2024
A multi-ancestry genetic study of pain intensity in 598,339 veterans
Toikumo S, Vickers-Smith R, Jinwala Z, Xu H, Saini D, Hartwell E, Pavicic M, Sullivan K, Xu K, Jacobson D, Gelernter J, Rentsch C, Stahl E, Cheatle M, Zhou H, Waxman S, Justice A, Kember R, Kranzler H. A multi-ancestry genetic study of pain intensity in 598,339 veterans. Nature Medicine 2024, 30: 1075-1084. PMID: 38429522, DOI: 10.1038/s41591-024-02839-5.Peer-Reviewed Original ResearchPain intensityChronic painTreat chronic painCalcium channel blockersCross-ancestry meta-analysisGenome-wide association studiesExperience of painSamples of European ancestryPain phenotypesFunctional genomics dataGABAergic neuronsCalcium channelsAnalgesic effectB-blockersDrug groupMillion Veteran ProgramPainSubstance use disordersQuality of lifeDrug repurposing analysisOpioid crisisGenetic architectureCausal genesGenetic lociGenomic data
2023
9. THE GENETIC ARCHITECTURE OF PAIN INTENSITY IN THE MILLION VETERAN PROGRAM
Toikumo S, Vickers-Smith R, Jinwala Z, Xu H, Saini D, Hartwell E, Pavicic M, Sullivan K, Jacobson D, Cheatle M, Zhou H, Waxman S, Justice A, Kember R, Kranzler H. 9. THE GENETIC ARCHITECTURE OF PAIN INTENSITY IN THE MILLION VETERAN PROGRAM. European Neuropsychopharmacology 2023, 75: s60-s61. DOI: 10.1016/j.euroneuro.2023.08.120.Peer-Reviewed Original ResearchIndependent lociGenetic architectureMillion Veteran ProgramGenome-wide association testingIndependent genetic lociLinkage disequilibrium score regressionDrug-gene interaction databaseDisequilibrium score regressionNovel genetic variantsPotential drug targetsComplex traitsGWAS resultsCausal genesDruggable genomeDrug repurposing analysisGenetic lociDruggable genesInteraction databasesDrug targetsGenetic correlationsMolecular contributorsAssociation testingLociPsychiatric traitsScore regressionMulti-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program
Cheng Y, Dao C, Zhou H, Li B, Kember R, Toikumo S, Zhao H, Gelernter J, Kranzler H, Justice A, Xu K. Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program. Translational Psychiatry 2023, 13: 148. PMID: 37147289, PMCID: PMC10162964, DOI: 10.1038/s41398-023-02409-2.Peer-Reviewed Original ResearchConceptsSingle-trait genome-wide association studiesGenome-wide association studiesNovel lociPower of GWASJoint genome-wide association studyGenome-wide significant lociMillion Veteran ProgramGenome-wide associationSubstance use traitsGWAS summary statisticsNovel genetic variantsMulti-trait analysisFunctional annotationUse traitsSignificant lociHeritable traitMultiple lociAssociation studiesColocalization analysisLociPleiotropic effectsMTAgVeteran ProgramGenetic variantsTraitsRacial and Ethnic Bias in the Diagnosis of Alcohol Use Disorder in Veterans
Vickers-Smith R, Justice A, Becker W, Rentsch C, Curtis B, Fernander A, Hartwell E, Ighodaro E, Kember R, Tate J, Kranzler H. Racial and Ethnic Bias in the Diagnosis of Alcohol Use Disorder in Veterans. American Journal Of Psychiatry 2023, 180: 426-436. PMID: 37132202, PMCID: PMC10238581, DOI: 10.1176/appi.ajp.21111097.Peer-Reviewed Original ResearchConceptsAlcohol use disorderAlcohol consumptionAUD diagnosisHispanic veteransWhite veteransUse disordersPrevalence of AUDAlcohol Use Disorders Identification TestUnhealthy alcohol useICD-10 codesAUDIT-C scoresSelf-reported alcohol consumptionAlcohol-related disordersDiagnosis of AUDDisorders Identification TestMaximum scoreSelf-reported raceElectronic health recordsPrimary outcomeAlcohol consumption levelsPotential confoundersHigh prevalenceMillion Veteran ProgramGreater oddsICD-9
2022
Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction
Kember RL, Vickers-Smith R, Xu H, Toikumo S, Niarchou M, Zhou H, Hartwell EE, Crist RC, Rentsch CT, Davis L, Justice A, Sanchez-Roige S, Kampman K, Gelernter J, Kranzler H. Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction. Nature Neuroscience 2022, 25: 1279-1287. PMID: 36171425, PMCID: PMC9682545, DOI: 10.1038/s41593-022-01160-z.Peer-Reviewed Original ResearchConceptsOpioid use disorderGenome-wide association studiesWide significant lociGene expression enrichmentSignificant genetic correlationsCell type groupSignificant lociAssociation studiesExpression enrichmentMillion Veteran ProgramGenetic correlationsUse disordersLociBrain regionsExonic variantsIntronic variantsSubstance use disordersTraitsBiological basisOpioid epidemicPsychiatric disordersVeteran ProgramBrain diseasesTSNARE1FBXW4Genome-wide association study in individuals of European and African ancestry and multi-trait analysis of opioid use disorder identifies 19 independent genome-wide significant risk loci
