Restricted accessResearch articleFirst published online 2021-11
Polygenic Score for Smoking Is Associated With Externalizing Psychopathology and Disinhibited Personality Traits but Not Internalizing Psychopathology in Adolescence
We examined whether a polygenic score (PGS) for smoking measured genetic risk for general behavioral disinhibition by estimating its associations with externalizing and internalizing psychopathology and related personality traits at multiple time points in adolescence (ages 11, 14, and 17 years; N = 3,225). The smoking PGS had strong associations with the stable variance across time for all the externalizing measures (mean standardized β = 0.27), agreeableness (β = −0.22, 95% confidence interval [CI] = [−0.28, −0.16]), and conscientiousness (β = −0.19, 95% CI = [−0.24, −0.13]) but was not significantly associated with internalizing measures (mean β = 0.06) or extraversion (β = 0.01, 95% CI = [−0.05, 0.07]). After controlling for smoking at age 17 years, the associations with externalizing, low agreeableness, and low conscientiousness remained statistically significant. The smoking PGS measures genetic influences that contribute to a spectrum of phenotypes related to behavioral disinhibition, including externalizing psychopathology and normal-range personality traits related to behavioral control but not internalizing psychopathology.
AgrawalA.DickD. M.BucholzK. K.MaddenP. A. F.CooperM. L.SherK. J.HeathA. C. (2008). Drinking expectancies and motives: A genetic study of young adult women. Addiction, 103(2), 194–204. https://doi.org/10.1111/j.1360-0443.2007.02074.x
2.
AkingbuwaW. A.HammerschlagA. R.JamiE. S.AllegriniA. G.KarhunenV.SallisH.AskH.AskelandR. B.BaselmansB.DiemerE.HagenbeekF. A.HavdahlA.HottengaJ. J.MbarekH.RivadeneiraF.TesliM.van BeijsterveldtC.BreenG.LewisC. M., . . . Bipolar Disorder and Major Depressive Disorder Working Groups of the Psychiatric Genomics Consortium. (2020). Genetic associations between childhood psychopathology and adult depression and associated traits in 42,998 individuals: A meta-analysis. JAMA Psychiatry, 77(7), 715–728.
3.
AllenH. L.EstradaK.LettreG.BerndtS. I.WeedonM. N.RivadeneiraF.WillerC. J.JacksonA. U.VedantamS.RaychaudhuriS.FerreiraT.WoodA. R.WeyantR. J.SegrèA. V.SpeliotesE. K.WheelerE.SoranzoN.ParkJ. H.YangJ.. . . HirschhornJ. N. (2010). Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature, 467(7317), 832–838. https://doi.org/10.1038/nature09410
4.
American Psychiatric Association. (1987). Diagnostic statistical manual of mental disorders (3rd ed., text rev).
5.
ByrneE. M.ZhuZ. H.QiT.SkeneN. G.BryoisJ.PardinasA. F.StahlE.SmollerJ. W.RietschelM., Bipolar Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, OwenM. J.WaltersJ.O’DonovanM. C.McGrathJ. G.Hjerling-LefflerJ.SullivanP. F.GoddardM. E.VisscherP. M.YangJ.WrayN. R. (2021). Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders. Molecular Psychiatry, 26, 2070–2081. https://doi.org/10.1038/s41380-020-0705-9
6.
ChangC. C.ChowC. C.TellierL.VattikutiS.PurcellS. M.LeeJ. J. (2015). Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience, 4(1), Article s13742–015–0047–8. https://doi.org/10.1186/s13742-015-0047-8
7.
ChangL. H.Couvy-DuchesneB.LiuM. Z.MedlandS. E.VerhulstB.BenotschE. G.HickieI. B.MartinN. G.GillespieN. A. & GSCAN Consortium. (2019). Association between polygenic risk for tobacco or alcohol consumption and liability to licit and illicit substance use in young Australian adults. Drug and Alcohol Dependence, 197, 271–279. https://doi.org/10.1016/j.drugalcdep.2019.01.015
8.
ClarkD. A.DurbinC. E.IaconoW. G.McGueM.HicksB. M. (2020). Personality and sexual development in adolescence: Selection, corresponsive effects, and genetic and environmental influences [unpublished manuscript].
9.
Cross-Disorder Group of the Psychiatric Genomics Consortium. (2013a). Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nature Genetics, 45(9), 984–994. https://doi.org/10.1038/ng.2711
10.
