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
2024
Multiple metal exposures associate with higher amyotrophic lateral sclerosis risk and mortality independent of genetic risk and correlate to self-reported exposures: a case-control study
Jang D, Dou J, Koubek E, Teener S, Zhou L, Bakulski K, Mukherjee B, Batterman S, Feldman E, Goutman S. Multiple metal exposures associate with higher amyotrophic lateral sclerosis risk and mortality independent of genetic risk and correlate to self-reported exposures: a case-control study. Journal Of Neurology Neurosurgery & Psychiatry 2024, jnnp-2024-333978. PMID: 39107037, DOI: 10.1136/jnnp-2024-333978.Peer-Reviewed Original ResearchAmyotrophic lateral sclerosis riskEnvironmental risk scoreAssociated with ALS riskALS riskGenetic riskRisk scorePolygenic risk scoresSelf-reported exposureGenome-wide association studiesStudy investigated associationsCase-control studySingle-nucleotide polymorphismsAssociation studiesExposure mixturesControl participantsExposure sourcesRiskParticipantsAmyotrophic lateral sclerosisSurvival modelsScoresAssociationEnvironmental factorsUrine metalsUrine samples
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
The construction of cross-population polygenic risk scores using transfer learning
Zhao Z, Fritsche L, Smith J, Mukherjee B, Lee S. The construction of cross-population polygenic risk scores using transfer learning. American Journal Of Human Genetics 2022, 109: 1998-2008. PMID: 36240765, PMCID: PMC9674947, DOI: 10.1016/j.ajhg.2022.09.010.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesPolygenic risk scoresAncestry groupsTransferability of PRSPRS-CSPolygenic risk score methodsEuropean ancestry cohortsIndividuals of African ancestryIndividuals of South Asian ancestryNon-European ancestry groupsNon-European ancestrySouth Asian ancestryAssociation studiesDichotomous traitsSouth Asian sampleEuropean ancestryGenetic researchPRS modelAncestryAsian ancestryAfrican ancestryAfrican samplesUK BiobankRisk scoreAsian samplesPrediction of telomere length and telomere attrition using a genetic risk score: The multi-ethnic study of atherosclerosis (MESA)
Castro-Diehl C, Smith J, Zhao W, Wang X, Mukherjee B, Seeman T, Needham B. Prediction of telomere length and telomere attrition using a genetic risk score: The multi-ethnic study of atherosclerosis (MESA). Frontiers In Aging 2022, 3: 1021051. PMID: 36304436, PMCID: PMC9592760, DOI: 10.3389/fragi.2022.1021051.Peer-Reviewed Original ResearchMulti-Ethnic Study of AtherosclerosisGenetic risk scoreMulti-Ethnic StudyGenome-wide association studiesStudy of AtherosclerosisAssociated with TLEuropean ancestry genome-wide association studiesEuropean ancestryRisk scoreTL-associated genetic variantsShorter TLEuropean ancestry populationsPredictive of telomere lengthHispanic participantsRace/ethnic groupsLinear mixed effects modelsShorter telomere lengthMixed effects modelsAfrican AmericansTelomere attritionExam 1Association studiesRelative TLTelomere lengthT/S ratioExPRSweb: An online repository with polygenic risk scores for common health-related exposures
Ma Y, Patil S, Zhou X, Mukherjee B, Fritsche L. ExPRSweb: An online repository with polygenic risk scores for common health-related exposures. American Journal Of Human Genetics 2022, 109: 1742-1760. PMID: 36152628, PMCID: PMC9606385, DOI: 10.1016/j.ajhg.2022.09.001.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresChronic conditionsPhenome-wide association studyMichigan Genomics InitiativeRisk scoreAssociation studiesHealth-related exposuresGenome-wide association studiesUK BiobankGenetic risk factorsPRS methodsFollow-up studyRisk factorsComplex traitsGenome InitiativeGenetic modifiersBiobankInfluence of exposureEnvironmental variablesScoresLipid levelsExpRLifestyleSmokingOnline repository
2021
On cross-ancestry cancer polygenic risk scores
Fritsche L, Ma Y, Zhang D, Salvatore M, Lee S, Zhou X, Mukherjee B. On cross-ancestry cancer polygenic risk scores. PLOS Genetics 2021, 17: e1009670. PMID: 34529658, PMCID: PMC8445431, DOI: 10.1371/journal.pgen.1009670.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresGenome-wide association studiesProstate cancer polygenic risk scoresPolygenic risk score distributionRecruitment of diverse participantsAncestry groupsPolygenic risk score methodsRisk scoreNon-genetic risk factorsElectronic health recordsBreast cancer casesHealth recordsUK BiobankGWAS effortsDisease risk assessmentCancer casesAssociation studiesGenetic dataEuropean ancestryPersonalized risk stratificationSummary statisticsRisk factorsAncestryDiverse participantsField of cancerEfficient 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
