Featured Publications
Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data
Jiang W, Chen L, Girgenti M, Zhao H. Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data. Nature Communications 2024, 15: 24. PMID: 38169469, PMCID: PMC10762162, DOI: 10.1038/s41467-023-44009-0.Peer-Reviewed Original ResearchBayes TheoremGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMultifactorial InheritancePolymorphism, Single NucleotideRisk FactorsA statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases
Liu W, Deng W, Chen M, Dong Z, Zhu B, Yu Z, Tang D, Sauler M, Lin C, Wain L, Cho M, Kaminski N, Zhao H. A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases. PLOS Genetics 2023, 19: e1010825. PMID: 37523391, PMCID: PMC10414598, DOI: 10.1371/journal.pgen.1010825.Peer-Reviewed Original ResearchMeSH KeywordsBreast NeoplasmsFemaleGene Expression ProfilingGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLungPolymorphism, Single NucleotidePulmonary Disease, Chronic ObstructiveConceptsCell typesDisease-associated tissuesWide association studyComplex diseasesCell type proportionsDisease-relevant tissuesReal GWAS dataFunctional genesTranscriptomic dataGWAS dataGenetic dataAssociation studiesNovel statistical frameworkChronic obstructive pulmonary diseaseStatistical frameworkObstructive pulmonary diseaseIdiopathic pulmonary fibrosisBreast cancer riskType proportionsBlood CD8Pulmonary diseasePulmonary fibrosisPredictive biomarkersLung tissueBreast cancerSDPRX: 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 ResearchMeSH KeywordsGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHumansMultifactorial InheritanceRisk FactorsConceptsStatistical methodsJoint distributionWide association study (GWAS) summary statisticsNon-European populationsReal traitsSummary statisticsCross-population predictionPrediction accuracyGenome-wide association study summary statisticsLinkage disequilibrium differencesPrediction performancePolygenic risk scoresComplex traitsStatisticsSimulationsApplicationsTraitsM-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits
Xie Y, Li M, Dong W, Jiang W, Zhao H. M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits. PLOS Genetics 2021, 17: e1009849. PMID: 34735430, PMCID: PMC8568192, DOI: 10.1371/journal.pgen.1009849.Peer-Reviewed Original ResearchAlgorithmsAutistic DisorderData Interpretation, StatisticalGenetic Predisposition to DiseaseHeart Defects, CongenitalHumansMutationSUPERGNOVA: 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 ResearchMeSH KeywordsAutism Spectrum DisorderCognitionComputer SimulationGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMultifactorial InheritanceQuantitative Trait, HeritableRisk FactorsSoftwareConceptsLocal 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 estimationSimilarityA fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics
Zhou G, Zhao H. A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics. PLOS Genetics 2021, 17: e1009697. PMID: 34310601, PMCID: PMC8341714, DOI: 10.1371/journal.pgen.1009697.Peer-Reviewed Original ResearchConceptsBayesian nonparametric methodParameter tuningNonparametric methodsExternal reference panelSummary statisticsComputational resourcesParallel algorithmBlock structureExplicit assumptionsExisting methodsStatisticsSeparate validation dataAccurate risk prediction modelsAssumptionPrediction modelPredictionAlgorithmTranscriptomic organization of the human brain in post-traumatic stress disorder
