Featured Publications
Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions
Levey DF, Stein MB, Wendt FR, Pathak GA, Zhou H, Aslan M, Quaden R, Harrington KM, Nuñez YZ, Overstreet C, Radhakrishnan K, Sanacora G, McIntosh AM, Shi J, Shringarpure SS, Concato J, Polimanti R, Gelernter J. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions. Nature Neuroscience 2021, 24: 954-963. PMID: 34045744, PMCID: PMC8404304, DOI: 10.1038/s41593-021-00860-2.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association studyMillion Veteran ProgramTranscriptome-wide association study (TWAS) analysisGenomic risk lociComplex psychiatric traitsGenetic architectureRisk lociGene expressionAssociation studiesLikely pathogenicityPsychiatric traitsVeteran ProgramNew therapeutic directionEuropean ancestryNew insightsAncestryUK BiobankAfrican ancestrySubstantial replicationExpressionLarge independent cohortsGWASTherapeutic directionsGenesLociDepression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses
Als T, Kurki M, Grove J, Voloudakis G, Therrien K, Tasanko E, Nielsen T, Naamanka J, Veerapen K, Levey D, Bendl J, Bybjerg-Grauholm J, Zeng B, Demontis D, Rosengren A, Athanasiadis G, Bækved-Hansen M, Qvist P, Bragi Walters G, Thorgeirsson T, Stefánsson H, Musliner K, Rajagopal V, Farajzadeh L, Thirstrup J, Vilhjálmsson B, McGrath J, Mattheisen M, Meier S, Agerbo E, Stefánsson K, Nordentoft M, Werge T, Hougaard D, Mortensen P, Stein M, Gelernter J, Hovatta I, Roussos P, Daly M, Mors O, Palotie A, Børglum A. Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses. Nature Medicine 2023, 29: 1832-1844. PMID: 37464041, PMCID: PMC10839245, DOI: 10.1038/s41591-023-02352-1.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphism heritabilityGenome-wide analysisLikely causal genesFunctional genomics dataRisk variantsWide association studyPolygenic burdenPsychiatric disordersCausal genesPolygenic architectureGenomic dataRisk lociAssociation studiesSubgroups of depressionCause of disabilityDepression genetic riskCommon psychiatric disordersPrecision medicine approachCases of depressionOligodendrocyte lineageGenesLociConsiderable sex differencesGABAergic neuronsPsychiatric comorbidityGenome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways
Watanabe K, Jansen PR, Savage JE, Nandakumar P, Wang X, Hinds D, Gelernter J, Levey D, Polimanti R, Stein M, Van Someren E, Smit A, Posthuma D. Genome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways. Nature Genetics 2022, 54: 1125-1132. PMID: 35835914, DOI: 10.1038/s41588-022-01124-w.Peer-Reviewed Original ResearchConceptsRisk lociGenome-wide association studiesSpecific gene setsPrevious genome-wide association studyGene prioritization strategyExternal biological resourcesExtreme polygenicityExpression specificityAssociated lociSignaling functionsGene setsAssociation studiesNeuronal differentiationFunctional interactionGenesLociBiological resourcesPolygenicityNovel strategyPrioritization strategiesSpecific hypothesesDifferentiationPathwayStatistical powerLarge number
2024
Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder
