Featured Publications
SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure
Tang D, Park S, Zhao H. SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure. Genome Biology 2022, 23: 129. PMID: 35706040, PMCID: PMC9199219, DOI: 10.1186/s13059-022-02688-w.Peer-Reviewed Original ResearchConceptsCell type-specific gene expressionType-specific gene expressionCell type proportionsDifferential expression analysisCell type-specific gene expression profilesExpression analysisGene expressionSingle-cell RNA-seq dataRNA-seq dataGene differential expression analysisGene expression profilesType proportionsExpression profilesExpressionGenesCells
2024
DNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism
Xiao C, Peng G, Conneely K, Zhao H, Felger J, Wommack E, Higgins K, Shin D, Saba N, Bruner D, Miller A. DNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism. Molecular Psychiatry 2024, 1-8. PMID: 38977918, DOI: 10.1038/s41380-024-02652-z.Peer-Reviewed Original ResearchGene expressionMethylation lociAssociated with gene expressionHead and neck cancerDNA methylation profilesProtein markersLipid metabolismInvolvement of genesIllumina MethylationEPICDNA methylationRelevant gene expressionEpigenetic modificationsExpression pairsInflammatory markersInflammatory responseLociHead and neck cancer patientsAssociated with inflammatory markersGenesDNAMarkers of inflammationAssociated with fatigueExpressionMethylationPost-radiotherapyStatistical methods for assessing the effects of de novo variants on birth defects
Xie Y, Wu R, Li H, Dong W, Zhou G, Zhao H. Statistical methods for assessing the effects of de novo variants on birth defects. Human Genomics 2024, 18: 25. PMID: 38486307, PMCID: PMC10938830, DOI: 10.1186/s40246-024-00590-z.Peer-Reviewed Original ResearchConceptsDe novo variantsAnalyzed de novo variantsDevelopment of next-generation sequencing technologiesNext-generation sequencing technologiesSequencing technologiesImprove statistical powerGenetic heterogeneitySequenced samplesStatistical powerBirth defectsDiseased individualsLow occurrenceCongenital heart diseaseVariantsGenesDeleterious effectsSequenceGeneral workflowStatistical methods
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 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 ResearchConceptsExpression quantitative trait lociComplex traitsNovel lociIdentification of eQTLGene expressionTranscriptome-wide association study methodLinkage disequilibriumQuantitative trait lociAssociation study methodsCombined Annotation Dependent Depletion (CADD) scoresAnnotation-dependent depletion scoreExpression levelsDisease-associated genesEpigenetic annotationsEpigenetic informationFunctional annotationTrait lociGenetic variationGenesPrevious GWASLociGenetic effectsTraitsComplex diseasesGWASGenome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals
Xu K, Li B, McGinnis KA, Vickers-Smith R, Dao C, Sun N, Kember RL, Zhou H, Becker WC, Gelernter J, Kranzler HR, Zhao H, Justice AC. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nature Communications 2020, 11: 5302. PMID: 33082346, PMCID: PMC7598939, DOI: 10.1038/s41467-020-18489-3.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesLarge genome-wide association studiesMillion Veteran ProgramAssociation studiesExpression quantitative trait lociQuantitative trait lociChromatin interactionsComplex traitsFunctional annotationTrait lociSequencing ConsortiumDozen genesSignificant lociSmoking phenotypesLociMultiple populationsNew insightsPhenotypeVeteran ProgramGenetic vulnerabilityGenesTraitsAnnotationEuropean AmericansConsortiumStatistical Methods in Genome-Wide Association Studies