Cross-Disorder Group of the Psychiatric Genomics Consortium. (2013b). Identification of risk loci with shared effects on five major psychiatric disorders: A genome-wide analysis. The Lancet, 381(9875), 1371–1379. https://doi.org/10.1016/s0140-6736(12)62129-1
DeakJ. D.ClarkD. A.LiuM.DurbinC. E.IaconoW. G.McGueM.VriezeS. I.HicksB. M. (2020). Polygenic risk scores predict the development of alcohol and nicotine use problems from adolescence through young adulthood. PsyArXiv. https://doi.org/10.31234/osf.io/axkqb
13.
DickD. M.RoseR. J.VikenR. J.KaprioJ.KoskenvuoM. (2001). Exploring gene-environment interactions: Socioregional moderation of alcohol use. Journal of Abnormal Psychology, 110(4), 625–632. https://doi.org/10.1037/0021-843x.110.4.625
14.
FosterK. T.HicksB. M.ZuckerR. A. (2018). Positive and negative effects of internalizing on alcohol use problems from childhood to young adulthood: The mediating and suppressing role of externalizing. Journal of Abnormal Psychology, 127(4), 394–403. https://doi.org/10.1037/abn0000337
15.
HallquistM. N.WileyJ. F. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 621–638. https://doi.org/10.1080/10705511.2017.1402334
16.
HamakerE. L.KuiperR. M.GrasmanR. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889
17.
HicksB. M.FosterK. T.IaconoW. G.McGueM. (2013). Genetic and environmental influences on the familial transmission of externalizing disorders in adoptive and twin offspring. JAMA Psychiatry, 70(10), 1076–1083.
18.
HicksB. M.IaconoW. G.McGueM. (2014). Identifying childhood characteristics that underlie premorbid risk for substance use disorders: Socialization and boldness. Development and Psychopathology, 26(1), 141–157. https://doi.org/10.1017/s0954579413000862
19.
IaconoW. G.CarlsonS. R.TaylorJ.ElkinsI. J.McGueM. (1999). Behavioral disinhibition and the development of substance-case disorders: Findings from the Minnesota Twin Family Study. Development and Psychopathology, 11(4), 869–900. https://doi.org/10.1017/s0954579499002369
20.
IaconoW. G.MaloneS. M.McGueM. (2008). Behavioral disinhibition and the development of early-onset addiction: Common and specific influences. Annual Review of Clinical Psychology, 4, 325–348.
21.
KendlerK. S.PrescottC. A.MyersJ.NealeM. C. (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. https://doi.org/10.1001/archpsyc.60.9.929
22.
KesslerR. C.ChiuW. T.DemlerO.WaltersE. E. (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62(6), 617–627. https://doi.org/10.1001/archpsyc.62.6.617
23.
KeyesM. A.MaloneS. M.ElkinsI. J.LegrandL. N.McGueM.IaconoW. G. (2009). The Enrichment Study of the Minnesota Twin Family Study: Increasing the yield of twin families at high risk for externalizing psychopathology. Twin Research and Human Genetics, 12(5), 489–501.
24.
KichaevG.BhatiaG.LohP. R.GazalS.BurchK.FreundM. K.SchoechA.PasaniucB.PriceA. L. (2019). Leveraging polygenic functional enrichment to improve GWAS power. American Journal of Human Genetics, 104(1), 65–75. https://doi.org/10.1016/j.ajhg.2018.11.008
25.
KruegerR. F.HicksB. M.PatrickC. J.CarlsonS. R.IaconoW. G.McGueM. (2002). Etiologic connections among substance dependence, antisocial behavior, and personality: Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111(3), 411–424. https://doi.org/10.1037//0021-843x.111.3.411
26.
LambertS. A.AbrahamG.InouyeM. (2019). Towards clinical utility of polygenic risk scores. Human Molecular Genetics, 28(R2), R133–R142. https://doi.org/10.1093/hmg/ddz187
27.
LinnerR. K.MallardT. T.BarrP. B.Sanchez-RoigeS.MadoleJ. W.DriverM. N.PooreH. E.GrotzingerA. D.TielbeekJ. J.JohnsonE. C.LiuM.ZhouH.KemberR. L.PasmanJ. A.VerweijK. J. HLuiD. J.VriezeS., COGA Collaborators, KranzlerH. R.. . . DickD. M. (2020). Multivariate genomic analysis of 1.5 million people identifies genes related to addiction, antisocial behavior, and health. bioRxiv. https://doi.org/10.1101/2020.10.16.342501
28.
LiuM. Z.JiangY.WedowR.LiY.BrazelD. M.ChenF.DattaG.Davila-VelderrainJ.McGuireD.TianC.ZhanX., 23andMe Research Team, HUNT All-In Psychiatry, ChoquetH.DochertyA. R.FaulJ. D.FoersterJ. R.FritscheL. G.GabrielsenM. E.. . . VriezeS. (2019). Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nature Genetics, 51(2), 237–244. https://doi.org/10.1038/s41588-018-0307-5
29.