Nievergelt C, Maihofer A, Atkinson E, Chen C, Choi K, Coleman J, Daskalakis N, Duncan L, Polimanti R, Aaronson C, Amstadter A, Andersen S, Andreassen O, Arbisi P, Ashley-Koch A, Austin S, Avdibegoviç E, Babić D, Bacanu S, Baker D, Batzler A, Beckham J, Belangero S, Benjet C, Bergner C, Bierer L, Biernacka J, Bierut L, Bisson J, Boks M, Bolger E, Brandolino A, Breen G, Bressan R, Bryant R, Bustamante A, Bybjerg-Grauholm J, Bækvad-Hansen M, Børglum A, Børte S, Cahn L, Calabrese J, Caldas-de-Almeida J, Chatzinakos C, Cheema S, Clouston S, Colodro-Conde L, Coombes B, Cruz-Fuentes C, Dale A, Dalvie S, Davis L, Deckert J, Delahanty D, Dennis M, Desarnaud F, DiPietro C, Disner S, Docherty A, Domschke K, Dyb G, Kulenović A, Edenberg H, Evans A, Fabbri C, Fani N, Farrer L, Feder A, Feeny N, Flory J, Forbes D, Franz C, Galea S, Garrett M, Gelaye B, Gelernter J, Geuze E, Gillespie C, Goleva S, Gordon S, Goçi A, Grasser L, Guindalini C, Haas M, Hagenaars S, Hauser M, Heath A, Hemmings S, Hesselbrock V, Hickie I, Hogan K, Hougaard D, Huang H, Huckins L, Hveem K, Jakovljević M, Javanbakht A, Jenkins G, Johnson J, Jones I, Jovanovic T, Karstoft K, Kaufman M, Kennedy J, Kessler R, Khan A, Kimbrel N, King A, Koen N, Kotov R, Kranzler H, Krebs K, Kremen W, Kuan P, Lawford B, Lebois L, Lehto K, Levey D, Lewis C, Liberzon I, Linnstaedt S, Logue M, Lori A, Lu Y, Luft B, Lupton M, Luykx J, Makotkine I, Maples-Keller J, Marchese S, Marmar C, Martin N, Martínez-Levy G, McAloney K, McFarlane A, McLaughlin K, McLean S, Medland S, Mehta D, Meyers J, Michopoulos V, Mikita E, Milani L, Milberg W, Miller M, Morey R, Morris C, Mors O, Mortensen P, Mufford M, Nelson E, Nordentoft M, Norman S, Nugent N, O’Donnell M, Orcutt H, Pan P, Panizzon M, Pathak G, Peters E, Peterson A, Peverill M, Pietrzak R, Polusny M, Porjesz B, Powers A, Qin X, Ratanatharathorn A, Risbrough V, Roberts A, Rothbaum A, Rothbaum B, Roy-Byrne P, Ruggiero K, Rung A, Runz H, Rutten B, de Viteri S, Salum G, Sampson L, Sanchez S, Santoro M, Seah C, Seedat S, Seng J, Shabalin A, Sheerin C, Silove D, Smith A, Smoller J, Sponheim S, Stein D, Stensland S, Stevens J, Sumner J, Teicher M, Thompson W, Tiwari A, Trapido E, Uddin M, Ursano R, Valdimarsdóttir U, Van Hooff M, Vermetten E, Vinkers C, Voisey J, Wang Y, Wang Z, Waszczuk M, Weber H, Wendt F, Werge T, Williams M, Williamson D, Winsvold B, Winternitz S, Wolf C, Wolf E, Xia Y, Xiong Y, Yehuda R, Young K, Young R, Zai C, Zai G, Zervas M, Zhao H, Zoellner L, Zwart J, deRoon-Cassini T, van Rooij S, van den Heuvel L, Stein M, Ressler K, Koenen K. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder. Nature Genetics 2024, 56: 792-808. PMID: 38637617, PMCID: PMC11396662, DOI: 10.1038/s41588-024-01707-9.Peer-Reviewed Original ResearchConceptsMeta-analysis of genome-wide association studiesGenome-wide significant lociMulti-ancestry meta-analysisGenome-wide association analysisGenome-wide association studiesIndividuals of European ancestryPotential causal genesNative American ancestryMulti-omics approachPost-traumatic stress disorderAdmixed individualsSignificant lociRisk lociCausal genesAssociation studiesAssociation analysisFunctional genesTranscription factorsGenetic studiesAmerican ancestryEuropean ancestryAxon guidanceSynaptic structureLociGenes
2022
Exploring the genetic overlap between twelve psychiatric disorders
