2023
eQTL studies: from bulk tissues to single cells
Zhang J, Zhao H. eQTL studies: from bulk tissues to single cells. Journal Of Genetics And Genomics 2023, 50: 925-933. PMID: 37207929, PMCID: PMC10656365, DOI: 10.1016/j.jgg.2023.05.003.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociBulk tissueIdentification of eQTLContext-dependent gene regulationCell typesQuantitative trait lociMost eQTL studiesSingle cellsComplex traitsGene regulationEQTL studiesFunctional genesTrait lociSpecific genesChromosomal regionsDynamic regulationGene expressionBiological processesDifferent tissuesGenetic variantsExpression levelsDisease mechanismsGenesRegulationRecent studiesMulti-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 variantsTraitsPredicting Breast Cancer Risk for Women Veterans of African Ancestry in the Million Veteran Program
Luoh S, Minnier J, Zhao H, Gao L. Predicting Breast Cancer Risk for Women Veterans of African Ancestry in the Million Veteran Program. Health Equity 2023, 7: 303-306. PMID: 37284538, PMCID: PMC10240329, DOI: 10.1089/heq.2023.0011.Peer-Reviewed Original ResearchBreast cancer riskBreast cancerMillion Veteran ProgramCancer riskPolygenic risk scoresWomen veteransHigher breast cancer riskRisk prediction instrumentsIncident breast cancerLow-risk womenBreast cancer preventionBreast cancer incidenceIndividual breast cancer riskBreast cancer mortalityPredicting Breast Cancer RiskBreast cancer screeningVeteran ProgramMajor health threatPrediction instrumentGenetic variantsEuropean ancestry womenProspective cohortRisk womenCancer mortalityCancer screening
2020
A large-scale genome-wide association study meta-analysis of cannabis use disorder
Johnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, Sanchez-Roige S, Paul SE, Wendt FR, Clarke TK, Lai D, Reginsson GW, Zhou H, He J, Baranger DAA, Gudbjartsson DF, Wedow R, Adkins DE, Adkins AE, Alexander J, Bacanu SA, Bigdeli TB, Boden J, Brown SA, Bucholz KK, Bybjerg-Grauholm J, Corley RP, Degenhardt L, Dick DM, Domingue BW, Fox L, Goate AM, Gordon SD, Hack LM, Hancock DB, Hartz SM, Hickie IB, Hougaard DM, Krauter K, Lind PA, McClintick JN, McQueen MB, Meyers JL, Montgomery GW, Mors O, Mortensen PB, Nordentoft M, Pearson JF, Peterson RE, Reynolds MD, Rice JP, Runarsdottir V, Saccone NL, Sherva R, Silberg JL, Tarter RE, Tyrfingsson T, Wall TL, Webb BT, Werge T, Wetherill L, Wright MJ, Zellers S, Adams MJ, Bierut LJ, Boardman JD, Copeland WE, Farrer LA, Foroud TM, Gillespie NA, Grucza RA, Harris KM, Heath AC, Hesselbrock V, Hewitt JK, Hopfer CJ, Horwood J, Iacono WG, Johnson EO, Kendler KS, Kennedy MA, Kranzler HR, Madden PAF, Maes HH, Maher BS, Martin NG, McGue M, McIntosh AM, Medland SE, Nelson EC, Porjesz B, Riley BP, Stallings MC, Vanyukov MM, Vrieze S, Workgroup P, Walters R, Polimanti R, Johnson E, McClintick J, Hatoum A, He J, Wendt F, Zhou H, Adams M, Adkins A, Aliev F, Bacanu S, Batzler A, Bertelsen S, Biernacka J, Bigdeli T, Chen L, Clarke T, Chou Y, Degenhardt F, Docherty A, Edwards A, Fontanillas P, Foo J, Fox L, Frank J, Giegling I, Gordon S, Hack L, Hartmann A, Hartz S, Heilmann-Heimbach S, Herms S, Hodgkinson C, Hoffman P, Hottenga J, Kennedy M, Alanne-Kinnunen M, Konte B, Lahti J, Lahti-Pulkkinen M, Lai D, Ligthart L, Loukola A, Maher B, Mbarek H, McIntosh A, McQueen M, Meyers J, Milaneschi Y, Palviainen T, Pearson J, Peterson R, Ripatti S, Ryu E, Saccone N, Salvatore J, Sanchez-Roige S, Schwandt M, Sherva R, Streit F, Strohmaier J, Thomas N, Wang J, Webb B, Wedow R, Wetherill L, Wills A, 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, Pederson 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, Davis L, Bogdan R, Gelernter J, Edenberg H, Stefansson K, Børglum A, Agrawal A. A large-scale genome-wide association study meta-analysis of cannabis use disorder. The Lancet Psychiatry 2020, 7: 1032-1045. PMID: 33096046, PMCID: PMC7674631, DOI: 10.1016/s2215-0366(20)30339-4.