2024
Genome-wide meta-analysis of myasthenia gravis uncovers new loci and provides insights into polygenic prediction
Braun A, Shekhar S, Levey D, Straub P, Kraft J, Panagiotaropoulou G, Heilbron K, Awasthi S, Meleka Hanna R, Hoffmann S, Stein M, Lehnerer S, Mergenthaler P, Elnahas A, Topaloudi A, Koromina M, Palviainen T, Asbjornsdottir B, Stefansson H, Skuladóttir A, Jónsdóttir I, Stefansson K, Reis K, Esko T, Palotie A, Leypoldt F, Stein M, Fontanillas P, Kaprio J, Gelernter J, Davis L, Paschou P, Tannemaat M, Verschuuren J, Kuhlenbäumer G, Gregersen P, Huijbers M, Stascheit F, Meisel A, Ripke S. Genome-wide meta-analysis of myasthenia gravis uncovers new loci and provides insights into polygenic prediction. Nature Communications 2024, 15: 9839. PMID: 39537604, PMCID: PMC11560923, DOI: 10.1038/s41467-024-53595-6.Peer-Reviewed Original ResearchConceptsPerformance of polygenic risk scoresGenome-wide significant hitsGenome-wide association studiesGenome-wide meta-analysisControls of European ancestryGenetic architecturePolygenic risk scoresSignificant hitsAssociation studiesPhenotypic variationPolygenic predictionEuropean ancestryAssociated with early-onsetHuman leukocyte antigen allelesLociEarly-onsetReplication studyNeuromuscular junctionMyasthenia gravisAutoantibody-mediated diseasesAntigen allelesAllelesAncestryDisease manifestationsLate-onset MG
2018
Genome‐wide association meta‐analysis of age at first cannabis use
Minică CC, Verweij KJH, van der Most P, Mbarek H, Bernard M, van Eijk K, Lind PA, Liu MZ, Maciejewski DF, Palviainen T, Sánchez‐Mora C, Sherva R, Taylor M, Walters RK, Abdellaoui A, Bigdeli TB, Branje SJT, Brown SA, Casas M, Corley RP, Davey‐Smith G, Davies GE, Ehli EA, Farrer L, Fedko IO, Garcia‐Martínez I, Gordon SD, Hartman CA, Heath AC, Hickie IB, Hickman M, Hopfer CJ, Hottenga JJ, Kahn RS, Kaprio J, Korhonen T, Kranzler HR, Krauter K, van Lier P, Madden PAF, Medland SE, Neale MC, Meeus WHJ, Montgomery GW, Nolte IM, Oldehinkel AJ, Pausova Z, Ramos‐Quiroga J, Richarte V, Rose RJ, Shin J, Stallings MC, Wall TL, Ware JJ, Wright MJ, Zhao H, Koot HM, Paus T, Hewitt JK, Ribasés M, Loukola A, Boks MP, Snieder H, Munafò MR, Gelernter J, Boomsma DI, Martin NG, Gillespie NA, Vink JM, Derks EM. Genome‐wide association meta‐analysis of age at first cannabis use. Addiction 2018, 113: 2073-2086. PMID: 30003630, PMCID: PMC7087375, DOI: 10.1111/add.14368.Peer-Reviewed Original ResearchConceptsGenome-wide associationSingle nucleotide polymorphismsLinkage disequilibriumTwin-based heritabilityGene-based testsHigh linkage disequilibriumATPase geneWide associationATP2C2 geneChromosome 16Heritability analysisHeritability of ageGenetic variantsNucleotide polymorphismsDiscovery sampleHeritabilityGenesATP2C2Replication sampleEnvironmental factorsRole of calciumIdentified associationsFirst cannabis useFirst cannabisATP2B2
2017
Sex Differences in Methamphetamine Use and Dependence in a Thai Treatment Center
Rungnirundorn T, Verachai V, Gelernter J, Malison RT, Kalayasiri R. Sex Differences in Methamphetamine Use and Dependence in a Thai Treatment Center. Journal Of Addiction Medicine 2017, 11: 19. PMID: 27649265, PMCID: PMC5291761, DOI: 10.1097/adm.0000000000000262.Peer-Reviewed Original ResearchConceptsMA dependenceMA useLifetime episodesTreatment centersLogistic regression analysisResidential drug treatmentChi-square testSex differencesSemi-Structured AssessmentAntisocial personality disorderMA diagnosisMA withdrawalTreatment cohortsComorbid antisocial personality disorderAssociated FactorsDrug treatmentSex-specific differencesNicotine dependencePsychomotor retardationDrug dependenceLogistic regressionMethamphetamine useMA usersPersonality disorderMethamphetamine