Romero C, Werme J, Jansen P, Gelernter J, Stein M, Levey D, Polimanti R, de Leeuw C, Posthuma D, Nagel M, van der Sluis S. Exploring the genetic overlap between twelve psychiatric disorders. Nature Genetics 2022, 54: 1795-1802. PMID: 36471075, DOI: 10.1038/s41588-022-01245-2.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphismsPleiotropic single nucleotide polymorphismsPositive genetic correlationStringent P-value thresholdGenetic architectureGenomic regionsGenetic covarianceBiological processesBiological pathwaysMolecular characterizationObserved phenotypicGenetic correlationsGenetic overlapBiological characterizationBiological mechanismsP-value thresholdOnly annotationGenesPleiotropicPairwise comparisonsPhenotypicPathwayAnnotationPolymorphismCharacterizationIntegrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder
Wingo TS, Gerasimov ES, Liu Y, Duong DM, Vattathil SM, Lori A, Gockley J, Breen MS, Maihofer AX, Nievergelt CM, Koenen KC, Levey DF, Gelernter J, Stein MB, Ressler KJ, Bennett DA, Levey AI, Seyfried NT, Wingo AP. Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder. Molecular Psychiatry 2022, 27: 3075-3084. PMID: 35449297, PMCID: PMC9233006, DOI: 10.1038/s41380-022-01544-4.Peer-Reviewed Original ResearchConceptsProteome-wide association studyTranscriptome-wide association studyGenome-wide association studiesBrain protein abundanceHuman brain proteomeBrain proteomeAssociation studiesProtein abundanceGenome-wide association dataHuman brain transcriptomePost-traumatic stress disorderGWAS resultsNovel proteinBrain transcriptomeRisk lociProteomeGenesAssociation dataPrecursor cellsPTSD pathogenesisBrain mRNA levelsMRNA levelsOligodendrocyte precursor cellsPromising targetNew insightsGenetically regulated multi-omics study for symptom clusters of posttraumatic stress disorder highlights pleiotropy with hematologic and cardio-metabolic traits
Pathak GA, Singh K, Wendt FR, Fleming TW, Overstreet C, Koller D, Tylee DS, De Angelis F, Cabrera Mendoza B, Levey DF, Koenen KC, Krystal JH, Pietrzak RH, O’ Donell C, Gaziano JM, Falcone G, Stein MB, Gelernter J, Pasaniuc B, Mancuso N, Davis LK, Polimanti R. Genetically regulated multi-omics study for symptom clusters of posttraumatic stress disorder highlights pleiotropy with hematologic and cardio-metabolic traits. Molecular Psychiatry 2022, 27: 1394-1404. PMID: 35241783, PMCID: PMC9210390, DOI: 10.1038/s41380-022-01488-9.Peer-Reviewed Original ResearchConceptsLocal genetic correlationsCell type-specific expressionVanderbilt University biorepositoryMulti-omics studiesMulti-omics investigationsDorsolateral prefrontal cortex tissueGenomic evidenceLaboratory traitsSpecific expressionCardio-metabolic traitsMillion Veteran ProgramPrefrontal cortex tissueMiR-148GenesGenetic correlationsRegulatory profileTraitsProtein expressionCardiometabolic traitsExpressionVeteran ProgramCortex tissueBiological heterogeneitySplicingPrioritization approach
2021
Pleiotropic effects of telomere length loci with brain morphology and brain tissue expression
Pathak GA, Wendt FR, Levey DF, Mecca AP, van Dyck CH, Gelernter J, Polimanti R. Pleiotropic effects of telomere length loci with brain morphology and brain tissue expression. Human Molecular Genetics 2021, 30: 1360-1370. PMID: 33831179, PMCID: PMC8255129, DOI: 10.1093/hmg/ddab102.Peer-Reviewed Original ResearchConceptsMethylation expressionGenetic variantsMapping gene functionTelomere lengthChromatin associationChromatin profilesGene functionGenetic colocalizationGene mappingGenomic relationshipsNeuropsychiatric traitsPleiotropic rolesDrug-gene interactionsCertain lociBrain tissue expressionGenesLociPleiotropic effectsBrain morphology measuresNucleotide polymorphismsAncestry populationsTissue expressionPhenotypic associationsPleiotropyAncestry groupsSex-stratified gene-by-environment genome-wide interaction study of trauma, posttraumatic-stress, and suicidality