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesGenome-wide significant lociLarge-scale genome-wide association studiesGenetic correlationsChromosome 7 locusTraits of interestLarge genome-wide association studiesLinkage disequilibrium score regressionChromosome 8 locusDifferent genetic underpinningsDifferent genetic correlationsWellcome Trust Case Control ConsortiumDisequilibrium score regressionNovel genetic variantsStrong genetic componentSignificant lociGenetic lociGenetic underpinningsGenetic componentLociScore regressionGenetic variantsGenetic overlapIntegrative sequencingStatistical 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
2017
Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease
Lu Q, Powles RL, Abdallah S, Ou D, Wang Q, Hu Y, Lu Y, Liu W, Li B, Mukherjee S, Crane PK, Zhao H. Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease. PLOS Genetics 2017, 13: e1006933. PMID: 28742084, PMCID: PMC5546707, DOI: 10.1371/journal.pgen.1006933.Peer-Reviewed Original ResearchConceptsTissue typesNon-coding elementsNon-coding genomeComplex human diseasesLate-onset Alzheimer's diseaseIndividual cell typesRelevant tissue typesGWAS traitsTranscriptomic annotationGenome annotationFunctional annotationDNA elementsHeritability enrichmentHuman genomeLarge international consortiaVariety of cellsGenomeHuman diseasesAnnotation dataCell typesGenetic variantsOrgan system categoriesComplex diseasesSimilar localizationAnnotationLeveraging functional annotations in genetic risk prediction for human complex diseases
Hu Y, Lu Q, Powles R, Yao X, Yang C, Fang F, Xu X, Zhao H. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Computational Biology 2017, 13: e1005589. PMID: 28594818, PMCID: PMC5481142, DOI: 10.1371/journal.pcbi.1005589.Peer-Reviewed Original ResearchMeSH KeywordsChromosome MappingData Interpretation, StatisticalData MiningDatabases, GeneticEpigenomicsGenetic Association StudiesGenetic Predisposition to DiseaseGenetic VariationGenome, HumanHumansLinkage DisequilibriumPolymorphism, Single NucleotideProportional Hazards ModelsQuantitative Trait LociRisk AssessmentConceptsGenome-wide association studiesFunctional annotationGenetic risk predictionDisease-associated genetic variantsLinkage disequilibriumIdentification of thousandsWide association studyHuman complex diseasesComplex diseasesGWAS summary statisticsHuman genetics researchAssociation studiesAnnoPredGenotype dataGenetic researchGenetic variantsRelevant variantsAnnotationDisequilibriumMost diseasesDiverse typesSummary statisticsVariantsBayesian frameworkPrecision medicine
2011
Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies
Chen M, Cho J, Zhao H. Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies. PLOS Genetics 2011, 7: e1001353. PMID: 21490723, PMCID: PMC3072362, DOI: 10.1371/journal.pgen.1001353.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesBiological pathwaysSingle gene-based methodsMarkov random field modelGene-based methodsPrior biological knowledgeRandom field modelGWAS analysisAssociation signalsMultiple genesPathway topologyGene associationsAssociation analysisGenesBiological knowledgeField modelGenetic variantsSpecific pathwaysReal data examplePathwayStatistical inferenceConditional modes algorithmExchangeable setRegression form