Featured Publications
Set-Based Tests for the Gene–Environment Interaction in Longitudinal Studies
He Z, Zhang M, Lee S, Smith J, Kardia S, Roux V, Mukherjee B. Set-Based Tests for the Gene–Environment Interaction in Longitudinal Studies. Journal Of The American Statistical Association 2017, 112: 966-978. PMID: 29780190, PMCID: PMC5954413, DOI: 10.1080/01621459.2016.1252266.Peer-Reviewed Original ResearchGene-environment interactionsMulti-Ethnic Study of AtherosclerosisSet-based testMeasures of neighborhood environmentMarginal genetic associationsEnvironmental exposuresMulti-Ethnic StudyStudy of AtherosclerosisNeighborhood environmentMeasurement of blood pressureGene-environmentMain-effects modelScore type testsMethod of sievesLongitudinal measures of blood pressureRobust to misspecificationGenetic associationGenetic variantsLongitudinal studyMain effectStudy periodEffects modelContinuous environmental exposurePotential biasIndependent conditions
2018
Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes
Yu Y, Xia L, Lee S, Zhou X, Stringham H, Boehnke M, Mukherjee B. Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes. Human Heredity 2018, 83: 283-314. PMID: 31132756, PMCID: PMC7034441, DOI: 10.1159/000496867.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesCholesterolCohort StudiesComputer SimulationC-Reactive ProteinFinlandGene FrequencyGene-Environment InteractionGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLipoproteins, LDLMeta-Analysis as TopicModels, GeneticPhenotypePolymorphism, Single NucleotideConceptsPresence of G-E interactionsGenetic associationHeterogeneity of genetic effectsDiscovery of genetic associationsGene-environment (G-EMarginal genetic effectsG-E interactionsGenome-wide association studiesGene-environment interactionsGenetic effectsData examplesSimulation studySingle nucleotide polymorphismsGene-environmentAssociation studiesAssociation analysisScreening toolMarginal associationNucleotide polymorphismsPresence of heterogeneityAssociationEnvironmental factorsIncreased powerMultiple studiesG-E
2017
Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies
Estes J, Rice J, Li S, Stringham H, Boehnke M, Mukherjee B. Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies. Statistics In Medicine 2017, 36: 3895-3909. PMID: 28744888, PMCID: PMC5624850, DOI: 10.1002/sim.7398.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAlpha-Ketoglutarate-Dependent Dioxygenase FTOBayes TheoremBiasBiometryBody Mass IndexCase-Control StudiesComputer SimulationDiabetes Mellitus, Type 2Gene-Environment InteractionHumansLogistic ModelsMeta-Analysis as TopicModels, GeneticModels, StatisticalPolymorphism, Single NucleotideRetrospective StudiesConceptsGene-environment independenceGene-environmentEmpirical Bayes estimatorsGene-environment interactionsCase-control studyMeta-analysis settingBayes estimatorsRetrospective likelihood frameworkShrinkage estimatorsMeta-analysisTesting gene-environment interactionsCombination of estimatesFactors body mass indexSimulation studyBody mass indexUnconstrained modelLikelihood frameworkInverse varianceMeta-analysis frameworkFTO geneMass indexGenetic markersEstimationStandard alternativeChatterjeeCurrent Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases
McAllister K, Mechanic L, Amos C, Aschard H, Blair I, Chatterjee N, Conti D, Gauderman W, Hsu L, Hutter C, Jankowska M, Kerr J, Kraft P, Montgomery S, Mukherjee B, Papanicolaou G, Patel C, Ritchie M, Ritz B, Thomas D, Wei P, Witte J, participants O. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. American Journal Of Epidemiology 2017, 186: 753-761. PMID: 28978193, PMCID: PMC5860428, DOI: 10.1093/aje/kwx227.Peer-Reviewed Original ResearchConceptsGene-environment interaction studiesStudies of complex diseasesGene-environmentAmerican Society of Human Genetics meetingMeasures of environmental exposureGene-environment interactionsComplex diseasesNational Institute of Environmental Health SciencesNational Cancer InstituteEnvironmental Health SciencesStudy designHealth SciencesCancer InstituteEnvironmental exposuresEnvironmental exposure assessmentNational InstituteLarge-scale studiesExposure assessmentNext-generation sequencing dataDisease outcomeNationalSequence dataThemesStudies of human populationsParticipantsUpdate 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 approachDataExposure enriched outcome dependent designs for longitudinal studies of gene–environment interaction
Sun Z, Mukherjee B, Estes J, Vokonas P, Park S. Exposure enriched outcome dependent designs for longitudinal studies of gene–environment interaction. Statistics In Medicine 2017, 36: 2947-2960. PMID: 28497531, PMCID: PMC5523112, DOI: 10.1002/sim.7332.Peer-Reviewed Original ResearchConceptsLongitudinal cohort studyCohort studyCase-only designLongitudinal studyG x E interactionNormative Aging StudyComplete-case analysisGene-environmentSampling designCase-controlVeterans AdministrationComplex human diseasesE interactionExposure informationAging StudyOutcome trajectoriesStratified samplingRetrospective genotypingIndividual exposureCovariate dataExposure effectsJoint effectsOutcomesTime-varying outcomeEnvironmental factors
2016
Classification and Clustering Methods for Multiple Environmental Factors in Gene–Environment Interaction
