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
2019
A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank
Bi W, Zhao Z, Dey R, Fritsche L, Mukherjee B, Lee S. A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank. American Journal Of Human Genetics 2019, 105: 1182-1192. PMID: 31735295, PMCID: PMC6904814, DOI: 10.1016/j.ajhg.2019.10.008.Peer-Reviewed Original ResearchConceptsCase-control ratioGenome-wide significance levelMeasures of environmental exposureGenome-wide analysisEuropean ancestry samplesGenetic association studiesSaddlepoint approximationCase-control imbalanceAnalysis of phenotypesGene-environment interactionsPopulation-based biobanksControlled type I error ratesAssociation studiesG x E effectsUK BiobankType I error rateGenetic variantsE analysisSPAGEComplex diseasesEnvironmental exposuresTest statisticsE studySimulation studyWald test
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 approachData
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
2014
Latent variable models for gene–environment interactions in longitudinal studies with multiple correlated exposures
Tao Y, Sánchez B, Mukherjee B. Latent variable models for gene–environment interactions in longitudinal studies with multiple correlated exposures. Statistics In Medicine 2014, 34: 1227-1241. PMID: 25545894, PMCID: PMC4355187, DOI: 10.1002/sim.6401.Peer-Reviewed Original ResearchMeSH KeywordsBiostatisticsChild, PreschoolComputer SimulationEnvironmental ExposureFemaleGene-Environment InteractionHemochromatosis ProteinHistocompatibility Antigens Class IHumansInfantInfant, NewbornLead PoisoningLongitudinal StudiesMembrane ProteinsMexicoModels, GeneticModels, StatisticalPolymorphism, Single NucleotidePregnancyPrenatal Exposure Delayed EffectsConceptsGene-environment interactionsOutcome measuresCohort studyHealth effects of environmental exposuresEnvironmental exposuresInvestigate health effectsGene-environment associationsEffects of environmental exposuresEarly life exposuresLV frameworkG x E effectsMultivariate exposuresGenotyped single nucleotide polymorphismsEffect modificationShrinkage estimatorsLife exposureExposure measurementsSingle nucleotide polymorphismsData-adaptive wayMultiple testingOutcome dataLongitudinal studyLongitudinal natureGenetic factorsNucleotide polymorphismsTesting departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting
Ko Y, Mukherjee B, Smith J, Park S, Kardia S, Allison M, Vokonas P, Chen J, Diez‐Roux A. Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting. Statistics In Medicine 2014, 33: 5177-5191. PMID: 25112650, PMCID: PMC4227925, DOI: 10.1002/sim.6281.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAgingAtherosclerosisBone and BonesComputer SimulationEnvironmental ExposureEthnicityFemaleGene-Environment InteractionHumansIronLeadLeast-Squares AnalysisLikelihood FunctionsLongitudinal StudiesMaleMiddle AgedModels, GeneticUnited StatesUnited States Department of Veterans AffairsConceptsGene-environment interactionsMulti-Ethnic Study of AtherosclerosisModel of gene-environment interactionMulti-Ethnic StudyTukey's modelLongitudinal settingStudy of AtherosclerosisNormative Aging StudyCase-control studyIncreasing categoriesAging StudyTested interactionsLongitudinal studyCategorical variablesRobust to misspecificationInteraction termsTest departuresShrinkage estimatorsWald testInteraction estimatesIncreased powerOne-degree-of-freedom modelInteraction effectsSetsEnvironmental markersThe impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification
Stenzel S, Ahn J, Boonstra P, Gruber S, Mukherjee B. The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification. European Journal Of Epidemiology 2014, 30: 413-423. PMID: 24894824, PMCID: PMC4256150, DOI: 10.1007/s10654-014-9908-1.Peer-Reviewed Original ResearchConceptsG-E interactionsExposure informationDetection of gene-environment interactionsPrevalence of exposureGene-environment interactionsSampling designCase-control studyRandom selection of subjectsPerformance of sampling designsCase-onlyExposure prevalenceJoint testExposure misclassificationCase-controlRare exposuresMarginal associationSelection of subjectsType I errorEmpirical simulation studyIdeal sampling schemesJoint effectsPrevalenceRandom selectionG-EMisclassificationThe Role of Environmental Heterogeneity in Meta‐Analysis of Gene–Environment Interactions With Quantitative Traits
Li S, Mukherjee B, Taylor J, Rice K, Wen X, Rice J, Stringham H, Boehnke M. The Role of Environmental Heterogeneity in Meta‐Analysis of Gene–Environment Interactions With Quantitative Traits. Genetic Epidemiology 2014, 38: 416-429. PMID: 24801060, PMCID: PMC4108593, DOI: 10.1002/gepi.21810.Peer-Reviewed Original ResearchMeSH KeywordsAlpha-Ketoglutarate-Dependent Dioxygenase FTOBiasBody Mass IndexCase-Control StudiesCholesterol, HDLCohort StudiesDiabetes Mellitus, Type 2Gene FrequencyGene-Environment InteractionGenetic Predisposition to DiseaseHumansMeta-Analysis as TopicModels, GeneticPhenotypePolymorphism, Single NucleotideProteinsQuantitative Trait, HeritableConceptsIndividual level dataMeta-analysisInverse-variance weighted meta-analysisEnvironmental heterogeneityGene-environment interaction studiesInverse-variance weighted estimatorMeta-analysis of interactionsStudy of type 2 diabetesGene-environment interactionsBody mass indexMeta-regression approachSingle nucleotide polymorphismsAdaptive weighted estimatorFTO geneType 2 diabetesMass indexMeta-regressionQuantitative traitsSummary statisticsCholesterol dataNucleotide polymorphismsLevel dataUnivariate summary statisticsData harmonizationEnvironmental covariates
