A statistical method for cross-population prediction of complex traits
Zhou G, Chen T, Zhao H. SDPRX: A statistical method for cross-population prediction of complex traits. American Journal Of Human Genetics 2022, 110: 13-22. PMID: 36460009, PMCID: PMC9892700, DOI: 10.1016/j.ajhg.2022.11.007.Peer-Reviewed Original ResearchConceptsStatistical methodsJoint distributionWide association study (GWAS) summary statisticsNon-European populationsReal traitsSummary statisticsCross-population predictionPrediction accuracyGenome-wide association study summary statisticsLinkage disequilibrium differencesPrediction performancePolygenic risk scoresComplex traitsStatisticsSimulationsApplicationsTraitsLeveraging 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 statisticsRelatednessSUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits
Zhang Y, Lu Q, Ye Y, Huang K, Liu W, Wu Y, Zhong X, Li B, Yu Z, Travers BG, Werling DM, Li JJ, Zhao H. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biology 2021, 22: 262. PMID: 34493297, PMCID: PMC8422619, DOI: 10.1186/s13059-021-02478-w.Peer-Reviewed Original ResearchConceptsLocal genetic correlationsComplex traitsGenetic correlationsGenomic regionsLocal genetic correlation analysisGenome-wide association studiesLocal genomic regionsSpecific genomic regionsGenetic correlation analysisDistinct genetic signaturesGenetic similarityGenetic signaturesAssociation studiesTraitsSample overlapStatistical frameworkSummary statisticsDisequilibriumRegionAccurate estimationSimilarity
2024
LDER-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
2023
Shared genetic architecture of blood eosinophil counts and asthma in UK Biobank
Li B, Wang Y, Wang Z, Li X, Kay S, Chupp G, Zhao H, Gomez J. Shared genetic architecture of blood eosinophil counts and asthma in UK Biobank. ERJ Open Research 2023, 9: 00291-2023. PMID: 37650091, PMCID: PMC10463033, DOI: 10.1183/23120541.00291-2023.Peer-Reviewed Original ResearchGenome-wide association studiesGenetic architectureGenetic correlation analysisUK BiobankGWAS resultsTranscription factorsInterleukin-4 SignalingBlood eosinophil countsAssociation studiesDoctor-diagnosed asthmaSignificant variantsEosinophil countEuropean ancestryTraitsPathwayGenetic linkType 2 immune responsesType 2 inflammationSignalingCritical associationImmune responseHeterogeneous diseaseTAGCSevere asthmaTherapeutic interventionsMulti-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program
Cheng Y, Dao C, Zhou H, Li B, Kember R, Toikumo S, Zhao H, Gelernter J, Kranzler H, Justice A, Xu K. Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program. Translational Psychiatry 2023, 13: 148. PMID: 37147289, PMCID: PMC10162964, DOI: 10.1038/s41398-023-02409-2.Peer-Reviewed Original ResearchConceptsSingle-trait genome-wide association studiesGenome-wide association studiesNovel lociPower of GWASJoint genome-wide association studyGenome-wide significant lociMillion Veteran ProgramGenome-wide associationSubstance use traitsGWAS summary statisticsNovel genetic variantsMulti-trait analysisFunctional annotationUse traitsSignificant lociHeritable traitMultiple lociAssociation studiesColocalization analysisLociPleiotropic effectsMTAgVeteran ProgramGenetic variantsTraitsWhole-Exome Sequencing Analyses Support a Role of Vitamin D Metabolism in Ischemic Stroke
