Polygenic risk scores for alcohol involvement relate to brain structure in substance-naïve children: Results from the ABCD study.
paper
Cited
Public
- Authors
- Hatoum, Alexander S; Johnson, Emma C; Baranger, David A A; Paul, Sarah E; Agrawal, Arpana; Bogdan, Ryan
- Year
- 2021
- Journal
- Genes, brain, and behavior
- PMID
- 34092032
- DOI
- 10.1111/gbb.12756
- PMCID
- PMC8645657
Brain imaging-derived structural correlates of alcohol involvement have largely been speculated to arise as a consequence of alcohol exposure. However, they may also reflect predispositional risk. In substance naïve children of European ancestry who completed the baseline session of the Adolescent Brain Cognitive Development (ABCD) Study (n = 3013), mixed-effects models estimated whether polygenic risk scores (PRS) for problematic alcohol use (PAU-PRS) and drinks per week (DPW-PRS) are associated with magnetic resonance imaging-derived brain structure phenotypes (i.e., total and regional: cortical thickness, surface area and volume; subcortical volume; white matter volume, fractional anisotropy, mean diffusivity). Follow-up analyses evaluated whether any identified regions were also associated with polygenic risk among substance naïve children of African ancestry (n = 898). After adjustment for multiple testing correction, polygenic risk for PAU was associated with lower volume of the left frontal pole and greater cortical thickness of the right supramarginal gyrus (|βs| > 0.009; ps < 0.001; ps < 0.046; r s < 0.004). PAU PRS and DPW PRS showed nominally significant associations with a host of other regional brain structure phenotypes (e.g., insula surface area and volume). None of these regions showed any, even nominal association among children of African ancestry. Genomic liability to alcohol involvement may manifest as variability in brain structure during middle childhood prior to alcohol use initiation. Broadly, alcohol-related variability in brain morphometry may partially reflect predisposing genomic influence. Larger discovery genome-wide association studies and target samples of diverse ancestries are needed to determine whether observed associations may generalize across ancestral origins.
Results of polygenic risk prediction for Problematic Alcohol Use (PAU) plotted on the cortex. The results are shown for those modalities that had significant regions in the Desikan atlas after controlling for mean of the modality, prenatal alcohol exposure, age, sex, age by sex, first 20 principal components, SES, and accounting for family and site as random nested effects. Red indicates areas where higher PAU polygenic risk predicted more of that modality. In the top panel, we focus on supramarginal gyrus cortical thickness prediction by PRS, as this association was significant after multiple testing correction. In the bottom panel, we focus on volume of the temporal pole, as this was significantly associated with PAU PRS after multiple testing correction.
1–20 of 28
Next →
| Name | Type |
|---|---|
| 1000 Genomes Project | cohort |
| 3,013 substance-naïve children local | cohort |
| ABCD consortium local | cohort |
| ABCD sample local | cohort |
| ABCD study | cohort |
| Adolescent Brain and Cognitive Development Study local | cohort |
| African American | cohort |
| African-ancestry sample local | cohort |
| African individuals | cohort |
| alcohol | phenotype |
| alcohol abuse | phenotype |
| alcohol dependence | phenotype |
| Alcohol involvement PRS local | phenotype |
| Alcohol Use | phenotype |
| Alcohol Use Disorder | phenotype |
| Alzheimer’s disease | phenotype |
| amygdala | anatomy |
| AUDIT-P problem subscale local | phenotype |
| behavioral risk factors local | phenotype |
| Brain alterations local | anatomy |
| brain development | phenotype |
| brain structure | anatomy |
