A Family-Based Genome Wide Association Study of Externalizing Behaviors.
paper
Primary
Public
- Authors
- Barr, Peter B; Salvatore, Jessica E; Wetherill, Leah; Anokhin, Andrey; Chan, Grace; Edenberg, Howard J; Kuperman, Samuel; Meyers, Jacquelyn; Nurnberger, John; Porjesz, Bernice; Schuckit, Mark; Dick, Danielle M
- Year
- 2020
- Journal
- Behavior genetics
- PMID
- 32239439
- DOI
- 10.1007/s10519-020-09999-3
- PMCID
- PMC7243710
Shared genetic factors contribute to the high degree of comorbidity among externalizing problems (e.g. substance use and antisocial behavior). We leverage this common genetic etiology to identify genetic influences externalizing problems in participants from the Collaborative Study on the Genetics of Alcoholism (European ancestry = 7568; African ancestry = 3274). We performed a family-based genome-wide association study (GWAS) on externalizing scores derived from criterion counts of five DSM disorders (alcohol dependence, alcohol abuse, illicit drug dependence, illicit drug abuse, and either antisocial personality disorder or conduct disorder). We meta analyzed these results with a similar measure of externalizing in an independent sample, Spit for Science (combined sample N = 15,112). We did not discover any robust genome-wide significant signals. Polygenic scores derived from the ancestry-specific GWAS summary statistics predicted externalizing problems in an independent European ancestry sample, but not in those of African ancestry. However, these PRS were no longer significant after adjusting for multiple testing. Larger samples with deep phenotyping are necessary for the discovery of SNPs related to externalizing problems.
Manhattan plots for inverse-variance weighted meta analysis of ancestry specific GWAS results. The y axis represents – log(P values) for association of variants with externalizing problem scores in each ancestry group (European Ancestry N = 7568; African Ancestry N = 3274). The dotted horizontal line represents the threshold for genome-wide significance (p < 5 × 10−8).
Manhattan plot for meta-analytic results from gene-based analyses (inverse variance fixed effects meta-analysis). The y axis represents – log(P values) for association of variants with externalizing problem scores. The dotted horizontal line represents the threshold for genome-wide significance (p < 2.78 × 10−6).
← Prev
21–24 of 24
No entities extracted from this document yet.
No uploaded files.
Not in any collection.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| American Psychiatric Association. (1987). Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition. Washington, D. C. | — | — | — |
| American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) Washington, D.C. | — | — | — |
| AnneyRJL, Lasky-SuJ, Ó’DúshláineC, KennyE, NealeBM, MulliganA, … GillM (2008). Conduct disorder and ADHD: Evaluation of conduct problems as a categorical and quantitative trait in the international multicentre ADHD genetics study. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 147B(8), 1369–1378. doi:10.1002/ajmg.b.3087118951430 | — | — | — |
| BandaY, KvaleMN, HoffmannTJ, HesselsonSE, RanatungaD, TangH, … RischN (2015). Characterizing Race/Ethnicity and Genetic Ancestry for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort. Genetics, 200(4), 1285.2609271610.1534/genetics.115.178616PMC4574246 | — | — | — |
| BegleiterH, ReichT, HesselbrockV, PorjeszB, LiTK, SchuckitMA, … RiceJP (1995). The Collaborative Study on the Genetics of Alcoholism. Alcohol Health & Research World, 19, 228–236.PMC687576831798102 | — | — | — |