Wendt FR, Pathak GA, Levey DF, Nuñez YZ, Overstreet C, Tyrrell C, Adhikari K, De Angelis F, Tylee DS, Goswami A, Krystal JH, Abdallah CG, Stein MB, Kranzler HR, Gelernter J, Polimanti R. Sex-stratified gene-by-environment genome-wide interaction study of trauma, posttraumatic-stress, and suicidality. Neurobiology Of Stress 2021, 14: 100309. PMID: 33665242, PMCID: PMC7905234, DOI: 10.1016/j.ynstr.2021.100309.Peer-Reviewed Original ResearchGenome-wide interaction studyRisk lociChromatin interaction profilesExtracellular matrix biologyGene-based analysisMatrix biologyMolecular basisTranscriptomic profilesInteraction studiesMultivariate geneGenetic perspectiveSNP effectsSuicidal behavior severityLociNovel targetGenesInteraction profilesSynaptic plasticityCellsInteractorsGenetic riskBiologyStressGxEIndependent cohort
2020
Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits
Zhou H, Sealock JM, Sanchez-Roige S, Clarke TK, Levey DF, Cheng Z, Li B, Polimanti R, Kember RL, Smith RV, Thygesen JH, Morgan MY, Atkinson SR, Thursz MR, Nyegaard M, Mattheisen M, Børglum AD, Johnson EC, Justice AC, Palmer AA, McQuillin A, Davis LK, Edenberg HJ, Agrawal A, Kranzler HR, Gelernter J. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nature Neuroscience 2020, 23: 809-818. PMID: 32451486, PMCID: PMC7485556, DOI: 10.1038/s41593-020-0643-5.Peer-Reviewed Original ResearchConceptsRegulatory genomic regionsGenome-wide association studiesNovel risk lociEuropean ancestry individualsPolygenic risk score analysisIndependent risk variantsGenetic architectureGenomic regionsRisk lociAssociation studiesGenetic relationshipsRisk genesGenetic correlationsPsychiatric traitsRisk variantsRisk score analysisTraitsGenetic heritabilityYields insightsBiobank samplesMendelian randomizationGenesLociBiologyHeritability
2019
International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci
Nievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen CY, Choi KW, Coleman JRI, Dalvie S, Duncan LE, Gelernter J, Levey DF, Logue MW, Polimanti R, Provost AC, Ratanatharathorn A, Stein MB, Torres K, Aiello AE, Almli LM, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegovic E, Babić D, Bækvad-Hansen M, Baker DG, Beckham JC, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Børglum AD, Bradley B, Brashear M, Breen G, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Calabrese JR, Caldas- de- Almeida J, Dale AM, Daly MJ, Daskalakis NP, Deckert J, Delahanty DL, Dennis MF, Disner SG, Domschke K, Dzubur-Kulenovic A, Erbes CR, Evans A, Farrer LA, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Geuze E, Gillespie C, Uka AG, Gordon SD, Guffanti G, Hammamieh R, Harnal S, Hauser MA, Heath AC, Hemmings SMJ, Hougaard DM, Jakovljevic M, Jett M, Johnson EO, Jones I, Jovanovic T, Qin XJ, Junglen AG, Karstoft KI, Kaufman ML, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kranzler HR, Kremen WS, Lawford BR, Lebois LAM, Lewis CE, Linnstaedt SD, Lori A, Lugonja B, Luykx JJ, Lyons MJ, Maples-Keller J, Marmar C, Martin AR, Martin NG, Maurer D, Mavissakalian MR, McFarlane