| Brain structure correlates local | anatomy |
| brain structure phenotypes local | phenotype |
| children of parents with AUD local | cohort |
| Children of parents with AUD local | cohort |
| cognition | phenotype |
| Consortium on Asthma among African-ancestry Populations in the Americas local | cohort |
| cortex region local | anatomy |
| cortical surface area | anatomy |
| cortical thickness | phenotype |
| dbGaP | cohort |
| discovery GWAS | cohort |
| DPW-PRS local | drug |
| DPW-PRS local | variant |
| drinks per week | phenotype |
| Drinks per week (DPW) local | phenotype |
| European ancestry | cohort |
| Europeans | cohort |
| externalizing disorders | phenotype |
| fractional anisotropy | phenotype |
| frontal pole | anatomy |
| frontal pole gray matter volume local | phenotype |
| full-scale IQ | phenotype |
| Full scale IQ estimation local | phenotype |
| Genomic risk local | phenotype |
| gray matter cortex local | anatomy |
| Gray matter cortex local | anatomy |
| gray matter metrics local | phenotype |
| GSCAN consortium local | cohort |
| GWAS | cohort |
| GWAS & Sequencing Consortium of Alcohol and Nicotine use | cohort |
| Haplotype Reference Consortium | cohort |
| heavy drinking | phenotype |
| hippocampus | anatomy |
| impulsivity | phenotype |
| inferior and caudal frontal gyrus local | anatomy |
| inferior parietal lobe | anatomy |
| insula | anatomy |
| interior-temporal gyrus local | anatomy |
| language | phenotype |
| left frontal pole | anatomy |
| left frontal pole gray matter volume local | phenotype |
| Lower subcortical volume local | phenotype |
| mean diffusivity | phenotype |
| Mega-analysis of Alcohol Use Disorder in adults local | cohort |
| middle childhood | cohort |
| Million Veteran Program | cohort |
| nicotine | drug |
| non-Hispanic European ancestry cohort local | cohort |
| nucleus accumbens | anatomy |
| orbitofrontal cortex | anatomy |
| original GWAS of individuals of African ancestry local | cohort |
| PAU-PRS local | drug |
| PAU-PRS local | phenotype |
| PAU-PRS local | variant |
| polygenic risk score | cohort |
| Polygenic risk score for alcohol dependence | phenotype |
| precentral gyrus | anatomy |
| Pre-dispositional risk factors local | phenotype |
| problematic alcohol use | phenotype |
| Problematic alcohol use (PAU) local | phenotype |
| Problem subscale of the Alcohol Use Disorders local | phenotype |
| Psychiatric Genomics Consortium | cohort |
| psychiatric traits | phenotype |
| putamen | anatomy |
| Reduced cognition local | phenotype |
| regional volume | phenotype |
| ricopili pipeline | drug |
| Ricopili pipeline local | cohort |
| right supramarginal gyrus cortical thickness local | phenotype |
| risk taking | phenotype |
| Rutgers University Cell and DNA repository | cohort |
| Smokescreen array local | drug |
| SNP | cohort |
| Stage-based addiction transitions local | phenotype |
| subcortex | anatomy |
| subcortical volumes | anatomy |
| substance exposure | phenotype |
| substance naïve children local | cohort |
| Substance naïve children local | cohort |
| Substance-naïve children (African ancestry) local | cohort |
| Substance-naïve children (European ancestry) local | cohort |
| substance-naïve children of alcohol dependent individuals local | cohort |
| substance use | phenotype |
| superior frontal cortex | anatomy |
| superior parietal cortex | anatomy |
| supramarginal gyrus | anatomy |
| supramarginal gyrus cortical thickness local | phenotype |
| surface area | phenotype |
| temporal partial junction local | anatomy |
| thickness | phenotype |
| total cortical thickness local | phenotype |
| Twin and family studies local | cohort |
| UK Biobank | cohort |
| VA Cooperative Studies Program local | cohort |
| Veterans Health Administration | cohort |
| white matter | anatomy |
No uploaded files.
