Featured Publications
Set‐based tests for genetic association in longitudinal studies
He Z, Zhang M, Lee S, Smith J, Guo X, Palmas W, Kardia S, Diez Roux A, Mukherjee B. Set‐based tests for genetic association in longitudinal studies. Biometrics 2015, 71: 606-615. PMID: 25854837, PMCID: PMC4601568, DOI: 10.1111/biom.12310.Peer-Reviewed Original ResearchConceptsMulti-Ethnic Study of AtherosclerosisGenome-wide association studiesJoint effect of multiple variantsLinkage disequilibriumAssociation studiesEffects of multiple variantsMarkers of chronic diseaseGenetic variantsSet-based testGene-based testsLongitudinal outcomesMulti-Ethnic StudyGenetic association studiesStudy of AtherosclerosisChronic diseasesPhenotypic variationGenetic associationObservational studyLongitudinal analysisWithin-subject correlationMultiple variantsScore type testsJoint testJoint effectsMarker tests
2022
Incorporating family disease history and controlling case–control imbalance for population-based genetic association studies
Zhuang Y, Wolford B, Nam K, Bi W, Zhou W, Willer C, Mukherjee B, Lee S. Incorporating family disease history and controlling case–control imbalance for population-based genetic association studies. Bioinformatics 2022, 38: 4337-4343. PMID: 35876838, PMCID: PMC9477535, DOI: 10.1093/bioinformatics/btac459.Peer-Reviewed Original ResearchConceptsEmpirical saddlepoint approximationFamily disease historyCase-control imbalanceSaddlepoint approximationGenome-wide association analysisPopulation-based genetic association studiesGenetic association testsVariant-phenotype associationsDisease historyGenetic association studiesLow detection powerType I error inflationCorrelation of phenotypesWhite British sampleSupplementary dataAssociation studiesPopulation-based biobanksIncreased phenotypic correlationsKorean GenomeSimulation studyPhenotype distributionPhenotypeAssociation TestBioinformaticsPhenotypic correlations
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
2019
A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank
Bi W, Zhao Z, Dey R, Fritsche L, Mukherjee B, Lee S. A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank. American Journal Of Human Genetics 2019, 105: 1182-1192. PMID: 31735295, PMCID: PMC6904814, DOI: 10.1016/j.ajhg.2019.10.008.Peer-Reviewed Original ResearchConceptsCase-control ratioGenome-wide significance levelMeasures of environmental exposureGenome-wide analysisEuropean ancestry samplesGenetic association studiesSaddlepoint approximationCase-control imbalanceAnalysis of phenotypesGene-environment interactionsPopulation-based biobanksControlled type I error ratesAssociation studiesG x E effectsUK BiobankType I error rateGenetic variantsE analysisSPAGEComplex diseasesEnvironmental exposuresTest statisticsE studySimulation studyWald test
2006
Bayesian modeling for genetic association in case-control studies: accounting for unknown population substructure
Zhang L, Mukherjee B, Ghosh M, Wu R. Bayesian modeling for genetic association in case-control studies: accounting for unknown population substructure. Statistical Modelling 2006, 6: 352-372. DOI: 10.1177/1471082006071841.Peer-Reviewed Original ResearchPopulation substructureCase-control studyGenetic association studiesLog odds ratio parametersOdds ratio parametersAssociation studiesAllele frequenciesGenetic associationParametric Bayesian methodsArgentinean populationBayesian modelCredible intervalsGenetic factorsBayesian methodsStatistical propertiesNumerical integration techniquesPosterior probabilityAssociation modelPopulationAllelesGenesAssociationIntegration techniqueMarkovObesity