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
2024
Avocational exposure associations with ALS risk, survival, and phenotype: A Michigan-based case-control study
Goutman S, Boss J, Jang D, Piecuch C, Farid H, Batra M, Mukherjee B, Feldman E, Batterman S. Avocational exposure associations with ALS risk, survival, and phenotype: A Michigan-based case-control study. Journal Of The Neurological Sciences 2024, 457: 122899. PMID: 38278093, PMCID: PMC11060628, DOI: 10.1016/j.jns.2024.122899.Peer-Reviewed Original ResearchConceptsALS riskLower educational attainmentAssociated with ALS riskCase-control studyExercise 5Onset ageSelf-completionExposure variablesYard workExposure associationsRecreational danceIdentified exposureExerciseEducational attainmentAL burdenEnvironmental exposuresParticipantsAL factorPersonal participationAvocational exposureRiskExposomeHobbiesALS onsetComparison correction
2022
Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification
Beesley L, Mukherjee B. Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification. Statistics In Medicine 2022, 41: 5501-5516. PMID: 36131394, PMCID: PMC9826451, DOI: 10.1002/sim.9579.Peer-Reviewed Original ResearchConceptsElectronic health recordsElectronic health record data analysisElectronic health record settingsLeverages external data sourcesElectronic health record dataPopulation-based data sourcesEHR-based researchLongitudinal health informationUniversity of Michigan Health SystemHealth record dataSelection biasPopulation-based researchMichigan Health SystemMultiple sources of biasFactors related to selectionPatient-level dataHealth recordsHealth systemHealth informationPhenotype misclassificationSummary estimatesPhenotyping errorsCancer diagnosisSources of biasRecord dataIncorporating 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
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 levelMAFTraitsRevisiting the genome-wide significance threshold for common variant GWAS
Chen Z, Boehnke M, Wen X, Mukherjee B. Revisiting the genome-wide significance threshold for common variant GWAS. G3: Genes, Genomes, Genetics 2021, 11: jkaa056. PMID: 33585870, PMCID: PMC8022962, DOI: 10.1093/g3journal/jkaa056.Peer-Reviewed Original ResearchConceptsGenome-wide significance thresholdP-value thresholdGWAS meta-analysesMeta-analysis consortiumExcessive false positive ratesSignificance thresholdGene set enrichmentBenjamini-Yekutieli procedureModest-sized studiesFDR-controlling proceduresGlobal lipidsMeta-analysesPathway analysisGWASReplication studyP-valueIncreased discoveryMultiple testing strategiesSample sizePositive discoveriesBenjamini-HochbergLipid levelsTesting strategiesDownstream workFDR
2020
Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks
Salvatore M, Beesley L, Fritsche L, Hanauer D, Shi X, Mondul A, Pearce C, Mukherjee B. Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks. Journal Of Biomedical Informatics 2020, 113: 103652. PMID: 33279681, PMCID: PMC7855433, DOI: 10.1016/j.jbi.2020.103652.Peer-Reviewed Original ResearchConceptsElectronic health recordsPolygenic risk scoresElectronic health record dataMichigan Genomics InitiativePhenotype risk scoreHigh-risk individualsPancreatic cancer diagnosisBody mass indexRisk scoreCancer diagnosisMedical phenomeUK Biobank (UKBHealth record dataSource of patient informationRisk predictionHypothesis-generating associationsDisease risk predictionHealth recordsUnadjusted associationsDrinking statusSmoking statusEpidemiological covariatesUKBPatient informationMultivariate associationsCancer 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 repositoryMichiganMedicineEvaluationA Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank
Bi W, Fritsche L, Mukherjee B, Kim S, Lee S. A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank. American Journal Of Human Genetics 2020, 107: 222-233. PMID: 32589924, PMCID: PMC7413891, DOI: 10.1016/j.ajhg.2020.06.003.Peer-Reviewed Original ResearchConceptsControlled type I error ratesTime-to-event data analysisType I error rateGenetic studies of human diseasesGenome-wide significance levelTime-to-event phenotypesSaddlepoint approximationGenome-wide analysisEuropean ancestry samplesMinor allele frequencyStudy of human diseaseElectronic health recordsCox PH regression modelRegression modelsStandard Wald testProportional hazardsBinary phenotypesData analysisAncestry samplesGenetic studiesHealth recordsUK BiobankAllele frequenciesInpatient dataCox proportional hazardsAn analytic framework for exploring sampling and observation process biases in genome and phenome‐wide association studies using electronic health records
Beesley L, Fritsche L, Mukherjee B. An analytic framework for exploring sampling and observation process biases in genome and phenome‐wide association studies using electronic health records. Statistics In Medicine 2020, 39: 1965-1979. PMID: 32198773, DOI: 10.1002/sim.8524.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsAssociation studiesObservational health care databasesElectronic health record dataLongitudinal biorepository effortPhenome-wide association studyMichigan Genomics InitiativeHealth record dataHealth care databasesDisease-gene association studiesMichigan Health SystemCare databaseHealth systemPhenotype misclassificationStudy biasRecord dataNonprobability samplingAssociation analysisData sourcesGenome InitiativeMisclassificationAnalysis approachRecordsSensitivity analysis
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 testExploring 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 InitiativePheWASTargeted Assessment of G0S2 Methylation Identifies a Rapidly Recurrent, Routinely Fatal Molecular Subtype of Adrenocortical Carcinoma
Mohan D, Lerario A, Else T, Mukherjee B, Almeida M, Vinco M, Rege J, Mariani B, Zerbini M, Mendonca B, Latronico A, Marie S, Rainey W, Giordano T, Fragoso M, Hammer G. Targeted Assessment of G0S2 Methylation Identifies a Rapidly Recurrent, Routinely Fatal Molecular Subtype of Adrenocortical Carcinoma. Clinical Cancer Research 2019, 25: 3276-3288. PMID: 30770352, PMCID: PMC7117545, DOI: 10.1158/1078-0432.ccr-18-2693.Peer-Reviewed Original ResearchConceptsUpregulation of cell cycleDNA damage response programsAdrenocortical carcinomaTargeted bisulfite sequencingCancer Genome Atlas projectBisulfite sequencingCpG island hypermethylation phenotypeHypermethylation phenotypeAggressive adrenocortical carcinomasCell cycleMolecular markersBiological processesHypermethylationMolecular diagnosticsShorter disease-freeCancers of patientsBiomarker methylationAtlas projectEfficacious adjuvant therapyLocoregional diseaseOverall survivalAdjuvant therapyAdrenocortical tumorsDismal outcomeSilencing
2018
Subset-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
2016
Microsatellite Alterations With Allelic Loss at 9p24.2 Signify Less-Aggressive Colorectal Cancer Metastasis
Koi M, Garcia M, Choi C, Kim H, Koike J, Hemmi H, Nagasaka T, Okugawa Y, Toiyama Y, Kitajima T, Imaoka H, Kusunoki M, Chen Y, Mukherjee B, Boland C, Carethers J. Microsatellite Alterations With Allelic Loss at 9p24.2 Signify Less-Aggressive Colorectal Cancer Metastasis. Gastroenterology 2016, 150: 944-955. PMID: 26752111, PMCID: PMC4808397, DOI: 10.1053/j.gastro.2015.12.032.