A, McGlinchey RE, McLaughlin KA, McLean SA, McLeay S, Mehta D, Milberg WP, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Neale BM, Nelson EC, Nordentoft M, Norman SB, O’Donnell M, Orcutt HK, Panizzon MS, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Rice JP, Ripke S, Risbrough VB, Roberts AL, Rothbaum AO, Rothbaum BO, Roy-Byrne P, Ruggiero K, Rung A, Rutten BPF, Saccone NL, Sanchez SE, Schijven D, Seedat S, Seligowski AV, Seng JS, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Trapido E, Uddin M, Ursano RJ, van den Heuvel LL, Van Hooff M, Vermetten E, Vinkers CH, Voisey J, Wang Y, Wang Z, Werge T, Williams MA, Williamson DE, Winternitz S, Wolf C, Wolf EJ, Wolff JD, Yehuda R, Young RM, Young KA, Zhao H, Zoellner LA, Liberzon I, Ressler KJ, Haas M, Koenen KC. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nature Communications 2019, 10: 4558. PMID: 31594949, PMCID: PMC6783435, DOI: 10.1038/s41467-019-12576-w.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesDisease genesAssociation studiesGenome-wide significant lociAfrican-ancestry analysesNon-coding RNAsGenetic risk lociParkinson's disease genesEuropean ancestry populationsNovel genesSignificant lociGenetic variationSpecific lociRisk lociAdditional lociLociAncestry populationsCommon variantsHeritability estimatesGenesGWASRNABiologySNPsPARK2
2014
Genetic risk prediction and neurobiological understanding of alcoholism
Levey DF, Le-Niculescu H, Frank J, Ayalew M, Jain N, Kirlin B, Learman R, Winiger E, Rodd Z, Shekhar A, Schork N, Kiefe F, Wodarz N, Müller-Myhsok B, Dahmen N, Nöthen M, Sherva R, Farrer L, Smith A, Kranzler H, Rietschel M, Gelernter J, Niculescu A. Genetic risk prediction and neurobiological understanding of alcoholism. Translational Psychiatry 2014, 4: e391-e391. PMID: 24844177, PMCID: PMC4035721, DOI: 10.1038/tp.2014.29.Peer-Reviewed Original ResearchConceptsTop candidate genesCandidate genesGenetic risk predictionGenome-wide association study dataFunctional genomics approachConvergent functional genomics approachAssociation study dataGene expression dataInitial discovery stepGenomic approachesKey genesSignal transductionSignificant genetic overlapTop genesRelevant genesBiological pathwaysExpression dataTop findingsGenesStrict Bonferroni correctionGenetic overlapProtein knockout miceSmall panelFatty acidsKnockout mice
2012
Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction
Ayalew M, Le-Niculescu H, Levey DF, Jain N, Changala B, Patel SD, Winiger E, Breier A, Shekhar A, Amdur R, Koller D, Nurnberger JI, Corvin A, Geyer M, Tsuang MT, Salomon D, Schork NJ, Fanous AH, O'Donovan MC, Niculescu AB. Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction. Molecular Psychiatry 2012, 17: 887-905. PMID: 22584867, PMCID: PMC3427857, DOI: 10.1038/mp.2012.37.Peer-Reviewed Original ResearchConceptsTop candidate genesCandidate genesG protein-coupled receptor signalingGenome-wide association study dataFunctional genomics approachReceptor signalingConvergent functional genomics approachAssociation study dataGene expression studiesFunctional genomicsGenomic approachesGlutamate receptor signalingSignificant genetic overlapSingle nucleotide polymorphismsEnvironmental stressTop genesExpression studiesPathway analysisBiological pathwaysGenetic risk predictionGenesCell adhesionBiological landscapeGenetic overlapSignaling