2020
Social regulation of inflammation related gene expression in the multi-ethnic study of atherosclerosis
Brown K, Diez-Roux A, Smith J, Needham B, Mukherjee B, Ware E, Liu Y, Cole S, Seeman T, Kardia S. Social regulation of inflammation related gene expression in the multi-ethnic study of atherosclerosis. Psychoneuroendocrinology 2020, 117: 104654. PMID: 32387875, PMCID: PMC7685527, DOI: 10.1016/j.psyneuen.2020.104654.Peer-Reviewed Original ResearchConceptsGene expressionMulti-Ethnic Study of AtherosclerosisGene set enrichment testGene Ontology databaseGene expression dataMulti-Ethnic StudyLifetime discriminationStudy of AtherosclerosisDifferential gene expressionInflammation-related genesExpression dataGene setsChronic burdenSocial factorsGene transcriptionRelated gene expressionOntology databaseGenesInflammatory responseTranscriptionGlobal analysisElastic net penalized regressionAdverse social factorsGlobal expressionExpression of inflammation-related genes
2019
Expression of socially sensitive genes: The multi-ethnic study of atherosclerosis
Brown K, Diez-Roux A, Smith J, Needham B, Mukherjee B, Ware E, Liu Y, Cole S, Seeman T, Kardia S. Expression of socially sensitive genes: The multi-ethnic study of atherosclerosis. PLOS ONE 2019, 14: e0214061. PMID: 30973896, PMCID: PMC6459532, DOI: 10.1371/journal.pone.0214061.Peer-Reviewed Original ResearchConceptsGene expressionSocial factorsAdult socioeconomic statusPopulation-based cohortMulti-Ethnic StudyMarginal significant associationPercentage of genesInfluence gene expressionLinear regressionExpression of genesLifetime discriminationSocioeconomic statusChronic burdenOverall healthInvestigate associationsGene approachGene setsLinear regression analysisSignificant associationGenesExpression patternsMultiple testingSensitive to social factorsCell typesRegression analysis
2018
Biobank-driven genomic discovery yields new insight into atrial fibrillation biology
Nielsen J, Thorolfsdottir R, Fritsche L, Zhou W, Skov M, Graham S, Herron T, McCarthy S, Schmidt E, Sveinbjornsson G, Surakka I, Mathis M, Yamazaki M, Crawford R, Gabrielsen M, Skogholt A, Holmen O, Lin M, Wolford B, Dey R, Dalen H, Sulem P, Chung J, Backman J, Arnar D, Thorsteinsdottir U, Baras A, O’Dushlaine C, Holst A, Wen X, Hornsby W, Dewey F, Boehnke M, Kheterpal S, Mukherjee B, Lee S, Kang H, Holm H, Kitzman J, Shavit J, Jalife J, Brummett C, Teslovich T, Carey D, Gudbjartsson D, Stefansson K, Abecasis G, Hveem K, Willer C. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nature Genetics 2018, 50: 1234-1239. PMID: 30061737, PMCID: PMC6530775, DOI: 10.1038/s41588-018-0171-3.Peer-Reviewed Original ResearchConceptsNear genesRisk variantsGenome-wide association studiesFunctional candidate genesIndependent risk variantsIdentified risk variantsFunctional enrichment analysisDeleterious mutationsAssociation studiesCandidate genesAtrial fibrillationGenetic variationGenomic discoveriesStriated muscle functionEnrichment analysisNKX2-5Fetal heart developmentResponse to stressGenesCardiac structural remodelingAtrial fibrillation casesHeart developmentHeart defectsAdult heartCardiac arrhythmias
2017
Update on the State of the Science for Analytical Methods for Gene-Environment Interactions