Girgenti MJ, Wang J, Ji D, Cruz DA, Stein M, Gelernter J, Young K, Huber B, Williamson D, Friedman M, Krystal J, Zhao H, Duman R. Transcriptomic organization of the human brain in post-traumatic stress disorder. Nature Neuroscience 2020, 24: 24-33. PMID: 33349712, DOI: 10.1038/s41593-020-00748-7.Peer-Reviewed Original ResearchMeSH KeywordsAdultAutopsyBrain ChemistryCohort StudiesDepressive Disorder, MajorFemaleGene Expression RegulationGene Regulatory NetworksGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansInterneuronsMaleMiddle AgedNerve Tissue ProteinsSex CharacteristicsStress Disorders, Post-TraumaticTranscriptomeYoung AdultConceptsGenome-wide association studiesSignificant gene networksDifferential gene expressionSystems-level evidenceSignificant genetic liabilityMajor depressive disorder cohortGene networksTranscriptomic organizationTranscriptomic landscapeDownregulated setsGenomic networksGene expressionAssociation studiesMolecular determinantsExtensive remodelingGenotype dataSexual dimorphismSignificant divergenceMolecular profileNetwork analysisELFN1TranscriptsDimorphismPostmortem tissueDivergence
2023
Multi-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 ResearchMeSH KeywordsAlcohol DrinkingAlcoholismGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideSmokingVeteransConceptsSingle-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 variantsTraitsEarly breast cancer risk detection: a novel framework leveraging polygenic risk scores and machine learning
Tao L, Ye Y, Zhao H. Early breast cancer risk detection: a novel framework leveraging polygenic risk scores and machine learning. Journal Of Medical Genetics 2023, 60: 960-964. PMID: 37055164, DOI: 10.1136/jmg-2022-108582.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceBreast NeoplasmsFemaleGenetic Predisposition to DiseaseHumansMachine LearningRisk FactorsConceptsBreast cancerPolygenic risk scoresRisk scoreBC risk assessmentClinical breast examNon-genetic risk factorsHigh-risk individualsFemale participantsBreast examCancer deathCommon cancerBC screeningRisk factorsBC diagnosisDisease risk predictionDiagnostic stepsPopulation screeningGenetic riskRisk predictionUK BiobankCancerDiagnosisDiagnostic pipelineWomenDetection testIdentification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans
Kimbrel N, Ashley-Koch A, Qin X, Lindquist J, Garrett M, Dennis M, Hair L, Huffman J, Jacobson D, Madduri R, Trafton J, Coon H, Docherty A, Mullins N, Ruderfer D, Harvey P, McMahon B, Oslin D, Beckham J, Hauser E, Hauser M, Agarwal K, Ashley-Koch A, Aslan M, Beckham J, Begoli E, Bhattacharya T, Brown B, Calhoun P, Cheung K, Choudhury S, Cliff A, Cohn J, Crivelli S, Cuellar-Hengartner L, Deangelis H, Dennis M, Dhaubhadel S, Finley P, Ganguly K, Garvin M, Gelernter J, Hair L, Harvey P, Hauser E, Hauser M, Hengartner N, Jacobson D, Jones P, Kainer D, Kaplan A, Katz I, Kember R, Kimbrel N, Kirby A, Ko J, Kolade B, Lagergren J, Lane M, Levey D, Levin D, Lindquist J, Liu X, Madduri R, Manore C, Martins S, McCarthy J, McDevitt-Cashman M, McMahon B, Miller I, Morrow D, Oslin D, Pavicic-Venegas M, Pestian J, Pyarajan S, Qin X, Rajeevan N, Ramsey C, Ribeiro R, Rodriguez A, Romero J, Santel D, Schaefferkoetter N, Shi Y, Stein M, Sullivan K, Sun N, Tamang S, Townsend A, Trafton J, Walker A, Wang X, Wangia-Anderson V, Yang R, Yoon H, Yoo S, Zamora-Resendiz R, Zhao H, Docherty A, Mullins N, Coleman J, Shabalin A, Kang J, Murnyak B, Wendt F, Adams M, Campos A, DiBlasi E, Fullerton J, Kranzler H, Bakian A, Monson E, Rentería M, Andreassen O, Bulik C, Edenberg H, Kessler R, Mann J, Nurnberger J, Pistis G, Streit F, Ursano R, Awasthi S, Bergen A, Berrettini W, Bohus M, Brandt H, Chang X, Chen H, Chen W, Christensen E, Crawford S, Crow S, Duriez P, Edwards A, Fernández-Aranda F, Fichter M, Galfalvy H, Gallinger S, Gandal M, Gorwood P, Guo Y, Hafferty J, Hakonarson H, Halmi K, Hishimoto A, Jain S, Jamain S, Jiménez-Murcia S, Johnson C, Kaplan A, Kaye