Sun N, Zhao H. Statistical Methods in Genome-Wide Association Studies. Annual Review Of Biomedical Data Science 2020, 3: 1-24. DOI: 10.1146/annurev-biodatasci-030320-041026.Peer-Reviewed Original ResearchGenome-wide association studiesAssociation studiesTraits of interestGenetic architectureIdentification of variantsGWAS dataStatistical methodologyStatistical challengesGenetic risk prediction modelsGenetic markersStatistical methodsHuman diseasesPhenotype informationGenetic variantsTraitsGenotype informationScientific goalsRecent progressGenesVariantsTens of thousandsHundreds of thousandsPrediction modelPathwayThousands
2019
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 ResearchConceptsGenome-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 estimatesGenesGWASRNABiologySNPsPARK2O2‐10‐03: LEVERAGING TISSUE SPECIFIC GENE EXPRESSION REGULATION TO IDENTIFY GENES ASSOCIATED WITH ALZHEIMER'S DISEASE
Liu W, Li M, Zhang W, Zhou G, Wu X, Wang J, Zhao H. O2‐10‐03: LEVERAGING TISSUE SPECIFIC GENE EXPRESSION REGULATION TO IDENTIFY GENES ASSOCIATED WITH ALZHEIMER'S DISEASE. Alzheimer's & Dementia 2019, 15: p564-p565. DOI: 10.1016/j.jalz.2019.06.4507.Peer-Reviewed Original ResearchA statistical framework for cross-tissue transcriptome-wide association analysis
Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 2019, 51: 568-576. PMID: 30804563, PMCID: PMC6788740, DOI: 10.1038/s41588-019-0345-7.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association analysisAssociation analysisGene-trait associationsGene expression dataGene expression levelsGenetic architectureComplex traitsMore genesGene expressionSingle tissueExpression dataAssociation resultsExpression levelsPowerful approachImputation modelHuman tissuesImputation accuracyGenotypesStatistical frameworkTissueGenesKey componentTraitsPowerful metricExpression
2018
Integrative functional genomic analysis of human brain development and neuropsychiatric risks
Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, Sunkin SM, Li Z, Shin Y, Zhu Y, Sousa AMM, Werling DM, Kitchen RR, Kang HJ, Pletikos M, Choi J, Muchnik S, Xu X, Wang D, Lorente-Galdos B, Liu S, Giusti-Rodríguez P, Won H, de Leeuw C, Pardiñas AF, Hu M, Jin F, Li Y, Owen M, O’Donovan M, Walters J, Posthuma D, Reimers M, Levitt P, Weinberger D, Hyde T, Kleinman J, Geschwind D, Hawrylycz M, State M, Sanders S, Sullivan P, Gerstein M, Lein E, Knowles J, Sestan N, Willsey A, Oldre A, Szafer A, Camarena A, Cherskov A, Charney A, Abyzov A, Kozlenkov A, Safi A, Jones A, Ashley-Koch A, Ebbert A, Price A, Sekijima A, Kefi A, Bernard A, Amiri A, Sboner A, Clark A, Jaffe A, Tebbenkamp A, Sodt A, Guillozet-Bongaarts A, Nairn A, Carey A, Huttner A, Chervenak A, Szekely A, Shieh A, Harmanci A, Lipska B, Carlyle B, Gregor B, Kassim B, Sheppard B, Bichsel C, Hahn C, Lee C, Chen C, Kuan C, Dang C, Lau C, Cuhaciyan C, Armoskus C, Mason C, Liu C, Slaughterbeck C, Bennet C, Pinto D, Polioudakis D, Franjic D, Miller D, Bertagnolli D, Lewis D, Feng D, Sandman D, Clarke D, Williams D, DelValle D, Fitzgerald D, Shen E, Flatow E, Zharovsky E, Burke E, Olson E, Fulfs E, Mattei E, Hadjimichael E, Deelman E, Navarro F, Wu F, Lee F, Cheng F, Goes F, Vaccarino F, Liu F, Hoffman G, Gürsoy G, Gee G, Mehta G, Coppola G, Giase G, Sedmak G, Johnson G, Wray G, Crawford G, Gu G, van Bakel H, Witt H, Yoon H, Pratt H, Zhao H, Glass I, Huey J, Arnold J, Noonan J, Bendl J, Jochim J, Goldy J, Herstein J, Wiseman J, Miller J, Mariani J, Stoll J, Moore J, Szatkiewicz J, Leng J, Zhang J, Parente J, Rozowsky J, Fullard J, Hohmann J, Morris J, Phillips J, Warrell J, Shin J, An J, Belmont J, Nyhus J, Pendergraft J, Bryois J, Roll K, Grennan K, Aiona K, White K, Aldinger K, Smith K, Girdhar K, Brouner K, Mangravite L, Brown L, Collado-Torres L, Cheng L, Gourley L, Song L, Ubieta L, Habegger L, Ng L, Hauberg M, Onorati M, Webster M, Kundakovic M, Skarica M, Reimers M, Johnson M, Chen M, Garrett M, Sarreal M, Reding M, Gu M, Peters M, Fisher M, Gandal M, Purcaro M, Smith M, Brown M, Shibata M, Brown M, Xu M, Yang