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 ResearchConceptsPredictive 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
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 associations
2019
Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction?
Wang X, Mukherjee B, Park S. Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction? Journal Of The American Heart Association 2019, 8: e013571. PMID: 31631727, PMCID: PMC6898859, DOI: 10.1161/jaha.119.013571.Peer-Reviewed Original ResearchConceptsCardiovascular diseaseNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk factorsStudy sampleCardiovascular disease risk factorsCardiovascular disease mortalityCardiovascular disease risk assessmentImprove CVD risk predictionC-statisticNutrition Examination SurveyCardiovascular mortality predictionCVD risk predictionCox modelBlood markersExamination SurveyPrecision healthRisk scorePairwise interaction termsBlood metalsIntegrated discrimination improvementRisk predictionReclassification improvementMortality predictionInteraction terms
2014
Environmental Risk Score as a New Tool to Examine Multi-Pollutants in Epidemiologic Research: An Example from the NHANES Study Using Serum Lipid Levels
Park S, Tao Y, Meeker J, Harlow S, Mukherjee B. Environmental Risk Score as a New Tool to Examine Multi-Pollutants in Epidemiologic Research: An Example from the NHANES Study Using Serum Lipid Levels. PLOS ONE 2014, 9: e98632. PMID: 24901996, PMCID: PMC4047033, DOI: 10.1371/journal.pone.0098632.Peer-Reviewed Original ResearchConceptsEnvironmental risk scoreLipid outcomesEpidemiological researchNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRisk scoreNutrition Examination SurveyAdverse health responsesSocio-demographic factorsMulti-pollutant exposuresDevelopment of chronic diseasesBody mass indexExamination SurveySerum nutrient levelsMulti-pollutant approachSociodemographic factorsHealth responseChronic diseasesSingle-pollutantDisease riskMass indexEpidemiological studiesNHANES studyRisk predictionMulti-pollutants
2013
Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons
Sun Z, Tao Y, Li S, Ferguson K, Meeker J, Park S, Batterman S, Mukherjee B. Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons. Environmental Health 2013, 12: 85. PMID: 24093917, PMCID: PMC3857674, DOI: 10.1186/1476-069x-12-85.Peer-Reviewed Original ResearchConceptsMultipollutant modelsHealth impacts of environmental factorsEffect estimatesExposure-response associationsExposure to multiple pollutantsTime series designConsequence of environmental exposureSample sizeHealth impactsEnvironmental exposuresPresence of multicollinearityRisk predictionPotential interactive effectsInitial screeningPollutant mixturesImpact of environmental factorsSupervised principal component analysisModel dimensionsStatistical literatureData examplesTree-based methodsMultiple pollutantsVariable selectionSimulation studyReduce model dimension