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 diagnosisIncorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction
Zhuang Y, Kim N, Fritsche L, Mukherjee B, Lee S. Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction. BMC Bioinformatics 2024, 25: 65. PMID: 38336614, PMCID: PMC11323637, DOI: 10.1186/s12859-024-05664-2.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMultifactorial InheritanceRisk FactorsSoftwareConceptsPredictive performance of polygenic risk scoresFunctional annotationGenetic architecturePerformance of polygenic risk scoresPRS-CSAnnotation informationPolygenic risk predictionGenetic risk predictionPolygenic risk scoresFunctional annotation informationKyoto Encyclopedia of GenesRisk predictionProportion of variantsEncyclopedia of GenesGenomes (KEGGSource of annotationTrait heritabilityAnnotation groupsPathway informationQuantitative traitsKyoto EncyclopediaFunctional categoriesBackgroundGenetic variantsHeritable contributionReal world data sources
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 ResearchMeSH KeywordsBiological Specimen BanksCOVID-19Genetic Predisposition to DiseaseGenome-Wide Association StudyHumansPopulation HealthPreexisting Condition CoverageRisk FactorsConceptsPolygenic 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 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 samplesExPRSweb: 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 ResearchMeSH KeywordsGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLipidsMultifactorial InheritanceRisk FactorsConceptsPolygenic 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 repositoryIncorporating 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 correlationsPolygenic Liability to Depression Is Associated With Multiple Medical Conditions in the Electronic Health Record: Phenome-wide Association Study of 46,782 Individuals
Fang Y, Fritsche L, Mukherjee B, Sen S, Richmond-Rakerd L. Polygenic Liability to Depression Is Associated With Multiple Medical Conditions in the Electronic Health Record: Phenome-wide Association Study of 46,782 Individuals. Biological Psychiatry 2022, 92: 923-931. PMID: 35965108, PMCID: PMC10712651, DOI: 10.1016/j.biopsych.2022.06.004.Peer-Reviewed Original ResearchMeSH KeywordsDepressionDepressive Disorder, MajorElectronic Health RecordsGenome-Wide Association StudyHumansMultifactorial InheritanceConceptsPhenome-wide association studyPolygenic risk scoresMDD PRSHealth recordsRisk scoreAssociation studiesGenome-wide polygenic risk scoreAssociated with multiple medical conditionsMeasures of genetic riskMichigan Genomics InitiativePsychiatric traitsElectronic health recordsEuropean ancestry participantsMajor depressive disorderAssociated with tobacco use disorderTests of associationMultiple medical conditionsGenitourinary conditionsTobacco use disorderDisease-associated disabilityMolecular genetic toolsMolecular genetic discoveriesPsychiatric disease categoriesHealth outcomesSubstance-related disorders
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 ResearchMeSH KeywordsBreast NeoplasmsFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMultifactorial InheritanceConceptsPolygenic 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 ResearchMeSH KeywordsBiological Specimen BanksChildComputer SimulationFemaleGenome-Wide Association StudyHumansMaleModels, GeneticPhenotypeResearch DesignUnited KingdomConceptsOrdinal 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 ResearchMeSH KeywordsBiological Specimen BanksElectronic Health RecordsGenome-Wide Association StudyHumansMichiganPancreatic NeoplasmsPhenotypeRisk FactorsConceptsElectronic 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 ResearchMeSH KeywordsBiological Specimen BanksCase-Control StudiesData AnalysisGene FrequencyGenome-Wide Association StudyHumansLogistic ModelsPhenotypeProportional Hazards ModelsSample SizeUnited KingdomWhite PeopleConceptsControlled 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 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
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 ResearchMeSH KeywordsAtrial FibrillationBiological Specimen BanksGenetic Predisposition to DiseaseGenome-Wide Association StudyGenomicsHeart Defects, CongenitalHumansMutationRiskConceptsNear 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 arrhythmiasNovel 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