Featured Publications
Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative
Fritsche L, Gruber S, Wu Z, Schmidt E, Zawistowski M, Moser S, Blanc V, Brummett C, Kheterpal S, Abecasis G, Mukherjee B. Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative. American Journal Of Human Genetics 2018, 102: 1048-1061. PMID: 29779563, PMCID: PMC5992124, DOI: 10.1016/j.ajhg.2018.04.001.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresElectronic health recordsAssociations of polygenic risk scoresPhenome-wide significant associationsPolygenic risk score associationsLongitudinal biorepository effortNon-cancer diagnosesPatients' electronic health recordsPhenome-wide association studyAnalysis of temporal orderMichigan Genomics InitiativeRisk scoreAssociated with multiple phenotypesFemale breast cancerNHGRI-EBI CatalogRisk profileGenetic risk profilesMeasures of genomic variationCancer traitsCase-control studyPheWAS analysisHealth recordsHealth systemMichigan MedicineCancer diagnosis
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
2019
The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities
Beesley L, Salvatore M, Fritsche L, Pandit A, Rao A, Brummett C, Willer C, Lisabeth L, Mukherjee B. The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities. Statistics In Medicine 2019, 39: 773-800. PMID: 31859414, PMCID: PMC7983809, DOI: 10.1002/sim.8445.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsMichigan Genomics InitiativeBiobank-based studiesHealth-related researchUK BiobankHealth researchDisease-gene associationsStudy designAgnostic searchBiobankDisease-treatmentInformatics infrastructureHypothesis-generating studyPhenotypic identificationGenome InitiativeMissing dataResource catalogExploratory questionsCurrent bodyBiobank researchData typesMedical researchRecruitment mechanismsPractical guidanceExploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb
Fritsche L, Beesley L, VandeHaar P, Peng R, Salvatore M, Zawistowski M, Taliun S, Das S, LeFaive J, Kaleba E, Klumpner T, Moser S, Blanc V, Brummett C, Kheterpal S, Abecasis G, Gruber S, Mukherjee B. Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb. PLOS Genetics 2019, 15: e1008202. PMID: 31194742, PMCID: PMC6592565, DOI: 10.1371/journal.pgen.1008202.Peer-Reviewed Original ResearchConceptsMichigan Genomics InitiativeElectronic health recordsPolygenic risk scoresSkin cancer subtypesPheWAS resultsUK BiobankElectronic health record dataLongitudinal biorepository effortPhenome-wide association studyRisk scoreHealth record dataUK Biobank dataPrediction of disease riskPublicly-available sourcesHealth recordsGenetic architectureBiobank dataMichigan MedicineRecord dataSecondary phenotypesDisease riskVisual catalogAssociation studiesGenome InitiativePheWAS
2018
Biobank-driven genomic discovery yields new insight into atrial fibrillation biology
Nielsen J, Thorolfsdottir R, Fritsche L, Zhou W, Skov M, Graham S, Herron T, McCarthy S, Schmidt E, Sveinbjornsson G, Surakka I, Mathis M, Yamazaki M, Crawford R, Gabrielsen M, Skogholt A, Holmen O, Lin M, Wolford B, Dey R, Dalen H, Sulem P, Chung J, Backman J, Arnar D, Thorsteinsdottir U, Baras A, O’Dushlaine C, Holst A, Wen X, Hornsby W, Dewey F, Boehnke M, Kheterpal S, Mukherjee B, Lee S, Kang H, Holm H, Kitzman J, Shavit J, Jalife J, Brummett C, Teslovich T, Carey D, Gudbjartsson D, Stefansson K, Abecasis G, Hveem K, Willer C. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nature Genetics 2018, 50: 1234-1239. PMID: 30061737, PMCID: PMC6530775, DOI: 10.1038/s41588-018-0171-3.Peer-Reviewed Original ResearchConceptsNear genesRisk variantsGenome-wide association studiesFunctional candidate genesIndependent risk variantsIdentified risk variantsFunctional enrichment analysisDeleterious mutationsAssociation studiesCandidate genesAtrial fibrillationGenetic variationGenomic discoveriesStriated muscle functionEnrichment analysisNKX2-5Fetal heart developmentResponse to stressGenesCardiac structural remodelingAtrial fibrillation casesHeart developmentHeart defectsAdult heartCardiac arrhythmias
2016
Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification
Boonstra P, Mukherjee B, Gruber S, Ahn J, Schmit S, Chatterjee N. Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification. American Journal Of Epidemiology 2016, 183: 237-247. PMID: 26755675, PMCID: PMC4724093, DOI: 10.1093/aje/kwv198.Peer-Reviewed Original ResearchConceptsG-E interactionsPresence of exposure misclassificationExposure misclassificationImpact of exposure misclassificationGene-environment (G-EGene-environment interactionsGenome-wide levelGenome-wide searchGenome-wide testingGenetic susceptibility lociJoint testDisease-gene relationshipsGene-environmentGenetic risk factorsType I error rateFamily-wise type I error rateSusceptibility lociG-EGenetic associationRisk factorsStatistical powerJoint effectsSimulation studyMisclassificationPublished simulation studies
2012
Incorporating auxiliary information for improved prediction in high-dimensional datasets: an ensemble of shrinkage approaches
Boonstra P, Taylor J, Mukherjee B. Incorporating auxiliary information for improved prediction in high-dimensional datasets: an ensemble of shrinkage approaches. Biostatistics 2012, 14: 259-272. PMID: 23087411, PMCID: PMC3590922, DOI: 10.1093/biostatistics/kxs036.Peer-Reviewed Original ResearchConceptsHigh-dimensional datasetsAuxiliary informationRidge estimatorBayesian alternativeOutcome YSimulation studyEstimates of BShrinkage approachBiological processesRidge regressionGene expression datasetsDatasetGenomic technologiesMicroarray technologyOptimal choiceBalance efficiencyX.EstimationPrediction errorPolymerase chain reactionBiological phenomenaInformationTechnologyQuantitative real-time polymerase chain reaction