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 diagnosisSet‐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
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 ResearchMeSH KeywordsGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMachine LearningMultifactorial InheritancePolymorphism, Single NucleotideRisk FactorsConceptsGenome-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 samplesIncorporating 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 ResearchMeSH KeywordsCase-Control StudiesComputer SimulationGenome-Wide Association StudyPhenotypePolymorphism, Single NucleotideConceptsEmpirical 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 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 ResearchMeSH KeywordsBiasElectronic Health RecordsGenome-Wide Association StudyMichiganPhenotypePolymorphism, Single NucleotideConceptsElectronic 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 InitiativePheWASA comprehensive gene–environment interaction analysis in Ovarian Cancer using genome‐wide significant common variants
Kim S, Wang M, Tyrer J, Jensen A, Wiensch A, Liu G, Lee A, Ness R, Salvatore M, Tworoger S, Whittemore A, Anton‐Culver H, Sieh W, Olson S, Berchuck A, Goode E, Goodman M, Doherty J, Chenevix‐Trench G, Rossing M, Webb P, Giles G, Terry K, Ziogas A, Fortner R, Menon U, Gayther S, Wu A, Song H, Brooks‐Wilson A, Bandera E, Cook L, Cramer D, Milne R, Winham S, Kjaer S, Modugno F, Thompson P, Chang‐Claude J, Harris H, Schildkraut J, Le N, Wentzensen N, Trabert B, Høgdall E, Huntsman D, Pike M, Pharoah P, Pearce C, Mukherjee B. A comprehensive gene–environment interaction analysis in Ovarian Cancer using genome‐wide significant common variants. International Journal Of Cancer 2019, 144: 2192-2205. PMID: 30499236, PMCID: PMC6399057, DOI: 10.1002/ijc.32029.Peer-Reviewed Original ResearchConceptsOral contraceptive pill useExcess risk due to additive interactionOvarian cancer risk factorsOral contraceptive pillsGene-environment interaction analysisCancer risk factorsGene-environment analysisOvarian cancer casesOCP useCase-control studyGenome-wide association analysisAdditive scaleCancer casesOvarian cancerOdds ratioCommon variantsDuration of OCP useRisk allelesRisk factorsGenetic variantsAdditive interactionAssociation analysisWomenFollow-upC allele
2018
Novel Common Genetic Susceptibility Loci for Colorectal Cancer
Schmit SL, Edlund CK, Schumacher FR, Gong J, Harrison TA, Huyghe JR, Qu C, Melas M, Van Den Berg DJ, Wang H, Tring S, Plummer SJ, Albanes D, Alonso MH, Amos CI, Anton K, Aragaki AK, Arndt V, Barry EL, Berndt SI, Bezieau S, Bien S, Bloomer A, Boehm J, Boutron-Ruault MC, Brenner H, Brezina S, Buchanan DD, Butterbach K, Caan BJ, Campbell PT, Carlson CS, Castelao JE, Chan AT, Chang-Claude J, Chanock SJ, Cheng I, Cheng YW, Chin LS, Church JM, Church T, Coetzee GA, Cotterchio M, Correa M, Curtis KR, Duggan D, Easton DF, English D, Feskens EJM, Fischer R, FitzGerald LM, Fortini BK, Fritsche LG, Fuchs CS, Gago-Dominguez M, Gala M, Gallinger SJ, Gauderman WJ, Giles GG, Giovannucci EL, Gogarten SM, Gonzalez-Villalpando C, Gonzalez-Villalpando EM, Grady WM, Greenson JK, Gsur A, Gunter M, Haiman CA, Hampe J, Harlid S, Harju JF, Hayes RB, Hofer P, Hoffmeister M, Hopper JL, Huang SC, Huerta JM, Hudson TJ, Hunter DJ, Idos GE, Iwasaki M, Jackson RD, Jacobs EJ, Jee SH, Jenkins MA, Jia WH, Jiao S, Joshi AD, Kolonel LN, Kono S, Kooperberg C, Krogh V, Kuehn T, Küry S, LaCroix A, Laurie CA, Lejbkowicz F, Lemire M, Lenz HJ, Levine D, Li CI, Li L, Lieb W, Lin Y, Lindor NM, Liu YR, Loupakis F, Lu Y, Luh F, Ma J, Mancao C, Manion FJ, Markowitz SD, Martin V, Matsuda K, Matsuo K, McDonnell KJ, McNeil CE, Milne R, Molina AJ, Mukherjee B, Murphy N, Newcomb PA, Offit K, Omichessan H, Palli D, Cotoré JPP, Pérez-Mayoral J, Pharoah PD, Potter JD, Qu C, Raskin L, Rennert G, Rennert HS, Riggs BM, Schafmayer C, Schoen RE, Sellers