Featured Publications
Exploiting Gene-Environment Independence for Analysis of Case–Control Studies: An Empirical Bayes-Type Shrinkage Estimator to Trade-Off Between Bias and Efficiency
Mukherjee B, Chatterjee N. Exploiting Gene-Environment Independence for Analysis of Case–Control Studies: An Empirical Bayes-Type Shrinkage Estimator to Trade-Off Between Bias and Efficiency. Biometrics 2007, 64: 685-694. PMID: 18162111, DOI: 10.1111/j.1541-0420.2007.00953.x.Peer-Reviewed Original ResearchConceptsGene-environment independenceShrinkage estimatorsLog odds ratio parametersCase-control dataGene-environment independence assumptionOdds ratio parametersCase-control estimatorsData-adaptive fashionData exampleProspective logistic regression analysisBinary exposureGene-environment associationsIndependence assumptionLogistic regression analysisCase-onlyMaximum likelihood frameworkEstimationSample sizeBinary genesRegression analysisChatterjeeExamplesWeighted averageAssumptions
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 alternativeChatterjee
2013
Environmental 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
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
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 methodIndependence
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
2007
Semiparametric Bayesian Analysis of Case–Control Data under Conditional Gene-Environment Independence
Mukherjee B, Zhang L, Ghosh M, Sinha S. Semiparametric Bayesian Analysis of Case–Control Data under Conditional Gene-Environment Independence. Biometrics 2007, 63: 834-844. PMID: 17489972, DOI: 10.1111/j.1541-0420.2007.00750.x.Peer-Reviewed Original ResearchConceptsGene-environment independenceSemiparametric Bayesian approachTraditional logistic regression analysisParametric model assumptionsSemiparametric Bayesian modelCase-control studyPopulation-based case-control studySimulation studyBayesian approachRobust alternativeLogistic regression analysisUnderlying populationEfficient estimation techniqueBayesian modelEnvironmental exposuresModel assumptionsScientific evidenceRegression analysisAssociated with diseaseEstimation techniquesOvarian cancerControl populationPopulationIndependenceCovariates