Featured Publications
Genome-wide association studies and cross-population meta-analyses investigating short and long sleep duration
Austin-Zimmerman I, Levey D, Giannakopoulou O, Deak J, Galimberti M, Adhikari K, Zhou H, Denaxas S, Irizar H, Kuchenbaecker K, McQuillin A, Concato J, Buysse D, Gaziano J, Gottlieb D, Polimanti R, Stein M, Bramon E, Gelernter J. Genome-wide association studies and cross-population meta-analyses investigating short and long sleep duration. Nature Communications 2023, 14: 6059. PMID: 37770476, PMCID: PMC10539313, DOI: 10.1038/s41467-023-41249-y.Peer-Reviewed Original ResearchConceptsAssociation studiesGenome-wide association studiesGenetic correlationsWide association studyLinkage disequilibrium scorePositive genetic correlationSleep traitsIndependent lociMillion Veteran ProgramTraitsAncestryUK BiobankVeteran ProgramMendelian randomisationLociHeritabilitySNPsPhenotypeEast AsiansSimilar patternCardiometabolic phenotypesGenome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program
Stein MB, Levey DF, Cheng Z, Wendt FR, Harrington K, Pathak GA, Cho K, Quaden R, Radhakrishnan K, Girgenti MJ, Ho YA, Posner D, Aslan M, Duman RS, Zhao H, Polimanti R, Concato J, Gelernter J. Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program. Nature Genetics 2021, 53: 174-184. PMID: 33510476, PMCID: PMC7972521, DOI: 10.1038/s41588-020-00767-x.Peer-Reviewed Original ResearchConceptsGenome-wide association analysisAssociation analysisMillion Veteran ProgramGenomic structural equation modelingSignificant lociGenetic varianceGene expressionDrug repositioning candidatesBiological coherenceVeteran ProgramMultiple testing correctionSymptom phenotypeLociRepositioning candidatesAfrican ancestryHeritabilityPhenotypeAncestryExpressionPTSD symptom factorsRegionSubdomainsEnrichmentGenome-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
2023
MULTI-ANCESTRY PHENOME-WIDE ASSOCIATION ANALYSES OF GENETIC LIABILITY FOR PROBLEMATIC ALCOHOL USE
Kember R, Johnson J, Johnston K, Lee H, Xu J, Davis L, Smoller J, Mallard T, Huckins L, Sanchez-Roige S, Kranzler H, Gelernter J, Zhou H. MULTI-ANCESTRY PHENOME-WIDE ASSOCIATION ANALYSES OF GENETIC LIABILITY FOR PROBLEMATIC ALCOHOL USE. European Neuropsychopharmacology 2023, 75: s10. DOI: 10.1016/j.euroneuro.2023.08.027.Peer-Reviewed Original ResearchGenetic Decomposition of the Heritable Component of Reported Childhood Maltreatment
Kuile A, Hübel C, Cheesman R, Coleman J, Peel A, Levey D, Stein M, Gelernter J, Rayner C, Eley T, Breen G. Genetic Decomposition of the Heritable Component of Reported Childhood Maltreatment. Biological Psychiatry Global Open Science 2023, 3: 716-724. PMID: 37881567, PMCID: PMC10593925, DOI: 10.1016/j.bpsgos.2023.03.003.Peer-Reviewed Original ResearchGenetic componentGenomic structural equationGenome-wide association study summary statisticsCommon genetic variantsResidual genetic varianceGeneral risk toleranceGenetic variancePutative traitsHeritable componentBehavioral traitsGenetic correlationsTraitsGenetic variantsHeritable characteristicsHeritable factorsHeritabilityEnvironmental factorsDecades of researchGenetic influencesSummary statisticsPhenotype
2022
Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond
Gaddis N, Mathur R, Marks J, Zhou L, Quach B, Waldrop A, Levran O, Agrawal A, Randesi M, Adelson M, Jeffries PW, Martin NG, Degenhardt L, Montgomery GW, Wetherill L, Lai D, Bucholz K, Foroud T, Porjesz B, Runarsdottir V, Tyrfingsson T, Einarsson G, Gudbjartsson DF, Webb BT, Crist RC, Kranzler HR, Sherva R, Zhou H, Hulse G, Wildenauer D, Kelty E, Attia J, Holliday EG, McEvoy M, Scott RJ, Schwab SG, Maher BS, Gruza R, Kreek MJ, Nelson EC, Thorgeirsson T, Stefansson K, Berrettini WH, Gelernter J, Edenberg HJ, Bierut L, Hancock DB, Johnson EO. Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond. Scientific Reports 2022, 12: 16873. PMID: 36207451, PMCID: PMC9546890, DOI: 10.1038/s41598-022-21003-y.Peer-Reviewed Original ResearchConceptsGenome-wide significant associationMulti-trait genome-wide association studyNovel genome-wide significant associationsGenome-wide association studiesGenomic structural equationGene-based analysisRelated traitsAssociation studiesGenetic correlationsEuropean ancestryA118G variantConsortium dataNew geneticsG variantGWASPPP6CLociPleiotropicGeneticsVariantsTraitsPhenotypeOA phenotypeFurinAncestry