Not in any collection.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| AchenbachTM, McConaughySH, HowellCT. Child/adolescent behavioral and emotional problems: Implications of cross-informant correlations for situational specificity. Psychol Bull [Internet]. 1987 [cited 2016 Jun 2];101(2):213–32. Available from: http://doi.apa.org/getdoi.cfm?doi=10.1037/0033-2909.101.2.2133562706 | — | — | — |
| BarangerDAA, DemersCH, ElsayedNM, KnodtAR, RadtkeSR, DesmaraisA, Convergent Evidence for Predispositional Effects of Brain Gray Matter Volume on Alcohol Consumption. Biol Psychiatry. 2019 9;10.1016/j.biopsych.2019.08.029PMC741271531699293 | — | — | — |
| BatesD, MächlerM, BolkerBM, WalkerSC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015 10 1;67(1):1–48. | — | — | — |
| BaurleyJW, EdlundCK, PardameanCI, ContiDV., Bergen AW. Smokescreen: A targeted genotyping array for addiction research. BMC Genomics. 2016;17(1).10.1186/s12864-016-2495-7PMC476952926921259 | — | — | — |
| BogdanR, BarangerDAA, AgrawalA. Polygenic Risk Scores in Clinical Psychology: Bridging Genomic Risk to Individual Differences. Annu Rev Clin Psychol. 2018 5 7;14(1).10.1146/annurev-clinpsy-050817-084847PMC777293929579395 | — | — | — |
| CaseyBJ, CannonierT, ConleyMI, CohenAO, BarchDM, HeitzegMM, The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev Cogn Neurosci [Internet]. 2018 8 1 [cited 2019 Mar 27];32:43–54. Available from: https://www.sciencedirect.com/science/article/pii/S18789293173012142956737610.1016/j.dcn.2018.03.001PMC5999559 | — | — | — |
| CrewsFT. Alcohol-related neurodegeneration and recovery: mechanisms from animal models. Alcohol Res Health [Internet]. 2008 [cited 2019 Dec 15];31(4):377–88. Available from: http://www.ncbi.nlm.nih.gov/pubmed/2358401123584011PMC3860462 | — | — | — |
| DesikanRS, SégonneF, FischlB, QuinnBT, DickersonBC, BlackerD, An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage [Internet]. 2006 7 1 [cited 2020 Aug 26];31(3):968–80. Available from: https://pubmed.ncbi.nlm.nih.gov/16530430/1653043010.1016/j.neuroimage.2006.01.021 | — | — | — |
| GershonRC, WagsterMV., HendrieHC, FoxNA, CookKF, NowinskiCJ. NIH toolbox for assessment of neurological and behavioral function. Neurology. 2013;80(11 Suppl 3).10.1212/WNL.0b013e3182872e5fPMC366233523479538 | — | — | — |
| GeT, ChenCY, NiY, FengYCA, SmollerJW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019 Dec 1;10(1):1–10.10.1038/s41467-019-09718-5PMC646799830992449 | — | — | — |
| GoldsteinRZ, VolkowND. Drug addiction and its underlying neurobiological basis: Neuroimaging evidence for the involvement of the frontal cortex. Vol. 159, American Journal of Psychiatry. 2002. p. 1642–52.10.1176/appi.ajp.159.10.1642PMC120137312359667 | — | — | — |
| GröpperS, SpenglerS, StukeH, GawronCK, ParnackJ, GutwinskiS, Behavioral impulsivity mediates the relationship between decreased frontal gray matter volume and harmful alcohol drinking: A voxel-based morphometry study. J Psychiatr Res. 2016 12 1;83:16–23.2752964810.1016/j.jpsychires.2016.08.006 | — | — | — |
| HaglerDJ, HattonSN, CornejoMD, MakowskiC, FairDA, DickAS, Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. Neuroimage. 2019 11 15;202.10.1016/j.neuroimage.2019.116091PMC698127831415884 | — | — | — |
| HamptonWH, HanikIM, OlsonIR. Substance abuse and white matter: Findings, limitations, and future of diffusion tensor imaging research. Vol. 197, Drug and Alcohol Dependence. Elsevier Ireland Ltd; 2019. p. 288–98.3087565010.1016/j.drugalcdep.2019.02.005PMC6440853 | — | — | — |
| HeikkinenN, NiskanenE, KönönenM, TolmunenT, KekkonenV, KivimäkiP, Alcohol consumption during adolescence is associated with reduced grey matter volumes. Addiction. 2017 4 1;112(4):604–13.2786503910.1111/add.13697 | — | — | — |
| HollaB, BharathRD, VenkatasubramanianG, BenegalV. Altered brain cortical maturation is found in adolescents with a family history of alcoholism. Addict Biol. 2019 7 1;24(4):835–45.3005876110.1111/adb.12662 | — | — | — |
