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 phenotypes
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
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
2018
GWAS and network analysis of co‐occurring nicotine and alcohol dependence identifies significantly associated alleles and network
Xiang B, Yang B, Zhou H, Kranzler H, Gelernter J. GWAS and network analysis of co‐occurring nicotine and alcohol dependence identifies significantly associated alleles and network. American Journal Of Medical Genetics Part B Neuropsychiatric Genetics 2018, 180: 3-11. PMID: 30488612, PMCID: PMC6918694, DOI: 10.1002/ajmg.b.32692.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlcoholismAllelesBlack or African AmericanComorbidityEthanolFemaleG(M2) Activator ProteinGene FrequencyGene Regulatory NetworksGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMiddle AgedNicotinePolymorphism, Single NucleotideProtein Interaction MapsRisk FactorsTobacco Use DisorderWhite PeopleConceptsGene subnetworksProtein-protein interaction (PPI) network analysisGenome-wide significant variantsInteraction network analysisGene-set analysisFunctional enrichment analysisSignificant SNPsQuantitative lociNerve growth factor pathwayGene enrichmentEnrichment analysisNetwork analysisGenetic traitsGrowth factor pathwaysRisk genesSignificant variantsGenesStudy of AddictionSNPsFactor pathwayGM2AAmphetamine addictionGenetic riskGWASSubnetworks
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 brainAncestry informative markers for distinguishing between Thai populations based on genome-wide association datasets
Vongpaisarnsin K, Listman JB, Malison RT, Gelernter J. Ancestry informative markers for distinguishing between Thai populations based on genome-wide association datasets. Legal Medicine 2015, 17: 245-250. PMID: 25759192, PMCID: PMC4480199, DOI: 10.1016/j.legalmed.2015.02.004.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAncestry informative markersAssociation studiesGenome-wide association datasetInformative markersGenetic structureHapMap populationsSNP dataInternational HapMap databaseSuch SNPsEast Asian populationsHapMap databaseSNPsMarkersPopulationDiversityAsian populationsEfficient strategyThai population
2014
The effects of a MAP2K5 microRNA target site SNP on risk for anxiety and depressive disorders
Jensen KP, Kranzler HR, Stein MB, Gelernter J. The effects of a MAP2K5 microRNA target site SNP on risk for anxiety and depressive disorders. American Journal Of Medical Genetics Part B Neuropsychiatric Genetics 2014, 165: 175-183. PMID: 24436253, PMCID: PMC4174417, DOI: 10.1002/ajmg.b.32219.Peer-Reviewed Original ResearchConceptsGene-trait relationshipsGWAS-identified variantsRegulation of mRNAGWAS signalsComplex traitsTrait associationsTarget genesStudy signalsSNP associationsRisk genesFunctional variantsSNPsGenesAnxiety-related traitsGene SNPsTraitsMRNAMAP2K5MicroRNAsMajor psychiatric disordersVariantsSite informationMitogenRegulationPathway
2011
Association between polymorphisms in catechol‐O‐methyltransferase (COMT) and cocaine‐induced paranoia in European‐American and African‐American populations
Ittiwut R, Listman JB, Ittiwut C, Cubells JF, Weiss RD, Brady K, Oslin D, Farrer LA, Kranzler HR, Gelernter J. Association between polymorphisms in catechol‐O‐methyltransferase (COMT) and cocaine‐induced paranoia in European‐American and African‐American populations. American Journal Of Medical Genetics Part B Neuropsychiatric Genetics 2011, 156: 651-660. PMID: 21656904, PMCID: PMC3864552, DOI: 10.1002/ajmg.b.31205.Peer-Reviewed Original Research
2010
Variation in Nicotinic Acetylcholine Receptor Genes is Associated with Multiple Substance Dependence Phenotypes
Sherva R, Kranzler HR, Yu Y, Logue MW, Poling J, Arias AJ, Anton RF, Oslin D, Farrer LA, Gelernter J. Variation in Nicotinic Acetylcholine Receptor Genes is Associated with Multiple Substance Dependence Phenotypes. Neuropsychopharmacology 2010, 35: 1921-1931. PMID: 20485328, PMCID: PMC3055642, DOI: 10.1038/npp.2010.64.Peer-Reviewed Original ResearchMeSH KeywordsAdultBlack or African AmericanChromosomes, Human, Pair 15Family HealthFemaleGene FrequencyGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHumansLinkage DisequilibriumMaleMiddle AgedPhenotypePolymorphism, Single NucleotideReceptors, NicotinicSubstance-Related DisordersWhite PeopleConceptsGene clusterAssociation studiesNicotinic receptor gene clusterNicotinic acetylcholine receptor genesAcetylcholine receptor genesReceptor gene clusterStrongest association signalSubstance dependence phenotypesAssociation signalsImportance of variationChromosome 15q25.1Opposite risk allelePermutation-based correctionDependence phenotypesReplication setReceptor geneMultiple polymorphismsSNPs
2008
Addictions Biology: Haplotype-Based Analysis for 130 Candidate Genes on a Single Array
Hodgkinson CA, Yuan Q, Xu K, Shen PH, Heinz E, Lobos EA, Binder EB, Cubells J, Ehlers CL, Gelernter J, Mann J, Riley B, Roy A, Tabakoff B, Todd RD, Zhou Z, Goldman D. Addictions Biology: Haplotype-Based Analysis for 130 Candidate Genes on a Single Array. Alcohol And Alcoholism 2008, 43: 505-515. PMID: 18477577, PMCID: PMC2724863, DOI: 10.1093/alcalc/agn032.Peer-Reviewed Original ResearchConceptsWhole-genome arraysCandidate genesHigh-quality SNPsGene of interestAncestry informative markersCase/control populationInformative markersHigh-throughput assaysAverage call rateComparison of haplotypesCall rateFull haplotype informationHaplotype-based analysisHaplotype coverageHaplotype informationGenesThroughput assaysSNPsDNA qualityTag SNPsHaplotypesReplication rate