Ko Y, Mukherjee B, Smith J, Kardia S, Allison M, Roux A. Classification and Clustering Methods for Multiple Environmental Factors in Gene–Environment Interaction. Epidemiology 2016, 27: 870-878. PMID: 27479650, PMCID: PMC5039086, DOI: 10.1097/ede.0000000000000548.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAtherosclerosisBayes TheoremCluster AnalysisData Interpretation, StatisticalEnvironmental ExposureEpidemiologic Research DesignFemaleFollow-Up StudiesGene-Environment InteractionGenetic Predisposition to DiseaseHumansMiddle AgedModels, StatisticalRegression AnalysisRisk FactorsConceptsMultiple environmental exposuresGene-environment interactionsG x EEnvironmental exposuresMultiethnic Study of AtherosclerosisStudy of AtherosclerosisGene-environmentEffect modificationMultiethnic StudyEnvironmental factorsExposure subgroupsEnvironmental exposure profilesMain effectExposure profilesE studyEfficient analysis strategyE analysisMultiple environmental factorsSubgroupsAnalysis strategyFactorsExposureProduct termsTests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification
Boonstra P, Mukherjee B, Gruber S, Ahn J, Schmit S, Chatterjee N. Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification. American Journal Of Epidemiology 2016, 183: 237-247. PMID: 26755675, PMCID: PMC4724093, DOI: 10.1093/aje/kwv198.Peer-Reviewed Original ResearchConceptsG-E interactionsPresence of exposure misclassificationExposure misclassificationImpact of exposure misclassificationGene-environment (G-EGene-environment interactionsGenome-wide levelGenome-wide searchGenome-wide testingGenetic susceptibility lociJoint testDisease-gene relationshipsGene-environmentGenetic risk factorsType I error rateFamily-wise type I error rateSusceptibility lociG-EGenetic associationRisk factorsStatistical powerJoint effectsSimulation studyMisclassificationPublished simulation studies
2013
Novel Likelihood Ratio Tests for Screening Gene‐Gene and Gene‐Environment Interactions With Unbalanced Repeated‐Measures Data
Ko Y, Saha‐Chaudhuri P, Park S, Vokonas P, Mukherjee B. Novel Likelihood Ratio Tests for Screening Gene‐Gene and Gene‐Environment Interactions With Unbalanced Repeated‐Measures Data. Genetic Epidemiology 2013, 37: 581-591. PMID: 23798480, PMCID: PMC4009698, DOI: 10.1002/gepi.21744.Peer-Reviewed Original ResearchConceptsGene-environment interactionsGene-gene interactionsTesting gene-gene interactionsModel gene-gene interactionsRepeated-measures studyLongitudinal cohort studyNormative Aging StudyCumulative lead exposureCase-control studyGene-environmentGene-geneType I error rateCohort studyScreening toolAging StudyLikelihood ratio testMain effectEpistasis patternsRatio testLead exposureHemochromatosis genePower propertiesPulse pressureRegression-based approachRestrictive assumptionsEnvironmental Confounding in Gene-Environment Interaction Studies
Vanderweele T, Ko Y, Mukherjee B. Environmental Confounding in Gene-Environment Interaction Studies. American Journal Of Epidemiology 2013, 178: 144-152. PMID: 23821317, PMCID: PMC3698991, DOI: 10.1093/aje/kws439.Peer-Reviewed Original ResearchConceptsGene-environment independenceGene-environment interaction studiesGene-environment interactionsEnvironmental confoundersGenetic factorsJoint testGene-environmentGenetic effectsEnvironmental factorsConfounding variablesConfoundingInteraction studiesSimulation studyJoint nullSample sizeBias estimatesFactorsIndependenceStudyTest
2012
Efficient designs of gene–environment interaction studies: implications of Hardy–Weinberg equilibrium and gene–environment independence
Chen J, Kang G, VanderWeele T, Zhang C, Mukherjee B. Efficient designs of gene–environment interaction studies: implications of Hardy–Weinberg equilibrium and gene–environment independence. Statistics In Medicine 2012, 31: 2516-2530. PMID: 22362617, PMCID: PMC3448495, DOI: 10.1002/sim.4460.Peer-Reviewed Original ResearchConceptsPresence of G-E interactionsG-E interactionsSubsample of casesGene-environmentHardy-Weinberg equilibriumG-E independenceGene-environment interaction studiesGene-environment independenceRandom subsampleGenetic susceptibility variantsCase-control sampleEnvironmental risk factorsSusceptibility variantsExternal control dataRisk factorsGenetic effectsWald statisticInteraction studiesSubsampleVariable EControl dataEnvironmental effectsIndependenceDataWald
2011
A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures
Sánchez B, Kang S, Mukherjee B. A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures. Biometrics 2011, 68: 466-476. PMID: 21955029, PMCID: PMC4405908, DOI: 10.1111/j.1541-0420.2011.01677.x.Peer-Reviewed Original ResearchMeSH KeywordsAnalysis of VarianceBiasBiometryBirth WeightCase-Control StudiesComputer SimulationEnvironmental ExposureEpidemiologic FactorsFemaleGene-Environment InteractionHumansInfant, NewbornIronLead PoisoningModels, StatisticalPregnancyPrenatal Exposure Delayed EffectsPrincipal Component AnalysisConceptsGene-environment interactionsGene-environmentEnvironmental epidemiologyCohort studyGene-environment dependenceBurden of multiple testingStudy gene-environment interactionsEnvironmental exposuresExposure dataEarly life exposuresLV frameworkG x E effectsHealth StudyCorrelated exposuresG x EDisease riskLife exposureMultiple testingFunction of environmental exposureE studyGenotype categoriesStudy of lead exposureBirth weightIron metabolism genesAdaptive trade-off