Xie Y, Acosta J, Ye Y, Demarais Z, Conlon C, Chen M, Zhao H, Falcone G. Whole-Exome Sequencing Analyses Support a Role of Vitamin D Metabolism in Ischemic Stroke. Stroke 2023, 54: 800-809. PMID: 36762557, PMCID: PMC10467223, DOI: 10.1161/strokeaha.122.040883.Peer-Reviewed Original ResearchConceptsGene-based testingRare genetic variationGene-based analysisGenetic variationAssociation studiesGenome-wide association studiesSingle-variant association analysisWide significance levelSusceptibility risk lociWide association studyDeleterious missense variantsMissense rare variantsBonferroni-corrected thresholdWhole-exome sequencing dataRare variantsSingle variant analysisHeritable traitRisk lociExome-wide studySequencing dataExome sequencing analysisAssociation analysisSequencing analysisMissense variantsTraitsRobustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression
Lin C, Liu W, Jiang W, Zhao H. Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression. Biology Methods And Protocols 2023, 8: bpad024. PMID: 37901453, PMCID: PMC10599978, DOI: 10.1093/biomethods/bpad024.Peer-Reviewed Original ResearchExpression quantitative trait lociGenome-wide association studiesComplex traitsGene expression regulationGenetic association signalsQuantitative trait lociScore regressionDisease-associated variantsSNP annotationGene annotationExpression regulationGWAS resultsTrait lociTrait heritabilityEQTL effectsAssociation signalsGene expressionAssociation studiesGene effectsSNP effectsHuman diseasesHeritabilityTraitsBiological realityAnnotation
2022
Genome-Wide Investigation of Maximum Habitual Alcohol Intake in US Veterans in Relation to Alcohol Consumption Traits and Alcohol Use Disorder
Deak JD, Levey DF, Wendt FR, Zhou H, Galimberti M, Kranzler HR, Gaziano JM, Stein MB, Polimanti R, Gelernter J, Muralidhar S, Moser J, Deen J, Gaziano J, Beckham J, Chang K, Tsao P, Luoh S, Casas J, Churby L, Whitbourne S, Brewer J, Brophy M, Selva L, Shayan S, Cho K, Pyarajan S, DuVall S, Connor T, Argyres D, Aslan M, Stephens B, Concato J, Gelernter J, Gleason T, Huang G, Koenen K, Marx C, Radhakrishnan K, Schork N, Stein M, Zhao H, Kaufman J, Nunez Y, Pietrzak R, Beck D, Cissell S, Crutchfield P, Lance W, Cheung K, Li Y, Sun N, Chen Q, Rajeevan N, Sayward F, Gagnon D, Harrington K, Quaden R, O'Leary T, Ramoni R. Genome-Wide Investigation of Maximum Habitual Alcohol Intake in US Veterans in Relation to Alcohol Consumption Traits and Alcohol Use Disorder. JAMA Network Open 2022, 5: e2238880. PMID: 36301540, PMCID: PMC9614582, DOI: 10.1001/jamanetworkopen.2022.38880.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide significant lociGenomic structural equation modelingSignificant lociAlcohol traitsAssociation studiesAfrican ancestry participantsGenome-wide investigationAncestry-specific genome-wide association studiesGenetic correlationsPsychiatric traitsLinkage disequilibrium score regressionGenetic associationStrong genetic correlationSingle nucleotide variantsGenetic architectureGenetic association studiesGenetic lociTop associationsNegative rgEuropean ancestry participantsNucleotide variantsFunctional variantsScore regressionTraitsSex-specific genetic association between psychiatric disorders and cognition, behavior and brain imaging in children and adults