Gauderman W, Mukherjee B, Aschard H, Hsu L, Lewinger J, Patel C, Witte J, Amos C, Tai C, Conti D, Torgerson D, Lee S, Chatterjee N. Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. American Journal Of Epidemiology 2017, 186: 762-770. PMID: 28978192, PMCID: PMC5859988, DOI: 10.1093/aje/kwx228.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesG x EGene-environment interactionsAssociation studiesAnalysis of gene-environment interactionsQuantitative trait studiesComplex traitsGenetic dataGene setsTrait studiesGene-environmentCase-controlEnvironmental dataConsortium settingFormation of consortiaGenesConsortiumAnalytical challengesTraitsSetsStudyInteractionStatistical approachData
2016
Identification of Susceptibility Loci and Genes for Colorectal Cancer Risk
Zeng C, Matsuda K, Jia W, Chang J, Kweon S, Xiang Y, Shin A, Jee S, Kim D, Zhang B, Cai Q, Guo X, Long J, Wang N, Courtney R, Pan Z, Wu C, Takahashi A, Shin M, Matsuo K, Matsuda F, Gao Y, Oh J, Kim S, Jung K, Ahn Y, Ren Z, Li H, Wu J, Shi J, Wen W, Yang G, Li B, Ji B, Brenner H, Schoen R, Küry S, Gruber S, Schumacher F, Stenzel S, Casey G, Hopper J, Jenkins M, Kim H, Jeong J, Park J, Tajima K, Cho S, Kubo M, Shu X, Lin Y, Zeng Y, Zheng W, Baron J, Berndt S, Bezieau S, Brenner H, Caan B, Carlson C, Casey G, Chan A, Chang-Claude J, Chanock S, Conti D, Curtis K, Duggan D, Fuchs C, Gallinger S, Giovannucci E, Gruber S, Haile R, Harrison T, Hayes R, Hoffmeister M, Hopper J, Hsu L, Hudson T, Hunter D, Hutter C, Jackson R, Jenkins M, Jiao S, Küry S, Le Marchand L, Lemire M, Lindor N, Ma J, Newcomb P, Peters U, Potter J, Qu C, Schoen R, Schumacher F, Seminara D, Slattery M, Thibodeau S, White E, Zanke B, Blalock K, Campbell P, Casey G, Conti D, Edlund C, Figueiredo J, Gauderman W, Gong J, Green R, Gruber S, Harju J, Harrison T, Jacobs E, Jenkins M, Jiao S, Li L, Lin D, Manion F, Moreno V, Mukherjee B, Peters U, Raskin L, Schumacher F, Seminara D, Severi G, Stenzel S, Thomas D. Identification of Susceptibility Loci and Genes for Colorectal Cancer Risk. Gastroenterology 2016, 150: 1633-1645. PMID: 26965516, PMCID: PMC4909543, DOI: 10.1053/j.gastro.2016.02.076.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAsian PeopleBasic Helix-Loop-Helix Leucine Zipper Transcription FactorsCase-Control StudiesColorectal NeoplasmsEukaryotic Initiation Factor-3FemaleGenetic LociGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMiddle AgedPolymorphism, Single NucleotideQb-SNARE ProteinsRibosomal ProteinsRisk FactorsSteroid 17-alpha-HydroxylaseSuppressor of Cytokine Signaling ProteinsYoung AdultConceptsEukaryotic translation initiation factor 3Translation initiation factor 3Ribosomal protein S2Initiation factor 3Transcription factor EBSOCS boxProtein S2Risk variantsReceptor domainSusceptibility lociProtein-coding genesGenome-wide association studiesFactor 3East Asian ancestryNearby genesEpigenomic databasesGenetic variationRisk lociGene expressionAutophagy pathwayAssociation studiesProtein synthesisLociGenesSignificant variants
2006
Bayesian modeling for genetic association in case-control studies: accounting for unknown population substructure
Zhang L, Mukherjee B, Ghosh M, Wu R. Bayesian modeling for genetic association in case-control studies: accounting for unknown population substructure. Statistical Modelling 2006, 6: 352-372. DOI: 10.1177/1471082006071841.Peer-Reviewed Original ResearchPopulation substructureCase-control studyGenetic association studiesLog odds ratio parametersOdds ratio parametersAssociation studiesAllele frequenciesGenetic associationParametric Bayesian methodsArgentinean populationBayesian modelCredible intervalsGenetic factorsBayesian methodsStatistical propertiesNumerical integration techniquesPosterior probabilityAssociation modelPopulationAllelesGenesAssociationIntegration techniqueMarkovObesity