2025
Incorporating local ancestry information to predict genetically associated DNA methylation in admixed populations
Cheng Y, Zhou G, Li H, Zhang X, Justice A, Martinez C, Aouizerat B, Xu K, Zhao H. Incorporating local ancestry information to predict genetically associated DNA methylation in admixed populations. Briefings In Bioinformatics 2025, 26: bbaf325. PMID: 40622482, PMCID: PMC12232425, DOI: 10.1093/bib/bbaf325.Peer-Reviewed Original ResearchConceptsMethylome-wide association studiesAdmixed populationsComplex traitsLocal ancestryAssociation studiesDNA methylationAssociated with complex traitsLocal ancestry informationPopulations of European ancestryCpG methylation levelsNon-European populationsMeasurement of methylationAncestry informationCpG sitesMethylation levelsEuropean ancestryEpigenetic underpinningsCpGAncestryTraitsMethylationAmerican populationAfrican American populationDNAPopulation
2024
HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data
Cheng Y, Cai B, Li H, Zhang X, D’Souza G, Shrestha S, Edmonds A, Meyers J, Fischl M, Kassaye S, Anastos K, Cohen M, Aouizerat B, Xu K, Zhao H. HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data. Genome Biology 2024, 25: 273. PMID: 39407252, PMCID: PMC11476968, DOI: 10.1186/s13059-024-03411-7.Peer-Reviewed Original ResearchConceptsMethylation quantitative trait lociQuantitative trait lociTrait lociMethylation dataFunctional annotation of genetic variantsAnnotation of genetic variantsGenetic variantsBisulfite sequencing dataEffects of genetic variantsBiologically relevant cell typesDNA methylation levelsCell typesFunctional annotationSequence dataComplex traitsMethylation datasetsRelevant cell typesMeQTLsMethylation levelsMethylation regulatorsReal data analysesLociVariantsMethylationDNADNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism
Xiao C, Peng G, Conneely K, Zhao H, Felger J, Wommack E, Higgins K, Shin D, Saba N, Bruner D, Miller A. DNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism. Molecular Psychiatry 2024, 30: 76-83. PMID: 38977918, DOI: 10.1038/s41380-024-02652-z.Peer-Reviewed Original ResearchGene expressionMethylation lociAssociated with gene expressionHead and neck cancerDNA methylation profilesProtein markersLipid metabolismInvolvement of genesIllumina MethylationEPICDNA methylationRelevant gene expressionEpigenetic modificationsExpression pairsInflammatory markersInflammatory responseLociHead and neck cancer patientsAssociated with inflammatory markersGenesDNAMarkers of inflammationAssociated with fatigueExpressionMethylationPost-radiotherapy
2005
Integrating mRNA Decay Information into Co-Regulation Study
Chen L, Zhao H. Integrating mRNA Decay Information into Co-Regulation Study. Journal Of Computer Science And Technology 2005, 20: 434-438. DOI: 10.1007/s11390-005-0434-1.Peer-Reviewed Original ResearchMRNA decay ratesTranscript amountsTranscription rateBioinformatics analysisTranscriptional regulatory networksCo-regulated genesRelative transcript amountsMRNA degradation ratesHigh-throughput technologiesGene regulationGenomic signalsRegulatory networksDifferent genesGene clusteringMRNA synthesisMRNA transcriptsDownstream analysisGenesDNAMotifSimilarity analysisTranscriptsRegulationIdentification
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