2020
Supervariants identification for breast cancer
Hu J, Li T, Wang S, Zhang H. Supervariants identification for breast cancer. Genetic Epidemiology 2020, 44: 934-947. PMID: 32808324, PMCID: PMC7924970, DOI: 10.1002/gepi.22350.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesCombination of allelesRare variantsNovel lociChromosome 2UK Biobank databaseChromosome 1Multiple lociAssociation studiesLociComplex diseasesGenesBiobank databaseAssociation methodGenomeVariantsTens of thousandsAllelesPolymorphismNovel resultsSignalsClassic conceptIdentificationDepth importance in precision medicine (DIPM): a tree- and forest-based method for right-censored survival outcomes
Chen V, Zhang H. Depth importance in precision medicine (DIPM): a tree- and forest-based method for right-censored survival outcomes. Biostatistics 2020, 23: 157-172. PMID: 32424406, PMCID: PMC8759439, DOI: 10.1093/biostatistics/kxaa021.Peer-Reviewed Original Research
2018
A univariate perspective of multivariate genome‐wide association analysis
Guo X, Zhu J, Fan Q, He M, Wang X, Zhang H. A univariate perspective of multivariate genome‐wide association analysis. Genetic Epidemiology 2018, 42: 470-479. PMID: 29781551, DOI: 10.1002/gepi.22128.Peer-Reviewed Original ResearchMeSH KeywordsComputer SimulationGenome-Wide Association StudyHumansModels, GeneticMultivariate AnalysisPhenotypeConceptsGenome-wide association studiesMultivariate genome-wide association studyMultivariate genome-wide association analysisGenome-wide association analysisMultiple correlated phenotypesGenetic signalsAssociation studiesCorrelated phenotypesAssociation analysisMultiple phenotypesSingle phenotypePhenotypeSubtype Classification and Heterogeneous Prognosis Model Construction in Precision Medicine
You N, He S, Wang X, Zhu J, Zhang H. Subtype Classification and Heterogeneous Prognosis Model Construction in Precision Medicine. Biometrics 2018, 74: 814-822. PMID: 29359319, DOI: 10.1111/biom.12843.Peer-Reviewed Original ResearchConceptsRegularization regressionVariable selectionHigh-dimensional predictorsNecessary statistical methodsVariable selection methodsExpectation-maximization algorithmOracle propertyPenalty parameterSemiparametric modelStatistical methodsParametric modelNumerical calculationsProper choiceModel constructionSelection methodGene expression datasetsModelEstimatorSubtype-specific risk factorsRegularizerSurvival probabilityHigh-throughput technologiesExpression datasetsAlgorithmSimulations
2015
Association Tests of Multiple Phenotypes: ATeMP
Guo X, Li Y, Ding X, He M, Wang X, Zhang H. Association Tests of Multiple Phenotypes: ATeMP. PLOS ONE 2015, 10: e0140348. PMID: 26479245, PMCID: PMC4610695, DOI: 10.1371/journal.pone.0140348.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyComputer SimulationGenome-Wide Association StudyGenotypeHumansModels, TheoreticalPhenotypeConceptsExtensive simulation studyStatistical literatureJoint association analysisMultiPhenSimulation studyEquivalence relationshipProportional odds modelReal case studyMeasurement errorMultivariate methodsOdds modelMultiple intermediate phenotypesJoint analysisMultiple phenotypesExplanatory variablesEquivalenceEstimationDistributionPhenotypic distributionATempSolutionError
2012
Simulating Realistic Genomic Data With Rare Variants
Xu Y, Wu Y, Song C, Zhang H. Simulating Realistic Genomic Data With Rare Variants. Genetic Epidemiology 2012, 37: 163-172. PMID: 23161487, PMCID: PMC3543480, DOI: 10.1002/gepi.21696.Peer-Reviewed Original ResearchGenetic Association Test for Multiple Traits at Gene Level
Guo X, Liu Z, Wang X, Zhang H. Genetic Association Test for Multiple Traits at Gene Level. Genetic Epidemiology 2012, 37: 122-129. PMID: 23032486, PMCID: PMC3524409, DOI: 10.1002/gepi.21688.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesMultiple traitsGene levelSingle nucleotide polymorphismsGenetic association testsCommon genesAssociation studiesAssociation TestNucleotide polymorphismsTraitsStudy of AddictionComplex diseasesBiological mechanismsDisease of interestAssociation informationGenesGeneticsSuch studiesStrong evidencePolymorphismPrevious findingsLevelsMultiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-Varying Covariates
Skup M, Zhu H, Zhang H. Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-Varying Covariates. Biometrics 2012, 68: 1083-1092. PMID: 22551084, PMCID: PMC3767131, DOI: 10.1111/j.1541-0420.2012.01767.x.Peer-Reviewed Original Research