| BenjaminiY, & HochbergY (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300. | — | — | — |
| BierutLJ, GoateAM, BreslauN, JohnsonEO, BertelsenS, FoxL, … EdenbergHJ (2011). ADH1B is associated with alcohol dependence and alcohol consumption in populations of European and African ancestry. Mol Psychiatry, 17, 445. doi:10.1038/mp.2011.12410.1038/mp.2011.124https://www.nature.com/articles/mp2011124#supplementary-informationhttps://www.nature.com/articles/mp2011124#supplementary-information2196892810.1038/mp.2011.124PMC3252425 | — | — | — |
| BreslauJ, LaneM, SampsonN, & KesslerRC (2008). Mental disorders and subsequent educational attainment in a US national sample. Journal of psychiatric research, 42(9), 708–716. doi:10.1016/j.jpsychires.2008.01.01618331741PMC2748981 | — | — | — |
| BucholzKK, CadoretR, CloningerCR, DinwiddieSH, HesselbrockVM, NurnbergerJI, … SchuckitMA (1994). A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. Journal of Studies on Alcohol, 55(2), 149–158. doi:10.15288/jsa.1994.55.1498189735 | — | — | — |
| BucholzKK, McCutcheonVV, AgrawalA, DickDM, HesselbrockVM, KramerJ, PorjeszB (2017). Comparison of Parent, Peer, Psychiatric, and Cannabis Use Influences Across Stages of Offspring Alcohol Involvement: Evidence from the COGA Prospective Study. Alcoholism: Clinical and Experimental Research, 41(2), 359–368. doi:10.1111/acer.13293PMC527277628073157 | — | — | — |
| Bulik-SullivanBK, LohPR, FinucaneHK, RipkeS, YangJ, Schizophrenia Working Group of the Psychiatric Genomics, C., … NealeBM (2015). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet, 47(3), 291–295. doi:10.1038/ng.321125642630PMC4495769 | — | — | — |
| CampbellMC, & TishkoffSA (2008). African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping. Annu Rev Genomics Hum Genet, 9, 403–433. doi:10.1146/annurev.genom.9.081307.16425818593304PMC2953791 | — | — | — |
| ChenM-H, & YangQ (2010). GWAF: an R package for genome-wide association analyses with family data. Bioinformatics, 26(4), 580–581. doi:10.1093/bioinformatics/btp71020040588PMC2852219 | — | — | — |
| DickDM, AlievF, WangJC, & (2008). Using dimensional models of externalizing psychopathology to aid in gene identification. Archives of general psychiatry, 65(3), 310–318.1831667710.1001/archpsyc.65.3.310 | — | — | — |
| DickDM, BarrP, GuyM, NasimA, & ScottD (2017). Review: Genetic research on alcohol use outcomes in African American populations: A review of the literature, associated challenges, and implications. The American Journal on Addictions, 26(5), 486–493. doi:10.1111/ajad.1249528240821PMC5884102 | — | — | — |
| DickDM, NasimA, EdwardsAC, SalvatoreJE, ChoSB, AdkinsA, … KendlerKS (2014). Spit for Science: launching a longitudinal study of genetic and environmental influences on substance use and emotional health at a large US university. Frontiers in Genetics, 5, 47. doi:10.3389/fgene.2014.0004724639683PMC3944794 | — | — | — |
| EdenbergHJ (2002). The collaborative study on the genetics of alcoholism: an update. Alcohol Res Health, 26(3), 214–218.12875050PMC6683843 | — | — | — |
| GelernterJ, KranzlerHR, ShervaR, AlmasyL, KoestererR, SmithAH, … FarrerLA (2014). Genome-wide association study of alcohol dependence:significant findings in African- and European-Americans including novel risk loci. Mol Psychiatry, 19(1), 41–49. doi:10.1038/mp.2013.14524166409PMC4165335 | — | — | — |
| GrotzingerAD, RhemtullaM, de VlamingR, RitchieSJ, MallardTT, HillWD, … Tucker-DrobEM (2019). Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behaviour. doi:10.1038/s41562-019-0566-xPMC652014630962613 | — | — | — |
