2025
Robust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk
Hu J, Ye Y, Zhang C, Ruan Y, Natarajan P, Zhao H. Robust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk. PLOS Computational Biology 2025, 21: e1013191. PMID: 40570042, PMCID: PMC12212871, DOI: 10.1371/journal.pcbi.1013191.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresCAD-PRSUK BiobankCoronary artery disease polygenic risk scoreSummary-level dataCAD-related traitsSamples of European ancestryCoronary artery diseaseHigh-risk individualsPotential genetic heterogeneityCurrent smokingPolygenic scoresPolygenic riskTargeted interventionsEuropean ancestryRisk scorePleiotropic regionsRisk predictionGenetic heterogeneityBiological functionsPleiotropySignificant interactionPhenotypic heterogeneityBlood pressureDisease interpretationJointPRS: 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 ResearchMeSH KeywordsComputer SimulationGenetic Predisposition to DiseaseGenetics, PopulationGenome-Wide Association StudyHumansModels, GeneticPolymorphism, Single NucleotideConceptsGenome-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 scenarioTraitsGenomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes
Sierant M, Jin S, Bilguvar K, Morton S, Dong W, Jiang W, Lu Z, Li B, López-Giráldez F, Tikhonova I, Zeng X, Lu Q, Choi J, Zhang J, Nelson-Williams C, Knight J, Zhao H, Cao J, Mane S, Sedore S, Gruber P, Lek M, Goldmuntz E, Deanfield J, Giardini A, Mital S, Russell M, Gaynor J, King E, Wagner M, Srivastava D, Shen Y, Bernstein D, Porter G, Newburger J, Seidman J, Roberts A, Yandell M, Yost H, Tristani-Firouzi M, Kim R, Chung W, Gelb B, Seidman C, Brueckner M, Lifton R. Genomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2420343122. PMID: 40127276, PMCID: PMC12002227, DOI: 10.1073/pnas.2420343122.Peer-Reviewed Original ResearchMeSH KeywordsFemaleGenes, DominantGenetic Predisposition to DiseaseGenomicsHeart Defects, CongenitalHumansInfantMaleMutationMutation, MissenseMyosin Heavy ChainsReceptor, Notch1ConceptsCongenital heart disease genesCongenital heart diseaseDamaging variantsMissense variantsAnalyzing de novo mutationsCHD probandsEpidermal growth factor (EGF)-like domainsNeurodevelopmental delayLoss of function variantsParent-offspring triosSyndromic congenital heart diseaseHeart disease genesDisease genesGenomic analysisCongenital heart disease subtypesAssociated with neurodevelopmental delayTetralogy of FallotFunctional variantsIncomplete penetranceCHD phenotypesGenesAssociated with developmentGenetic testingMolecular diagnosticsExtracardiac abnormalitiesRecessive genetic contribution to congenital heart disease in 5,424 probands
Dong W, Jin S, Sierant M, Lu Z, Li B, Lu Q, Morton S, Zhang J, López-Giráldez F, Nelson-Williams C, Knight J, Zhao H, Cao J, Mane S, Gruber P, Lek M, Goldmuntz E, Deanfield J, Giardini A, Mital S, Russell M, Gaynor J, Cnota J, Wagner M, Srivastava D, Bernstein D, Porter G, Newburger J, Roberts A, Yandell M, Yost H, Tristani-Firouzi M, Kim R, Seidman J, Chung W, Gelb B, Seidman C, Lifton R, Brueckner M. Recessive genetic contribution to congenital heart disease in 5,424 probands. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2419992122. PMID: 40030011, PMCID: PMC11912448, DOI: 10.1073/pnas.2419992122.Peer-Reviewed Original ResearchConceptsRecessive genotypeCHD probandsCongenital heart diseaseAssociated with laterality defectsGene-based analysisAnalyzed whole-exome sequencingLeft-sided congenital heart diseaseWhole-exome sequencingCongenital heart disease phenotypeAshkenazi Jewish probandsOffspring of consanguineous unionsSingle-cell transcriptomicsCHD geneExome sequencingMouse notochordSecreted proteinsConsanguineous familyFounder variantGenesSignificant enrichmentLaterality phenotypesHeart diseaseProbandsAbnormal contractile functionConsanguineous unionsPolygenic Susceptibility to Diabetes and Poor Glycemic Control in Stroke Survivors
Demarais Z, Conlon C, Rivier C, Clocchiatti-Tuozzo S, Renedo D, Torres-Lopez V, Sheth K, Meeker D, Zhao H, Ohno-Machado L, Acosta J, Huo S, Falcone G. Polygenic Susceptibility to Diabetes and Poor Glycemic Control in Stroke Survivors. Neurology 2025, 104: e210276. PMID: 39889253, DOI: 10.1212/wnl.0000000000210276.Peer-Reviewed Original ResearchMeSH KeywordsAgedBlood GlucoseCross-Sectional StudiesDiabetes Mellitus, Type 2FemaleGenetic Predisposition to DiseaseGlycated HemoglobinGlycemic ControlHumansMaleMiddle AgedMultifactorial InheritanceStrokeSurvivorsConceptsStroke survivorsWorse glycemic controlPoor glycemic controlStroke patientsAssociated with worse glycemic controlGlycemic controlPolygenic risk scoresClinical management of stroke patientsAssociated with poor glycemic controlManagement of stroke patientsCross-sectional designGenetic association studiesUncontrolled diabetesSusceptibility to T2DMUK BiobankType 2 diabetes mellitusAdverse vascular outcomesRisk scoreAssociation studiesHemoglobin A1cSurvivorsVascular outcomesSusceptibility to diabetesStrokeDiabetesRefining breast cancer genetic risk and biology through multi-ancestry fine-mapping analyses of 192 risk regions
Jia G, Chen Z, Ping J, Cai Q, Tao R, Li C, Bauer J, Xie Y, Ambs S, Barnard M, Chen Y, Choi J, Gao Y, Garcia-Closas M, Gu J, Hu J, Iwasaki M, John E, Kweon S, Li C, Matsuda K, Matsuo K, Nathanson K, Nemesure B, Olopade O, Pal T, Park S, Park B, Press M, Sanderson M, Sandler D, Shen C, Troester M, Yao S, Zheng Y, Ahearn T, Brewster A, Falusi A, Hennis A, Ito H, Kubo M, Lee E, Makumbi T, Ndom P, Noh D, O’Brien K, Ojengbede O, Olshan A, Park M, Reid S, Yamaji T, Zirpoli G, Butler E, Huang M, Low S, Obafunwa J, Weinberg C, Zhang H, Zhao H, Cote M, Ambrosone C, Huo D, Li B, Kang D, Palmer J, Shu X, Haiman C, Guo X, Long J, Zheng W. Refining breast cancer genetic risk and biology through multi-ancestry fine-mapping analyses of 192 risk regions. Nature Genetics 2025, 57: 80-87. PMID: 39753771, PMCID: PMC12184877, DOI: 10.1038/s41588-024-02031-y.Peer-Reviewed Original ResearchMeSH KeywordsAsian PeopleBlack PeopleBreast NeoplasmsCase-Control StudiesChromosome MappingFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPolymorphism, Single NucleotideRisk FactorsWhiteWhite PeopleConceptsFine-mapping analysisAssociation signalsRisk lociBreast cancer genetic riskGenome-wide association study dataBreast cancer risk lociFemale breast cancer casesGenome-wide association studiesCancer genetic riskBreast cancer geneticsBreast cancer riskFunctional genomics dataCredible causal variantsCancer risk lociGenetic risk lociBreast cancer casesSingle-cell RNA sequencingBreast cancerCausal variantsFine-mappingGenomic dataAssociation studiesCancer riskCancer geneticsCancer casesPerformance of Polygenic Risk Scores for Primary Open-Angle Glaucoma in Populations of African Descent