W, Keel P, Kennedy J, Kim M, Klump K, Levey D, Li D, Liao S, Lieb K, Lilenfeld L, Lori A, Magistretti P, Marshall C, Mitchell J, Myers R, Okazaki S, Otsuka I, Pinto D, Powers A, Ramoz N, Ripke S, Roepke S, Rozanov V, Scherer S, Schmahl C, Sokolowski M, Starnawska A, Strober M, Su M, Thornton L, Treasure J, Ware E, Watson H, Witt S, Woodside D, Yilmaz Z, Zillich L, Agerbo E, Børglum A, Breen G, Demontis D, Erlangsen A, Esko T, Gelernter J, Glatt S, Hougaard D, Hwu H, Kuo P, Lewis C, Li Q, Liu C, Martin N, McIntosh A, Medland S, Mors O, Nordentoft M, Nurnberger J, Olsen C, Porteous D, Smith D, Stahl E, Stein M, Wasserman D, Werge T, Whiteman D, Willour V, Coon H, Ruderfer D, Dedert E, Elbogen E, Fairbank J, Hurley R, Kilts J, Martindale S, Marx C, McDonald S, Moore S, Morey R, Naylor J, Rowland J, Shura R, Swinkels C, Tupler L, Van Voorhees E, Yoash-Gantz R, Gaziano J, Muralidhar S, Ramoni R, Chang K, O’Donnell C, Tsao P, Breeling J, Hauser E, Sun Y, Huang G, Casas J, Moser J, Whitbourne S, Brewer J, Conner T, Argyres D, Stephens B, Brophy M, Humphries D, Selva L, Do N, Shayan S, Cho K, Churby L, Wilson P, McArdle R, Dellitalia L, Mattocks K, Harley J, Whittle J, Jacono F, Wells J, Gutierrez S, Gibson G, Hammer K, Kaminsky L, Villareal G, Kinlay S, Xu J, Hamner M, Mathew R, Bhushan S, Iruvanti P, Godschalk M, Ballas Z, Ivins D, Mastorides S, Moorman J, Gappy S, Klein J, Ratcliffe N, Florez H, Okusaga O, Murdoch M, Sriram P, Yeh S, Tandon N, Jhala D, Liangpunsakul S, Oursler K, Whooley M, Ahuja S, Constans J, Meyer P, Greco J, Rauchman M, Servatius R, Gaddy M, Wallbom A, Morgan T, Stapley T, Sherman S, Ross G, Strollo P, Boyko E, Meyer L, Gupta S, Huq M, Fayad J, Hung A, Lichy J, Hurley R, Robey B, Striker R. Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans. JAMA Psychiatry 2023, 80: 135-145. PMID: 36515925, PMCID: PMC9857322, DOI: 10.1001/jamapsychiatry.2022.3896.Peer-Reviewed Original ResearchConceptsMolecular genetic basisRisk lociSingle nucleotide variantsGWS lociGenetic basisGenomic risk lociRisk genesGenome-wide association studiesSignificant enrichmentGene-based analysisGenetic risk lociCandidate risk genesCyclic adenosine monophosphate (cAMP) signalingIdentification of novelPolygenic risk score analysisGene clusterFocal adhesionsGenetic substructureUbiquitination processChromosome 2Enrichment analysisAssociation studiesAxon guidanceAfrican ancestryNCAM1-TTC12
2022
Sex-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 ResearchMeSH KeywordsAdolescentAdultChildCognitionFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMental DisordersMultifactorial InheritanceNeuroimagingRisk FactorsConceptsCognitive functionFluid intelligenceCognitive traitsAdolescent Brain Cognitive Development (ABCD) studyPsychiatric disordersCognitive Development StudyMediation effectMost psychiatric disordersPolygenic risk scoresBrain functionBrain structuresBrain imagingEarly etiologySex differencesDevelopment studiesPsychiatric traitsChildrenIntelligenceDisordersSchizophreniaGenetic riskAdultsTraitsCognitionAutismGlaucoma Genetic Risk Scores in the Million Veteran Program
Waksmunski A, Kinzy T, Cruz L, Nealon C, Halladay C, Simpson P, Canania R, Anthony S, Roncone D, Rogers L, Leber J, Dougherty J, Greenberg P, Sullivan J, Wu W, Iyengar S, Crawford D, Peachey N, Bailey J, 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, Tsao P, Stephens B, Brophy M, Humphries D, Do N, Shayan S, Nguyen X, O’Donnell C, Pyarajan S, Cho K, Pyarajan S, 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, Tsao P, Sonel E, Boyko E, Meyer L, Gupta S, Fayad J, Hung A, Lichy J, Hurley R, Robey B, Striker R. Glaucoma Genetic Risk Scores in the Million Veteran Program. Ophthalmology 2022, 129: 1263-1274. PMID: 35718050, PMCID: PMC9997524, DOI: 10.1016/j.ophtha.2022.06.012.