Gui Y, Zhou X, Wang Z, Zhang Y, Wang Z, Zhou G, Zhao Y, Liu M, Lu H, Zhao H. Sex-specific genetic association between psychiatric disorders and cognition, behavior and brain imaging in children and adults. Translational Psychiatry 2022, 12: 347. PMID: 36028495, PMCID: PMC9418275, DOI: 10.1038/s41398-022-02041-6.Peer-Reviewed Original ResearchConceptsCognitive functionFluid intelligenceCognitive traitsAdolescent Brain Cognitive Development (ABCD) studyPsychiatric disordersCognitive Development StudyMediation effectMost psychiatric disordersPolygenic risk scoresBrain functionBrain structuresBrain imagingEarly etiologySex differencesDevelopment studiesPsychiatric traitsChildrenIntelligenceDisordersSchizophreniaGenetic riskAdultsTraitsCognitionAutism
2020
Leveraging functional annotation to identify genes associated with complex diseases
Liu W, Li M, Zhang W, Zhou G, Wu X, Wang J, Lu Q, Zhao H. Leveraging functional annotation to identify genes associated with complex diseases. PLOS Computational Biology 2020, 16: e1008315. PMID: 33137096, PMCID: PMC7660930, DOI: 10.1371/journal.pcbi.1008315.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociComplex traitsNovel lociIdentification of eQTLGene expressionTranscriptome-wide association study methodLinkage disequilibriumQuantitative trait lociAssociation study methodsCombined Annotation Dependent Depletion (CADD) scoresAnnotation-dependent depletion scoreExpression levelsDisease-associated genesEpigenetic annotationsEpigenetic informationFunctional annotationTrait lociGenetic variationGenesPrevious GWASLociGenetic effectsTraitsComplex diseasesGWASGenome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals
Xu K, Li B, McGinnis KA, Vickers-Smith R, Dao C, Sun N, Kember RL, Zhou H, Becker WC, Gelernter J, Kranzler HR, Zhao H, Justice AC. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nature Communications 2020, 11: 5302. PMID: 33082346, PMCID: PMC7598939, DOI: 10.1038/s41467-020-18489-3.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesLarge genome-wide association studiesMillion Veteran ProgramAssociation studiesExpression quantitative trait lociQuantitative trait lociChromatin interactionsComplex traitsFunctional annotationTrait lociSequencing ConsortiumDozen genesSignificant lociSmoking phenotypesLociMultiple populationsNew insightsPhenotypeVeteran ProgramGenetic vulnerabilityGenesTraitsAnnotationEuropean AmericansConsortiumStatistical Methods in Genome-Wide Association Studies
Sun N, Zhao H. Statistical Methods in Genome-Wide Association Studies. Annual Review Of Biomedical Data Science 2020, 3: 1-24. DOI: 10.1146/annurev-biodatasci-030320-041026.Peer-Reviewed Original ResearchGenome-wide association studiesAssociation studiesTraits of interestGenetic architectureIdentification of variantsGWAS dataStatistical methodologyStatistical challengesGenetic risk prediction modelsGenetic markersStatistical methodsHuman diseasesPhenotype informationGenetic variantsTraitsGenotype informationScientific goalsRecent progressGenesVariantsTens of thousandsHundreds of thousandsPrediction modelPathwayThousands
2019
Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies
Fang H, Hui Q, Lynch J, Honerlaw J, Assimes T, Huang J, Vujkovic M, Damrauer S, Pyarajan S, Gaziano J, DuVall S, O’Donnell C, Cho K, Chang K, Wilson P, Tsao P, Sun Y, Tang H, Gaziano J, Ramoni R, Breeling J, Chang K, Huang G, Muralidhar S, O’Donnell C, Tsao P, Muralidhar S, Moser J, Whitbourne S, Brewer J, Concato J, Warren S, Argyres D, Stephens B, Brophy M, Humphries D, Do N, Shayan S, Nguyen X, Pyarajan S, Cho K, Hauser E, Sun Y, Zhao H, Wilson P, McArdle R, Dellitalia L, Harley J, Whittle J, Beckham J, Wells J, Gutierrez S, Gibson G, Kaminsky L, Villareal G, Kinlay S, Xu J, Hamner M, Haddock K, Bhushan S, Iruvanti P, Godschalk M, Ballas Z, Buford M, Mastorides S, Klein J, Ratcliffe N, Florez H, Swann A, Murdoch M, Sriram P, Yeh S, Washburn R, Jhala D, Aguayo S, Cohen D, Sharma S, Callaghan J, Oursler K, Whooley M, Ahuja S, Gutierrez A, Schifman R, Greco J, Rauchman M, Servatius R, Oehlert M, Wallbom A, Fernando R, Morgan T, Stapley T, Sherman S, Anderson G, Sonel E, Boyko E, Meyer L, Gupta S, Fayad J, Hung A, Lichy J, Hurley R, Robey B, Striker R. Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies. American Journal Of Human Genetics 2019, 105: 763-772. PMID: 31564439, PMCID: PMC6817526, DOI: 10.1016/j.ajhg.2019.08.012.Peer-Reviewed Original ResearchImproving Genetic Association Analysis through Integration of Functional Annotations of the Human Genome
Lu Q, Zhao H. Improving Genetic Association Analysis through Integration of Functional Annotations of the Human Genome. 2019, 679-30. DOI: 10.1002/9781119487845.ch24.Peer-Reviewed Original ResearchGenome-wide association studiesFunctional annotationHuman genomeAssociation analysisAnnotation dataFunctional annotation dataPost-GWAS analysisSummary association statisticsGenetic association analysisGWAS findingsGWAS dataIntegrative analysisAssociation studiesComplex diseasesAssociation statisticsGenetic associationGenomeComputational methodsAnnotationTraitsDirect applicationStatistical powerMost diseasesInterpretable metricsTens of thousandsGenome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations
Kranzler HR, Zhou H, Kember RL, Vickers Smith R, Justice AC, Damrauer S, Tsao PS, Klarin D, Baras A, Reid J, Overton J, Rader DJ, Cheng Z, Tate JP, Becker WC, Concato J, Xu K, Polimanti R, Zhao H, Gelernter J. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nature Communications 2019, 10: 1499. PMID: 30940813, PMCID: PMC6445072, DOI: 10.1038/s41467-019-09480-8.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesMillion Veteran Program sampleGenetic correlationsWide significant lociSignificant genetic correlationsPolygenic risk scoresCell type groupSignificant lociHeritable traitEnrichment analysisTraitsMultiple populationsLociPhenotypeProgram samplesA 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 metricExpressionM31 GENOME-WIDE ASSOCIATION STUDY OF COMORBID ALCOHOL DEPENDENCE AND MAJOR DEPRESSION
Zhou H, Polimanti R, Yang B, Wang Q, Han S, Sherva R, Nunez Y, Zhao H, Farrer L, Kranzler H, Gelernter J. M31 GENOME-WIDE ASSOCIATION STUDY OF COMORBID ALCOHOL DEPENDENCE AND MAJOR DEPRESSION. European Neuropsychopharmacology 2019, 29: s971. DOI: 10.1016/j.euroneuro.2017.08.338.Peer-Reviewed Original Research
2018
Integrative functional genomic analysis of human brain development and neuropsychiatric risks
Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, Sunkin SM, Li Z, Shin Y, Zhu Y, Sousa AMM, Werling DM, Kitchen RR, Kang HJ, Pletikos M, Choi J, Muchnik S, Xu X, Wang D, Lorente-Galdos B, Liu S, Giusti-Rodríguez P, Won H, de Leeuw C, Pardiñas AF, Hu M, Jin F, Li Y, Owen M, O’Donovan M, Walters J, Posthuma D, Reimers M, Levitt P, Weinberger D, Hyde T, Kleinman J, Geschwind D, Hawrylycz M, State M, Sanders S, Sullivan P, Gerstein M, Lein E, Knowles J, Sestan N, Willsey A, Oldre A, Szafer A, Camarena A, Cherskov A, Charney A, Abyzov A, Kozlenkov A, Safi A, Jones A, Ashley-Koch A, Ebbert A, Price A, Sekijima A, Kefi A, Bernard A, Amiri A, Sboner A, Clark A, Jaffe A, Tebbenkamp A, Sodt A, Guillozet-Bongaarts A, Nairn A, Carey A, Huttner A, Chervenak A, Szekely A, Shieh A, Harmanci A, Lipska B, Carlyle B, Gregor B, Kassim B, Sheppard B, Bichsel C, Hahn C, Lee C, Chen C, Kuan C, Dang C, Lau C, Cuhaciyan C, Armoskus C, Mason C, Liu C, Slaughterbeck C, Bennet C, Pinto D, Polioudakis D, Franjic D, Miller D, Bertagnolli D, Lewis D, Feng D, Sandman D, Clarke D, Williams D, DelValle D, Fitzgerald D, Shen E, Flatow E, Zharovsky E, Burke E, Olson E, Fulfs E, Mattei E, Hadjimichael E, Deelman E, Navarro F, Wu F, Lee F, Cheng F, Goes F, Vaccarino F, Liu F, Hoffman G, Gürsoy G, Gee G, Mehta G, Coppola G, Giase G, Sedmak G, Johnson G, Wray G, Crawford G, Gu G, van Bakel H, Witt H, Yoon H, Pratt H, Zhao H, Glass I, Huey J, Arnold J, Noonan J, Bendl J, Jochim J, Goldy J, Herstein J, Wiseman J, Miller J, Mariani J, Stoll J, Moore J, Szatkiewicz J, Leng J, Zhang J, Parente J, Rozowsky J, Fullard J, Hohmann J, Morris J, Phillips J, Warrell J, Shin J, An J, Belmont J, Nyhus J, Pendergraft J, Bryois J, Roll K, Grennan K, Aiona K, White K, Aldinger K, Smith K, Girdhar K, Brouner K, Mangravite L, Brown L, Collado-Torres L, Cheng L, Gourley L, Song L, Ubieta L, Habegger L, Ng L, Hauberg M, Onorati M, Webster M, Kundakovic M, Skarica M, Reimers M, Johnson M, Chen M, Garrett M, Sarreal M, Reding M, Gu M, Peters M, Fisher M, Gandal M, Purcaro M, Smith M, Brown M, Shibata M, Brown M, Xu M, Yang M, Ray M, Shapovalova N, Francoeur N, Sjoquist N, Mastan N, Kaur N, Parikshak N, Mosqueda N, Ngo N, Dee N, Ivanov N, Devillers O, Roussos P, Parker P, Manser P, Wohnoutka P, Farnham P, Zandi P, Emani P, Dalley R, Mayani R, Tao R, Gittin R, Straub R, Lifton R, Jacobov R, Howard R, Park R, Dai R, Abramowicz S, Akbarian S, Schreiner S, Ma S, Parry S, Shapouri S, Weissman S, Caldejon S, Mane S, Ding S, Scuderi S, Dracheva S, Butler S, Lisgo S, Rhie S, Lindsay S, Datta S, Souaiaia T, Roychowdhury T, Gomez T, Naluai-Cecchini T, Beach T, Goodman T, Gao T, Dolbeare T, Fliss T, Reddy T, Chen T, Hyde T, Brunetti T, Lemon T, Desta T, Borrman T, Haroutunian V, Spitsyna V, Swarup V, Shi X, Jiang Y, Xia Y, Chen Y, Jiang Y, Wang Y, Chae Y, Yang Y, Kim Y, Riley Z, Krsnik Z, Deng Z, Weng Z, Lin Z, Li Z. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 2018, 362 PMID: 30545854, PMCID: PMC6413317, DOI: 10.1126/science.aat7615.Peer-Reviewed Original ResearchConceptsIntegrative functional genomic analysisFunctional genomic analysisCell typesGene coexpression modulesDistinct cell typesCell type-specific dynamicsGenomic basisEpigenomic reorganizationEpigenomic landscapeEpigenomic regulationGenomic analysisCoexpression modulesIntegrative analysisHuman brain developmentFetal transitionHuman neurodevelopmentGenetic associationCellular compositionNeuropsychiatric riskBrain developmentNeurodevelopmental processesGenesTraitsPostnatal developmentNeuropsychiatric disorders