| KranzlerHR, ZhouH, KemberRL, Vickers SmithR, JusticeAC, DamrauerS, Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat Commun [Internet]. 2019 12 1 [cited 2021 Jan 7];10(1). Available from: https://pubmed.ncbi.nlm.nih.gov/30940813/10.1038/s41467-019-09480-8PMC644507230940813 | — | — | — |
| LamM, AwasthiS, WatsonHJ, GoldsteinJ, PanagiotaropoulouG, TrubetskoyV, RICOPILI: Rapid Imputation for COnsortias PIpeLIne. bioRxiv. 2019 4 11;587196.10.1093/bioinformatics/btz633PMC786804531393554 | — | — | — |
| LiuM, JiangY, WedowR, LiY, BrazelDM, ChenF, Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Vol. 51, Nature Genetics. Nature Publishing Group; 2019. p. 237–44.3064325110.1038/s41588-018-0307-5PMC6358542 | — | — | — |
| MackeyS, AllgaierN, ChaaraniB, SpechlerP, OrrC, BunnJ, Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects. Am J Psychiatry [Internet]. 2019 2 [cited 2019 Dec 15];176(2):119–28. Available from: http://ajp.psychiatryonline.org/doi/10.1176/appi.ajp.2018.170404153033670510.1176/appi.ajp.2018.17040415PMC6427822 | — | — | — |
| MartinAR, GignouxCR, WaltersRK, WojcikGL, NealeBM, GravelS, Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am J Hum Genet [Internet]. 2017 4 6 [cited 2020 Dec 13];100(4):635–49. Available from: 10.1016/j.ajhg.2017.03.004.28366442PMC5384097 | — | — | — |
| PaulSE, HatoumAS, FineJD, JohnsonEC, HansenI, KarcherNR, Associations between Prenatal Cannabis Exposure and Childhood Outcomes: Results from the ABCD Study. JAMA Psychiatry [Internet]. 2021 1 1 [cited 2021 Mar 16];78(1):64–76. Available from: https://jamanetwork.com/journals/jamapsychiatry/fullarticle/27709643296549010.1001/jamapsychiatry.2020.2902PMC7512132 | — | — | — |
| RobertGH, LuoQ, YuT, ChuC, IngA, JiaT, Association of Gray Matter and Personality Development with Increased Drunkenness Frequency during Adolescence. JAMA Psychiatry. 2019;10.1001/jamapsychiatry.2019.4063PMC699080331851304 | — | — | — |
| Sanchez-RoigeS, PalmerAA, FontanillasP, ElsonSL, AdamsMJ, HowardDM, Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts. Am J Psychiatry [Internet]. 2019 2 19 [cited 2019 Feb 22];176(2):107–18. Available from: http://ajp.psychiatryonline.org/doi/10.1176/appi.ajp.2018.180403693033670110.1176/appi.ajp.2018.18040369PMC6365681 | — | — | — |
| SelemonLD. A role for synaptic plasticity in the adolescent development of executive function. Vol. 3, Translational psychiatry. Nature Publishing Group; 2013. p. e238–e238.2346298910.1038/tp.2013.7PMC3625918 | — | — | — |
| ShnitkoTA, LiuZ, WangX, GrantKA, KroenkeCD. Chronic alcohol drinking slows brain development in adolescent and young adult nonhuman primates. eNeuro [Internet]. 2019 3 1 [cited 2020 Jul 15];6(2). Available from: /pmc/articles/PMC6464511/?report=abstract10.1523/ENEURO.0044-19.2019PMC646451130993181 | — | — | — |
| SicilianoCA, NoamanyH, ChangCJ, BrownAR, ChenX, LeibleD, A cortical-brainstem circuit predicts and governs compulsive alcohol drinking. Science (80- ) [Internet]. 2019 11 22 [cited 2020 Aug 26];366(6468):1008–12. Available from: https://pubmed.ncbi.nlm.nih.gov/31754002/10.1126/science.aay1186PMC698910031754002 | — | — | — |
| StoeckelC, GoughPM, WatkinsKE, DevlinJT. Supramarginal gyrus involvement in visual word recognition. Cortex. 2009 10 1;45(9):1091–6.1923258310.1016/j.cortex.2008.12.004PMC2726132 | — | — | — |
| VerhulstB, NealeMC, KendlerKS. The heritability of alcohol use disorders: A meta-analysis of twin and adoption studies. Psychol Med. 2015 4 15;45(5):1061–72.2517159610.1017/S0033291714002165PMC4345133 | — | — | — |
| VolkowND, KoobGF, CroyleRT, BianchiDW, GordonJA, KoroshetzWJ, The conception of the ABCD study: From substance use to a broad NIH collaboration. Vol. 32, Developmental Cognitive Neuroscience. Elsevier Ltd; 2018. p. 4–7.2905102710.1016/j.dcn.2017.10.002PMC5893417 | — | — | — |
