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 variantsAncestryIndividualsRecessive 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 unions
2024
Benchmarking 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 studyIntegration of expression QTLs with fine mapping via SuSiE.
Zhang X, Jiang W, Zhao H. Integration of expression QTLs with fine mapping via SuSiE. PLOS Genetics 2024, 20: e1010929. PMID: 38271473, PMCID: PMC10846745, DOI: 10.1371/journal.pgen.1010929.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociGenome-wide association studiesFine-mapping methodsLinkage disequilibriumBody mass indexFine-mappingExpression quantitative trait loci informationGenome-wide association study resultsExpression quantitative trait loci analysisPresence of linkage disequilibriumExternal reference panelGenetic fine-mappingQuantitative trait lociPosterior inclusion probabilitiesInclusion probabilitiesAlzheimer's diseaseExpression QTLsLD patternsComplex traitsCandidate variantsAssociation studiesTrait lociAssociation to causationReference panelFunctional variants
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 ResearchBenchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies
Zhang C, Zhang Y, Zhang Y, Zhao H. Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies. Briefings In Bioinformatics 2023, 24: bbad407. PMID: 37974509, PMCID: PMC10654488, DOI: 10.1093/bib/bbad407.Peer-Reviewed Original ResearchStatistical assessment of biomarker replicability using MAJAR method
Xie Y, Zhai S, Jiang W, Zhao H, Mehrotra D, Shen J. Statistical assessment of biomarker replicability using MAJAR method. Statistical Methods In Medical Research 2023, 32: 1961-1972. PMID: 37519295, DOI: 10.1177/09622802231188519.Peer-Reviewed Original ResearchConceptsBayesian false discovery rateDifferent data generation processesNovel statistical frameworkExtensive simulation studyExpectation-maximization algorithmStatistical frameworkComputational efficiencyGWAS summary statistics dataSimulation studyData generation processStatistical assessmentSimulation resultsStatistical powerFalse discovery rateResponse predictionSummary statistics dataDiscovery rateSample sizeLimited powerOutliersAlgorithmGeneration processRobustnessPowerSmall sample sizeMulti-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 variantsTraitsA genome-wide association study of frailty identifies significant genetic correlation with neuropsychiatric, cardiovascular, and inflammation pathways
Ye Y, Noche R, Szejko N, Both C, Acosta J, Leasure A, Brown S, Sheth K, Gill T, Zhao H, Falcone G. A genome-wide association study of frailty identifies significant genetic correlation with neuropsychiatric, cardiovascular, and inflammation pathways. GeroScience 2023, 45: 2511-2523. PMID: 36928559, PMCID: PMC10651618, DOI: 10.1007/s11357-023-00771-z.Peer-Reviewed Original ResearchConceptsFried frailty scoreBiology of frailtyEuropean descent participantsOccurrence of frailtyGenome-wide association studiesMendelian randomization analysisFrailty scoreChronic painJoint disordersPolygenic risk scoresRespiratory diseaseInflammation pathwaysRisk scoreClinical phenotypeBrain tissueCausal associationFrailtyAge-related pathwaysRandomization analysisGenetic factorsAssociation studiesUK BiobankRetirement StudyPerson's vulnerabilitySignificant genetic correlationsWhole-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 ResearchMeSH KeywordsExome SequencingGenetic TestingGenome-Wide Association StudyHumansIschemic StrokePhenotypeConceptsGene-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 variantsTraits
2022
Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics
Hu X, Zhao J, Lin Z, Wang Y, Peng H, Zhao H, Wan X, Yang C. Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. Proceedings Of The National Academy Of Sciences Of The United States Of America 2022, 119: e2106858119. PMID: 35787050, PMCID: PMC9282238, DOI: 10.1073/pnas.2106858119.Peer-Reviewed Original ResearchLeveraging 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