Chang-Wolf J, Kinzy T, Driessen S, Cruz L, Iyengar S, Peachey N, Aung T, Khor C, Williams S, Ramsay M, Olawoye O, Ashaye A, Klaver C, Hauser M, Thiadens A, Cooke Bailey J, Bonnemaijer P, Sanywia A, Cook C, Hassan H, Kanyaro N, Ntomoka C, Allingham R, van der Heide C, Taylor K, Rotter J, Wang S, ABDULLAHI S, Abu-Amero K, Anderson M, Akafo S, ALHASSAN M, Asimadu I, Ayyagari R, BAKAYOKO S, BIANGOUP NYAMSI P, Bowden D, Bromley W, Budenz D, Carmichael T, Challa P, Chen Y, Chuka-Okosa C, Costa V, Cruz D, DuBiner H, Ervin J, Feldman R, Flamme-Wiese M, Gaasterland D, Garnai S, Girkin C, GUIROU N, Guo X, Haines J, Hammond C, Herndon L, Hoffmann T, Hulette C, Hydara A, Igo Jr. R, Jorgenson E, KABWE J, KILANGALANGA N, Kizor-Akaraiwe N, Kuchtey R, LAMARI H, Li Z, Liebmann J, Liu Y, Loos R, Melo M, Moroi S, Msosa J, Mullins R, Nadkarni G, NAPO A, Ng M, Nunes H, Obeng-Nyarkoh E, Okeke A, Okeke S, OLANIYI O, Oliveira M, Pasquale L, Perez-Grossmann R, Pericak-Vance M, Qin X, RESNIKOFF S, Richards J, Schimiti R, Sim K, Sponsel W, Svidnicki P, Uche N, van Duijn C, Vasconcellos J, Wiggs J, Zangwill L, Risch N, Milea D, Weinreb R, Ashley-Koch A, Fingert J, Aslan M, Antonelli M, de Asis M, Bauer M, Brophy M, Concato J, Cunningham F, Freedman R, Gaziano M, Gleason T, Harvey P, Huang G, Kelsoe J, Kosten T, Lehner T, Lohr J, Marder S, Miller P, O Leary T, Patterson T, Peduzzi P, Przygodski R, Siever L, Sklar P, Strakowski S, Zhao H, Fanous A, Farwell W, Malhorta A, Mane S, Palacios P, Bigdeli T, Corsey M, Zaluda L, Johnson J, Sueiro M, Cavaliere D, Jeanpaul V, Maffucci A, Mancini L, Deen J, Muldoon G, Whitbourne S, Canive J, Adamson L, Calais L, Fuldauer G, Kushner R, Toney G, Lackey M, Mank A, Mahdavi N, Villarreal G, Muly E, Amin F, Dent M, Wold J, Fischer B, Elliott A, Felix C, Gill G, Parker P, Logan C, McAlpine J, DeLisi L, Reece S, Hammer M, Agbor‐Tabie D, Goodson W, Aslam M, Grainger M, Richtand N, Rybalsky A, Al Jurdi R, Boeckman E, Natividad T, Smith D, Stewart M, Torres S, Zhao Z, Mayeda A, Green A, Hofstetter J, Ngombu S, Scott M, Strasburger A, Sumner J, Paschall G, Mucciarelli J, Owen R, Theus S, Tompkins D, Potkin S, Reist C, Novin M, Khalaghizadeh S, Douyon R, Kumar N, Martinez B, Sponheim S, Bender T, Lucas H, Lyon A, Marggraf M, Sorensen L, Surerus C, Sison C, Amato J, Johnson D, Pagan‐Howard N, Adler L, Alerpin S, Leon T, Mattocks K, Araeva N, Sullivan J, Suppes T, Bratcher K, Drag L, Fischer E, Fujitani L, Gill S, Grimm D, Hoblyn J, Nguyen T, Nikolaev E, Shere L, Relova R, Vicencio A, Yip M, Hurford I, Acheampong S, Carfagno G, Haas G, Appelt C, Brown E, Chakraborty B, Kelly E, Klima G, Steinhauer S, Hurley R, Belle R, Eknoyan D, Johnson K, Lamotte J, Granholm E, Bradshaw K, Holden J, Jones R, Le T, Molina I, Peyton M, Ruiz I, Sally L, Tapp A, Devroy S, Jain V, Kilzieh N, Maus L, Miller K, Pope H, Wood A, Meyer E, Givens P, Hicks P, Justice S, McNair K, Pena J, Tharp D, Davis L, Ban M, Cheatum L, Darr P, Grayson W, Munford J, Whitfield B, Wilson E, Melnikoff S, Schwartz B, Tureson M, D Souza D, Forselius K, Ranganathan M, Rispoli L, Sather M, Colling C, Haakenson C, Kruegar D, Muralidhar S, Ramoni