| VolkowND, KoobGF, McLellanAT. Neurobiologic Advances from the Brain Disease Model of Addiction. LongoDL, editor. N Engl J Med [Internet]. 2016 1 28 [cited 2020 Jun 1];374(4):363–71. Available from: http://www.nejm.org/doi/10.1056/NEJMra15114802681601310.1056/NEJMra1511480PMC6135257 | — | — | — |
| WaltersGD. THE HERITABILITY OF ALCOHOL ABUSE AND DEPENDENCE: A META-ANALYSIS OF BEHAVIOR GENETIC RESEARCH. Am J Drug Alcohol Abuse [Internet]. 2002 1 5 [cited 2020 Jan 28];28(3):557–84. Available from: http://www.tandfonline.com/doi/full/10.1081/ADA-1200067421221136610.1081/ada-120006742 | — | — | — |
| WaltersRK, PolimantiR, JohnsonEC, McClintickJN, AdamsMJ, AdkinsAE, Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci. 2018 12 1;21(12):1656–69.3048294810.1038/s41593-018-0275-1PMC6430207 | — | — | — |
| WintersKC, ArriaA. Adolescent Brain Development and Drugs. Prev Res [Internet]. 2011 [cited 2020 Jun 19];18(2):21–4. Available from: http://www.ncbi.nlm.nih.gov/pubmed/2282229822822298PMC3399589 | — | — | — |
| World Health Organization. Global status report on alcohol and health 2018. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA 3.0 IGO. PoznyakV, RekveD, editors. 2018. 478 p. | — | — | — |
| YuR, DeochandC, KrotowA, Le’aoR, TongM, AgarwalAR, Tobacco Smoke-Induced Brain White Matter Myelin Dysfunction: Potential Co-Factor Role of Smoking in Neurodegeneration. J Alzheimer’s Dis. 2016 1 1;50(1):133–48.2663997210.3233/JAD-150751PMC5577392 | — | — | — |
| ZhouH, SealockJM, Sanchez-RoigeS, ClarkeT-K, LeveyDF, ChengZ, Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat Neurosci [Internet]. 2020 5 25 [cited 2020 May 30];1–10. Available from: http://www.nature.com/articles/s41593-020-0643-53245148610.1038/s41593-020-0643-5PMC7485556 | — | — | — |
In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features. | 2023 | 37232664 |
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Longitudinal Sex-at-Birth and Age Analyses of Cortical Structure in the ABCD Study. | Marshall AT et al. | — | 2025 | → |
| Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder. | Schilling L et al. | — | 2025 | → |
| The effects of alcohol use severity and polygenic risk on gray matter volumes in young adults. | Chen Y et al. | — | 2025 | → |
| The effects of polygenic risks for alcohol misuse on negative emotion processing in young adult binge drinkers. | Chen Y et al. | — | 2025 | → |
| The ABCD and HBCD Studies: Longitudinal Studies to Inform Prevention Science. | Dowling GJ et al. | — | 2024 | → |
| A genetically informed Registered Report on adverse childhood experiences and mental health. | Baldwin JR et al. | — | 2023 | → |
| Alcohol use and grey matter structure: Disentangling predispositional and causal contributions in human studies. | Baranger DAA et al. | — | 2023 | → |
| Genotype Data and Derived Genetic Instruments of Adolescent Brain Cognitive Development Study<sup>®</sup> for Better Understanding of Human Brain Development. | Fan CC et al. | — | 2023 | → |
| Inferring the Genetic Influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition. | Hatoum AS et al. | — | 2023 | → |
| Neuroanatomical Predictors of Alcohol Consumption in Adolescents via in vivo Structural Imaging: A Systematic Review of Longitudinal Studies | Honarvar F et al. | — | 2023 | — |
| Neuroanatomical predictors of problematic alcohol consumption in adolescents: a systematic review of longitudinal studies. | Honarvar F et al. | — | 2023 | → |
| Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features. | Kamarajan C et al. | — | 2023 | → |
| The Genetically Informed Neurobiology of Addiction (GINA) model. | Bogdan R et al. | — | 2023 | → |
| Transforming the Future of Adolescent Health: Opportunities From the Adolescent Brain Cognitive Development Study. | Hoffman EA et al. | — | 2022 | → |