2024
Multiple metal exposures associate with higher amyotrophic lateral sclerosis risk and mortality independent of genetic risk and correlate to self-reported exposures: a case-control study
Jang D, Dou J, Koubek E, Teener S, Zhou L, Bakulski K, Mukherjee B, Batterman S, Feldman E, Goutman S. Multiple metal exposures associate with higher amyotrophic lateral sclerosis risk and mortality independent of genetic risk and correlate to self-reported exposures: a case-control study. Journal Of Neurology Neurosurgery & Psychiatry 2024, jnnp-2024-333978. PMID: 39107037, DOI: 10.1136/jnnp-2024-333978.Peer-Reviewed Original ResearchAmyotrophic lateral sclerosis riskEnvironmental risk scoreAssociated with ALS riskALS riskGenetic riskRisk scorePolygenic risk scoresSelf-reported exposureGenome-wide association studiesStudy investigated associationsCase-control studySingle-nucleotide polymorphismsAssociation studiesExposure mixturesControl participantsExposure sourcesRiskParticipantsAmyotrophic lateral sclerosisSurvival modelsScoresAssociationEnvironmental factorsUrine metalsUrine samples
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
Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies
Patel C, Kerr J, Thomas D, Mukherjee B, Ritz B, Chatterjee N, Jankowska M, Madan J, Karagas M, McAllister K, Mechanic L, Fallin M, Ladd-Acosta C, Blair I, Teitelbaum S, Amos C. Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiology Biomarkers & Prevention 2017, 26: 1370-1380. PMID: 28710076, PMCID: PMC5581729, DOI: 10.1158/1055-9965.epi-17-0459.Peer-Reviewed Original ResearchConceptsFollow-up of study participantsInfluence cancer riskExposure and behaviourPopulation-based studyExposure assessmentCase-control studyPhysical activityCancer riskCorrelated exposuresStudy participantsEpidemiological studiesGenetic susceptibilityEnvironmental exposure assessmentFollow-upData collectionMultidimensional indicatorsCancer developmentEvaluated 1Indicators of exposureComplex effects of environmental factorsEpidemiological investigationsOccupational exposure assessmentAssessmentEnvironmental factorsDisease developmentExposure 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 terms
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 estimatesFactorsIndependenceStudyTest