2024
Cross-Sectional Associations between Prenatal Per- and Poly-Fluoroalkyl Substances and Bioactive Lipids in Three Environmental Influences on Child Health Outcomes (ECHO) Cohorts
Suthar H, Manea T, Pak D, Woodbury M, Eick S, Cathey A, Watkins D, Strakovsky R, Ryva B, Pennathur S, Zeng L, Weller D, Park J, Smith S, DeMicco E, Padula A, Fry R, Mukherjee B, Aguiar A, Geiger S, Ng S, Huerta-Montanez G, Vélez-Vega C, Rosario Z, Cordero J, Zimmerman E, Woodruff T, Morello-Frosch R, Schantz S, Meeker J, Alshawabkeh A, Aung M, Outcomes O. Cross-Sectional Associations between Prenatal Per- and Poly-Fluoroalkyl Substances and Bioactive Lipids in Three Environmental Influences on Child Health Outcomes (ECHO) Cohorts. Environmental Science And Technology 2024, 58: 8264-8277. PMID: 38691655, PMCID: PMC11097396, DOI: 10.1021/acs.est.4c00094.Peer-Reviewed Original ResearchConceptsPFAS mixtureLinear mixed modelsBioactive lipidsChild health outcomesCross-sectional associationsPrenatal PFAS exposureBioactive lipid levelsPoly-fluoroalkyl substancesQuantile g-computationMixed modelsGestational outcomesHealth outcomesPregnancy outcomesPregnant womenCombined cohortG-computationCohort analysisProgram cohortQuartile increaseLipid levelsCohortPositive associationMeta-analysisEnvironmental influencesPFAS exposure
2023
Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks
Fritsche L, Nam K, Du J, Kundu R, Salvatore M, Shi X, Lee S, Burgess S, Mukherjee B. Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks. PLOS Genetics 2023, 19: e1010907. PMID: 38113267, PMCID: PMC10763941, DOI: 10.1371/journal.pgen.1010907.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresMichigan Genomics InitiativeUK BiobankPre-existing conditionsPhenome-wide association studyAssociation studiesCohort-specific analysesPolygenic risk score approachUK Biobank cohortMeta-analysisIncreased risk of hospitalizationGenome-wide association studiesBody mass indexRisk of hospitalizationIdentified novel associationsRisk score approachCOVID-19 outcome dataCOVID-19 hospitalizationCOVID-19Mass indexRisk scoreBiobankCardiovascular conditionsCOVID-19 severityIncreased risk
2022
Meta-analysis of nationwide SARS-CoV-2 infection fatality rates in India
Zimmermann L, Mukherjee B. Meta-analysis of nationwide SARS-CoV-2 infection fatality rates in India. PLOS Global Public Health 2022, 2: e0000897. PMID: 36962545, PMCID: PMC10021252, DOI: 10.1371/journal.pgph.0000897.Peer-Reviewed Original ResearchMortality dataExcess deathsTotal deathUnderreporting of deathsMeta-analysisGlobal Index MedicusSARS-CoV-2 infection fatality rateFatality rateInfection fatality rateUnderserved populationsSearch of databases PubMedMeta-analysis frameworkAscertainment rateSystematic reviewPooled estimatesDatabases PubMedPRISMA guidelinesConfidence intervalsAt-riskMeta-analyzedIndex MedicusDeath studiesCOVID mortalityUnderreportingDemographic groupsGlobal Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review
Chen C, Haupert S, Zimmermann L, Shi X, Fritsche L, Mukherjee B. Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review. The Journal Of Infectious Diseases 2022, 226: 1593-1607. PMID: 35429399, PMCID: PMC9047189, DOI: 10.1093/infdis/jiac136.Peer-Reviewed Original ResearchConceptsPost-COVID-19 conditionCondition prevalenceMeta-analysisGlobal prevalenceHealth effects of COVID-19Prevalence of post-COVID-19 conditionRegional prevalence estimationHealthcare systemPrevalence estimatesPooled prevalencePost-COVID-19Systematic reviewDerSimonian-Laird estimatorMeta-analyzedMemory problemsHealth effectsPrevalenceEffects of COVID-19Post-coronavirus disease 2019Long COVIDCOVID-19COVID-19 conditionsNonhospitalized patientsUnited States