TA, Seminara D, Severi G, Shi W, Shibata D, Shu XO, Siegel EM, Slattery ML, Southey M, Stadler ZK, Stern MC, Stintzing S, Taverna D, Thibodeau SN, Thomas DC, Trichopoulou A, Tsugane S, Ulrich CM, van Duijnhoven FJB, van Guelpan B, Vijai J, Virtamo J, Weinstein SJ, White E, Win AK, Wolk A, Woods M, Wu AH, Wu K, Xiang YB, Yen Y, Zanke BW, Zeng YX, Zhang B, Zubair N, Kweon SS, Figueiredo JC, Zheng W, Le Marchand L, Lindblom A, Moreno V, Peters U, Casey G, Hsu L, Conti DV, Gruber SB. Novel Common Genetic Susceptibility Loci for Colorectal Cancer. Journal Of The National Cancer Institute 2018, 111: 146-157. PMID: 29917119, PMCID: PMC6555904, DOI: 10.1093/jnci/djy099.Peer-Reviewed Original ResearchSubset-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
Rare‐variant association tests in longitudinal studies, with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA)
He Z, Lee S, Zhang M, Smith J, Guo X, Palmas W, Kardia S, Ionita‐Laza I, Mukherjee B. Rare‐variant association tests in longitudinal studies, with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA). Genetic Epidemiology 2017, 41: 801-810. PMID: 29076270, PMCID: PMC5696115, DOI: 10.1002/gepi.22081.Peer-Reviewed Original ResearchMeSH KeywordsAtherosclerosisBlood PressureDNA-Binding ProteinsEthnicityGenome-Wide Association StudyHumansModels, GeneticPolymorphism, Single NucleotideConceptsMulti-Ethnic Study of AtherosclerosisMulti-Ethnic StudyStudy of AtherosclerosisType I error rateRare-variant association testsRare variantsGene-based association testsRare-variant associationsAssociation TestLongitudinal outcomesLongitudinal studyExome sequencing dataMeasurement of blood pressureGenomic regionsSequence dataTrait heritabilitySequencing studiesMeasured outcomesGenetic variantsVariant analysisModerate sample sizesIndividual variantsRobust to misspecificationWithin-subject correlationStatistical powerMeta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies
Estes J, Rice J, Li S, Stringham H, Boehnke M, Mukherjee B. Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies. Statistics In Medicine 2017, 36: 3895-3909. PMID: 28744888, PMCID: PMC5624850, DOI: 10.1002/sim.7398.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAlpha-Ketoglutarate-Dependent Dioxygenase FTOBayes TheoremBiasBiometryBody Mass IndexCase-Control StudiesComputer SimulationDiabetes Mellitus, Type 2Gene-Environment InteractionHumansLogistic ModelsMeta-Analysis as TopicModels, GeneticModels, StatisticalPolymorphism, Single NucleotideRetrospective StudiesConceptsGene-environment independenceGene-environmentEmpirical Bayes estimatorsGene-environment interactionsCase-control studyMeta-analysis settingBayes estimatorsRetrospective likelihood frameworkShrinkage estimatorsMeta-analysisTesting gene-environment interactionsCombination of estimatesFactors body mass indexSimulation studyBody mass indexUnconstrained modelLikelihood frameworkInverse varianceMeta-analysis frameworkFTO geneMass indexGenetic markersEstimationStandard alternativeChatterjee
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
Applying Novel Methods for Assessing Individual- and Neighborhood-Level Social and Psychosocial Environment Interactions with Genetic Factors in the Prediction of Depressive Symptoms in the Multi-Ethnic Study of Atherosclerosis
Ware E, Smith J, Mukherjee B, Lee S, Kardia S, Diez-Roux A. Applying Novel Methods for Assessing Individual- and Neighborhood-Level Social and Psychosocial Environment Interactions with Genetic Factors in the Prediction of Depressive Symptoms in the Multi-Ethnic Study of Atherosclerosis. Behavior Genetics 2015, 46: 89-99. PMID: 26254610, PMCID: PMC4720563, DOI: 10.1007/s10519-015-9734-6.Peer-Reviewed Original ResearchConceptsDepressive symptom scoresMulti-Ethnic Study of AtherosclerosisGene regionNeighborhood levelMulti-Ethnic StudyPredictive of depressive symptomsStudy of AtherosclerosisMultiple