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesCross-Sectional StudiesGenetic Predisposition to DiseaseGenome-Wide Association StudyGlaucoma, Open-AngleHumansPolymorphism, Single NucleotideRisk FactorsVeteransConceptsPrimary open-angle glaucomaInvasive glaucoma surgeryRisk stratificationMillion Veteran ProgramEffect estimatesPOAG casesEuropean ancestryOpen-angle glaucomaCross-sectional studyDegenerative eye diseasesAfrican ancestryVeteran ProgramGenetic risk scoreAggressive treatmentGlaucoma surgeryEarly treatmentIrreversible blindnessEye diseaseHigh riskRisk scoreIncremental riskVisual impairmentGenetic riskVeteransRisk variants
2021
Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection
Zekavat SM, Lin SH, Bick AG, Liu A, Paruchuri K, Wang C, Uddin MM, Ye Y, Yu Z, Liu X, Kamatani Y, Bhattacharya R, Pirruccello JP, Pampana A, Loh PR, Kohli P, McCarroll SA, Kiryluk K, Neale B, Ionita-Laza I, Engels EA, Brown DW, Smoller JW, Green R, Karlson EW, Lebo M, Ellinor PT, Weiss ST, Daly MJ, Terao C, Zhao H, Ebert B, Reilly M, Ganna A, Machiela M, Genovese G, Natarajan P. Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection. Nature Medicine 2021, 27: 1012-1024. PMID: 34099924, PMCID: PMC8245201, DOI: 10.1038/s41591-021-01371-0.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overAgingBiological Specimen BanksChromosome AberrationsCommunicable DiseasesDigestive System DiseasesFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHematologic NeoplasmsHumansMaleMiddle AgedMosaicismPneumoniaRisk FactorsSepsisUrogenital AbnormalitiesYoung AdultConceptsMosaic chromosomal alterationsLeukocyte cell countDominant risk factorChromosomal alterationsBlood-derived DNAInfectious disease riskIncident infectionsSystem infectionGenitourinary infectionsImmune cellsRisk factorsHematological malignanciesHematological cancersCell countDisease riskInfectionInfectious diseasesClonal hematopoiesisSomatic variantsAgeRiskAlterationsWide association studyAutosomal mosaic chromosomal alterationsAssociation studies
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 ResearchMeSH KeywordsEpigenesis, GeneticGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMolecular Sequence AnnotationPolymorphism, Single NucleotideQuantitative Trait LociConceptsExpression 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 diseasesGWASAutomated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms
Li X, Zhao H. Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms. PLOS Genetics 2020, 16: e1009089. PMID: 33075057, PMCID: PMC7595622, DOI: 10.1371/journal.pgen.1009089.Peer-Reviewed Original ResearchLeveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies
Song S, Jiang W, Hou L, Zhao H. Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies. PLOS Computational Biology 2020, 16: e1007565. PMID: 32045423, PMCID: PMC7039528, DOI: 10.1371/journal.pcbi.1007565.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyComputer SimulationFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLinkage DisequilibriumMaleMultifactorial InheritanceSoftwareConceptsEffect size distributionClass of methodsReal data applicationOnly summary statisticsTheoretical resultsSummary statisticsExtensive simulation resultsLD informationSimulation resultsData applicationsFirst methodImportant problemOptimal propertiesGenetic risk predictionAccurate predictionPrediction accuracyStandard PRSStatisticsPrediction method
2019
International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci
Nievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen CY, Choi KW, Coleman JRI, Dalvie S, Duncan LE, Gelernter J, Levey DF, Logue MW, Polimanti R, Provost AC, Ratanatharathorn A, Stein MB, Torres K, Aiello AE, Almli LM, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegovic E, Babić D, Bækvad-Hansen M, Baker DG, Beckham JC, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Børglum AD, Bradley B, Brashear M, Breen G, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Calabrese JR, Caldas- de- Almeida J, Dale AM, Daly MJ, Daskalakis NP, Deckert J, Delahanty DL, Dennis MF, Disner SG, Domschke K, Dzubur-Kulenovic A, Erbes CR, Evans A, Farrer LA, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Geuze E, Gillespie C, Uka AG, Gordon SD, Guffanti G, Hammamieh R, Harnal S, Hauser MA, Heath AC, Hemmings SMJ, Hougaard DM, Jakovljevic M, Jett M, Johnson EO, Jones I, Jovanovic T, Qin XJ, Junglen AG, Karstoft KI, Kaufman ML, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kranzler HR, Kremen WS, Lawford BR, Lebois LAM, Lewis CE, Linnstaedt SD, Lori A, Lugonja B, Luykx JJ, Lyons MJ, Maples-Keller J, Marmar C, Martin AR, Martin NG, Maurer D, Mavissakalian MR, McFarlane A, McGlinchey RE, McLaughlin KA, McLean SA, McLeay S, Mehta D, Milberg WP, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Neale BM, Nelson EC, Nordentoft M, Norman SB, O’Donnell M, Orcutt HK, Panizzon MS, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Rice JP, Ripke S, Risbrough VB, Roberts AL, Rothbaum AO, Rothbaum BO, Roy-Byrne P, Ruggiero K, Rung A, Rutten BPF, Saccone NL, Sanchez SE, Schijven D, Seedat S, Seligowski AV, Seng JS, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Trapido E, Uddin M, Ursano RJ, van den Heuvel LL, Van Hooff M, Vermetten E, Vinkers CH, Voisey J, Wang Y, Wang Z, Werge T, Williams MA, Williamson DE, Winternitz S, Wolf C, Wolf EJ, Wolff JD, Yehuda R, Young RM, Young KA, Zhao H, Zoellner LA, Liberzon I, Ressler KJ, Haas M, Koenen KC. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nature Communications 2019, 10: 4558. PMID: 31594949, PMCID: PMC6783435, DOI: 10.1038/s41467-019-12576-w.Peer-Reviewed Original ResearchMeSH KeywordsBlack PeopleDatasets as TopicFemaleGenetic LociGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleSex FactorsStress Disorders, Post-TraumaticUbiquitin-Protein LigasesVeteransWhite PeopleConceptsGenome-wide association studiesDisease genesAssociation studiesGenome-wide significant lociAfrican-ancestry analysesNon-coding RNAsGenetic risk lociParkinson's disease genesEuropean ancestry populationsNovel genesSignificant lociGenetic variationSpecific lociRisk lociAdditional lociLociAncestry populationsCommon variantsHeritability estimatesGenesGWASRNABiologySNPsPARK2Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans
Gelernter J, Sun N, Polimanti R, Pietrzak R, Levey DF, Bryois J, Lu Q, Hu Y, Li B, Radhakrishnan K, Aslan M, Cheung KH, Li Y, Rajeevan N, Sayward F, Harrington K, Chen Q, Cho K, Pyarajan S, Sullivan PF, Quaden R, Shi Y, Hunter-Zinck H, Gaziano JM, Concato J, Zhao H, Stein MB. Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans. Nature Neuroscience 2019, 22: 1394-1401. PMID: 31358989, PMCID: PMC6953633, DOI: 10.1038/s41593-019-0447-7.Peer-Reviewed Original ResearchMeSH KeywordsAdultCohort StudiesFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleStress Disorders, Post-TraumaticUnited StatesVeteransVeterans HealthConceptsGenome-wide association studiesAssociation studiesHigh linkage disequilibrium regionLinkage disequilibrium regionWide association studyDisequilibrium regionBioinformatics analysisTranscriptomic profilesMillion Veteran ProgramChromosome 17Genetic risk factorsNew insightsUK Biobank dataReexperiencing of traumaStriatal medium spiny neuronsVeteran ProgramSignificant regionsCAMKVEuropean AmericansBiobank dataMedium spiny neuronsTCF4BiologyKANSL1African American cohort