2013
Distinct Loci in the CHRNA5/CHRNA3/CHRNB4 Gene Cluster Are Associated With Onset of Regular Smoking
Stephens SH, Hartz SM, Hoft NR, Saccone NL, Corley RC, Hewitt JK, Hopfer CJ, Breslau N, Coon H, Chen X, Ducci F, Dueker N, Franceschini N, Frank J, Han Y, Hansel NN, Jiang C, Korhonen T, Lind PA, Liu J, Lyytikäinen L, Michel M, Shaffer JR, Short SE, Sun J, Teumer A, Thompson JR, Vogelzangs N, Vink JM, Wenzlaff A, Wheeler W, Yang B, Aggen SH, Balmforth AJ, Baumeister SE, Beaty TH, Benjamin DJ, Bergen AW, Broms U, Cesarini D, Chatterjee N, Chen J, Cheng Y, Cichon S, Couper D, Cucca F, Dick D, Foroud T, Furberg H, Giegling I, Gillespie NA, Gu F, Hall AS, Hällfors J, Han S, Hartmann AM, Heikkilä K, Hickie IB, Hottenga JJ, Jousilahti P, Kaakinen M, Kähönen M, Koellinger PD, Kittner S, Konte B, Landi M, Laatikainen T, Leppert M, Levy SM, Mathias RA, McNeil DW, Medland SE, Montgomery GW, Murray T, Nauck M, North KE, Paré PD, Pergadia M, Ruczinski I, Salomaa V, Viikari J, Willemsen G, Barnes KC, Boerwinkle E, Boomsma DI, Caporaso N, Edenberg HJ, Francks C, Gelernter J, Grabe HJ, Hops H, Jarvelin M, Johannesson M, Kendler KS, Lehtimäki T, Magnusson PK, Marazita ML, Marchini J, Mitchell BD, Nöthen MM, Penninx BW, Raitakari O, Rietschel M, Rujescu D, Samani NJ, Schwartz AG, Shete S, Spitz M, Swan GE, Völzke H, Veijola J, Wei Q, Amos C, Cannon DS, Grucza R, Hatsukami D, Heath A, Johnson EO, Kaprio J, Madden P, Martin NG, Stevens VL, Weiss RB, Kraft P, Bierut LJ, Ehringer MA. Distinct Loci in the CHRNA5/CHRNA3/CHRNB4 Gene Cluster Are Associated With Onset of Regular Smoking. Genetic Epidemiology 2013, 37: 846-859. PMID: 24186853, PMCID: PMC3947535, DOI: 10.1002/gepi.21760.Peer-Reviewed Original ResearchConceptsGene clusterAssociation signalsEarly smoking behaviourSmoking behaviorCHRNA5/A3/B4 gene clusterNicotinic acetylcholine receptor genesRobust association signalsNeuronal nicotinic acetylcholine receptor geneAcetylcholine receptor genesNicotine dependenceCHRNB4 gene clusterSignificant associationB4 gene clusterDistinct lociLung cancer riskRegular tobacco useAssociation resultsNicotine dependence phenotypesDependence phenotypesReceptor geneCotinine levelsRs1948PhenotypeRegular smokingProtective effectRate of progression from first use to dependence on cocaine or opioids: A cross-substance examination of associated demographic, psychiatric, and childhood risk factors
Sartor CE, Kranzler HR, Gelernter J. Rate of progression from first use to dependence on cocaine or opioids: A cross-substance examination of associated demographic, psychiatric, and childhood risk factors. Addictive Behaviors 2013, 39: 473-479. PMID: 24238782, PMCID: PMC3855905, DOI: 10.1016/j.addbeh.2013.10.021.Peer-Reviewed Original ResearchMeSH KeywordsAdultAge of OnsetAgedAlcohol-Related DisordersBehavior, AddictiveCase-Control StudiesChildChild AbuseCluster AnalysisCocaine-Related DisordersConnecticutCross-Sectional StudiesDiagnosis, Dual (Psychiatry)Disease ProgressionFemaleHumansInterviews as TopicLogistic ModelsMaleMental DisordersMiddle AgedOpioid-Related DisordersPennsylvaniaRisk FactorsSocial EnvironmentSocioeconomic