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
2017
Rare‐variant association tests in longitudinal studies, with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA)
He Z, Lee S, Zhang M, Smith J, Guo X, Palmas W, Kardia S, Ionita‐Laza I, Mukherjee B. Rare‐variant association tests in longitudinal studies, with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA). Genetic Epidemiology 2017, 41: 801-810. PMID: 29076270, PMCID: PMC5696115, DOI: 10.1002/gepi.22081.Peer-Reviewed Original ResearchConceptsMulti-Ethnic Study of AtherosclerosisMulti-Ethnic StudyStudy of AtherosclerosisType I error rateRare-variant association testsRare variantsGene-based association testsRare-variant associationsAssociation TestLongitudinal outcomesLongitudinal studyExome sequencing dataMeasurement of blood pressureGenomic regionsSequence dataTrait heritabilitySequencing studiesMeasured outcomesGenetic variantsVariant analysisModerate sample sizesIndividual variantsRobust to misspecificationWithin-subject correlationStatistical powerExposure 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
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 markers
2012
Where science meets policy: comparing longitudinal and cross-sectional designs to address diarrhoeal disease burden in the developing world
Markovitz A, Goldstick J, Levy K, Cevallos W, Mukherjee B, Trostle J, Eisenberg J. Where science meets policy: comparing longitudinal and cross-sectional designs to address diarrhoeal disease burden in the developing world. International Journal Of Epidemiology 2012, 41: 504-513. PMID: 22253314, PMCID: PMC3324455, DOI: 10.1093/ije/dyr194.Peer-Reviewed Original ResearchConceptsCross-sectional studyCross-sectional designEffect estimatesLongitudinal studyRisk factorsDisease risk factorsRisk factor distributionInforming public health policyPublic health policiesPublic health communityRisk factor effectsHousehold risk factorsDiarrhoeal disease burdenFactor effect estimatesHealth policyDiarrhoeal disease surveillanceEcuadorian villageNational policy decisionsHealth communityDisease burdenCross-sectionDisease surveillanceFactor distributionRiskGeographic regions