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
2024
Avocational exposure associations with ALS risk, survival, and phenotype: A Michigan-based case-control study
Goutman S, Boss J, Jang D, Piecuch C, Farid H, Batra M, Mukherjee B, Feldman E, Batterman S. Avocational exposure associations with ALS risk, survival, and phenotype: A Michigan-based case-control study. Journal Of The Neurological Sciences 2024, 457: 122899. PMID: 38278093, PMCID: PMC11060628, DOI: 10.1016/j.jns.2024.122899.Peer-Reviewed Original ResearchConceptsALS riskLower educational attainmentAssociated with ALS riskCase-control studyExercise 5Onset ageSelf-completionExposure variablesYard workExposure associationsRecreational danceIdentified exposureExerciseEducational attainmentAL burdenEnvironmental exposuresParticipantsAL factorPersonal participationAvocational exposureRiskExposomeHobbiesALS onsetComparison correction
2023
Environmental risk scores of persistent organic pollutants associate with higher ALS risk and shorter survival in a new Michigan case/control cohort
Goutman S, Boss J, Jang D, Mukherjee B, Richardson R, Batterman S, Feldman E. Environmental risk scores of persistent organic pollutants associate with higher ALS risk and shorter survival in a new Michigan case/control cohort. Journal Of Neurology Neurosurgery & Psychiatry 2023, 95: 241-248. PMID: 37758454, PMCID: PMC11060633, DOI: 10.1136/jnnp-2023-332121.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreAmyotrophic lateral sclerosis riskPersistent organic pollutantsALS riskInterquartile increaseHigher ALS riskAssociated with ALS riskModify disease riskOrganochlorine pesticidesAssociated with riskRisk reduction strategiesPersistent organic pollutant analysisIndividual persistent organic pollutantsPersistent organic pollutant mixturesHazard ratioDisease riskRisk scoreCase/control cohortEnvironmental exposuresControl participantsGenetic susceptibilityPolychlorinated biphenylsAlpha-hexachlorocyclohexaneOrganic pollutantsMortality rate
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 testHigh plasma concentrations of organic pollutants negatively impact survival in amyotrophic lateral sclerosis
Goutman S, Boss J, Patterson A, Mukherjee B, Batterman S, Feldman E. High plasma concentrations of organic pollutants negatively impact survival in amyotrophic lateral sclerosis. Journal Of Neurology Neurosurgery & Psychiatry 2019, 90: 907. PMID: 30760645, PMCID: PMC6625908, DOI: 10.1136/jnnp-2018-319785.Peer-Reviewed Original ResearchConceptsPersistent organic pollutantsPolychlorinated biphenylsHigh concentrations of persistent organic pollutantsAmyotrophic lateral sclerosisConcentrations of persistent organic pollutantsEffects of persistent organic pollutantsMeasurement of persistent organic pollutantsHazard rate of mortalitySymptom onset to diagnosisMedian diagnostic ageNegatively impacts survivalLowest quartileIndependent of ageOrganic pollutantsSummary measuresMedian timeClinical featuresMedical recordsRate of mortalityDiagnostic ageEnvironmental exposuresBulbar onsetALS diseaseCervical onsetPlasma concentrations
2018
Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes
Narisetty N, Mukherjee B, Chen Y, Gonzalez R, Meeker J. Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes. Statistics In Medicine 2018, 38: 1582-1600. PMID: 30586682, PMCID: PMC7134269, DOI: 10.1002/sim.8059.Peer-Reviewed Original Research
2017
Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES
Park S, Zhao Z, Mukherjee B. Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. Environmental Health 2017, 16: 102. PMID: 28950902, PMCID: PMC5615812, DOI: 10.1186/s12940-017-0310-9.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreBayesian kernel machine regressionNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk scoreAssociated with odds ratiosNutrition Examination SurveyAssociated with systolicExamination SurveyMulti-pollutant approachKernel machine regressionPollutant mixturesSD increaseEpidemiological researchDiastolic blood pressureMortality outcomesOdds ratioBayesian additive regression treesDisease endpointsHealth endpointsCumulative riskPositive associationEnvironmental exposuresIntermediate markersCardiovascular diseaseCurrent 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 populationsParticipants
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 terms
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 polymorphisms
2013
Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons
Sun Z, Tao Y, Li S, Ferguson K, Meeker J, Park S, Batterman S, Mukherjee B. Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons. Environmental Health 2013, 12: 85. PMID: 24093917, PMCID: PMC3857674, DOI: 10.1186/1476-069x-12-85.Peer-Reviewed Original ResearchConceptsMultipollutant modelsHealth impacts of environmental factorsEffect estimatesExposure-response associationsExposure to multiple pollutantsTime series designConsequence of environmental exposureSample sizeHealth impactsEnvironmental exposuresPresence of multicollinearityRisk predictionPotential interactive effectsInitial screeningPollutant mixturesImpact of environmental factorsSupervised principal component analysisModel dimensionsStatistical literatureData examplesTree-based methodsMultiple pollutantsVariable selectionSimulation studyReduce model dimension
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
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
2007
Accounting for error due to misclassification of exposures in case–control studies of gene–environment interaction
Zhang L, Mukherjee B, Ghosh M, Gruber S, Moreno V. Accounting for error due to misclassification of exposures in case–control studies of gene–environment interaction. Statistics In Medicine 2007, 27: 2756-2783. PMID: 17879261, DOI: 10.1002/sim.3044.Peer-Reviewed Original ResearchConceptsCase-control studyCase-control study of colorectal cancerGene-environment independence assumptionStudy of gene-environment interactionsStudy of colorectal cancerCase-control study designEnvironmental exposuresDisease-exposure associationsCase-control dataMisclassification of exposureGene-environment interactionsDegree of misclassificationStudy designConfidence intervalsGenotyping errorsValidation subsampleColorectal cancerAnalysis of dataMisclassification error rateGenetic factorsIndependence assumptionMisclassificationMisclassified dataAnalytical formEstimation strategySemiparametric 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