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
2018
Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering
Tang M, Gao C, Goutman S, Kalinin A, Mukherjee B, Guan Y, Dinov I. Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering. Neuroinformatics 2018, 17: 407-421. PMID: 30460455, PMCID: PMC6527505, DOI: 10.1007/s12021-018-9406-9.Peer-Reviewed Original ResearchMeSH KeywordsAdultAmyotrophic Lateral SclerosisBayes TheoremCluster AnalysisDisease ProgressionFemaleHumansLinear ModelsMachine LearningMaleMiddle AgedReproducibility of ResultsConceptsAmyotrophic Lateral Sclerosis Functional Rating ScaleClusters of participantsModel-basedAmyotrophic lateral sclerosisRating ScaleComputable phenotypeFunctional Rating ScaleSets of featuresUnsupervised clusteringUnsupervised machine learning methodsClinical decision supportMachine learning methodsImputation of missing values in a large job exposure matrix using hierarchical information
Roberts B, Cheng W, Mukherjee B, Neitzel R. Imputation of missing values in a large job exposure matrix using hierarchical information. Journal Of Exposure Science & Environmental Epidemiology 2018, 28: 615-648. PMID: 29789667, PMCID: PMC9929916, DOI: 10.1038/s41370-018-0037-x.Peer-Reviewed Original Research
2013
Urinary phthalate metabolite concentrations among pregnant women in Northern Puerto Rico: Distribution, temporal variability, and predictors
Cantonwine D, Cordero J, Rivera-González L, Del Toro L, Ferguson K, Mukherjee B, Calafat A, Crespo N, Jiménez-Vélez B, Padilla I, Alshawabkeh A, Meeker J. Urinary phthalate metabolite concentrations among pregnant women in Northern Puerto Rico: Distribution, temporal variability, and predictors. Environment International 2013, 62: 1-11. PMID: 24161445, PMCID: PMC3874859, DOI: 10.1016/j.envint.2013.09.014.Peer-Reviewed Original ResearchConceptsMono-3-carboxypropyl phthalateMono-ethyl phthalateUrinary phthalate metabolite concentrationsMono-carboxyisooctyl phthalatePhthalate metabolite concentrationsPregnant womenMono-carboxyisononyl phthalatePhthalate metabolitesMono-2-ethylhexyl phthalatePhthalate biomarkersDetectable concentrations of phthalate metabolitesMono-2-ethyl-5-oxohexyl phthalateMetabolite concentrationsMono-2-ethyl-5-carboxypentyl phthalateConcentrations of phthalate metabolitesMono-2-ethyl-5-carboxypentylUrinary phthalate biomarkersMono-2-ethyl-5-hydroxyhexyl phthalateWomen of reproductive ageUrine samplesAssociated with phthalate exposureMono-isobutyl phthalateMono-n-butyl phthalateMono-benzyl phthalateIntraclass correlation coefficient
2009
Shrinkage estimation for robust and efficient screening of single‐SNP association from case‐control genome‐wide association studies
Luo S, Mukherjee B, Chen J, Chatterjee N. Shrinkage estimation for robust and efficient screening of single‐SNP association from case‐control genome‐wide association studies. Genetic Epidemiology 2009, 33: 740-750. PMID: 19434716, PMCID: PMC3103068, DOI: 10.1002/gepi.20428.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesComputational BiologyComputer SimulationData Interpretation, StatisticalFalse Positive ReactionsGenetic MarkersGenomeGenome, HumanGenome-Wide Association StudyGenotypeHumansLikelihood FunctionsModels, StatisticalPolymorphism, Single NucleotideReproducibility of ResultsConceptsHardy-Weinberg equilibriumAssociation TestPopulation-based case-control designGenome-wide association scanGenome-wide association studiesSingle-SNP associationsCase-control designCase-control studyAssociation scansAssociation studiesGenetic markersSusceptibility SNPsRecessive effectUnderlying populationAssociationFalse-positive resultsEfficient screeningSNPsRare diseaseShrinkage estimatorsSimulation studyStudyTestTwo-degrees-of-freedomPopulation
2008
Tests for gene‐environment interaction from case‐control data: a novel study of type I error, power and designs
Mukherjee B, Ahn J, Gruber S, Rennert G, Moreno V, Chatterjee N. Tests for gene‐environment interaction from case‐control data: a novel study of type I error, power and designs. Genetic Epidemiology 2008, 32: 615-626. PMID: 18473390, DOI: 10.1002/gepi.20337.Peer-Reviewed Original ResearchConceptsGene-environment independence assumptionCase-control studyGene-environment interactionsGene-environment associationsCase-onlyCase-control study of colorectal cancerDetection of gene-environment interactionsType I errorGene-environment dependenceStudy of colorectal cancerGene-environment independenceEffect of genetic susceptibilityCase-only methodCase-only estimatorCase-control estimatorsCase-control dataGene-environment effectsCase-control designCase-control methodCase-control analysisGlutathione S-transferase M1Empirical-BayesEpidemiological researchCase-controlColorectal cancer