M, Ray M, Shapovalova N, Francoeur N, Sjoquist N, Mastan N, Kaur N, Parikshak N, Mosqueda N, Ngo N, Dee N, Ivanov N, Devillers O, Roussos P, Parker P, Manser P, Wohnoutka P, Farnham P, Zandi P, Emani P, Dalley R, Mayani R, Tao R, Gittin R, Straub R, Lifton R, Jacobov R, Howard R, Park R, Dai R, Abramowicz S, Akbarian S, Schreiner S, Ma S, Parry S, Shapouri S, Weissman S, Caldejon S, Mane S, Ding S, Scuderi S, Dracheva S, Butler S, Lisgo S, Rhie S, Lindsay S, Datta S, Souaiaia T, Roychowdhury T, Gomez T, Naluai-Cecchini T, Beach T, Goodman T, Gao T, Dolbeare T, Fliss T, Reddy T, Chen T, Hyde T, Brunetti T, Lemon T, Desta T, Borrman T, Haroutunian V, Spitsyna V, Swarup V, Shi X, Jiang Y, Xia Y, Chen Y, Jiang Y, Wang Y, Chae Y, Yang Y, Kim Y, Riley Z, Krsnik Z, Deng Z, Weng Z, Lin Z, Li Z. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 2018, 362 PMID: 30545854, PMCID: PMC6413317, DOI: 10.1126/science.aat7615.Peer-Reviewed Original ResearchConceptsIntegrative functional genomic analysisFunctional genomic analysisCell typesGene coexpression modulesDistinct cell typesCell type-specific dynamicsGenomic basisEpigenomic reorganizationEpigenomic landscapeEpigenomic regulationGenomic analysisCoexpression modulesIntegrative analysisHuman brain developmentFetal transitionHuman neurodevelopmentGenetic associationCellular compositionNeuropsychiatric riskBrain developmentNeurodevelopmental processesGenesTraitsPostnatal developmentNeuropsychiatric disorders
2015
Gene-based and pathway-based genome-wide association study of alcohol dependence
Lingjun Z, ZHANG CK, SAYWARD FG, CHEUNG KH, Kesheng W, KRYSTAL JH, Hongyu Z, Xingguang L. Gene-based and pathway-based genome-wide association study of alcohol dependence. General Psychiatry 2015, 27: 111-118. PMID: 26120261, PMCID: PMC4466852, DOI: 10.11919/j.issn.1002-0829.215031.Peer-Reviewed Original ResearchGenome-wide association studiesRisk genesAssociation studiesBiological signaling processesPXN geneGene pathwaysSignaling processesGlycan degradationInteraction pathwayGenetic markersTransporter pathwaysGenesDiscovery samplePathwayReplication sampleAfrican American casesRisk pathwaysMultiple testingBonferroni correctionNew evidence
2014
Graphical Modeling of Biological Pathways in Genome-wide Association Studies
Chen M, Cho J, Zhao H. Graphical Modeling of Biological Pathways in Genome-wide Association Studies. 2014, 294-317. DOI: 10.1093/acprof:oso/9780198709022.003.0012.Peer-Reviewed Original ResearchGenome-wide association studiesAssociation studiesAbstract Genome-wide association studiesBiological pathwaysDisease-associated genesPrior biological knowledgeNeighboring genesPathway topologyAssociation analysisBiological knowledgeGenesSpecific pathwaysPathwayCrohn's disease cohortDiscoveryExchangeable set
2013
Guilt by rewiring: gene prioritization through network rewiring in Genome Wide Association Studies
Hou L, Chen M, Zhang CK, Cho J, Zhao H. Guilt by rewiring: gene prioritization through network rewiring in Genome Wide Association Studies. Human Molecular Genetics 2013, 23: 2780-2790. PMID: 24381306, PMCID: PMC3990172, DOI: 10.1093/hmg/ddt668.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesWide association studyDisease-associated genesGWAS signalsNetwork rewiringAssociation studiesFunctional genomic informationGene expression networksCo-expression networkDisease-associated pathwaysExpression networksGene networksGenomic informationAssociation signalsGene prioritizationDisease genesDisease locusSusceptibility lociGenesAssociation principleRewiringDisease associationsLociMillions of candidatesDisease conditions
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
2006
A Misclassification Model for Inferring Transcriptional Regulatory Networks