FactorsSouth CarolinaTime FactorsConceptsChildhood risk factorsOpioid dependenceOpioid-dependent participantsRisk factorsCocaine dependenceDependent participantsImportant public health goalRate of progressionPublic health goalsLogistic regression modelsBlack/African AmericanChildhood physical abuseOrdinal logistic regression modelsCocaine-dependent participantsMean ageSlow progressionElevated riskAssociated demographicPsychiatric disordersMulti-site studyDependence diagnosisGreater riskHealth goalsSubstance dependenceConduct disorder
2012
Increased Genetic Vulnerability to Smoking at CHRNA5 in Early-Onset Smokers
Hartz SM, Short SE, Saccone NL, Culverhouse R, Chen L, Schwantes-An TH, Coon H, Han Y, Stephens SH, Sun J, Chen X, Ducci F, Dueker N, Franceschini N, Frank J, Geller F, Gubjartsson D, Hansel NN, Jiang C, Keskitalo-Vuokko K, Liu Z, Lyytikäinen LP, Michel M, Rawal R, Rosenberger A, Scheet P, Shaffer JR, Teumer A, Thompson JR, Vink JM, Vogelzangs N, Wenzlaff AS, Wheeler W, Xiao X, Yang BZ, Aggen SH, Balmforth AJ, Baumeister SE, Beaty T, Bennett S, Bergen AW, Boyd HA, Broms U, Campbell H, Chatterjee N, Chen J, Cheng YC, Cichon S, Couper D, Cucca F, Dick DM, Foroud T, Furberg H, Giegling I, Gu F, Hall AS, Hällfors J, Han S, Hartmann AM, Hayward C, Heikkilä K, Hewitt JK, Hottenga JJ, Jensen MK, Jousilahti P, Kaakinen M, Kittner SJ, Konte B, Korhonen T, Landi MT, Laatikainen T, Leppert M, Levy SM, Mathias RA, McNeil DW, Medland SE, Montgomery GW, Muley T, Murray T, Nauck M, North K, Pergadia M, Polasek O, Ramos EM, Ripatti S, Risch A, Ruczinski I, Rudan I, Salomaa V, Schlessinger D, Styrkársdóttir U, Terracciano A, Uda M, Willemsen G, Wu X, Abecasis G, Barnes K, Bickeböller H, Boerwinkle E, Boomsma DI, Caporaso N, Duan J, Edenberg HJ, Francks C, Gejman PV, Gelernter J, Grabe HJ, Hops H, Jarvelin MR, Viikari J, Kähönen M, Kendler KS, Lehtimäki T, Levinson DF, Marazita ML, Marchini J, Melbye M, Mitchell BD, Murray JC, Nöthen MM, Penninx BW, Raitakari O, Rietschel M, Rujescu D, Samani NJ, Sanders AR, Schwartz AG, Shete S, Shi J, Spitz M, Stefansson K, Swan GE, Thorgeirsson T, Völzke H, Wei Q, Wichmann H, Amos CI, Breslau N, Cannon DS, Ehringer M, Grucza R, Hatsukami D, Heath A, Johnson EO, Kaprio J, Madden P, Martin NG, Stevens VL, Stitzel JA, Weiss RB, Kraft P, Bierut LJ. Increased Genetic Vulnerability to Smoking at CHRNA5 in Early-Onset Smokers. JAMA Psychiatry 2012, 69: 854-860. PMID: 22868939, PMCID: PMC3482121, DOI: 10.1001/archgenpsychiatry.2012.124.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdolescent DevelopmentAdultAge of OnsetEuropeFemaleGene-Environment InteractionGenetic Association StudiesGenetic Predisposition to DiseaseHumansMaleNerve Tissue ProteinsNicotinePolymorphism, Single NucleotideReceptors, NicotinicSeverity of Illness IndexSmokingTobacco Use DisorderConceptsEarly-onset smokersLate-onset smokersHeavy smokersRisk allelesGenetic vulnerabilityRs16969968 genotypeLight smokersLight smokingRegular smokingSmokersSmokingMeta-AnalysisLogistic regressionRs16969968Single nucleotide polymorphismsAgeNonsynonymous single nucleotide polymorphismsCHRNA5Recent studiesAvailable genetic studiesAssociationSample sizeStudyCigarettesGenetic studies
2011
Empirically derived subtypes of opioid use and related behaviors