race/ethnicitiesMultiple testing correctionAssess individual-SKAT analysisNeighborhood factorsEtiology of depressive illnessDepressive symptomsPsychosocial stressorsSymptom scoresComplex illnessTesting correctionRace/ethnicityRace/ethnicitiesEthnic groupsDepressive illnessGenetic predispositionIndividual-Genetic factorsGenome-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 ResearchMeSH KeywordsCase-Control StudiesColorectal NeoplasmsGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansOdds RatioPolymorphism, Single NucleotideConceptsNew 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
2014
Latent variable models for gene–environment interactions in longitudinal studies with multiple correlated exposures
Tao Y, Sánchez B, Mukherjee B. Latent variable models for gene–environment interactions in longitudinal studies with multiple correlated exposures. Statistics In Medicine 2014, 34: 1227-1241. PMID: 25545894, PMCID: PMC4355187, DOI: 10.1002/sim.6401.Peer-Reviewed Original ResearchMeSH KeywordsBiostatisticsChild, PreschoolComputer SimulationEnvironmental ExposureFemaleGene-Environment InteractionHemochromatosis ProteinHistocompatibility Antigens Class IHumansInfantInfant, NewbornLead PoisoningLongitudinal StudiesMembrane ProteinsMexicoModels, GeneticModels, StatisticalPolymorphism, Single NucleotidePregnancyPrenatal Exposure Delayed EffectsConceptsGene-environment interactionsOutcome measuresCohort studyHealth effects of environmental exposuresEnvironmental exposuresInvestigate health effectsGene-environment associationsEffects of environmental exposuresEarly life exposuresLV frameworkG x E effectsMultivariate exposuresGenotyped single nucleotide polymorphismsEffect modificationShrinkage estimatorsLife exposureExposure measurementsSingle nucleotide polymorphismsData-adaptive wayMultiple testingOutcome dataLongitudinal studyLongitudinal natureGenetic factorsNucleotide polymorphismsThe 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
2013
Interaction of Fatty Acid Genotype and Diet on Changes in Colonic Fatty Acids in a Mediterranean Diet Intervention Study
Porenta S, Ko Y, Raskin L, Gruber S, Mukherjee B, Baylin A, Ren J, Djuric Z. Interaction of Fatty Acid Genotype and Diet on Changes in Colonic Fatty Acids in a Mediterranean Diet Intervention Study. Cancer Prevention Research 2013, 6: 1212-1221. PMID: 24022589, PMCID: PMC3840911, DOI: 10.1158/1940-6207.capr-13-0131.Peer-Reviewed Original ResearchConceptsFatty acid desaturaseHealthy Eating dietArachidonic acid concentrationIntake of n-3Fatty acid desaturase genotypeIncreased risk of colon cancerN-6 fatty acidsEating dietsIntake of n-6 fatty acidsRisk of colon cancerFatty acid desaturase gene clusterDietary fat intakeColon cancer riskSerum eicosapentaenoic acidDiet intervention studyInteraction of polymorphismsSerum arachidonic acidFatty acid desaturase geneFatty acidsSingle-nucleotide polymorphismsFat intakeN-3N-6Colon cancerAssociated with genotype
2012
Likelihood‐based methods for regression analysis with binary exposure status assessed by pooling
Lyles R, Tang L, Lin J, Zhang Z, Mukherjee B. Likelihood‐based methods for regression analysis with binary exposure status assessed by pooling. Statistics In Medicine 2012, 31: 2485-2497. PMID: 22415630, PMCID: PMC3528351, DOI: 10.1002/sim.4426.Peer-Reviewed Original ResearchConceptsPopulation-based case-control study of colorectal cancerCase-control study of colorectal cancerPopulation-based case-control studyStudy of colorectal cancerExposure statusBinary outcomesRegression modelsCase-control sampleLogistic regression modelsGene-disease associationsObserved binary outcomeStudy designEpidemiological studiesColorectal cancerAssess exposureMaximum likelihood analysisRegression analysisLikelihood-based methodsExposure assessmentMaximum likelihood approachLikelihood approachCross-sectionSimulation studyOutcomesLikelihood analysis