| HesselbrockM, EastonC, BucholzKK, SchuckitM, & HesselbrockV (1999). A validity study of the SSAGA--a comparison with the SCAN. Addiction, 94(9), 1361–1370. doi:10.1046/j.1360-0443.1999.94913618.x10615721 | — | — | — |
| HicksBM, SchaletBD, MaloneSM, IaconoWG, & McGueM (2011). Psychometric and Genetic Architecture of Substance Use Disorder and Behavioral Disinhibition Measures for Gene Association Studies. Behavior genetics, 41(4), 459–475. doi:10.1007/s10519-010-9417-221153693PMC3123412 | — | — | — |
| Karlsson LinnérR, BiroliP, KongE, MeddensSFW, WedowR, FontanaMA, … Social Science Genetic Association, C. (2019). Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nature Genetics, 51(2), 245–257. doi:10.1038/s41588-018-0309-330643258PMC6713272 | — | — | — |
| KendlerKS, PrescottCA, MyersJ, & NealeMC (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of general psychiatry, 60(9), 929–937. doi:10.1001/archpsyc.60.9.92912963675 | — | — | — |
| KesslerRC, ChiuWT, DemlerO, & WaltersEE (2005). Prevalence, Severity, and Comorbidity of Twelve-month DSM-IV Disorders in the National Comorbidity Survey Replication (NCS-R). Archives of general psychiatry, 62(6), 617–627. doi:10.1001/archpsyc.62.6.61715939839PMC2847357 | — | — | — |
| KranzlerHR, ZhouH, KemberRL, Vickers SmithR, JusticeAC, DamrauerS, … GelernterJ (2019). Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat Commun, 10(1), 1499. doi:10.1038/s41467-019-09480-830940813PMC6445072 | — | — | — |
| KruegerRF, HicksBM, PatrickCJ, CarlsonSR, IaconoWG, & McGueM (2002).Etiologic connections among substance dependence, antisocial behavior and personality: Modeling the externalizing spectrum. Journal of abnormal psychology, 111(3), 411–424. doi:10.1037//0021-843x.111.3.41112150417 | — | — | — |
| LiuM, JiangY, WedowR, LiY, BrazelDM, ChenF, … PsychiatryHA-I (2019). Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nature Genetics. doi:10.1038/s41588-018-0307-5PMC635854230643251 | — | — | — |
| McGueM, ZhangY, MillerMB, BasuS, VriezeS, HicksB, … IaconoWG (2013). A Genome-Wide Association Study of Behavioral Disinhibition. Behavior genetics, 43(5), 363–373. doi:10.1007/s10519-013-9606-x23942779PMC3886341 | — | — | — |
| McLeodJD, UemuraR, & RohrmanS (2012). Adolescent mental health, behavior problems, and academic achievement. J Health Soc Behav, 53(4), 482–497.doi:10.1177/002214651246288823197485PMC3752654 | — | — | — |
| National Drug Intelligence Center. (2011). National drug threat assessment. Retrieved from Washington, DC: | — | — | — |
| OkbayA, BeauchampJP, FontanaMA, LeeJJ, PersTH, RietveldCA, … BenjaminDJ (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533, 539–542. doi:10.1038/nature1767127225129PMC4883595 | — | — | — |
| OtowaT, HekK, LeeM, ByrneEM, MirzaSS, NivardMG, … HettemaJM(2016). Meta-analysis of genome-wide association studies of anxiety disorders. Mol Psychiatry, 21, 1485. doi:10.1038/mp.2016.1126857599 | — | — | — |
| PasmanJA, VerweijKJH, GerringZ, StringerS, Sanchez-RoigeS, TreurJL, … International Cannabis, C. (2018). GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nature Neuroscience, 21(9), 1161–1170. doi:10.1038/s41593-018-0206-130150663PMC6386176 | — | — | — |
| PetersonRE, EdwardsAC, BacanuSA, DickDM, KendlerKS, & WebbBT (2017). The utility of empirically assigning ancestry groups in cross-population genetic studies of addiction. Am J Addict, 26(5), 494–501. doi:10.1111/ajad.1258628714599PMC5646819 | — | — | — |
| SacksJJ, GonzalesKR, BoucheryEE, TomediLE, & BrewerRD (2015). 2010 National and State Costs of Excessive Alcohol Consumption. American Journal of Preventitive Medicine, 49(5), e73–e79. doi:10.1016/j.amepre.2015.05.03126477807 | — | — | — |