Chan G, Gelernter J, Oslin D, Farrer L, Kranzler HR. Empirically derived subtypes of opioid use and related behaviors. Addiction 2011, 106: 1146-1154. PMID: 21306596, PMCID: PMC3164489, DOI: 10.1111/j.1360-0443.2011.03390.x.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge of OnsetAgedAnalgesics, OpioidChildCluster AnalysisComorbidityDiagnostic and Statistical Manual of Mental DisordersFamily HealthFemaleGenetic Association StudiesGenetic Predisposition to DiseaseGenotypeHumansInterview, PsychologicalMaleMental DisordersMiddle AgedOpioid-Related DisordersPhenotypePrevalenceSiblingsSubstance Abuse, IntravenousUnited StatesYoung AdultConceptsOpioid usePsychiatric disordersHomogeneous subtypesRelated behaviorsSemi-Structured AssessmentGeneral community sampleOpioid dependenceMedical historyPrevalence ratesCocaine dependenceDrug dependenceSubtypesParticipant demographicsSubstance useCase-control genetic studyDemographicsDisordersCommunity sampleGenetic studies
2009
Adolescent cannabis use increases risk for cocaine-induced paranoia
Kalayasiri R, Gelernter J, Farrer L, Weiss R, Brady K, Gueorguieva R, Kranzler HR, Malison RT. Adolescent cannabis use increases risk for cocaine-induced paranoia. Drug And Alcohol Dependence 2009, 107: 196-201. PMID: 19944543, PMCID: PMC2821949, DOI: 10.1016/j.drugalcdep.2009.10.006.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdolescent BehaviorAge FactorsAge of OnsetCannabisCatechol O-MethyltransferaseCocaine-Related DisordersFemaleGene FrequencyGenetic Predisposition to DiseaseHumansLogistic ModelsMaleParanoid DisordersPolymerase Chain ReactionPrevalencePsychiatric Status Rating ScalesRisk FactorsSeverity of Illness IndexSiblingsUnited StatesConceptsAdolescent onset cannabisEarly cannabis exposureCocaine-dependent individualsCocaine-induced paranoiaCannabis exposureRisk factorsCOMT genotypeSemi-Structured AssessmentCatechol-O-methyl transferase (COMT) geneCOMT Val158Met genotypeCannabis abuseIncrease riskPsychotic symptomsOnset interactionPsychotic disordersStimulant abuseDrug dependenceFamily-based studyLogistic regressionEarly exposureAdolescent cannabisCannabisGenetic factorsSignificant predictorsVal158Met genotype
2006
Apolipoprotein E ε4 Allele Is Unrelated to Cognitive or Functional Decline in Alzheimer’s Disease: Retrospective and Prospective Analysis
Kleiman T, Zdanys K, Black B, Rightmer T, Grey M, Garman K, MacAvoy M, Gelernter J, van Dyck C. Apolipoprotein E ε4 Allele Is Unrelated to Cognitive or Functional Decline in Alzheimer’s Disease: Retrospective and Prospective Analysis. Dementia And Geriatric Cognitive Disorders 2006, 22: 73-82. PMID: 16699282, DOI: 10.1159/000093316.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseFunctional declineProspective analysisAD patientsEpsilon4 doseMultiple subsequent time pointsApolipoprotein E (APOE) ε4 alleleGenotype groupsApolipoprotein E epsilon4 alleleDuration of symptomsAD patient samplesGenetic risk factorsSubsequent time pointsTests of cognitionRisk factorsAPOE epsilon4Disease onsetDisease progressionEpsilon4 alleleFunctional impairmentRetrospective analysisΕ4 alleleDaily functionPatient samplesFunctional measuresRisk factors for cocaine-induced paranoia in cocaine-dependent sibling pairs
Kalayasiri R, Kranzler HR, Weiss R, Brady K, Gueorguieva R, Panhuysen C, Yang BZ, Farrer L, Gelernter J, Malison RT. Risk factors for cocaine-induced paranoia in cocaine-dependent sibling pairs. Drug And Alcohol Dependence 2006, 84: 77-84. PMID: 16413147, DOI: 10.1016/j.drugalcdep.2005.12.002.Peer-Reviewed Original Research