2021
A 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 modelPredictionAlgorithm
2020
Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studies
Munn‐Chernoff M, Johnson EC, Chou Y, Coleman JRI, Thornton LM, Walters RK, Yilmaz Z, Baker JH, Hübel C, Gordon S, Medland SE, Watson HJ, Gaspar HA, Bryois J, Hinney A, Leppä VM, Mattheisen M, Ripke S, Yao S, Giusti‐Rodríguez P, Hanscombe KB, Adan RAH, Alfredsson L, Ando T, Andreassen OA, Berrettini WH, Boehm I, Boni C, Perica V, Buehren K, Burghardt R, Cassina M, Cichon S, Clementi M, Cone RD, Courtet P, Crow S, Crowley JJ, Danner UN, Davis OSP, de Zwaan M, Dedoussis G, Degortes D, DeSocio JE, Dick DM, Dikeos D, Dina C, Dmitrzak‐Weglarz M, Docampo E, Duncan LE, Egberts K, Ehrlich S, Escaramís G, Esko T, Estivill X, Farmer A, Favaro A, Fernández‐Aranda F, Fichter MM, Fischer K, Föcker M, Foretova L, Forstner AJ, Forzan M, Franklin CS, Gallinger S, Giegling I, Giuranna J, Gonidakis F, Gorwood P, Mayora M, Guillaume S, Guo Y, Hakonarson H, Hatzikotoulas K, Hauser J, Hebebrand J, Helder SG, Herms S, Herpertz‐Dahlmann B, Herzog W, Huckins LM, Hudson JI, Imgart H, Inoko H, Janout V, Jiménez‐Murcia S, Julià A, Kalsi G, Kaminská D, Karhunen L, Karwautz A, Kas MJH, Kennedy JL, Keski‐Rahkonen A, Kiezebrink K, Kim Y, Klump KL, Knudsen GPS, La Via MC, Le Hellard S, Levitan RD, Li D, Lilenfeld L, Lin BD, Lissowska J, Luykx J, Magistretti PJ, Maj M, Mannik K, Marsal S, Marshall CR, Mattingsdal M, McDevitt S, McGuffin P, Metspalu A, Meulenbelt I, Micali N, Mitchell K, Monteleone AM, Monteleone P, Nacmias B, Navratilova M, Ntalla I, O'Toole JK, Ophoff RA, Padyukov L, Palotie A, Pantel J, Papezova H, Pinto D, Rabionet R, Raevuori A, Ramoz N, Reichborn‐Kjennerud T, Ricca V, Ripatti S, Ritschel F, Roberts M, Rotondo A, Rujescu D, Rybakowski F, Santonastaso P, Scherag A, Scherer SW, Schmidt U, Schork NJ, Schosser A, Seitz J, Slachtova L, Slagboom PE, Landt M, Slopien A, Sorbi S, Świątkowska B, Szatkiewicz JP, Tachmazidou I, Tenconi E, Tortorella A, Tozzi F, Treasure J, Tsitsika A, Tyszkiewicz‐Nwafor M, Tziouvas K, van Elburg A, van Furth E, Wagner G, Walton E, Widen E, Zeggini E, Zerwas S, Zipfel S, Bergen AW, Boden JM, Brandt H, Crawford S, Halmi KA, Horwood LJ, Johnson C, Kaplan AS, Kaye WH, Mitchell J, Olsen CM, Pearson JF, Pedersen NL, Strober M, Werge T, Whiteman DC, Woodside DB, Grove J, Henders AK, Larsen JT, Parker R, Petersen LV, Jordan J, Kennedy MA, Birgegård A, Lichtenstein P, Norring C, Landén M, Mortensen PB, Polimanti R, McClintick JN, Adkins AE, Aliev F, Bacanu S, Batzler A, Bertelsen S, Biernacka JM, Bigdeli TB, Chen L, Clarke T, Degenhardt F, Docherty AR, Edwards AC, Foo JC, Fox L, Frank J, Hack LM, Hartmann AM, Hartz SM, Heilmann‐Heimbach S, Hodgkinson C, Hoffmann P, Hottenga J, 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, Peterson RE, Ryu E, Saccone NL, Salvatore JE, Sanchez‐Roige S, Schwandt M, Sherva R, Streit F, Strohmaier J, Thomas N, Wang J, Webb BT, Wedow R, Wetherill L, Wills AG, Zhou H, Boardman JD, Chen D, Choi D, Copeland WE, Culverhouse RC, Dahmen N, Degenhardt L, Domingue BW, Frye MA, Gäebel W, Hayward C, Ising M, Keyes M, Kiefer F, Koller G, Kramer J, Kuperman S, Lucae S, Lynskey MT, Maier W, Mann K, Männistö S, Müller‐Myhsok B, Murray AD, Nurnberger JI, Preuss U, Räikkönen K, Reynolds MD, Ridinger M, Scherbaum N, Schuckit MA, Soyka M, Treutlein J, Witt SH, Wodarz N, Zill P, Adkins DE, Boomsma DI, Bierut LJ, Brown SA, Bucholz KK, Costello EJ, de Wit H, Diazgranados N, Eriksson JG, Farrer LA, Foroud TM, Gillespie NA, Goate AM, Goldman D, Grucza RA, Hancock DB, Harris KM, Hesselbrock V, Hewitt JK, Hopfer CJ, Iacono WG, Johnson EO, Karpyak VM, Kendler KS, Kranzler HR, Krauter K, Lind PA, McGue M, MacKillop J, Madden PAF, Maes HH, Magnusson PKE, Nelson EC, Nöthen MM, Palmer AA, Penninx BWJH, Porjesz B, Rice JP, Rietschel M, Riley BP, Rose RJ, Shen P, Silberg J, Stallings MC, Tarter RE, Vanyukov MM, Vrieze S, Wall TL, Whitfield JB, Zhao H, Neale BM, Wade TD, Heath AC, Montgomery GW, Martin NG, Sullivan PF, Kaprio J, Breen G, Gelernter J, Edenberg HJ, Bulik CM, Agrawal A. Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studies. Addiction Biology 2020, 26: e12880. PMID: 32064741, PMCID: PMC7429266, DOI: 10.1111/adb.12880.Peer-Reviewed Original Research
