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
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
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