2020
An efficient and computationally robust statistical method for analyzing case-control mother–offspring pair genetic association studies
Zhang H, Mukherjee B, Arthur V, Hu G, Hochner H, Chen J. An efficient and computationally robust statistical method for analyzing case-control mother–offspring pair genetic association studies. The Annals Of Applied Statistics 2020, 14: 560-584. DOI: 10.1214/19-aoas1298.Peer-Reviewed Original ResearchEnvironmental risk factorsRisk factorsMaternal environmental risk factorsOffspring genetic effectsPerinatal environmental risk factorsGenetic association studiesFinite sample performancePregnancy healthGenetic risk factorsAssessment of pre-Extensive simulation studyGestational diabetes mellitusIncreased statistical efficiencyLogistic regressionAssociation studiesMaternal genotypeSample performanceMendelian transmissionProfile likelihoodRegression modelsOffspring genotypesEarly-lifeInference proceduresLagrange multiplier methodLikelihood method
2012
Air-Pollution and Cardiometabolic Diseases (AIRCMD): A prospective study investigating the impact of air pollution exposure and propensity for type II diabetes
Sun Z, Mukherjee B, Brook R, Gatts G, Yang F, Sun Q, Brook J, Fan Z, Rajagopalan S. Air-Pollution and Cardiometabolic Diseases (AIRCMD): A prospective study investigating the impact of air pollution exposure and propensity for type II diabetes. The Science Of The Total Environment 2012, 448: 72-78. PMID: 23182147, PMCID: PMC4548977, DOI: 10.1016/j.scitotenv.2012.10.087.Peer-Reviewed Original ResearchConceptsAir pollution exposureAir pollutionAmbient fine particulate matterMeasures of air pollution exposureImpact of air pollution exposureFine particulate matterExposure to PM2.5Air pollution measurementsPersonal exposure measurementsCreation of novel methodologiesSub-acute exposurePolluted urban environmentsImpact of environmental factorsParticulate matterPollution exposureStudy visitsPollution measurementsType II diabetesProspective cohort studyEnvironmental risk factorsAmbient measurementsII diabetesCohort studyUrban environmentScreening visitEfficient 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