2019
Molecular genetic overlap between posttraumatic stress disorder and sleep phenotypes
Lind M, Brick L, Gehrman P, Duncan L, Gelaye B, Maihofer A, Nievergelt C, Nugent N, Stein M, Amstadter A, Aiello A, Almli L, Amstadter A, Andersen S, Andreassen O, Arbisi P, Ashley-Koch A, Atkinson E, Austin S, Avdibegovic E, Babić D, Bækvad-Hansen M, Baker D, Beckham J, Bierut L, Bisson J, Boks M, Bolger E, Børglum A, Bradley B, Brashear M, Breen G, Bryant R, Bustamante A, Bybjerg-Grauholm J, Calabrese J, Caldas-de-Almeida J, Chen C, Coleman J, Dale A, Dalvie S, Daly M, Daskalakis N, Deckert J, Delahanty D, Dennis M, Disner S, Domschke K, Duncan L, Dzubur-Kulenovic A, Erbes C, Evans A, Farrer L, Feeny N, Flory J, Forbes D, Franz C, Galea S, Garrett M, Gelaye B, Gelernter J, Geuze E, Gillespie C, Uka A, Gordon S, Guffanti G, Haas M, Hammamieh R, Hauser M, Heath A, Hemmings S, Hougaard D, Jakovljevic M, Jett M, Johnson E, Jones I, Jovanovic T, Junglen A, Karstoft K, Kaufman M, Kessler R, Khan A, Kimbrel N, King A, Koen N, Koenen K, Kranzler H, Kremen W, Lawford B, Lebois L, Lewis C, Liberzon I, Linnstaedt S, Logue M, Lori A, Lugonja B, Luykx J, Lyons M, Maihofer A, Maples-Keller J, Marmar C, Martin N, Maurer D, Mavissakalian M, McFarlane A, McGlinchey R, McLaughlin K, McLean S, McLeay S, Mehta D, Milberg W, Miller M, Morey R, Morris C, Mors O, Mortensen P, Nelson E, Nievergelt C, Nordentoft M, Norman S, O’Donnell M, Orcutt H, Panizzon M, Peters E, Peterson A, Peverill M, Pietrzak R, Polusny M, Qin X, Ratanatharathorn A, Ressler K, Rice J, Risbrough V, Roberts A, Rothbaum A, Rothbaum B, Roy-Byrne P, Ruggiero K, Rung A, Rutten B, Saccone N, Sanchez S, Schijven D, Seedat S, Seligowski A, Seng J, Sheerin C, Silove D, Smith A, Smoller J, Sponheim S, Stein D, Stein M, Stevens J, Teicher M, Thompson W, Torres K, Trapido E, Uddin M, Ursano R, van den Heuvel L, van Hooff M, Vermetten E, Vinkers C, Voisey J, Wang Y, Wang Z, Werge T, Williams M, Williamson D, Winternitz S, Wolf C, Wolf E, Wolff J, Yehuda R, Young K, Young R, Zhao H, Zoellner L. Molecular genetic overlap between posttraumatic stress disorder and sleep phenotypes. Sleep 2019, 43: zsz257. PMID: 31802129, PMCID: PMC7157187, DOI: 10.1093/sleep/zsz257.Peer-Reviewed Original ResearchGenome-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 samples
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 ResearchConceptsGenetic 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
Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data
Wang Y, Fang H, Yang D, Zhao H, Deng M. Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data. IEEE Transactions On Computational Biology And Bioinformatics 2017, 16: 1743-1752. PMID: 28858811, DOI: 10.1109/tcbb.2017.2743711.Peer-Reviewed Original ResearchConceptsExponential-family random graph modelsRandom graph modelsGraph modelStatistical network modelsHeterogeneity of networksLarge-scale genetic interaction networksReal social networksERGM parametersSubset of nodesOnline graphStatistical modelData sizeObserved networkEM algorithmNetwork informationGraph nodesMixture problemSocial networksFlexible wayNetwork modelNetwork clustersClassical methodsIncredible setInteraction dataNetworkJoint 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
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