R, Breeling J, Chang K, O Donnell C, Tsao P, Moser J, Brewer J, Warren S, Argyres D, Stevens B, Humphries D, Do N, Shayan S, Nguyen X, Pyarajan S, Cho K, Hauser E, Sun Y, Wilson P, McArdle R, Dellitalia L, Harley J, Whittle J. Performance of Polygenic Risk Scores for Primary Open-Angle Glaucoma in Populations of African Descent. JAMA Ophthalmology 2025, 143: 7-14. PMID: 39541127, PMCID: PMC11565374, DOI: 10.1001/jamaophthalmol.2024.4784.Peer-Reviewed Original ResearchConceptsPrimary open-angle glaucomaEuropean ancestry groupsArea under the receiver operating characteristic curveAfrican descentSouth AfricaOpen-angle glaucomaCross-sectional studyIndividuals of African descentBaseline of ageAfrican ancestryOdds ratioGlaucoma patientsRisk stratificationMillion Veteran ProgramPolygenic risk scoresGenetics of glaucomaRisk scorePatients of African descentEuropean ancestryRisk quintileReceiver operating characteristic curveGhanaiansGhanaPopulations of African descentAmerican individuals
2024
Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction
Huang X, Arora J, Erzurumluoglu A, Stanhope S, Lam D, Arora J, Erzurumluoglu A, Lam D, Khoueiry P, Jensen J, Cai J, Lawless N, Kriegl J, Ding Z, de Jong J, Zhao H, Ding Z, Wang Z, de Jong J. Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction. Journal Of The American Medical Informatics Association 2024, 32: 435-446. PMID: 39723811, PMCID: PMC11833479, DOI: 10.1093/jamia/ocae297.Peer-Reviewed Original ResearchMeSH KeywordsColitis, UlcerativeCrohn DiseaseDeep LearningElectronic Health RecordsFamily RelationsGenetic Predisposition to DiseaseHumansMachine LearningPedigreeRisk AssessmentConceptsElectronic health recordsDisease risk predictionElectronic health record researchFamily health historyGenetic aspects of diseaseRisk predictionInflammatory bowel disease subtypeHealth recordsHealth historyAspects of diseaseFamily relationsHealthcare ResearchPatient's disease riskInfluence of geneticsDisease riskDiagnosis dataFamily pedigreeEnvironmental exposuresRisk factorsDisease dependencyPatient representation learningClinical profileFamilyDisease subtypesRiskFine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes
Chen Z, Guo X, Tao R, Huyghe J, Law P, Fernandez-Rozadilla C, Ping J, Jia G, Long J, Li C, Shen Q, Xie Y, Timofeeva M, Thomas M, Schmit S, Díez-Obrero V, Devall M, Moratalla-Navarro F, Fernandez-Tajes J, Palles C, Sherwood K, Briggs S, Svinti V, Donnelly K, Farrington S, Blackmur J, Vaughan-Shaw P, Shu X, Lu Y, Broderick P, Studd J, Harrison T, Conti D, Schumacher F, Melas M, Rennert G, Obón-Santacana M, Martín-Sánchez V, Oh J, Kim J, Jee S, Jung K, Kweon S, Shin M, Shin A, Ahn Y, Kim D, Oze I, Wen W, Matsuo K, Matsuda K, Tanikawa C, Ren Z, Gao Y, Jia W, Hopper J, Jenkins M, Win A, Pai R, Figueiredo J, Haile R, Gallinger S, Woods M, Newcomb P, Duggan D, Cheadle J, Kaplan R, Kerr R, Kerr D, Kirac I, Böhm J, Mecklin J, Jousilahti P, Knekt P, Aaltonen L, Rissanen H, Pukkala E, Eriksson J, Cajuso T, Hänninen U, Kondelin J, Palin K, Tanskanen T, Renkonen-Sinisalo L, Männistö S, Albanes D, Weinstein S, Ruiz-Narvaez E, Palmer J, Buchanan D, Platz E, Visvanathan K, Ulrich C, Siegel E, Brezina S, Gsur A, Campbell P, Chang-Claude J, Hoffmeister M, Brenner H, Slattery M, Potter J, Tsilidis K, Schulze M, Gunter M, Murphy