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 estimatesFactorsIndependenceStudyTestBayesian semiparametric analysis for two-phase studies of gene-environment interaction
Ahn J, Mukherjee B, Gruber S, Ghosh M. Bayesian semiparametric analysis for two-phase studies of gene-environment interaction. The Annals Of Applied Statistics 2013, 7: 543-569. PMID: 24587840, PMCID: PMC3935248, DOI: 10.1214/12-aoas599.Peer-Reviewed Original ResearchBayesian variable selection algorithmTwo-phase sampling designGene-environment independencePseudo-likelihood methodJoint effects of genotypeGene-environment interactionsHigh-dimensional modelsWeighted likelihoodCase-control study of colorectal cancerJoint distributionHierarchical priorsSemiparametric analysisRetrospective likelihoodGenetic markersCovariate informationLikelihood methodSimulation studyStudy of gene-environment interactionsStudy of colorectal cancerVariable selection algorithmBayesian approachPhase I dataSub-sample of casesBayesian methodsBayesian analysis
2012
Principal interactions analysis for repeated measures data: application to gene–gene and gene–environment interactions
Mukherjee B, Ko Y, VanderWeele T, Roy A, Park S, Chen J. Principal interactions analysis for repeated measures data: application to gene–gene and gene–environment interactions. Statistics In Medicine 2012, 31: 2531-2551. PMID: 22415818, PMCID: PMC4046647, DOI: 10.1002/sim.5315.Peer-Reviewed Original ResearchConceptsGene-environment interactionsGene-geneLongitudinal cohort studyNormative Aging StudyHealth outcomesMain effect termsMeasured outcomesAging StudyOccupational historyEpistasis modelsEnvironmental exposuresMain effectLongitudinal natureLongitudinal dataResampling-based methodsCell meansClassification arrayQuantitative traitsInteraction analysisRobust classLeading eigenvaluesSimulation studyTime-varying effectsSubject-specificOutcomes
2011
Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons
Mukherjee B, Ahn J, Gruber S, Chatterjee N. Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons. American Journal Of Epidemiology 2011, 175: 177-190. PMID: 22199027, PMCID: PMC3286201, DOI: 10.1093/aje/kwr367.Peer-Reviewed Original ResearchConceptsGene-environment independenceGene-environment interactionsCase-only methodTesting gene-environment interactionsCase-control testsExposure under studyCase-control association studyUnderlying populationCase-control methodCase-control analysisFraction of markersType I error propertiesGenome-wide scanClass of proceduresAssociation studiesData-adaptive wayComparative simulation studyLarge-scale studiesEmpirical-BayesIndependence assumptionFalse positivesPopulationReplication strategyHybrid methodIndependenceA 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
2009
Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis
Mukherjee B, Ahn J, Gruber S, Ghosh M, Chatterjee N. Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis. Biometrics 2009, 66: 934-948. PMID: 19930190, PMCID: PMC3103064, DOI: 10.1111/j.1541-0420.2009.01357.x.Peer-Reviewed Original ResearchConceptsGene-environment interactionsCase-control study of colorectal cancerStudy of gene-environment interactionsStudy of colorectal cancerGene-environment independenceRed meat consumptionBayesian designCase-control studyBayesian approachSample size determination criteriaCase-controlEpidemiological studiesColorectal cancerFrequentist counterpartsNatural wayMeat consumptionAnalyze current dataHypothesis testingDetermination criteriaSmokingEpidemiological exposureAnalysis strategyStudy
2008
Tests for gene‐environment interaction from case‐control data: a novel study of type I error, power and designs
Mukherjee B, Ahn J, Gruber S, Rennert G, Moreno V, Chatterjee N. Tests for gene‐environment interaction from case‐control data: a novel study of type I error, power and designs. Genetic Epidemiology 2008, 32: 615-626. PMID: 18473390, DOI: 10.1002/gepi.20337.Peer-Reviewed Original ResearchConceptsGene-environment independence assumptionCase-control studyGene-environment interactionsGene-environment associationsCase-onlyCase-control study of colorectal cancerDetection of gene-environment interactionsType I errorGene-environment dependenceStudy of colorectal cancerGene-environment independenceEffect of genetic susceptibilityCase-only methodCase-only estimatorCase-control estimatorsCase-control dataGene-environment effectsCase-control designCase-control methodCase-control analysisGlutathione S-transferase M1Empirical-BayesEpidemiological researchCase-controlColorectal cancer