MartinA. R.KanaiM.KamataniY.OkadaY.NealeB. M.DalyM. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584–591. https://doi.org/10.1038/s41588-019-0379-x
30.
MostafaviH.HarpakA.ConleyD.PritchardJ. K.PrzeworskiM. (2019). Variable prediction accuracy of polygenic scores within an ancestry group. bioRxiv. https://doi.org/10.7554/eLife.48376
31.
MuthénL. K.MuthénB. O. (2017). Mplus user’s guide (8th ed.).
32.
PriceA. L.PattersonN. J.PlengeR. M.WeinblattM. E.ShadickN. A.ReichD. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38(8), 904–909. https://doi.org/10.1038/ng1847
33.
QuachB. C.BrayM. J.GaddisN. C.LiuM.PalviainenT.MinicaC. C.ZellersS.ShervaR.AlievF.NothnagelM. (2020). Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits: Findings from the Nicotine Dependence GenOmics (iNDiGO) Consortium. bioRxiv. https://doi.org/10.1101/2020.01.15.898858
34.
R Core Team. (2016). R: A language and environment for statistical computing (Version 3.3.1) [Computer software]. R Foundation for Statistical Computing. http://www.R-project.org
35.
SalvatoreJ. E.SavageJ. E.BarrP.WolenA. R.AlievF.VuoksimaaE.LatvalaA.PulkkinenL.RoseR. J.KaprioJ.DickD. M. (2018). Incorporating functional genomic information to enhance polygenic signal and identify variants involved in gene-by-environment interaction for young adult alcohol problems. Alcoholism: Clinical and Experimental Research, 42(2), 413–423. https://doi.org/10.1111/acer.13551
36.
SaracenoL.MunafoM.HeronJ.CraddockN.van den BreeM. B. M. (2009). Genetic and non-genetic influences on the development of co-occurring alcohol problem use and internalizing symptomatology in adolescence: A review. Addiction, 104(7), 1100–1121. https://doi.org/10.1111/j.1360-0443.2009.02571.x
37.
TorkamaniA.WineingerN. E.TopolE. J. (2018). The personal and clinical utility of polygenic risk scores. Nature Reviews Genetics, 19(9), 581–590. https://doi.org/10.1038/s41576-018-0018-x
38.
TullyE. C.IaconoW. G. (2016). An integrative common liabilities model for the comorbidity of substance use disorders with externalizing and internalizing disorders. In SherK. J. (Ed.), The Oxford handbook of substance use and substance use disorders (pp. 187–212). Oxford University Press.
39.
VilhjalmssonB. J.YangJ.FinucaneH. K.GusevA.LindstromS.RipkeS.GenoveseG.LohP. R.BhatiaG.DoR.HayeckT.WonH. H., Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Study, KathiresanS.PatoM.PatoC.TamimiR.StahlE.. . . PriceA. L. (2015). Modeling linkage disequilibrium increases accuracy of polygenic risk scores. American Journal of Human Genetics, 97(4), 576–592. https://doi.org/10.1016/j.ajhg.2015.09.001
40.
VriezeS. I.HicksB. M.IaconoW. G.McGueM. (2012). Decline in genetic influence on the co-occurrence of alcohol, marijuana, and nicotine dependence symptoms declines from age 14 to 29. American Journal of Psychiatry, 169, 1073–1081.
WaszczukM. A.MiaoJ.DochertyA. R.ShabalilnA. A.JonasK. G.MicheliniG.KotovR. (2020, May22). General vs. specific vulnerabilities: Polygenic risk scores and higher-order psychopathology dimensions in the Adolescent Brain Cognitive Development (ABCD) Study. PsyArXiv. https://doi.org/10.31234/osf.io/km6v3
43.
WilsonS.HaroianK.IaconoW. G.KruegerR. F.LeeJ. M. J.LucianaM.MaloneS. M.McGueM.RoismanG. I.VriezeS. (2019). Minnesota Center for Twin and Family Research. Twin Research and Human Genetics, 22(6), 746–752. https://doi.org/10.1017/thg.2019.107
ZhouH.SealockJ. M.Sanchez-RoigeS.ClarkeT.-K.LeveyD.ChengZ.LiB.PolimantiR.KemberR. L.SmithR. V. (2019). Meta-analysis of problematic alcohol use in 435,563 individuals identifies 29 risk variants and yields insights into biology, pleiotropy and causality. bioRxiv. https://doi.org/10.1038/s41593-020-0643-5
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