Featured Publications
A 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 ResearchConceptsCell 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 cancerSUPERGNOVA: 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 ResearchConceptsLocal 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 estimationSimilarity
2019
Prediction analysis for microbiome sequencing data
Wang T, Yang C, Zhao H. Prediction analysis for microbiome sequencing data. Biometrics 2019, 75: 875-884. PMID: 30994187, DOI: 10.1111/biom.13061.Peer-Reviewed Original ResearchConceptsMonte Carlo expectation-maximization algorithmInverse regression modelReal data exampleTypes of covariatesNew statistical frameworkMaximum likelihood estimationExpectation-maximization algorithmDimension reduction structureInverse regressionStatistical frameworkData examplesStatistical challengesLikelihood estimationMicrobiome sequencing dataHuman microbiome studiesHuman microbiome compositionDifferent library sizesZerosPredictive analysisModelEstimationAlgorithmSimulationsRegression modelsFrameworkA 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
2015
A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data
Lu Q, Hu Y, Sun J, Cheng Y, Cheung KH, Zhao H. A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data. Scientific Reports 2015, 5: 10576. PMID: 26015273, PMCID: PMC4444969, DOI: 10.1038/srep10576.Peer-Reviewed Original ResearchConceptsHuman genomeFunctional regionsStatistical frameworkAnnotation dataFunctional annotation dataWhole-genome annotationNon-coding regionsGenomic conservationHigh-throughput experimentsENCODE projectExperimental annotationsGenomeUnsupervised statistical learningFunctional potentialHuman geneticsStatistical learningComputational predictionsIntegrated analysisAnnotationAnnotation methodDiverse typesPowerful toolGeneticsMajor goalWeb server
2012
Time course RNA-seq: A potential avenue with somewhat different approach in tandem of differential analysis
Oh S, Zhao H, Noonan J. Time course RNA-seq: A potential avenue with somewhat different approach in tandem of differential analysis. 2012, 1: 580-587. DOI: 10.1109/cisis.2012.204.Peer-Reviewed Original ResearchMonte Carlo simulation studySimulation studyReal data setsStatistical frameworkDifferential expression methodsStatistical approachDependent dataMarkov model approachInherent dependenciesTime seriesModel approachHidden Markov Model ApproachStandard approachTime-series RNA-seq dataData setsIntuitive solutionBiological systemsTrajectory indexTemporal complexityDifferential analysisDifferent approachesApproachConsiderable advantagesSolution
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