Featured Publications
SANTO: a coarse-to-fine alignment and stitching method for spatial omics
Li H, Lin Y, He W, Han W, Xu X, Xu C, Gao E, Zhao H, Gao X. SANTO: a coarse-to-fine alignment and stitching method for spatial omics. Nature Communications 2024, 15: 6048. PMID: 39025895, PMCID: PMC11258319, DOI: 10.1038/s41467-024-50308-x.Peer-Reviewed Original ResearchJoint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation
Shen J, Zhang Y, Zhu Z, Cheng Y, Cai B, Zhao Y, Zhao H. Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation. NeuroImage 2024, 297: 120739. PMID: 39009250, PMCID: PMC11367654, DOI: 10.1016/j.neuroimage.2024.120739.Peer-Reviewed Original ResearchGenetic networksComplex traitsGenetic architecture of complex traitsArchitecture of complex traitsGenome-wide association analysisGenetic correlationsGenetic architectureGenetic variationAssociation analysisGenetic basisPhenotypic similarityGenetic effectsFunctional variationRight hemisphereBrain regionsUK BiobankCortical thicknessTraitsCortical measuresCorrelation networkSignificant pairsHeritabilitySimilarity matrixBrainBrain lobesIntegration 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 variantsTuning 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 ResearchscNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Zhu B, Wang Y, Ku L, van Dijk D, Zhang L, Hafler D, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biology 2023, 24: 292. PMID: 38111007, PMCID: PMC10726524, DOI: 10.1186/s13059-023-03129-y.Peer-Reviewed Original ResearchA 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 cancerSDPRX: 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 ResearchConceptsStatistical methodsJoint distributionWide association study (GWAS) summary statisticsNon-European populationsReal traitsSummary statisticsCross-population predictionPrediction accuracyGenome-wide association study summary statisticsLinkage disequilibrium differencesPrediction performancePolygenic risk scoresComplex traitsStatisticsSimulationsApplicationsTraitsLow-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data
Park S, Lee E, Zhao H. Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data. Journal Of The American Statistical Association 2022, 119: 202-216. PMID: 38481466, PMCID: PMC10928550, DOI: 10.1080/01621459.2022.2105704.Peer-Reviewed Original ResearchAdditive Conditional Independence for Large and Complex Biological Structures
Lee K, Li B, Zhao H. Additive Conditional Independence for Large and Complex Biological Structures. Springer Handbooks Of Computational Statistics 2022, 153-171. DOI: 10.1007/978-3-662-65902-1_8.Peer-Reviewed Original ResearchNetwork assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease
Xie Y, Jiang W, Dong W, Li H, Jin SC, Brueckner M, Zhao H. Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease. PLOS Genetics 2022, 18: e1010252. PMID: 35671298, PMCID: PMC9205499, DOI: 10.1371/journal.pgen.1010252.Peer-Reviewed Original ResearchSCADIE: 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 profilesExpressionGenesCellsLeveraging 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 statisticsRelatednessCharacterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition
Liu T, Yuan M, Zhao H. Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition. Statistics In Biosciences 2022, 14: 485-513. DOI: 10.1007/s12561-021-09331-5.Peer-Reviewed Original ResearchLow-rank tensor decompositionTensor decompositionPower iterationClassical principal component analysisStatistical performanceNumerical experimentsTensor unfoldingStatistical methodsGene expression dataEfficient algorithmData matrixExpression dataTensor principal componentsBrain expression dataPrincipal component analysisIterationDecompositionSpatiotemporal transcriptomeImplicit assumptionAlgorithmDynamicsTrajectoriesGuaranteesAssumptionSpatial patternsNonparametric Functional Graphical Modeling Through Functional Additive Regression Operator
Lee K, Li L, Li B, Zhao H. Nonparametric Functional Graphical Modeling Through Functional Additive Regression Operator. Journal Of The American Statistical Association 2022, 118: 1718-1732. DOI: 10.1080/01621459.2021.2006667.Peer-Reviewed Original ResearchRegression operatorMultivariate random functionsGraphical modelsOne-dimensional kernelCurse of dimensionalityRandom variablesStatistical objectsRandom functionExisting graphical modelsError boundsEstimation consistencyLarge-scale networksDistributional assumptionsGaussian distributionNonparametric approachNonlinear relationOperatorsGraphical modelingOperator levelNeighborhood selectionExponential rateSupplementary materialElectroencephalography datasetAssumptionDifferent nodesM-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 ResearchSUPERGNOVA: 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 estimationSimilarityConditional Functional Graphical Models
Lee K, Ji D, Li L, Constable T, Zhao H. Conditional Functional Graphical Models. Journal Of The American Statistical Association 2021, 118: 257-271. PMID: 37193511, PMCID: PMC10181795, DOI: 10.1080/01621459.2021.1924178.Peer-Reviewed Original ResearchMultivariate random functionsFunctional graphical modelNew linear operatorMultivariate functional dataGraph structureConditional graphical modelGraphical modelsLinear operatorsGraphical modelingRandom functionPrecision matrixPrecision operatorCorresponding estimatorsUniform convergenceConditional graphFunctional settingGraph sizeCorrelation operatorPartial correlation matrixNonzero elementsConditioning setCorrelation matrixOperatorsGraphBrain functional connectivity networksA 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 modelPredictionAlgorithmStatistical Methods for Analyzing Tree-Structured Microbiome Data
Wang T, Zhao H. Statistical Methods for Analyzing Tree-Structured Microbiome Data. Frontiers In Probability And The Statistical Sciences 2021, 193-220. DOI: 10.1007/978-3-030-73351-3_8.Peer-Reviewed Original ResearchStatistical methodsOnly relative informationMicrobiome data analysisMicrobiome dataEmpirical Bayes estimationCompositional predictorsBayes estimationComputational challengesRelative informationDimension reductionAbundance matrixTaxa countsMultinomial modelMicrobiome datasetsPhylogenetic informationMicrobial taxaPhylogenetic treeSequencing technologiesOriginal ecosystemMicrobial compositionOrders of magnitudeMatrixExperimental methodsLibrary sizeZerosTranscriptomic organization of the human brain in post-traumatic stress disorder
Girgenti MJ, Wang J, Ji D, Cruz DA, Stein M, Gelernter J, Young K, Huber B, Williamson D, Friedman M, Krystal J, Zhao H, Duman R. Transcriptomic organization of the human brain in post-traumatic stress disorder. Nature Neuroscience 2020, 24: 24-33. PMID: 33349712, DOI: 10.1038/s41593-020-00748-7.Peer-Reviewed Original ResearchMeSH KeywordsAdultAutopsyBrain ChemistryCohort StudiesDepressive Disorder, MajorFemaleGene Expression RegulationGene Regulatory NetworksGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansInterneuronsMaleMiddle AgedNerve Tissue ProteinsSex CharacteristicsStress Disorders, Post-TraumaticTranscriptomeYoung AdultConceptsGenome-wide association studiesSignificant gene networksDifferential gene expressionSystems-level evidenceSignificant genetic liabilityMajor depressive disorder cohortGene networksTranscriptomic organizationTranscriptomic landscapeDownregulated setsGenomic networksGene expressionAssociation studiesMolecular determinantsExtensive remodelingGenotype dataSexual dimorphismSignificant divergenceMolecular profileNetwork analysisELFN1TranscriptsDimorphismPostmortem tissueDivergence