2013
Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data
Denny J, Bastarache L, Ritchie M, Carroll R, Zink R, Mosley J, Field J, Pulley J, Ramirez A, Bowton E, Basford M, Carrell D, Peissig P, Kho A, Pacheco J, Rasmussen L, Crosslin D, Crane P, Pathak J, Bielinski S, Pendergrass S, Xu H, Hindorff L, Li R, Manolio T, Chute C, Chisholm R, Larson E, Jarvik G, Brilliant M, McCarty C, Kullo I, Haines J, Crawford D, Masys D, Roden D. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nature Biotechnology 2013, 31: 1102-1111. PMID: 24270849, PMCID: PMC3969265, DOI: 10.1038/nbt.2749.Peer-Reviewed Original ResearchCharacterization of Statin Dose Response in Electronic Medical Records
Wei W, Feng Q, Jiang L, Waitara M, Iwuchukwu O, Roden D, Jiang M, Xu H, Krauss R, Rotter J, Nickerson D, Davis R, Berg R, Peissig P, McCarty C, Wilke R, Denny J. Characterization of Statin Dose Response in Electronic Medical Records. Clinical Pharmacology & Therapeutics 2013, 95: 331-338. PMID: 24096969, PMCID: PMC3944214, DOI: 10.1038/clpt.2013.202.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAllelesAtorvastatinCholesterol, LDLCohort StudiesDatabases, FactualDose-Response Relationship, DrugElectronic Health RecordsGenotypeHeptanoic AcidsHumansHydroxymethylglutaryl-CoA Reductase InhibitorsHyperlipidemiasLipid MetabolismLipidsPhenotypePolymorphism, Single NucleotidePyrrolesRandomized Controlled Trials as TopicSimvastatin
2012
Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks
Han B, Chen X, Talebizadeh Z, Xu H. Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks. BMC Systems Biology 2012, 6: s14. PMID: 23281790, PMCID: PMC3524021, DOI: 10.1186/1752-0509-6-s3-s14.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAlzheimer DiseaseArtificial IntelligenceAutistic DisorderBayes TheoremComputational BiologyComputer SimulationDatabases, GeneticEpistasis, GeneticGenome-Wide Association StudyHumansMacular DegenerationMarkov ChainsModels, GeneticMonte Carlo MethodPolymorphism, Single NucleotideConceptsEpistatic interaction detectionBayesian network structure learning methodTwo-layer Bayesian networkBayesian network-based methodBayesian networkInteraction detectionMarkov chain Monte Carlo methodsStructure learning methodReal disease dataNetwork-based methodReal GWAS datasetMonte Carlo methodHigh-order epistatic interactionsMachine learningSearch spaceLearning methodsDisease datasetCarlo methodTarget nodeModel complexityStatistical methodsReal dataNew scoring functionComplex human diseasesDatasetPredicting warfarin dosage in EuropeanAmericans and AfricanAmericans using DNA samples linked to an electronic health record
Ramirez A, Shi Y, Schildcrout J, Delaney J, Xu H, Oetjens M, Zuvich R, Basford M, Bowton E, Jiang M, Speltz P, Zink R, Cowan J, Pulley J, Ritchie M, Masys D, Roden D, Crawford D, Denny J. Predicting warfarin dosage in EuropeanAmericans and AfricanAmericans using DNA samples linked to an electronic health record. Pharmacogenomics 2012, 13: 407-418. PMID: 22329724, PMCID: PMC3361510, DOI: 10.2217/pgs.11.164.Peer-Reviewed Original ResearchAdultAgedAged, 80 and overAnticoagulantsAryl Hydrocarbon HydroxylasesBlack or African AmericanCalcium-Binding ProteinsCytochrome P-450 CYP2C9Cytochrome P-450 Enzyme SystemCytochrome P450 Family 4Dose-Response Relationship, DrugDrug Administration ScheduleElectronic Health RecordsFemaleHumansMaleMiddle AgedMixed Function OxygenasesPolymorphism, Single NucleotideSubstance-Related DisordersVitamin K Epoxide ReductasesWarfarinWhite PeopleThe use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients
Birdwell K, Grady B, Choi L, Xu H, Bian A, Denny J, Jiang M, Vranic G, Basford M, Cowan J, Richardson D, Robinson M, Ikizler T, Ritchie M, Stein C, Haas D. The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients. Pharmacogenetics And Genomics 2012, 22: 32-42. PMID: 22108237, PMCID: PMC3237759, DOI: 10.1097/fpc.0b013e32834e1641.Peer-Reviewed Original ResearchMeSH KeywordsAdultAge FactorsATP Binding Cassette Transporter, Subfamily BATP Binding Cassette Transporter, Subfamily B, Member 1Body WeightCytochrome P-450 CYP3ADatabases, Nucleic AcidDose-Response Relationship, DrugDrug MonitoringElectronic Health RecordsFemaleGenetic Association StudiesGenotypeHemoglobinsHumansImmunosuppressive AgentsKidney TransplantationLinkage DisequilibriumMaleMiddle AgedPolymorphism, Single NucleotidePregnane X ReceptorReceptors, SteroidTacrolimusConceptsTacrolimus dose requirementsKidney transplant recipientsDose requirementsElectronic medical recordsBlood concentrationsTransplant recipientsMedical recordsCYP3A5 rs776746Electronic medical record dataInterindividual pharmacokinetic variabilityTacrolimus blood concentrationsNarrow therapeutic indexDNA biobanksMedical record dataTherapeutic drug monitoringDrug-metabolizing enzymesKidney transplantationClinical factorsPrimary outcomeImmunosuppressive drugsPharmacokinetic variabilityTacrolimus clearanceClinical covariatesPharmacogenomic predictorsTherapeutic index