2023
Genetic Underpinnings of the Transition From Alcohol Consumption to Alcohol Use Disorder: Shared and Unique Genetic Architectures in a Cross-Ancestry Sample
Kember R, Vickers-Smith R, Zhou H, Xu H, Jennings M, Dao C, Davis L, Sanchez-Roige S, Justice A, Gelernter J, Vujkovic M, Kranzler H. Genetic Underpinnings of the Transition From Alcohol Consumption to Alcohol Use Disorder: Shared and Unique Genetic Architectures in a Cross-Ancestry Sample. American Journal Of Psychiatry 2023, 180: 584-593. PMID: 37282553, PMCID: PMC10731616, DOI: 10.1176/appi.ajp.21090892.Peer-Reviewed Original ResearchIdentifying genetic loci and phenomic associations of substance use traits: A multi‐trait analysis of GWAS (MTAG) study
Xu H, Toikumo S, Crist R, Glogowska K, Jinwala Z, Deak J, Justice A, Gelernter J, Johnson E, Kranzler H, Kember R. Identifying genetic loci and phenomic associations of substance use traits: A multi‐trait analysis of GWAS (MTAG) study. Addiction 2023, 118: 1942-1952. PMID: 37156939, PMCID: PMC10754226, DOI: 10.1111/add.16229.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesSignificant single nucleotide polymorphismsSubstance use traitsMulti-trait analysisAssociation studiesGenetic architectureUse traitsGenome-wide significant single nucleotide polymorphismsProtein-protein interaction analysisTrait genetic architectureNumber of lociPolygenic risk scoresEuropean ancestry individualsNovel lociSingle nucleotide polymorphismsGenetic lociGWAS studiesLociMultiple related phenotypesNucleotide polymorphismsRelated phenotypesTraitsNovel associationsMTAgBiobank samplesMulti-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 variantsTraits
2022
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
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 oneIdentifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks
Kim M, Huffman JE, Justice A, Goethert I, Agasthya G, Danciu I. Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks. BMC Medical Genomics 2022, 15: 151. PMID: 35794577, PMCID: PMC9258200, DOI: 10.1186/s12920-022-01298-6.Peer-Reviewed Original Research
2020
Validating 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
2019
Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations
Kranzler HR, Zhou H, Kember RL, Vickers Smith R, Justice AC, Damrauer S, Tsao PS, Klarin D, Baras A, Reid J, Overton J, Rader DJ, Cheng Z, Tate JP, Becker WC, Concato J, Xu K, Polimanti R, Zhao H, Gelernter J. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nature Communications 2019, 10: 1499. PMID: 30940813, PMCID: PMC6445072, DOI: 10.1038/s41467-019-09480-8.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesMillion Veteran Program sampleGenetic correlationsWide significant lociSignificant genetic correlationsPolygenic risk scoresCell type groupSignificant lociHeritable traitEnrichment analysisTraitsMultiple populationsLociPhenotypeProgram samples
2018
Using DNA methylation to validate an electronic medical record phenotype for smoking
McGinnis KA, Justice AC, Tate JP, Kranzler HR, Tindle HA, Becker WC, Concato J, Gelernter J, Li B, Zhang X, Zhao H, Crothers K, Xu K, Group F. Using DNA methylation to validate an electronic medical record phenotype for smoking. Addiction Biology 2018, 24: 1056-1065. PMID: 30284751, PMCID: PMC6541538, DOI: 10.1111/adb.12670.Peer-Reviewed Original ResearchConceptsVeterans Aging Cohort StudyAging Cohort StudyStrong associationDNA methylation sitesSmoking metricsCohort studyCurrent smokingSmoking statusSpearman correlation coefficientBiomarker cohortBlood samplesSmoking behaviorCriterion standardLogistic regressionSmokingSmoking phenotypesCurve analysisGroup assignmentText notesAssociationDescriptive statisticsPhenotypeCorrelation coefficientGenetic discoveriesPercentAUDIT‐C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations
Justice AC, Smith RV, Tate JP, McGinnis K, Xu K, Becker WC, Lee K, Lynch K, Sun N, Concato J, Fiellin DA, Zhao H, Gelernter J, Kranzler HR, Program O. AUDIT‐C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations. Addiction 2018, 113: 2214-2224. PMID: 29972609, PMCID: PMC6226338, DOI: 10.1111/add.14374.Peer-Reviewed Original Research
2017
Validating Harmful Alcohol Use as a Phenotype for Genetic Discovery Using Phosphatidylethanol and a Polymorphism in ADH1B
Justice AC, McGinnis KA, Tate JP, Xu K, Becker WC, Zhao H, Gelernter J, Kranzler HR. Validating Harmful Alcohol Use as a Phenotype for Genetic Discovery Using Phosphatidylethanol and a Polymorphism in ADH1B. Alcohol Clinical And Experimental Research 2017, 41: 998-1003. PMID: 28295416, PMCID: PMC5501250, DOI: 10.1111/acer.13373.Peer-Reviewed Original ResearchConceptsHarmful alcohol useAlcohol exposureAlcohol useElectronic health record dataEHR dataAUDIT-C scoresHealth record dataLongitudinal electronic health record dataLongitudinal trajectoriesChi-square testEHR-derived phenotypesStudy cohortBlood drawCommon missense polymorphismGenetic risk variantsBlood samplingMissense polymorphismAlcohol riskQuantitative biomarkersRecord dataMedianRisk variantsOverall sampleAfrican AmericansADH1B gene
2014
An Adapted Frailty-Related Phenotype and the VACS Index as Predictors of Hospitalization and Mortality in HIV-Infected and Uninfected Individuals
Akgün KM, Tate JP, Crothers K, Crystal S, Leaf DA, Womack J, Brown TT, Justice AC, Oursler KK. An Adapted Frailty-Related Phenotype and the VACS Index as Predictors of Hospitalization and Mortality in HIV-Infected and Uninfected Individuals. JAIDS Journal Of Acquired Immune Deficiency Syndromes 2014, 67: 397-404. PMID: 25202921, PMCID: PMC4213242, DOI: 10.1097/qai.0000000000000341.Peer-Reviewed Original ResearchConceptsHIV-1 RNAUndetectable HIV-1 RNAVACS IndexUninfected individualsHazard ratioC-statisticFrailty-related phenotypePredictors of hospitalizationCohort Study participantsLow physical activityAdverse health outcomesPhysiologic reserveGeriatric syndromesFrailty stateRisk factorsHospitalizationPhysical activityMortality riskHIVHealth behaviorsHealth outcomesSurvival analysisSystem biomarkersStudy participantsMortality