2019
A 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
2011
Hox genes from the parasitic flatworm Schistosoma japonicum
Gu J, Chen S, Dou T, Xu M, Xu J, Zhang L, Hu W, Wang S, Zhou Y. Hox genes from the parasitic flatworm Schistosoma japonicum. Genomics 2011, 99: 59-65. PMID: 22100282, DOI: 10.1016/j.ygeno.2011.10.008.Peer-Reviewed Original ResearchConceptsHox gene familyHox genesGene familyFirst genome-wide characterizationGenome-wide characterizationEvolutionary developmental biologyComparative genomic analysisReal-time polymerase chain reaction experimentsGene copy numberPolymerase chain reaction experimentsOrthology groupsAxis patterningGenome assemblyDevelopmental biologyRelated speciesPhylogenetic analysisGenomic analysisGene expressionSchistosomulum stagePeptide domainGenesCopy numberAnimal speciesHigh expressionSchistosoma japonicum
2009
Improving detection of differentially expressed gene sets by applying cluster enrichment analysis to Gene Ontology
Xu T, Gu J, Zhou Y, Du L. Improving detection of differentially expressed gene sets by applying cluster enrichment analysis to Gene Ontology. BMC Bioinformatics 2009, 10: 240. PMID: 19653916, PMCID: PMC2731756, DOI: 10.1186/1471-2105-10-240.Peer-Reviewed Original Research