2022
Methods for large‐scale single mediator hypothesis testing: Possible choices and comparisons
Du J, Zhou X, Clark‐Boucher D, Hao W, Liu Y, Smith J, Mukherjee B. Methods for large‐scale single mediator hypothesis testing: Possible choices and comparisons. Genetic Epidemiology 2022, 47: 167-184. PMID: 36465006, PMCID: PMC10329872, DOI: 10.1002/gepi.22510.Peer-Reviewed Original ResearchConceptsNull hypothesisTest statisticsMediation hypothesis testingComposite null hypothesisHypothesis testingClasses of methodsFalse positive rateAlternative hypothesisSimulation studyHypothesis testing methodContinuous mediatorReference distributionSobel test statisticsContinuous outcomesExposure-mediator interactionMulti-Ethnic Study of AtherosclerosisDNA methylation sitesClassCRANMethylation sites
2021
Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes
Bi W, Zhou W, Dey R, Mukherjee B, Sampson J, Lee S. Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes. American Journal Of Human Genetics 2021, 108: 825-839. PMID: 33836139, PMCID: PMC8206161, DOI: 10.1016/j.ajhg.2021.03.019.Peer-Reviewed Original ResearchConceptsOrdinal categorical phenotypesGenome-wide association studiesCategorical phenotypesGenome-wide significant variantsRare variantsPhenotype distributionControlled type I error ratesType I error rateMixed model approachArray genotypingAssociation studiesCommon variantsQuantitative traitsSignificant variantsLogistic mixed modelsLack of analysis toolsUK BiobankLinear mixed model approachPhenotypeAssociation TestVariantsMixed modelsSignificance levelMAFTraits
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
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 powerMeta‐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 alternativeChatterjeeUpdate 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
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 assumptions
2011
Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome
Boonstra P, Mukherjee B, Taylor J, Nilbert M, Moreno V, Gruber S. Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome. Biometrics 2011, 67: 1627-1637. PMID: 21627626, PMCID: PMC3176998, DOI: 10.1111/j.1541-0420.2011.01607.x.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge of OnsetAgedAnticipation, GeneticBayes TheoremChildChild, PreschoolColorectal Neoplasms, Hereditary NonpolyposisComputer SimulationDenmarkFemaleHumansInfantInfant, NewbornMaleMiddle AgedModels, GeneticModels, StatisticalMutationPolymorphism, Single NucleotidePrevalenceRisk AssessmentRisk FactorsYoung AdultConceptsLynch syndromeBirth cohortGenetic anticipationHereditary nonpolyposis colorectal cancerCancer registry dataNonpolyposis colorectal cancerDanish Cancer RegisterGenetic counseling clinicAge-specific incidenceHigh-risk familiesRandom-effects modelCancer RegisterRegistry dataCounseling clinicMismatch repairRandom effectsSecular trendsMedical practiceColorectal cancerSurvival analysis methodsEffects modelConfounding effectsLynchFlexible random effects modelModel fit diagnostics
2010
A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome
Boonstra P, Gruber S, Raymond V, Huang S, Timshel S, Nilbert M, Mukherjee B. A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome. Genetic Epidemiology 2010, 34: 756-768. PMID: 20878717, PMCID: PMC3894615, DOI: 10.1002/gepi.20534.Peer-Reviewed Original ResearchConceptsAffected parent-child pairsDanish HNPCC registerParent-child pairsLynch syndromePaired t-testGenetic anticipationLynch syndrome cohortCancer genetics clinicsT-testEvidence of genetic anticipationFamily membersClinic-based populationRandom-effects modelGenetics clinicAffected pairsMismatch repairUnaffected family membersFamilial correlationsAffected parentType I errorSyndrome cohortRegression modelsPedigree dataDecreasing ageAscertainment
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