2021
Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example
Hatoum AS, Wendt FR, Galimberti M, Polimanti R, Neale B, Kranzler HR, Gelernter J, Edenberg HJ, Agrawal A. Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example. Drug And Alcohol Dependence 2021, 229: 109115. PMID: 34710714, PMCID: PMC9358969, DOI: 10.1016/j.drugalcdep.2021.109115.Peer-Reviewed Original ResearchConceptsGenome-wide significant variantsCandidate gene predictionGenetic predictionRandom SNPsPolygenic traitRandom phenotypeCandidate SNPsSimulated phenotypesPsychiatric geneticsGenetic machineSignificant variantsBinary phenotypesCandidate variantsSNPsAncestryPhenotypeAllele frequenciesVariantsMachine learning modelsGenetic testsLearning model
2020
Characterizing the effect of background selection on the polygenicity of brain-related traits
Wendt FR, Pathak GA, Overstreet C, Tylee DS, Gelernter J, Atkinson EG, Polimanti R. Characterizing the effect of background selection on the polygenicity of brain-related traits. Genomics 2020, 113: 111-119. PMID: 33278486, PMCID: PMC7855394, DOI: 10.1016/j.ygeno.2020.11.032.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesBrain-related traitsGWAS of schizophreniaTrait-associated lociLocus effect sizesSubset of traitsGenotype networksGenetic architectureIntolerant regionsBrain-related phenotypesBackground selectionNatural selectionEvolutionary pressurePositive selectionSNP heritabilityLocal ancestryAssociation studiesTraitsFunctional significanceLociPolygenicityBinary annotationPhenotypeRisk allelesSize varianceExpanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits
Quach BC, Bray MJ, Gaddis NC, Liu M, Palviainen T, Minica CC, Zellers S, Sherva R, Aliev F, Nothnagel M, Young KA, Marks JA, Young H, Carnes MU, Guo Y, Waldrop A, Sey NYA, Landi MT, McNeil DW, Drichel D, Farrer LA, Markunas CA, Vink JM, Hottenga JJ, Iacono WG, Kranzler HR, Saccone NL, Neale MC, Madden P, Rietschel M, Marazita ML, McGue M, Won H, Winterer G, Grucza R, Dick DM, Gelernter J, Caporaso NE, Baker TB, Boomsma DI, Kaprio J, Hokanson JE, Vrieze S, Bierut LJ, Johnson EO, Hancock DB. Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits. Nature Communications 2020, 11: 5562. PMID: 33144568, PMCID: PMC7642344, DOI: 10.1038/s41467-020-19265-z.Peer-Reviewed Original ResearchConceptsGenome-wide significant lociGenome-wide association studiesNearby gene expressionExpression of genesSmoking traitsGenetic architectureSignificant lociGenetic variationMultiple traitsGene expressionAssociation studiesLociTraitsGenetic knowledgeComposite phenotypeUK BiobankExpressionTENM2GNAI1GenesGeneticsVariantsPhenotypeGenome-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 AmericansConsortium
2019
Genome-Wide Meta-Analyses of FTND and TTFC Phenotypes
Chen J, Loukola A, Gillespie NA, Peterson R, Jia P, Riley B, Maes H, Dick DM, Kendler KS, Damaj MI, Miles MF, Zhao Z, Li MD, Vink JM, Minica CC, Willemsen G, Boomsma DI, Qaiser B, Madden PAF, Korhonen T, Jousilahti P, Hällfors J, Gelernter J, Kranzler HR, Sherva R, Farrer L, Maher B, Vanyukov M, Taylor M, Ware JJ, Munafò MR, Lutz SM, Hokanson JE, Gu F, Landi MT, Caporaso NE, Hancock DB, Gaddis NC, Baker TB, Bierut LJ, Johnson EO, Chenoweth M, Lerman C, Tyndale R, Kaprio J, Chen X. Genome-Wide Meta-Analyses of FTND and TTFC Phenotypes. Nicotine & Tobacco Research 2019, 22: 900-909. PMID: 31294817, PMCID: PMC7249921, DOI: 10.1093/ntr/ntz099.Peer-Reviewed Original ResearchConceptsGenome-Wide Meta-AnalysisGene-based analysisChemokine signaling pathwaysGenetic architectureActin cytoskeletonNew lociReceptor recyclingMAPK signalingPathway interactionsBiological pathwaysSignaling pathwaysAxon guidanceNovel pathwayPhenotypeNovel candidatesEuropean ancestryPathwayReplication sampleNetwork analysisReplicationIno80CCytoskeletonCOPB2EndocytosisSORBS2