2018
Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders
Walters RK, Polimanti R, Johnson EC, McClintick JN, Adams MJ, Adkins AE, Aliev F, Bacanu SA, Batzler A, Bertelsen S, Biernacka JM, Bigdeli TB, Chen LS, Clarke TK, Chou YL, Degenhardt F, Docherty AR, Edwards AC, Fontanillas P, Foo JC, Fox L, Frank J, Giegling I, Gordon S, Hack LM, Hartmann AM, Hartz SM, Heilmann-Heimbach S, Herms S, Hodgkinson C, Hoffmann P, Jan Hottenga J, Kennedy MA, Alanne-Kinnunen M, Konte B, Lahti J, Lahti-Pulkkinen M, Lai D, Ligthart L, Loukola A, Maher BS, Mbarek H, McIntosh AM, McQueen MB, Meyers JL, Milaneschi Y, Palviainen T, Pearson JF, Peterson RE, Ripatti S, Ryu E, Saccone NL, Salvatore JE, Sanchez-Roige S, Schwandt M, Sherva R, Streit F, Strohmaier J, Thomas N, Wang JC, Webb BT, Wedow R, Wetherill L, Wills AG, Boardman J, Chen D, Choi D, Copeland W, Culverhouse R, Dahmen N, Degenhardt L, Domingue B, Elson S, Frye M, Gäbel W, Hayward C, Ising M, Keyes M, Kiefer F, Kramer J, Kuperman S, Lucae S, Lynskey M, Maier W, Mann K, Männistö S, Müller-Myhsok B, Murray A, Nurnberger J, Palotie A, Preuss U, Räikkönen K, Reynolds M, Ridinger M, Scherbaum N, Schuckit M, Soyka M, Treutlein J, Witt S, Wodarz N, Zill P, Adkins D, Boden J, Boomsma D, Bierut L, Brown S, Bucholz K, Cichon S, Costello E, de Wit H, Diazgranados N, Dick D, Eriksson J, Farrer L, Foroud T, Gillespie N, Goate A, Goldman D, Grucza R, Hancock D, Harris K, Heath A, Hesselbrock V, Hewitt J, Hopfer C, Horwood J, Iacono W, Johnson E, Kaprio J, Karpyak V, Kendler K, Kranzler H, Krauter K, Lichtenstein P, Lind P, McGue M, MacKillop J, Madden P, Maes H, Magnusson P, Martin N, Medland S, Montgomery G, Nelson E, Nöthen M, Palmer A, Pedersen N, Penninx B, Porjesz B, Rice J, Rietschel M, Riley B, Rose R, Rujescu D, Shen P, Silberg J, Stallings M, Tarter R, Vanyukov M, Vrieze S, Wall T, Whitfield J, Zhao H, Neale B, Gelernter J, Edenberg H, Agrawal A. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nature Neuroscience 2018, 21: 1656-1669. PMID: 30482948, PMCID: PMC6430207, DOI: 10.1038/s41593-018-0275-1.Peer-Reviewed Original ResearchMeSH KeywordsAlcoholismAllelesGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHumansMental DisordersPhenotypePolymorphism, Single NucleotideConceptsGenetic underpinningsGenome-wide association studiesGenome-wide dataLarge genome-wide association studiesGenome-wide significant effectComplex polygenic architectureSignificant genetic correlationsPolygenic architectureGenetic distinctionCommon genetic underpinningsAssociation studiesGenetic relationshipsGenetic correlationsGenetic ancestryFamily-based studyUse of cigarettesAttention deficit hyperactivity disorder
2017
A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics
Lu Q, Li B, Ou D, Erlendsdottir M, Powles RL, Jiang T, Hu Y, Chang D, Jin C, Dai W, He Q, Liu Z, Mukherjee S, Crane PK, Zhao H. A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics. American Journal Of Human Genetics 2017, 101: 939-964. PMID: 29220677, PMCID: PMC5812911, DOI: 10.1016/j.ajhg.2017.11.001.Peer-Reviewed Original ResearchConceptsGWAS summary statisticsGenome-wide association studiesComplex traitsSingle nucleotide polymorphismsGenetic covarianceGenetic architectureLarge-scale genome-wide association studiesStrong genetic covarianceDistinct genetic architecturesSignificant genetic covarianceLate-onset Alzheimer's diseaseHigh minor allele frequencyGenetic profileFunctional genomeAmyotrophic lateral sclerosisMajor neurodegenerative diseasesMinor allele frequencyGenetic basisAssociation studiesTraitsLarge-scale inferenceSummary statisticsBiological interpretabilityAllele frequenciesNeurodegenerative diseases