N, Castells A, Castellví-Bel S, Moreira L, Arndt V, Shcherbina A, Bishop D, Giles G, Southey M, Idos G, McDonnell K, Abu-Ful Z, Greenson J, Shulman K, Lejbkowicz F, Offit K, Su Y, Steinfelder R, Keku T, van Guelpen B, Hudson T, Hampel H, Pearlman R, Berndt S, Hayes R, Martinez M, Thomas S, Pharoah P, Larsson S, Yen Y, Lenz H, White E, Li L, Doheny K, Pugh E, Shelford T, Chan A, Cruz-Correa M, Lindblom A, Hunter D, Joshi A, Schafmayer C, Scacheri P, Kundaje A, Schoen R, Hampe J, Stadler Z, Vodicka P, Vodickova L, Vymetalkova V, Edlund C, Gauderman W, Shibata D, Toland A, Markowitz S, Kim A, Chanock S, van Duijnhoven F, Feskens E, Sakoda L, Gago-Dominguez M, Wolk A, Pardini B, FitzGerald L, Lee S, Ogino S, Bien S, Kooperberg C, Li C, Lin Y, Prentice R, Qu C, Bézieau S, Yamaji T, Sawada N, Iwasaki M, Le Marchand L, Wu A, Qu C, McNeil C, Coetzee G, Hayward C, Deary I, Harris S, Theodoratou E, Reid S, Walker M, Ooi L, Lau K, Zhao H, Hsu L, Cai Q, Dunlop M, Gruber S, Houlston R, Moreno V, Casey G, Peters U, Tomlinson I, Zheng W. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes. Nature Communications 2024, 15: 3557. PMID: 38670944, PMCID: PMC11053150, DOI: 10.1038/s41467-024-47399-x.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesCredible causal variantsColorectal cancer susceptibility genesSusceptibility genesAssociation signalsAnalysis of single-cell RNA-seq dataAnalysis of whole-exome sequencing dataGenome-wide association study dataColorectal cancer risk lociSingle-cell RNA-seq dataTarget genesWhole-exome sequencing dataFunctional genomic investigationsFine-mapping analysisRNA-seq dataExome sequencing dataTissue-specific transcriptomesColorectal cancerCancer susceptibility genesCausal variantsFine-mappingRisk lociMethylome dataSequence dataGenomic investigationsGenome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder
Nievergelt C, Maihofer A, Atkinson E, Chen C, Choi K, Coleman J, Daskalakis N, Duncan L, Polimanti R, Aaronson C, Amstadter A, Andersen S, Andreassen O, Arbisi P, Ashley-Koch A, Austin S, Avdibegoviç E, Babić D, Bacanu S, Baker D, Batzler A, Beckham J, Belangero S, Benjet C, Bergner C, Bierer L, Biernacka J, Bierut L, Bisson J, Boks M, Bolger E, Brandolino A, Breen G, Bressan R, Bryant R, Bustamante A, Bybjerg-Grauholm J, Bækvad-Hansen M, Børglum A, Børte S, Cahn L, Calabrese J, Caldas-de-Almeida J, Chatzinakos C, Cheema S, Clouston S, Colodro-Conde L, Coombes B, Cruz-Fuentes C, Dale A, Dalvie S, Davis L, Deckert J, Delahanty D, Dennis M, Desarnaud F, DiPietro C, Disner S, Docherty A, Domschke K, Dyb G, Kulenović A, Edenberg H, Evans A, Fabbri C, Fani N, Farrer L, Feder A, Feeny N, Flory J, Forbes D, Franz C, Galea S, Garrett M, Gelaye B, Gelernter J, Geuze E, Gillespie C, Goleva S, Gordon S, Goçi A, Grasser L, Guindalini C, Haas M, Hagenaars S, Hauser M, Heath A, Hemmings S, Hesselbrock V, Hickie I, Hogan K, Hougaard D, Huang H, Huckins L, Hveem K, Jakovljević M, Javanbakht A, Jenkins G, Johnson J, Jones I, Jovanovic T, Karstoft K, Kaufman M, Kennedy J, Kessler R, Khan A, Kimbrel N, King A, Koen N, Kotov R, Kranzler H, Krebs K, Kremen W, Kuan P, Lawford B, Lebois L, Lehto K, Levey D, Lewis C, Liberzon I, Linnstaedt S, Logue M, Lori A, Lu Y, Luft B, Lupton M, Luykx J, Makotkine I, Maples-Keller J, Marchese S, Marmar C, Martin