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 discoveriesPercentLocal adaptation in European populations affected the genetics of psychiatric disorders and behavioral traits
Polimanti R, Kayser MH, Gelernter J. Local adaptation in European populations affected the genetics of psychiatric disorders and behavioral traits. Genome Medicine 2018, 10: 24. PMID: 29580271, PMCID: PMC5870256, DOI: 10.1186/s13073-018-0532-7.Peer-Reviewed Original ResearchConceptsLocal adaptationPathogen diversityEuropean populationsBehavioral traitsGenome-wide investigationGenome-wide dataPolygenic risk score analysisProtozoan diversityWinter minimum temperaturesGenetic diversityEvolutionary mechanismsPositive selectionWidespread signalMolecular mechanismsTop findingsRisk score analysisDiversityTraitsCommon variationBehavioral phenotypesAdaptationGeneticsPopulationPhenotypeMechanism
2017
Genome-wide association study identifies a novel locus for cannabis dependence
Agrawal A, Chou YL, Carey CE, Baranger DAA, Zhang B, Sherva R, Wetherill L, Kapoor M, Wang JC, Bertelsen S, Anokhin AP, Hesselbrock V, Kramer J, Lynskey MT, Meyers JL, Nurnberger JI, Rice JP, Tischfield J, Bierut LJ, Degenhardt L, Farrer LA, Gelernter J, Hariri AR, Heath AC, Kranzler HR, Madden PAF, Martin NG, Montgomery GW, Porjesz B, Wang T, Whitfield JB, Edenberg HJ, Foroud T, Goate AM, Bogdan R, Nelson EC. Genome-wide association study identifies a novel locus for cannabis dependence. Molecular Psychiatry 2017, 23: 1293-1302. PMID: 29112194, PMCID: PMC5938138, DOI: 10.1038/mp.2017.200.Peer-Reviewed Original ResearchMeSH KeywordsAdultAllelesBlack or African AmericanCannabisCase-Control StudiesChromosomes, Human, Pair 10Cohort StudiesFemaleGene FrequencyGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHumansMaleMarijuana AbuseMiddle AgedPhenotypePolymorphism, Single NucleotideWhite PeopleYoung AdultConceptsWide significant lociSingle nucleotide polymorphismsSignificant lociGenome-wide significant lociGenome-wide association study dataGenome-wide association studiesAssociation study dataCorrelated single-nucleotide polymorphismsNovel lociTranscription factorsChromosome 10Association studiesModerate heritabilityNovel regionLociBiological contributionEA college studentsMinor alleleEuropean descentH3K4me1Criterion countsHeritabilityPhenotypeEnhancerIndependent cohortLargest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability
Duncan LE, Ratanatharathorn A, Aiello AE, Almli LM, Amstadter AB, Ashley-Koch AE, Baker DG, Beckham JC, Bierut LJ, Bisson J, Bradley B, Chen CY, Dalvie S, Farrer LA, Galea S, Garrett ME, Gelernter JE, Guffanti G, Hauser MA, Johnson EO, Kessler RC, Kimbrel NA, King A, Koen N, Kranzler HR, Logue MW, Maihofer AX, Martin AR, Miller MW, Morey RA, Nugent NR, Rice JP, Ripke S, Roberts AL, Saccone NL, Smoller JW, Stein DJ, Stein MB, Sumner JA, Uddin M, Ursano RJ, Wildman DE, Yehuda R, Zhao H, Daly MJ, Liberzon I, Ressler KJ, Nievergelt CM, Koenen KC. Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Molecular Psychiatry 2017, 23: 666-673. PMID: 28439101, PMCID: PMC5696105, DOI: 10.1038/mp.2017.77.Peer-Reviewed Original ResearchMeSH KeywordsAdultBipolar DisorderBlack or African AmericanCase-Control StudiesDepressive Disorder, MajorFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMiddle AgedMultifactorial InheritancePolymorphism, Single NucleotideRisk FactorsSchizophreniaSex CharacteristicsSex FactorsStress Disorders, Post-TraumaticWhite PeopleConceptsSingle nucleotide polymorphismsRisk lociSNP-level summary statisticsGenomic data resourcesGenome-wide significanceMolecular genetic dataComplex genetic disorderPolygenic risk predictionGenetic dataAncestral diversityLarge GWASGenetic indicesDiverse phenotypesGenetic riskHeritability estimatesLociSummary statisticsGenetic disordersHeritabilityGenetic influencesGWASDiversityPhenotypeTransethnicStrong evidence
2016
Meta-Analyses of Genome-Wide Association Data Hold New Promise for Addiction Genetics.
