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
2020
Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks
Fritsche L, Patil S, Beesley L, VandeHaar P, Salvatore M, Ma Y, Peng R, Taliun D, Zhou X, Mukherjee B. Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks. American Journal Of Human Genetics 2020, 107: 815-836. PMID: 32991828, PMCID: PMC7675001, DOI: 10.1016/j.ajhg.2020.08.025.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresGenome-wide association studiesMichigan Genomics InitiativeUK BiobankPopulation-based UK BiobankPolygenic risk score constructionPublished genome-wide association studiesLongitudinal biorepository effortAssociation studiesPredictive polygenic risk scoresRisk scoreNHGRI-EBI GWAS CatalogCancer traitsIndependent biobankMichigan MedicineGWAS CatalogGenome InitiativeBiobankScoresTraitsCancer researchOnline repositoryMichiganMedicineEvaluation
2017
Update 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