| SavageJE, SalvatoreJE, AlievF, EdwardsAC, HickmanM, KendlerKS, … DickDM (2018). Polygenic Risk Score Prediction of Alcohol Dependence Symptoms Across Population-Based and Clinically Ascertained Samples. Alcoholism: Clinical and Experimental Research, 42(3), 520–530. doi:10.1111/acer.13589PMC583258929405378 | — | — | — |
| SulJH, MartinLS, & EskinE (2018). Population structure in genetic studies: Confounding factors and mixed models. PLoS Genet, 14(12), e1007309. doi:10.1371/journal.pgen.100730930589851PMC6307707 | — | — | — |
| The 1000 Genomes Project Consortium. (2015). A global reference for human genetic variation. Nature, 526, 68. doi:10.1038/nature1539310.1038/nature15393https://www.nature.com/articles/nature15393#supplementary-informationhttps://www.nature.com/articles/nature15393#supplementary-information2643224510.1038/nature15393PMC4750478 | — | — | — |
| TielbeekJJ, JohanssonA, PoldermanTC, & (2017). Genome-wide association studies of a broad spectrum of antisocial behavior. JAMA Psychiatry, 74(12), 1242–1250. doi:10.1001/jamapsychiatry.2017.306928979981PMC6309228 | — | — | — |
| TuvbladC, GrannM, & LichtensteinP (2006). Heritability for adolescent antisocial behavior differs with socioeconomic status: gene–environment interaction. Journal of Child Psychology and Psychiatry, 47(7), 734–743. doi:10.1111/j.1469-7610.2005.01552.x16790008 | — | — | — |
| WaltersRK, PolimantiR, JohnsonEC, McClintickJN, AdamsMJ, AdkinsAE, andMe ResearchT (2018). Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nature Neuroscience, 21(12), 1656–1669. doi:10.1038/s41593-018-0275-130482948PMC6430207 | — | — | — |
| WatanabeK, TaskesenE, van BochovenA, & PosthumaD (2017). Functional mapping and annotation of genetic associations with FUMA. Nat Commun, 8(1), 1826. doi:10.1038/s41467-017-01261-529184056PMC5705698 | — | — | — |
| WillerCJ, LiY, & AbecasisGR (2010). METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics, 26(17), 2190–2191. doi:10.1093/bioinformatics/btq34020616382PMC2922887 | — | — | — |
| WilliamsDR, & MohammedSA (2009). Discrimination and racial disparities in health: evidence and needed research. J Behav Med, 32(1), 20–47. doi:10.1007/s10865-008-9185-019030981PMC2821669 | — | — | — |
| WojcikGL, GraffM, NishimuraKK, TaoR, HaesslerJ, GignouxCR, … CarlsonCS (2019). Genetic analyses of diverse populations improves discovery for complex traits. Nature, 570(7762), 514–518. doi:10.1038/s41586-019-1310-431217584PMC6785182 | — | — | — |
| World Health Organization. (2014). Global status report on alcohol and health, 2014. Retrieved from | — | — | — |
| YoungSE, StallingsMC, CorleyRP, KrauterKS, & HewittJK (2002). Genetic and environmental influences on behavioral disinhibition. American Journal of Medical Genetics, 96(5), 684–695. doi:10.1002/1096-8628(20001009)96:5<684::AID-AJMG16>3.0.CO;2-G11054778 | — | — | — |
No papers in this knowledge base cite this source.
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Associations of polygenic scores and developmental trajectories of externalizing behaviors. | Sasia AB et al. | — | 2025 | → |
| Developmental genetic effects on externalizing behavior and alcohol use: Examination across two longitudinal samples. | Elam KK et al. | — | 2024 | → |
| The collaborative study on the genetics of alcoholism: Genetics. | Johnson EC et al. | — | 2023 | → |
| Estimation of Parental Effects Using Polygenic Scores. | Balbona JV et al. | — | 2021 | → |
| Polygenic Risk for Aggression Predicts Adult Substance Use Disorder Diagnoses via Substance Use Offending in Emerging Adulthood and is Moderated by a Family-Centered Intervention. | Elam KK et al. | — | 2021 | → |