Associations between the CADM2 gene, substance use, risky sexual behavior, and self-control: A phenome-wide association study.
paper
Cited
Public
- Authors
- Arends, Rachel M; Pasman, Joëlle A; Verweij, Karin J H; Derks, Eske M; Gordon, Scott D; Hickie, Ian; Thomas, Nathaniel S; Aliev, Fazil; Zietsch, Brendan P; van der Zee, Matthijs D; Mitchell, Brittany L; Martin, Nicholas G; Dick, Danielle M; Gillespie, Nathan A; de Geus, Eco J C; Boomsma, Dorret I; Schellekens, Arnt F A; Vink, Jacqueline M
- Year
- 2021
- Journal
- Addiction biology
- PMID
- 33604983
- DOI
- 10.1111/adb.13015
- PMCID
- PMC8596397
Risky behaviors, such as substance use and unprotected sex, are associated with various physical and mental health problems. Recent genome-wide association studies indicated that variation in the cell adhesion molecule 2 (CADM2) gene plays a role in risky behaviors and self-control. In this phenome-wide scan for risky behavior, it was tested if underlying common vulnerability could be (partly) explained by pleiotropic effects of this gene and how large the effects were. Single nucleotide polymorphism (SNP)-level and gene-level association tests within four samples (25 and Up, Spit for Science, Netherlands Twin Register, and UK Biobank and meta-analyses over all samples (combined sample of 362,018 participants) were conducted to test associations between CADM2, substance- and sex-related risk behaviors, and various measures related to self-control. We found significant associations between the CADM2 gene, various risky behaviors, and different measures of self-control. The largest effect sizes were found for cannabis use, sensation seeking, and disinhibition. Effect sizes ranged from 0.01% to 0.26% for single top SNPs and from 0.07% to 3.02% for independent top SNPs together, with sufficient power observed only in the larger samples and meta-analyses. In the largest cohort, we found indications that risk-taking proneness mediated the association between CADM2 and latent factors for lifetime smoking and regular alcohol use. This study extends earlier findings that CADM2 plays a role in risky behaviors and self-control. It also provides insight into gene-level effect sizes and demonstrates the feasibility of testing mediation. These findings present a good starting point for investigating biological etiological pathways underlying risky behaviors.
Significance of associations between CADM2 and risk behavior factors, with and without a mediating effect of risk‐taking proneness. Path a: the effect of the predictor (CADM2) on the mediator (risk‐taking proneness); path b: the effect of the mediator on the outcome factors (tobacco (ab)use, lifetime smoking, and risky alcohol use); path c: the effect of the predictor on the outcome variables; path c': the effect of the predictor on the outcome variables controlling for the mediator. † C′ paths with attenuated p‐values, indicating a partial mediation effect
1–20 of 44
Next →
No entities extracted from this document yet.
No uploaded files.
Not in any collection.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| Albayrak Ö , Pütter C , Volckmar AL , et al. Common obesity risk alleles in childhood attention‐deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet. 2013;162(4):295‐305.10.1002/ajmg.b.3214423533005 | — | — | — |
| Allman JM , Hakeem A , Erwin JM , Nimchinsky E , Hof P . The anterior cingulate cortex. Ann N Y Acad Sci. 2001;935(1):107‐117.11411161 | — | — | — |
| Baban A , Craciun C . Changing health‐risk behaviors: a review of theory and evidence‐based interventions in health psychology. J Evidence‐Based Psychother. 2007;7(1):45‐67. | — | — | — |
| Baron RM , Kenny DA . The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173‐1182.380635410.1037//0022-3514.51.6.1173 | — | — | — |
| Benjamini Y , Hochberg Y . Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Methodol. 1995;57(1):289‐300. | — | — | — |
| Berndt SI , Gustafsson S , Mägi R , et al. Genome‐wide meta‐analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet. 2013;45(5):501‐512.2356360710.1038/ng.2606PMC3973018 | — | — | — |
| Bezdjian S , Baker LA , Tuvblad C . Genetic and environmental influences on impulsivity: a meta‐analysis of twin, family and adoption studies. Clin Psychol Rev. 2011;31(7):1209‐1223.2188943610.1016/j.cpr.2011.07.005PMC3176916 | — | — | — |
| Border R , Johnson EC , Evans LM , et al. No support for historical candidate gene or candidate gene‐by‐interaction hypotheses for major depression across multiple large samples. Am J Psychiatry. 2019;176(5):376‐387.3084582010.1176/appi.ajp.2018.18070881PMC6548317 | — | — | — |