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 arrhythmiasSubset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes
Yu Y, Xia L, Lee S, Zhou X, Stringham H, Boehnke M, Mukherjee B. Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes. Human Heredity 2018, 83: 283-314. PMID: 31132756, PMCID: PMC7034441, DOI: 10.1159/000496867.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesCholesterolCohort StudiesComputer SimulationC-Reactive ProteinFinlandGene FrequencyGene-Environment InteractionGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLipoproteins, LDLMeta-Analysis as TopicModels, GeneticPhenotypePolymorphism, Single NucleotideConceptsPresence of G-E interactionsGenetic associationHeterogeneity of genetic effectsDiscovery of genetic associationsGene-environment (G-EMarginal genetic effectsG-E interactionsGenome-wide association studiesGene-environment interactionsGenetic effectsData examplesSimulation studySingle nucleotide polymorphismsGene-environmentAssociation studiesAssociation analysisScreening toolMarginal associationNucleotide polymorphismsPresence of heterogeneityAssociationEnvironmental factorsIncreased powerMultiple studiesG-E
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
2016
A splicing variant of TERT identified by GWAS interacts with menopausal estrogen therapy in risk of ovarian cancer
Lee A, Bomkamp A, Bandera E, Jensen A, Ramus S, Goodman M, Rossing M, Modugno F, Moysich K, Chang‐Claude J, Rudolph A, Gentry‐Maharaj A, Terry K, Gayther S, Cramer D, Doherty J, Schildkraut J, Kjaer S, Ness R, Menon U, Berchuck A, Mukherjee B, Roman L, Pharoah P, Chenevix‐Trench G, Olson S, Hogdall E, Wu A, Pike M, Stram D, Pearce C, Consortium F. A splicing variant of TERT identified by GWAS interacts with menopausal estrogen therapy in risk of ovarian cancer. International Journal Of Cancer 2016, 139: 2646-2654. PMID: 27420401, PMCID: PMC5500237, DOI: 10.1002/ijc.30274.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAgedAged, 80 and overAllelesAlternative SplicingCase-Control StudiesDisease SusceptibilityEstrogen Replacement TherapyFemaleGene-Environment InteractionGenome-Wide Association StudyGenotypeHumansMenopauseMiddle AgedOdds RatioOvarian NeoplasmsPolymorphism, Single NucleotidePopulation SurveillanceRiskTelomeraseConceptsOvarian Cancer Association ConsortiumEstrogen-alone therapyOvarian cancer riskEndometrioid ovarian cancerOvarian cancerET usersET useT alleleAssociated with ovarian cancer riskCancer riskLong-term ET usersOvarian cancer susceptibility lociRisk of ovarian cancerSusceptibility variantsMenopausal estrogen therapyCancer susceptibility lociSerous ovarian cancerSplice variantsNon-usersCase-control studyConditional logistic regressionGenome-wide association studiesIncreased risk of diseaseEndometrioid histotypeEstrogen therapyIdentification of Susceptibility Loci and Genes for Colorectal Cancer Risk
Zeng C, Matsuda K, Jia W, Chang J, Kweon S, Xiang Y, Shin A, Jee S, Kim D, Zhang B, Cai Q, Guo X, Long J, Wang N, Courtney R, Pan Z, Wu C, Takahashi A, Shin M, Matsuo K, Matsuda F, Gao Y, Oh J, Kim S, Jung K, Ahn Y, Ren Z, Li H, Wu J, Shi J, Wen W, Yang G, Li B, Ji B, Brenner H, Schoen R, Küry S, Gruber S, Schumacher F, Stenzel S, Casey G, Hopper J, Jenkins M, Kim H, Jeong J, Park J, Tajima K, Cho S, Kubo M, Shu X, Lin Y, Zeng Y, Zheng W, Baron J, Berndt S, Bezieau S, Brenner H, Caan B, Carlson C, Casey G, Chan A, Chang-Claude J, Chanock S, Conti D, Curtis K, Duggan D, Fuchs C, Gallinger S, Giovannucci E, Gruber S, Haile R, Harrison T, Hayes R, Hoffmeister M, Hopper J, Hsu L, Hudson T, Hunter D, Hutter C, Jackson R, Jenkins M, Jiao S, Küry S, Le Marchand L, Lemire M, Lindor N, Ma J, Newcomb P, Peters U, Potter J, Qu C, Schoen R, Schumacher F, Seminara D, Slattery M, Thibodeau S, White E, Zanke B, Blalock K, Campbell P, Casey G, Conti D, Edlund C, Figueiredo J, Gauderman W, Gong J, Green R, Gruber S, Harju J, Harrison T, Jacobs E, Jenkins M, Jiao S, Li L, Lin D, Manion F, Moreno V, Mukherjee B, Peters U, Raskin L, Schumacher F, Seminara D, Severi G, Stenzel S, Thomas D. Identification of Susceptibility Loci and Genes for Colorectal Cancer Risk. Gastroenterology 2016, 150: 1633-1645. PMID: 26965516, PMCID: PMC4909543, DOI: 10.1053/j.gastro.2016.02.076.