2021
SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation
Zimmermann L, Bhattacharya S, Purkayastha S, Kundu R, Bhaduri R, Ghosh P, Mukherjee B. SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation. Studies In Microeconomics 2021, 9: 137-179. DOI: 10.1177/23210222211054324.Peer-Reviewed Original ResearchStudy end dateExcess deathsMeta-analysisCase fatality rateState-specific estimatesGlobal Index MedicusEnd dateFatality rateSARS-CoV-2-related deathInfection fatality rateSARS-CoV-2 infection fatality rateRandom-effects modelDeath dataDeath reportingExcess death estimatesImprove death reportingNationwide estimatesSystematic reviewDatabases PubMedPRISMA guidelinesWave 2DerSimonian-Laird estimatorSystematic searchWave 1Model-based estimates
2017
Meta-analysis of job-exposure matrix data from multiple sources
Cheng W, Roberts B, Mukherjee B, Neitzel R. Meta-analysis of job-exposure matrix data from multiple sources. Journal Of Exposure Science & Environmental Epidemiology 2017, 28: 259-274. PMID: 28975928, PMCID: PMC9936531, DOI: 10.1038/jes.2017.19.Peer-Reviewed Original ResearchMeta‐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 alternativeChatterjee
2015
Comparative genome-wide association studies of a depressive symptom phenotype in a repeated measures setting by race/ethnicity in the multi-ethnic study of atherosclerosis
Ware E, Mukherjee B, Sun Y, Diez-Roux A, Kardia S, Smith J. Comparative genome-wide association studies of a depressive symptom phenotype in a repeated measures setting by race/ethnicity in the multi-ethnic study of atherosclerosis. BMC Genomic Data 2015, 16: 118. PMID: 26459564, PMCID: PMC4603946, DOI: 10.1186/s12863-015-0274-0.Peer-Reviewed Original ResearchConceptsMulti-Ethnic StudyGenome-wide association studiesStudies of depressive symptomsMulti-Ethnic Study of AtherosclerosisDepressive symptomsStudy of AtherosclerosisGenome-wide suggestive levelMeasures analysisSingle-nucleotide polymorphismsMultiple ethnicitiesBaseline measurementsMeta-analysisEuropean AmericansLongitudinal measurementsGenome-wide analysisLongitudinal frameworkSuggestive levelAssociation studiesMethodsThis studyEthnicityGenetic predictorsP-valueMood disordersHealthNovel variantsAssociation between Stress Response Genes and Features of Diurnal Cortisol Curves in the Multi-Ethnic Study of Atherosclerosis: A New Multi-Phenotype Approach for Gene-Based Association Tests
He Z, Payne E, Mukherjee B, Lee S, Smith J, Ware E, Sánchez B, Seeman T, Kardia S, Roux A. Association between Stress Response Genes and Features of Diurnal Cortisol Curves in the Multi-Ethnic Study of Atherosclerosis: A New Multi-Phenotype Approach for Gene-Based Association Tests. PLOS ONE 2015, 10: e0126637. PMID: 25993632, PMCID: PMC4439141, DOI: 10.1371/journal.pone.0126637.Peer-Reviewed Original ResearchConceptsMulti-Ethnic Study of AtherosclerosisMarker association testsCortisol featuresMulti-Ethnic StudySingle marker association testsStudy of AtherosclerosisAssociation TestGene level association testsGene-based association testsEthnic-specific resultsMeta-analysisGenetic contribution to variabilityGene-level analysisStress-responsive genesSample of European AmericansGenotype-phenotype associationsDiurnal cortisol curveHispanic AmericansChronic diseasesMultiple physiological systemsDaily cortisol profilesAfrican AmericansGene approachGene-basedMultiple testing
2014
The 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