Vannucci M, Sun N, Zhao H. A Misclassification Model for Inferring Transcriptional Regulatory Networks. 2006, 347-365. DOI: 10.1017/cbo9780511584589.019.Peer-Reviewed Original ResearchTranscriptional regulatory networksGene expression dataRegulatory networksExpression dataUnderlying transcriptional regulatory networksProtein-DNA binding dataNetwork reconstructionSet of proteinsYeast cell cycleMutual regulatory interactionsRegulatory network reconstructionGene regulationRegulatory interactionsSpecific genesCell cycleGenesBiological researchExpression levelsProteinTRNBinding dataHigh connectivityTransient stimulationRecent advancesStatistical framework
2005
Integrating mRNA Decay Information into Co-Regulation Study
Chen L, Zhao H. Integrating mRNA Decay Information into Co-Regulation Study. Journal Of Computer Science And Technology 2005, 20: 434-438. DOI: 10.1007/s11390-005-0434-1.Peer-Reviewed Original ResearchMRNA decay ratesTranscript amountsTranscription rateBioinformatics analysisTranscriptional regulatory networksCo-regulated genesRelative transcript amountsMRNA degradation ratesHigh-throughput technologiesGene regulationGenomic signalsRegulatory networksDifferent genesGene clusteringMRNA synthesisMRNA transcriptsDownstream analysisGenesDNAMotifSimilarity analysisTranscriptsRegulationIdentification
2001
Light Control of Arabidopsis Development Entails Coordinated Regulation of Genome Expression and Cellular Pathways[W]
Ma L, Li J, Qu L, Hager J, Chen Z, Zhao H, Deng X. Light Control of Arabidopsis Development Entails Coordinated Regulation of Genome Expression and Cellular Pathways[W]. The Plant Cell 2001, 13: 2589-2607. PMID: 11752374, PMCID: PMC139475, DOI: 10.1105/tpc.010229.Peer-Reviewed Original ResearchMeSH KeywordsArabidopsisArabidopsis ProteinsCryptochromesDrosophila ProteinsExpressed Sequence TagsEye ProteinsFlavoproteinsGene Expression Regulation, DevelopmentalGene Expression Regulation, PlantGenome, PlantLightOligonucleotide Array Sequence AnalysisPhotoreceptor CellsPhotoreceptor Cells, InvertebratePhytochromePhytochrome APhytochrome BPlant ProteinsPlant ShootsReceptors, G-Protein-CoupledTranscription FactorsConceptsArabidopsis developmentExpression profilesGenome expressionCellular pathwaysLight-regulated genesSimilar gene expression profilesGenome expression profilesDistinct expression profilesGene expression profilesDifferent light qualitiesPhytochrome BSequence tagsCoordinated regulationPhytochrome ARegulatory pathwaysLight qualityLight conditionsDark transitionGenesPathwayLight treatmentLight controlBlue lightExpressionCryptochromesTest of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium Test
Zhang S, Zhang K, Li J, Sun F, Zhao H. Test of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium Test. Genetic Epidemiology 2001, 21: s370-s375. PMID: 11793701, DOI: 10.1002/gepi.2001.21.s1.s370.Peer-Reviewed Original ResearchConceptsQuantitative pedigree disequilibrium testPedigree disequilibrium testQuantitative traitsTraits of interestGenetic Analysis Workshop 12Disequilibrium testGeneral pedigreesSequence dataCandidate genesGenetic markersGenetic linkageQualitative traitsLinkage disequilibriumTraitsLarge pedigreePresence of linkagePedigreeStatistical methodsFamilyNuclear familiesTests of associationGenesUnrelated nuclear familiesLinkageDisequilibrium
1998
Stochastic modeling of the crossover process during meiosis
Zhao H, Speed T. Stochastic modeling of the crossover process during meiosis. Communication In Statistics- Theory And Methods 1998, 27: 1557-1580. DOI: 10.1080/03610929808832177.Peer-Reviewed Original ResearchHomologous chromosome pairsChromosome pairsGenetic mapHomologous chromosomesGenetic mappingNonsister chromatidsProper segregationGenetic variationCrossover interferenceDisease genesDifferent chromatidsHomologous chromatidsChromatidsGenetic studiesChiasma interferenceMeiosisChromatid interferenceReunion eventChromosomesGenesOrganismsExpression