2020
Super‐variants identification for brain connectivity
Li T, Hu J, Wang S, Zhang H. Super‐variants identification for brain connectivity. Human Brain Mapping 2020, 42: 1304-1312. PMID: 33236465, PMCID: PMC7927294, DOI: 10.1002/hbm.25294.Peer-Reviewed Original ResearchConceptsCombination of allelesSingle nucleotide polymorphismsNovel lociBrain connectivityUK Biobank databaseChromosome 1Multiple lociGenetic effectsGenetic variantsNucleotide polymorphismsAssociation detectionLociGenetic associationGenesNeurodegenerative disordersBiobank databaseBrain issuesGenetic biomarkersBrain functionBrain structuresGenomeRSPO2Discovery phaseAssociationTMEM74
2015
A Genome‐Wide Association Study of Early Spontaneous Preterm Delivery
Zhang H, Baldwin DA, Bukowski RK, Parry S, Xu Y, Song C, Andrews WW, Saade GR, Esplin MS, Sadovsky Y, Reddy UM, Ilekis J, Varner M, Biggio JR, Research F. A Genome‐Wide Association Study of Early Spontaneous Preterm Delivery. Genetic Epidemiology 2015, 39: 217-226. PMID: 25599974, PMCID: PMC4366311, DOI: 10.1002/gepi.21887.Peer-Reviewed Original ResearchConceptsSpontaneous preterm birthMaternal single nucleotide polymorphismsSPTB casesPreterm birthValidation cohortSingle nucleotide polymorphismsEarly spontaneous preterm deliveryP-valueTerm controlsTerm delivery controlsSpontaneous preterm deliveryMother-infant pairsCase-control studyIndependent validation cohortRace/ethnicityPreterm deliveryInfant morbidityMaternal ageControl groupMultiple testing adjustmentMultiple comparisonsCohortBirthNucleotide polymorphismsGenome-wide association studies
2013
NCK2 Is Significantly Associated with Opiates Addiction in African‐Origin Men
Liu Z, Guo X, Jiang Y, Zhang H. NCK2 Is Significantly Associated with Opiates Addiction in African‐Origin Men. The Scientific World JOURNAL 2013, 2013: 748979. PMID: 23533358, PMCID: PMC3603435, DOI: 10.1155/2013/748979.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphismsNCK2 geneGenome-wide significant associationGenome-wide significant levelWide association studyGene-based methodsNumerous genetic variantsGWAS discoveryChromosome 2Association studiesNck2Genetic variantsGenesNucleotide polymorphismsComplex diseasesFirst evidenceGenetic disordersDiscoverySignificant levelsPolymorphismVariantsSubstantial effort
2012
Genome‐Wide Significant Association Signals in IPO11‐HTR1A Region Specific for Alcohol and Nicotine Codependence
Zuo L, Zhang X, Wang F, Li C, Lu L, Ye L, Zhang H, Krystal JH, Deng H, Luo X. Genome‐Wide Significant Association Signals in IPO11‐HTR1A Region Specific for Alcohol and Nicotine Codependence. Alcohol Clinical And Experimental Research 2012, 37: 730-739. PMID: 23216389, PMCID: PMC3610804, DOI: 10.1111/acer.12032.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlcoholismBeta KaryopherinsBlack or African AmericanCase-Control StudiesChromosomes, Human, Pair 5FemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHumansMaleMiddle AgedPolymorphism, Single NucleotideQuantitative Trait LociReceptor, Serotonin, 5-HT1ATobacco Use DisorderWhite PeopleConceptsGenome-wide significance levelSingle nucleotide polymorphismsReplication cohortDiscovery cohortAlcohol dependenceExpression quantitative loci (eQTL) analysisPeripheral blood mononuclear cell samplesNeuropsychiatric disordersWide significant association signalsMononuclear cell samplesGenome-wide association studiesQuantitative loci analysisGene-disease association analysisCis-eQTL analysisTop single nucleotide polymorphismsCis-acting regulatory effectsSignificant association signalsBrain tissue samplesAmerican controlsEuropean American controlsRisk single nucleotide polymorphismsAfrican-American controlsSevere subtypeGenomic regionsAfrican American casesGenetic 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
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
Detecting significant single-nucleotide polymorphisms in a rheumatoid arthritis study using random forests
Wang M, Chen X, Zhang M, Zhu W, Cho K, Zhang H. Detecting significant single-nucleotide polymorphisms in a rheumatoid arthritis study using random forests. BMC Proceedings 2009, 3: s69. PMID: 20018063, PMCID: PMC2795970, DOI: 10.1186/1753-6561-3-s7-s69.Peer-Reviewed Original ResearchSignificant single nucleotide polymorphismsGenome-wide dataGenetic Analysis Workshop 16 Problem 1 dataGenes/SNPsSNP markersSignificant SNPsSingle nucleotide polymorphismsGenetic association studiesWhole genomeChromosome 6Association studiesRheumatoid arthritis statusAntigen geneTraitsSNPsForestHLA-DRAArray experimentsGenomeMarkersHuman leukocyte antigen (HLA) genesGenesFurther analysisIndividual markersHigh levelsWillows: a memory efficient tree and forest construction package
Zhang H, Wang M, Chen X. Willows: a memory efficient tree and forest construction package. BMC Bioinformatics 2009, 10: 130. PMID: 19416535, PMCID: PMC2683818, DOI: 10.1186/1471-2105-10-130.Peer-Reviewed Original ResearchConceptsMassive genotype dataUser-friendly interfaceExcessive memory demandsHigh-dimensional dataNew software packageGenomic dataFriendly interfaceUse of memoryDimensional dataMassive amountsRandom forestPartitioning techniquesHigh-throughput genomic dataPowerful bioinformatics toolsEfficient treeComputer memorySoftware packageMassive sizeForest methodMemory demandsConstruction packagesSingle nucleotide polymorphismsBioinformatics toolsSNP dataGenotyping platformsMachine learning in genome‐wide association studies
Szymczak S, Biernacka JM, Cordell HJ, González‐Recio O, König IR, Zhang H, Sun YV. Machine learning in genome‐wide association studies. Genetic Epidemiology 2009, 33: s51-s57. PMID: 19924717, DOI: 10.1002/gepi.20473.Peer-Reviewed Original ResearchConceptsGenome-wide SNP dataSingle nucleotide polymorphismsSNP dataAssociation studiesGenome-wide association studiesOverall genetic architectureMachine learning approachesGenetic Analysis Workshop 16Wide association studyComplex human diseasesMain genetic effectsGenetic architectureLearning approachGenetic risk variantsEnsemble methodHuman diseasesGenetic effectsRisk variantsGenetic variantsComplex diseasesMachineNew variable selection procedureNetwork analysisVariable selection procedureDifferent approaches
2006
Detection 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
2005
A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking
Ye Y, Zhong X, Zhang H. A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking. BMC Genomic Data 2005, 6: s135. PMID: 16451594, PMCID: PMC1866801, DOI: 10.1186/1471-2156-6-s1-s135.Peer-Reviewed Original ResearchConceptsAssociation analysisGenetic Analysis Workshop 14Single nucleotide polymorphism dataJoint association analysisNew genesSingle nucleotide polymorphismsGenetic mechanismsPolymorphism dataAssociation studiesDeterministic forestsGenetics of AlcoholismGenesTreesUseful candidateGeneticsForestPolymorphismFuture studiesStudy of alcoholism