Agrawal A, Edenberg HJ, Gelernter J. Meta-Analyses of Genome-Wide Association Data Hold New Promise for Addiction Genetics. Journal Of Studies On Alcohol And Drugs 2016, 77: 676-80. PMID: 27588522, PMCID: PMC5015465, DOI: 10.15288/jsad.2016.77.676.Peer-Reviewed Original ResearchThe role of genes involved in stress, neural plasticity, and brain circuitry in depressive phenotypes: Convergent findings in a mouse model of neglect
Montalvo-Ortiz JL, Bordner KA, Carlyle BC, Gelernter J, Simen AA, Kaufman J. The role of genes involved in stress, neural plasticity, and brain circuitry in depressive phenotypes: Convergent findings in a mouse model of neglect. Behavioural Brain Research 2016, 315: 71-74. PMID: 27506655, PMCID: PMC5396458, DOI: 10.1016/j.bbr.2016.08.010.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsDepressionDisease Models, AnimalGene Expression RegulationInhibitor of Differentiation ProteinsMaleMaternal DeprivationMaze LearningMiceMice, Inbred C57BLMice, Inbred DBAMicroarray AnalysisNerve Tissue ProteinsNeuronal PlasticityPrefrontal CortexReceptors, N-Methyl-D-AspartateRNA, MessengerStress, PsychologicalSwimmingConceptsTubulin Polymerization Promoting ProteinRole of genesGene expression dataEpigenetic changesGene expressionPhenotype dataExpression dataPrefrontal cortex tissueGenesSecondary analysisMedial prefrontal cortex (mPFC) tissueGlutamate NMDA receptorsAdult male miceId-3Early life stressPhenotypeSwimming testMale miceNMDA receptorsDepression riskMaternal separationMouse modelDepressive phenotypeBrain circuitryBehavioral differencesDNA co-methylation modules in postmortem prefrontal cortex tissues of European Australians with alcohol use disorders
Wang F, Xu H, Zhao H, Gelernter J, Zhang H. DNA co-methylation modules in postmortem prefrontal cortex tissues of European Australians with alcohol use disorders. Scientific Reports 2016, 6: 19430. PMID: 26763658, PMCID: PMC4725922, DOI: 10.1038/srep19430.Peer-Reviewed Original ResearchConceptsCo-methylation modulesPostmortem prefrontal cortex tissueDNA methylome alterationsCo-methylation analysisDNA methylation alterationsSubstance dependence phenotypesTranscriptional regulationDNA methylomeMethylation alterationsMethylome alterationsBiological processesPostmortem prefrontal cortexExpression relationshipsNeural developmentDifferential expressionPrefrontal cortex tissueGenesDependence phenotypesMultiple testing correctionCpGAUD subjectsFemale pairsCortex tissueMethylomePhenotype
2015
Evidence of CNIH3 involvement in opioid dependence
Nelson EC, Agrawal A, Heath AC, Bogdan R, Sherva R, Zhang B, Al-Hasani R, Bruchas MR, Chou YL, Demers CH, Carey CE, Conley ED, Fakira AK, Farrer LA, Goate A, Gordon S, Henders AK, Hesselbrock V, Kapoor M, Lynskey MT, Madden PA, Moron JA, Rice JP, Saccone NL, Schwab SG, Shand FL, Todorov AA, Wallace L, Wang T, Wray NR, Zhou X, Degenhardt L, Martin NG, Hariri AR, Kranzler HR, Gelernter J, Bierut LJ, Clark DJ, Montgomery GW. Evidence of CNIH3 involvement in opioid dependence. Molecular Psychiatry 2015, 21: 608-614. PMID: 26239289, PMCID: PMC4740268, DOI: 10.1038/mp.2015.102.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphismsGenome-wide association studiesComputational genetic analysisEpigenetic annotationsGenetic analysisAssociation studiesGenetic studiesStudy of AddictionVivo functionalityMouse strainsOpioid dependenceNeurogenetics StudySevere addictive disordersΑ-aminoGenesOpioid misusersGeneticsCnih3SNPsDuke Neurogenetics StudyHaplotypesPhenotypeA alleleAllelesFetal brain
2014
SLC6A4 polymorphism, population genetics, and psychiatric traits
Gelernter J. SLC6A4 polymorphism, population genetics, and psychiatric traits. Human Genetics 2014, 133: 459-461. PMID: 24385047, PMCID: PMC3992709, DOI: 10.1007/s00439-013-1412-2.Peer-Reviewed Original Research