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorChi-Square DistributionChromosome AberrationsChromosomes, Human, Pair 9Colorectal NeoplasmsDisease ProgressionDisease-Free SurvivalFemaleGenetic Predisposition to DiseaseHumansJapanKaplan-Meier EstimateLiver NeoplasmsLogistic ModelsLoss of HeterozygosityMaleMicrosatellite RepeatsMiddle AgedNeoplasm Recurrence, LocalNeoplasm StagingOdds RatioPhenotypeProportional Hazards ModelsProto-Oncogene Proteins B-rafProto-Oncogene Proteins p21(ras)Republic of KoreaRisk FactorsTime FactorsTreatment OutcomeConceptsPrimary colorectal tumorsLoss of heterozygosityLiver metastasesColorectal cancerColorectal tumorsElevated microsatellite alterationsMicrosatellite alterationsStage IICurative treatment of patientsStage III colorectal cancerOverall survival of patientsSurvival of patientsIII colorectal cancerTumor to liverColorectal cancer recurrenceTreatment of patientsMatched liver metastasesCancer cell nucleiMatched metastasesDisease recurrenceOverall survivalPrognostic factorsAllelic lossNo significant differenceCurative treatment
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 variants
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
2011
High Risk of Colorectal and Endometrial Cancer in Ashkenazi Families With the MSH2 A636P Founder Mutation
Mukherjee B, Rennert G, Ahn J, Dishon S, Lejbkowicz F, Rennert H, Shiovitz S, Moreno V, Gruber S. High Risk of Colorectal and Endometrial Cancer in Ashkenazi Families With the MSH2 A636P Founder Mutation. Gastroenterology 2011, 140: 1919-1926. PMID: 21419771, PMCID: PMC4835182, DOI: 10.1053/j.gastro.2011.02.071.Peer-Reviewed Original ResearchMeSH KeywordsAdultAge FactorsAgedAged, 80 and overCase-Control StudiesColorectal Neoplasms, Hereditary NonpolyposisEndometrial NeoplasmsFemaleFounder EffectGene FrequencyGenetic Predisposition to DiseaseGenetic TestingHeredityHumansIsraelJewsLikelihood FunctionsMaleMass ScreeningMiddle AgedMutationMutS Homolog 2 ProteinPedigreePenetrancePhenotypeProportional Hazards ModelsRegistriesRisk AssessmentRisk FactorsSex FactorsYoung AdultConceptsRisk of colorectal cancerHazard ratioColorectal cancerCumulative riskPopulation-basedLifetime risk of colorectal cancerCumulative risk of colorectal cancerEstimates of colorectal cancerAge-specific cumulative riskHigh risk of colorectalCases of colorectal cancerModified segregation analysisRisk of colorectalClinical genetics servicesClinic-based sampleEndometrial cancerRisk of ECCase-control studyGenetic servicesLynch syndromeCancer screeningEC riskLifetime riskAshkenazi familiesEstimated penetrance
2009
Risk of Pancreatic Cancer in Families With Lynch Syndrome
Kastrinos F, Mukherjee B, Tayob N, Wang F, Sparr J, Raymond V, Bandipalliam P, Stoffel E, Gruber S, Syngal S. Risk of Pancreatic Cancer in Families With Lynch Syndrome. JAMA 2009, 302: 1790-1795. PMID: 19861671, PMCID: PMC4091624, DOI: 10.1001/jama.2009.1529.Peer-Reviewed Original ResearchMeSH KeywordsAdaptor Proteins, Signal TransducingAdultAgedAged, 80 and overColorectal Neoplasms, Hereditary NonpolyposisDNA Mismatch RepairDNA Mutational AnalysisDNA-Binding ProteinsFemaleGenotypeGerm-Line MutationHumansMaleMiddle AgedMutL Protein Homolog 1MutS Homolog 2 ProteinNuclear ProteinsPancreatic NeoplasmsPedigreePhenotypeProportional Hazards ModelsRegistriesRiskSEER ProgramYoung AdultConceptsRisk of pancreatic cancerMutations of DNA mismatch repairPancreatic cancer riskGermline MMR gene mutationsMMR gene mutationsCancer riskHazard ratio estimatesLynch syndromeInherited cause of colorectal cancerAge-specific cumulative riskCumulative riskCumulative risk of pancreatic cancerFamily history of pancreatic cancerHistory of pancreatic cancerFamilial cancer registryGeneral populationModified segregation analysisCause of colorectal cancerUniversity of Michigan Comprehensive Cancer CenterComprehensive cancer centerGene mutation carriersCases of pancreatic cancerStudy start dateDana-Farber Cancer InstituteExtracolonic tumors