Featured Publications
Incorporating 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 sourcesRisk of Non-Melanoma Cancers in First-Degree Relatives of CDKN2A Mutation Carriers
Mukherjee B, DeLancey J, Raskin L, Everett J, Jeter J, Begg C, Orlow I, Berwick M, Armstrong B, Kricker A, Marrett L, Millikan R, Culver H, Rosso S, Zanetti R, Kanetsky P, From L, Gruber S, Investigators F. Risk of Non-Melanoma Cancers in First-Degree Relatives of CDKN2A Mutation Carriers. Journal Of The National Cancer Institute 2012, 104: 953-956. PMID: 22534780, PMCID: PMC3379723, DOI: 10.1093/jnci/djs221.Peer-Reviewed Original ResearchConceptsFirst-degree relatives of carriersCDKN2A mutation carriersFirst-degree relativesMutation carriersNon-melanoma cancersFirst-degree relatives of melanoma patientsFirst-degree relatives of mutation carriersKin-cohort methodConfidence intervalsRisk of cancerMelanoma patientsLifetime riskProband's genotypeNon-melanomaFamily membersIncreased riskGastrointestinal cancerCDKN2A mutationsWilms tumorRiskMelanoma StudyPancreatic cancerNoncarriersGenotype distributionMelanomaSet‐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
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 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 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 cancer
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
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
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
2017
Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases
McAllister K, Mechanic L, Amos C, Aschard H, Blair I, Chatterjee N, Conti D, Gauderman W, Hsu L, Hutter C, Jankowska M, Kerr J, Kraft P, Montgomery S, Mukherjee B, Papanicolaou G, Patel C, Ritchie M, Ritz B, Thomas D, Wei P, Witte J, participants O. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. American Journal Of Epidemiology 2017, 186: 753-761. PMID: 28978193, PMCID: PMC5860428, DOI: 10.1093/aje/kwx227.Peer-Reviewed Original ResearchMeSH KeywordsDiseaseGene-Environment InteractionGenetic Predisposition to DiseaseGenome-Wide Association StudyHigh-Throughput Nucleotide SequencingHumansSoftwareConceptsGene-environment interaction studiesStudies of complex diseasesGene-environmentAmerican Society of Human Genetics meetingMeasures of environmental exposureGene-environment interactionsComplex diseasesNational Institute of Environmental Health SciencesNational Cancer InstituteEnvironmental Health SciencesStudy designHealth SciencesCancer InstituteEnvironmental exposuresEnvironmental exposure assessmentNational InstituteLarge-scale studiesExposure assessmentNext-generation sequencing dataDisease outcomeNationalSequence dataThemesStudies of human populationsParticipantsUpdate 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 ResearchMeSH KeywordsBayes TheoremDiseaseGene-Environment InteractionGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLogistic ModelsModels, GeneticModels, StatisticalSequence Analysis, DNASoftwareConceptsGenome-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
Classification and Clustering Methods for Multiple Environmental Factors in Gene–Environment Interaction
Ko Y, Mukherjee B, Smith J, Kardia S, Allison M, Roux A. Classification and Clustering Methods for Multiple Environmental Factors in Gene–Environment Interaction. Epidemiology 2016, 27: 870-878. PMID: 27479650, PMCID: PMC5039086, DOI: 10.1097/ede.0000000000000548.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAtherosclerosisBayes TheoremCluster AnalysisData Interpretation, StatisticalEnvironmental ExposureEpidemiologic Research DesignFemaleFollow-Up StudiesGene-Environment InteractionGenetic Predisposition to DiseaseHumansMiddle AgedModels, StatisticalRegression AnalysisRisk FactorsConceptsMultiple environmental exposuresGene-environment interactionsG x EEnvironmental exposuresMultiethnic Study of AtherosclerosisStudy of AtherosclerosisGene-environmentEffect modificationMultiethnic StudyEnvironmental factorsExposure subgroupsEnvironmental exposure profilesMain effectExposure profilesE studyEfficient analysis strategyE analysisMultiple environmental factorsSubgroupsAnalysis strategyFactorsExposureProduct termsIdentification 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 variantsMicrosatellite 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
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