2023
Deep learning identified genetic variants for COVID‐19‐related mortality among 28,097 affected cases in UK Biobank
Liu Z, Dai W, Wang S, Yao Y, Zhang H. Deep learning identified genetic variants for COVID‐19‐related mortality among 28,097 affected cases in UK Biobank. Genetic Epidemiology 2023, 47: 215-230. PMID: 36691909, PMCID: PMC10006374, DOI: 10.1002/gepi.22515.Peer-Reviewed Original Research
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 conceptIdentification
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 phenotypePhenotype
2016
A method for integrating neuroimaging into genetic models of learning performance
Mehta CM, Gruen JR, Zhang H. A method for integrating neuroimaging into genetic models of learning performance. Genetic Epidemiology 2016, 41: 4-17. PMID: 27859682, PMCID: PMC5154929, DOI: 10.1002/gepi.22025.Peer-Reviewed Original Research
2014
TARV: Tree‐based Analysis of Rare Variants Identifying Risk Modifying Variants in CTNNA2 and CNTNAP2 for Alcohol Addiction
Song C, Zhang H. TARV: Tree‐based Analysis of Rare Variants Identifying Risk Modifying Variants in CTNNA2 and CNTNAP2 for Alcohol Addiction. Genetic Epidemiology 2014, 38: 552-559. PMID: 25041903, PMCID: PMC4154634, DOI: 10.1002/gepi.21843.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesSequence kernel association testRare variant dataTree-based analysisRare variantsNext-generation sequencing technologiesVariant dataGeneration sequencing technologyKernel association testGene-gene interactionsSequencing technologiesMultiple genesAssociation studiesDisease modelsRisk genesCTNNA2Genetic variantsSAGE dataComplex disease modelsGenesStudy of AddictionComplex diseasesCommon variantsSpecific variantsRisk of alcoholism
2012
Large Scale Association Analysis for Drug Addiction: Results from SNP to Gene
Guo X, Liu Z, Wang X, Zhang H. Large Scale Association Analysis for Drug Addiction: Results from SNP to Gene. The Scientific World JOURNAL 2012, 2012: 939584. PMID: 23365539, PMCID: PMC3543790, DOI: 10.1100/2012/939584.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesAssociation analysisGene-based association analysisLarge-scale association analysisSingle nucleotide polymorphism dataWide association studyComplex diseasesGene-based analysisGene-based methodsNucleotide polymorphism dataGenetic association studiesPolymorphism dataGene findingGenetic variantsIndividual SNPsStudy of AddictionSNPsGenetic etiologyGenesComprehensive analysisGeneticsVariantsSimulating 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 findingsLevels
2009
The Genetic Susceptibility to Respiratory Distress Syndrome
Levit O, Jiang Y, Bizzarro MJ, Hussain N, Buhimschi CS, Gruen JR, Zhang H, Bhandari V. The Genetic Susceptibility to Respiratory Distress Syndrome. Pediatric Research 2009, 66: 693-697. PMID: 19687775, PMCID: PMC2796284, DOI: 10.1203/pdr.0b013e3181bbce86.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 ResearchA genomic imprinting test for ordinal traits in pedigree data
Feng R, Zhang H. A genomic imprinting test for ordinal traits in pedigree data. Genetic Epidemiology 2007, 32: 132-142. PMID: 17922481, DOI: 10.1002/gepi.20270.Peer-Reviewed Original ResearchMeSH KeywordsAlcoholismGenetic Predisposition to DiseaseGenomic ImprintingHumansModels, GeneticModels, StatisticalPedigreeConceptsComplex genetic basisOrdinal traitsIdentical nucleotide sequencesGenomic imprintingNumerous common diseasesNovel lociNucleotide sequenceComplex inheritanceGenetic basisChromosome 3Non-genetic covariatesHuman disordersLinkage analysisHuman diseasesGenetics of AlcoholismChromosome 18Continuous traitsTraitsPedigree dataBinary traitsOrigin effectsImprinting testLociStrong signalImprintingA score test for linkage analysis of ordinal traits based on IBD sharing
Feng R, Zhang H. A score test for linkage analysis of ordinal traits based on IBD sharing. Biostatistics 2007, 9: 114-127. PMID: 17519391, DOI: 10.1093/biostatistics/kxm016.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 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
2004
Correcting the loss of cell-cycle synchrony in clustering analysis of microarray data using weights
Duan F, Zhang H. Correcting the loss of cell-cycle synchrony in clustering analysis of microarray data using weights. Bioinformatics 2004, 20: 1766-1771. PMID: 15166015, DOI: 10.1093/bioinformatics/bth169.Peer-Reviewed Original ResearchAlgorithmsBiological ClocksCell CycleCluster AnalysisGene Expression ProfilingGene Expression Regulation, FungalModels, GeneticModels, StatisticalOligonucleotide Array Sequence AnalysisPattern Recognition, AutomatedReproducibility of ResultsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSensitivity and SpecificityTime Factors
2002
Obsessive‐compulsive symptom dimensions in affected sibling pairs diagnosed with Gilles de la Tourette syndrome
Leckman JF, Pauls DL, Zhang H, Rosario‐Campos M, Katsovich L, Kidd KK, Pakstis AJ, Alsobrook JP, Robertson MM, McMahon WM, Walkup JT, van de Wetering BJ, King RA, Cohen DJ. Obsessive‐compulsive symptom dimensions in affected sibling pairs diagnosed with Gilles de la Tourette syndrome. American Journal Of Medical Genetics Part B Neuropsychiatric Genetics 2002, 116B: 60-68. PMID: 12497616, DOI: 10.1002/ajmg.b.10001.Peer-Reviewed Original Research
2000
Use of classification trees for association studies
Zhang H, Bonney G. Use of classification trees for association studies. Genetic Epidemiology 2000, 19: 323-332. PMID: 11108642, DOI: 10.1002/1098-2272(200012)19:4<323::aid-gepi4>3.0.co;2-5.Peer-Reviewed Original ResearchA Frailty Model of Segregation Analysis: Understanding the Familial Transmission of Alcoholism
Zhang H, Merikangas K. A Frailty Model of Segregation Analysis: Understanding the Familial Transmission of Alcoholism. Biometrics 2000, 56: 815-823. PMID: 10985221, DOI: 10.1111/j.0006-341x.2000.00815.x.Peer-Reviewed Original Research