Deak JD, Zhou H, Galimberti M, Levey DF, Wendt FR, Sanchez-Roige S, Hatoum AS, Johnson EC, Nunez YZ, Demontis D, Børglum AD, Rajagopal VM, Jennings MV, Kember RL, Justice AC, Edenberg HJ, Agrawal A, Polimanti R, Kranzler HR, Gelernter J. Genome-wide association study in individuals of European and African ancestry and multi-trait analysis of opioid use disorder identifies 19 independent genome-wide significant risk loci. Molecular Psychiatry 2022, 27: 3970-3979. PMID: 35879402, PMCID: PMC9718667, DOI: 10.1038/s41380-022-01709-1.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide significant risk lociAssociation studiesVariant associationsLarge-scale genome-wide association studiesGenetic correlationsSignificant risk lociPsychiatric Genomics ConsortiumMulti-trait analysisPolygenic risk score analysisSingle-variant associationsGWS lociGenetic architectureIndividuals of EuropeanGWS associationsRisk lociGene regionGenomics ConsortiumMillion Veteran ProgramSusceptibility lociAfrican ancestryLociRisk score analysisGenetic informativenessSNPs oneValidation of a multi-ancestry polygenic risk score and age-specific risks of prostate cancer: A meta-analysis within diverse populations
Chen F, Darst BF, Madduri RK, Rodriguez AA, Sheng X, Rentsch CT, Andrews C, Tang W, Kibel AS, Plym A, Cho K, Jalloh M, Gueye SM, Niang L, Ogunbiyi OJ, Popoola O, Adebiyi AO, Aisuodionoe-Shadrach OI, Ajibola HO, Jamda MA, Oluwole OP, Nwegbu M, Adusei B, Mante S, Darkwa-Abrahams A, Mensah JE, Adjei AA, Diop H, Lachance J, Rebbeck TR, Ambs S, Gaziano JM, Justice AC, Conti DV, Haiman CA. Validation of a multi-ancestry polygenic risk score and age-specific risks of prostate cancer: A meta-analysis within diverse populations. ELife 2022, 11: e78304. PMID: 35801699, PMCID: PMC9322982, DOI: 10.7554/elife.78304.Peer-Reviewed Original ResearchConceptsProstate cancer riskPolygenic risk scoresProstate cancerCancer riskOdds ratioMillion Veteran ProgramRisk scoreRisk stratification toolAge-specific absolute risksAfrican ancestry menCancer odds ratiosVeterans Health AdministrationCase-control studyNonaggressive prostate cancerProstate Cancer FoundationAge-specific riskAssociation of PRSPRS categoriesRisk-stratified screeningVeteran ProgramNational Cancer InstituteEuropean ancestry menStratification toolAbsolute riskEffect modification
2020
Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals
Xu K, Li B, McGinnis KA, Vickers-Smith R, Dao C, Sun N, Kember RL, Zhou H, Becker WC, Gelernter J, Kranzler HR, Zhao H, Justice AC. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nature Communications 2020, 11: 5302. PMID: 33082346, PMCID: PMC7598939, DOI: 10.1038/s41467-020-18489-3.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesLarge genome-wide association studiesMillion Veteran ProgramAssociation studiesExpression quantitative trait lociQuantitative trait lociChromatin interactionsComplex traitsFunctional annotationTrait lociSequencing ConsortiumDozen genesSignificant lociSmoking phenotypesLociMultiple populationsNew insightsPhenotypeVeteran ProgramGenetic vulnerabilityGenesTraitsAnnotationEuropean AmericansConsortiumValidating a non-invasive, ALT-based non-alcoholic fatty liver phenotype in the million veteran program
Serper M, Vujkovic M, Kaplan DE, Carr RM, Lee KM, Shao Q, Miller DR, Reaven PD, Phillips LS, O’Donnell C, Meigs JB, Wilson PWF, Vickers-Smith R, Kranzler HR, Justice AC, Gaziano JM, Muralidhar S, Pyarajan S, DuVall SL, Assimes TL, Lee JS, Tsao PS, Rader DJ, Damrauer SM, Lynch JA, Saleheen D, Voight BF, Chang KM, . Validating a non-invasive, ALT-based non-alcoholic fatty liver phenotype in the million veteran program. PLOS ONE 2020, 15: e0237430. PMID: 32841307, PMCID: PMC7447043, DOI: 10.1371/journal.pone.0237430.Peer-Reviewed Original ResearchMeSH Keywords17-Hydroxysteroid DehydrogenasesAbdomenAdaptor Proteins, Signal TransducingAgedAlanine TransaminaseElectronic Health RecordsFemaleGenetic LociGenetic Predisposition to DiseaseGenetic VariationHumansLipaseLiverLysophospholipaseMaleMembrane ProteinsMiddle AgedNon-alcoholic Fatty Liver DiseasePhenotypeRisk FactorsVeteransConceptsMetabolic risk factorsNAFLD phenotypeAlanine aminotransferaseUnits/LElectronic health recordsAdvanced fibrosisRisk factorsMillion Veteran ProgramAlcohol consumptionNon-invasive criteriaNormal alanine aminotransferaseNAFLD fibrosis scorePopulation-based studyGenetic variantsFatty liver phenotypeVeteran ProgramPNPLA3 locusNAFLD riskLiver biopsyLiver diseaseFibrosis scoreEHR reviewUS veteransBiopsy dataAbdominal imaging