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAsian PeopleBasic Helix-Loop-Helix Leucine Zipper Transcription FactorsCase-Control StudiesColorectal NeoplasmsEukaryotic Initiation Factor-3FemaleGenetic LociGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMiddle AgedPolymorphism, Single NucleotideQb-SNARE ProteinsRibosomal ProteinsRisk FactorsSteroid 17-alpha-HydroxylaseSuppressor of Cytokine Signaling ProteinsYoung AdultConceptsEukaryotic translation initiation factor 3Translation initiation factor 3Ribosomal protein S2Initiation factor 3Transcription factor EBSOCS boxProtein S2Risk variantsReceptor domainSusceptibility lociProtein-coding genesGenome-wide association studiesFactor 3East Asian ancestryNearby genesEpigenomic databasesGenetic variationRisk lociGene expressionAutophagy pathwayAssociation studiesProtein synthesisLociGenesSignificant variants
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 variantsGenome-wide association study of colorectal cancer identifies six new susceptibility loci
Schumacher FR, Schmit SL, Jiao S, Edlund CK, Wang H, Zhang B, Hsu L, Huang SC, Fischer CP, Harju JF, Idos GE, Lejbkowicz F, Manion FJ, McDonnell K, McNeil CE, Melas M, Rennert HS, Shi W, Thomas DC, Van Den Berg DJ, Hutter CM, Aragaki AK, Butterbach K, Caan BJ, Carlson CS, Chanock SJ, Curtis KR, Fuchs CS, Gala M, Giovannucci EL, Gogarten SM, Hayes RB, Henderson B, Hunter DJ, Jackson RD, Kolonel LN, Kooperberg C, Küry S, LaCroix A, Laurie CC, Laurie CA, Lemire M, Levine D, Ma J, Makar KW, Qu C, Taverna D, Ulrich CM, Wu K, Kono S, West DW, Berndt SI, Bezieau S, Brenner H, Campbell PT, Chan AT, Chang-Claude J, Coetzee GA, Conti DV, Duggan D, Figueiredo JC, Fortini BK, Gallinger SJ, Gauderman WJ, Giles G, Green R, Haile R, Harrison TA, Hoffmeister M, Hopper JL, Hudson TJ, Jacobs E, Iwasaki M, Jee SH, Jenkins M, Jia WH, Joshi A, Li L, Lindor NM, Matsuo K, Moreno V, Mukherjee B, Newcomb PA, Potter JD, Raskin L, Rennert G, Rosse S, Severi G, Schoen RE, Seminara D, Shu XO, Slattery ML, Tsugane S, White E, Xiang YB, Zanke BW, Zheng W, Le Marchand L, Casey G, Gruber SB, Peters U. Genome-wide association study of colorectal cancer identifies six new susceptibility loci. Nature Communications 2015, 6: 7138. PMID: 26151821, PMCID: PMC4967357, DOI: 10.1038/ncomms8138.Peer-Reviewed Original ResearchConceptsNew susceptibility lociAssociation studiesSusceptibility lociSignificant genetic lociGenome-wide association studiesGenome-wide thresholdCommon genetic variantsRare pathogenic mutationsTwo-stage association studyGenetic lociGenetic epidemiology studiesGenetic variantsLociUnderlying biological mechanismsPathogenic mutationsBiological mechanismsAsian ConsortiumGenetic susceptibilityMutationsAdditional insightColorectal cancerCancerVariants
2009
Shrinkage estimation for robust and efficient screening of single‐SNP association from case‐control genome‐wide association studies
Luo S, Mukherjee B, Chen J, Chatterjee N. Shrinkage estimation for robust and efficient screening of single‐SNP association from case‐control genome‐wide association studies. Genetic Epidemiology 2009, 33: 740-750. PMID: 19434716, PMCID: PMC3103068, DOI: 10.1002/gepi.20428.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesComputational BiologyComputer SimulationData Interpretation, StatisticalFalse Positive ReactionsGenetic MarkersGenomeGenome, HumanGenome-Wide Association StudyGenotypeHumansLikelihood FunctionsModels, StatisticalPolymorphism, Single NucleotideReproducibility of ResultsConceptsHardy-Weinberg equilibriumAssociation TestPopulation-based case-control designGenome-wide association scanGenome-wide association studiesSingle-SNP associationsCase-control designCase-control studyAssociation scansAssociation studiesGenetic markersSusceptibility SNPsRecessive effectUnderlying populationAssociationFalse-positive resultsEfficient screeningSNPsRare diseaseShrinkage estimatorsSimulation studyStudyTestTwo-degrees-of-freedomPopulation