2011
Propensity score‐based nonparametric test revealing genetic variants underlying bipolar disorder
Jiang Y, Zhang H. Propensity score‐based nonparametric test revealing genetic variants underlying bipolar disorder. Genetic Epidemiology 2011, 35: 125-132. PMID: 21254220, PMCID: PMC3077545, DOI: 10.1002/gepi.20558.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphismsGenetic variantsWellcome Trust Case Control ConsortiumRPGRIP1L geneGenetic studiesAssociation analysisHaplotype blocksChromosome 16Nucleotide polymorphismsComplex diseasesGenesComplex disorderStrong signalUnreported regionsVariantsImportant roleStrong evidencePolymorphismBipolar disorderRegion
2009
Analysis of Twin Data Using SAS
Feng R, Zhou G, Zhang M, Zhang H. Analysis of Twin Data Using SAS. Biometrics 2009, 65: 584-589. PMID: 18647295, PMCID: PMC2700843, DOI: 10.1111/j.1541-0420.2008.01098.x.Peer-Reviewed Original Research
2008
LOT: a tool for linkage analysis of ordinal traits for pedigree data
Zhang M, Feng R, Chen X, Hu B, Zhang H. LOT: a tool for linkage analysis of ordinal traits for pedigree data. Bioinformatics 2008, 24: 1737-1739. PMID: 18535081, PMCID: PMC2566542, DOI: 10.1093/bioinformatics/btn258.Peer-Reviewed Original ResearchModelling gene regulation networks via multivariate adaptive splines.
Chen X, Zhang H. Modelling gene regulation networks via multivariate adaptive splines. Cancer Genomics & Proteomics 2008, 5: 55-62. PMID: 18359980, PMCID: PMC3159687.Peer-Reviewed Original ResearchConceptsRegulatory networksRegulation networkMultivariate adaptive splinesTranscriptional regulation networkTranscriptional regulatory networksGene regulation networksDozens of genomesTranscriptome reprogrammingGene expression time series dataRegulatory motifsHuman genomeTranscription factorsPutative motifsRegulatory functionsRegulatory roleEukaryotesMotifGenomePhysiological conditionsReprogrammingYeastAdaptive splinesIdentification
2007
A forest-based approach to identifying gene and gene–gene interactions
Chen X, Liu CT, Zhang M, Zhang H. A forest-based approach to identifying gene and gene–gene interactions. Proceedings Of The National Academy Of Sciences Of The United States Of America 2007, 104: 19199-19203. PMID: 18048322, PMCID: PMC2148267, DOI: 10.1073/pnas.0709868104.Peer-Reviewed Original Research
2006
Family‐based association tests for ordinal traits adjusting for covariates
Wang X, Ye Y, Zhang H. Family‐based association tests for ordinal traits adjusting for covariates. Genetic Epidemiology 2006, 30: 728-736. PMID: 17086513, DOI: 10.1002/gepi.20184.Peer-Reviewed Original ResearchLinkage analysis of longitudinal data and design consideration
Zhang H, Zhong X. Linkage analysis of longitudinal data and design consideration. BMC Genomic Data 2006, 7: 37. PMID: 16768806, PMCID: PMC1550417, DOI: 10.1186/1471-2156-7-37.Peer-Reviewed Original ResearchA statistical framework for the classification of tensor morphologies in diffusion tensor images
Zhu H, Xu D, Raz A, Hao X, Zhang H, Kangarlu A, Bansal R, Peterson BS. A statistical framework for the classification of tensor morphologies in diffusion tensor images. Magnetic Resonance Imaging 2006, 24: 569-582. PMID: 16735178, PMCID: PMC2367261, DOI: 10.1016/j.mri.2006.01.004.Peer-Reviewed Original ResearchAscertainment adjustment in genetic studies of ordinal traits
Feng R, Zhang H. Ascertainment adjustment in genetic studies of ordinal traits. Human Genetics 2006, 119: 429-435. PMID: 16528520, DOI: 10.1007/s00439-006-0147-8.Peer-Reviewed Original ResearchDetection of Genes for Ordinal Traits in Nuclear Families and a Unified Approach for Association Studies
Zhang H, Wang X, Ye Y. Detection of Genes for Ordinal Traits in Nuclear Families and a Unified Approach for Association Studies. Genetics 2006, 172: 693-699. PMID: 16219774, PMCID: PMC1456175, DOI: 10.1534/genetics.105.049122.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphismsQuantitative traitsOrdinal traitsTraditional linkage studiesGenomewide association analysisAssociation of genesDetection of genesGametic disequilibriumLoci existAssociation studiesAssociation analysisGenesLinkage disequilibriumTraitsComplex diseasesLinkage studiesGrowth-associated protein 43Protein 43DisequilibriumPolymorphismFamilyMarkersNuclear families