N, Martínez-Levy G, McAloney K, McFarlane A, McLaughlin K, McLean S, Medland S, Mehta D, Meyers J, Michopoulos V, Mikita E, Milani L, Milberg W, Miller M, Morey R, Morris C, Mors O, Mortensen P, Mufford M, Nelson E, Nordentoft M, Norman S, Nugent N, O’Donnell M, Orcutt H, Pan P, Panizzon M, Pathak G, Peters E, Peterson A, Peverill M, Pietrzak R, Polusny M, Porjesz B, Powers A, Qin X, Ratanatharathorn A, Risbrough V, Roberts A, Rothbaum A, Rothbaum B, Roy-Byrne P, Ruggiero K, Rung A, Runz H, Rutten B, de Viteri S, Salum G, Sampson L, Sanchez S, Santoro M, Seah C, Seedat S, Seng J, Shabalin A, Sheerin C, Silove D, Smith A, Smoller J, Sponheim S, Stein D, Stensland S, Stevens J, Sumner J, Teicher M, Thompson W, Tiwari A, Trapido E, Uddin M, Ursano R, Valdimarsdóttir U, Van Hooff M, Vermetten E, Vinkers C, Voisey J, Wang Y, Wang Z, Waszczuk M, Weber H, Wendt F, Werge T, Williams M, Williamson D, Winsvold B, Winternitz S, Wolf C, Wolf E, Xia Y, Xiong Y, Yehuda R, Young K, Young R, Zai C, Zai G, Zervas M, Zhao H, Zoellner L, Zwart J, deRoon-Cassini T, van Rooij S, van den Heuvel L, Stein M, Ressler K, Koenen K. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder. Nature Genetics 2024, 56: 792-808. PMID: 38637617, PMCID: PMC11396662, DOI: 10.1038/s41588-024-01707-9.Peer-Reviewed Original ResearchMeSH KeywordsGenetic LociGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansNeurobiologyPolymorphism, Single NucleotideStress Disorders, Post-TraumaticWhite PeopleConceptsMeta-analysis of genome-wide association studiesGenome-wide significant lociMulti-ancestry meta-analysisGenome-wide association analysisGenome-wide association studiesIndividuals of European ancestryPotential causal genesNative American ancestryMulti-omics approachPost-traumatic stress disorderAdmixed individualsSignificant lociRisk lociCausal genesAssociation studiesAssociation analysisFunctional genesTranscription factorsGenetic studiesAmerican ancestryEuropean ancestryAxon guidanceSynaptic structureLociGenesTuning 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 Factors
2023
Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals
Zhou H, Kember R, Deak J, Xu H, Toikumo S, Yuan K, Lind P, Farajzadeh L, Wang L, Hatoum A, Johnson J, Lee H, Mallard T, Xu J, Johnston K, Johnson E, Nielsen T, Galimberti M, Dao C, Levey D, Overstreet C, Byrne E, Gillespie N, Gordon S, Hickie I, Whitfield J, Xu K, Zhao H, Huckins L, Davis L, Sanchez-Roige S, Madden P, Heath A, Medland S, Martin N, Ge T, Smoller J, Hougaard D, Børglum A, Demontis D, Krystal J, Gaziano J, Edenberg H, Agrawal A, Justice A, Stein M, Kranzler H, Gelernter J. Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. Nature Medicine 2023, 29: 3184-3192. PMID: 38062264, PMCID: PMC10719093, DOI: 10.1038/s41591-023-02653-5.Peer-Reviewed Original ResearchAlcoholismGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideRacial GroupsA 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 cancerMulti-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
SDPRX: 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 traitsStatisticsSimulationsApplicationsTraitsSex-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
M-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, CongenitalHumansMutation
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