| Boutwell B , Hinds D , Agee M , et al. Replication and characterization of CADM2 and MSRA genes on human behavior. Heliyon. 2017;3(7):e00349.2879515810.1016/j.heliyon.2017.e00349PMC5537199 | — | — | — |
| Bush G , Luu P , Posner MI . Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci. 2000;4(6):215‐222.1082744410.1016/s1364-6613(00)01483-2 | — | — | — |
| Bush WS , Oetjens MT , Crawford DC . Unravelling the human genome–phenome relationship using phenome‐wide association studies. Nat Gen Rev. 2016;17(3):129‐145.10.1038/nrg.2015.3626875678 | — | — | — |
| Chang CC , Chow CC , Tellier LC , Vattikuti S , Purcell SM , Lee JJ . Second‐generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4(7):1‐16.2572285210.1186/s13742-015-0047-8PMC4342193 | — | — | — |
| Clarke T‐K , Adams MJ , Davies G , et al. Genome‐wide association study of alcohol consumption and genetic overlap with other health‐related traits in UK Biobank (N=112 117). Mol Psychiatry. 2017;22(10):1376‐1384.2893769310.1038/mp.2017.153PMC5622124 | — | — | — |
| Davis C , Strachan S , Berkson M . Sensitivity to reward: implications for overeating and overweight. Appetite. 2004;42(2):131‐138.1501017610.1016/j.appet.2003.07.004 | — | — | — |
| Day FR , Helgason H , Chasman DI , et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat Genet. 2016;48(6):617‐623.2708918010.1038/ng.3551PMC5238953 | — | — | — |
| de Leeuw CA , Mooij JM , Heskes T , Posthuma D . MAGMA: generalized gene‐set analysis of GWAS data. PLoS Comp Biol. 2015;11(4):1‐19, e1004219.10.1371/journal.pcbi.1004219PMC440165725885710 | — | — | — |
| Dick D , Nasim A , Edwards AC , et al. Spit for science: launching a longitudinal study of genetic and environmental influences on substance use and emotional health at a large US university. Front Gen. 2014;5:47.10.3389/fgene.2014.00047PMC394479424639683 | — | — | — |
| Duncan LE , Keller MC . A critical review of the first 10 years of candidate gene‐by‐environment interaction research in psychiatry. Am J Psychiatry. 2011;168(10):1041‐1049.2189079110.1176/appi.ajp.2011.11020191PMC3222234 | — | — | — |
| Ezzati M , Lopez AD , Rodgers A , Vander Hoorn S , Murray CJ , Group CRAC . Selected major risk factors and global and regional burden of disease. Lancet. 2002;360(9343):1347‐1360.1242398010.1016/S0140-6736(02)11403-6 | — | — | — |
| Fabrigar LR , Wegener DT . Exploratory Factor Analysis. New York: Oxford University Press; 2012. | — | — | — |
| Faul F , Erdfelder E , Buchner A , Lang A‐G . Statistical power analyses using G* Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009;41(4):1149‐1160.1989782310.3758/BRM.41.4.1149 | — | — | — |
| Friedman HS . Encyclopedia of mental health. Oxford: Elsevier; 2016. | — | — | — |
| Gizer IR , Ficks C , Waldman ID . Candidate gene studies of ADHD: a meta‐analytic review. Hum Genet. 2009;126(1):51‐90.1950690610.1007/s00439-009-0694-x | — | — | — |
| Gläscher J , Adolphs R , Damasio H , et al. Lesion mapping of cognitive control and value‐based decision making in the prefrontal cortex. Proc Natl Acad Sci. 2012;109(36):14681‐14686.2290828610.1073/pnas.1206608109PMC3437894 | — | — | — |
| Goldman D , Oroszi G , Ducci F . The genetics of addictions: uncovering the genes. Focus. 2006;6(3):521‐525.10.1038/nrg163515995696 | — | — | — |
| Gore FM , Bloem PJ , Patton GC , et al. Global burden of disease in young people aged 10–24 years: a systematic analysis. Lancet. 2011;377(9783):2093‐2102.2165206310.1016/S0140-6736(11)60512-6 | — | — | — |
| GTExConsortium . Variant Page. https://www.gtexportal.org/home/snp/rs4637303. Published 2019. Accessed. | — | — | — |
| Haber SN . The place of dopamine in the cortico‐basal ganglia circuit. Neuroscience. 2014;282:248‐257.2544519410.1016/j.neuroscience.2014.10.008PMC5484174 | — | — | — |
| Hattersley AT , McCarthy MI . What makes a good genetic association study? Lancet. 2005;366(9493):1315‐1323.1621460310.1016/S0140-6736(05)67531-9 | — | — | — |
| Hayes AF . Beyond Baron and Kenny: statistical mediation analysis in the new millennium. Commun Monogr. 2009;76(4):408‐420. | — | — | — |
| Holmes AJ , Hollinshead MO , Roffman JL , Smoller JW , Buckner RL . Individual differences in cognitive control circuit anatomy link sensation seeking, impulsivity, and substance use. J Neurosci. 2016;36(14):4038‐4049.2705321010.1523/JNEUROSCI.3206-15.2016PMC4821913 | — | — | — |
| Hopfer CJ , Crowley TJ , Hewitt JK . Review of twin and adoption studies of adolescent substance use. J am Acad Child Adolesc Psychiatry. 2003;42(6):710‐719.1292147910.1097/01.CHI.0000046848.56865.54 | — | — | — |
| Hyman SE , Malenka RC , Nestler EJ . Neural mechanisms of addiction: the role of reward‐related learning and memory. Annu Rev Neurosci. 2006;29(1):565‐598.1677659710.1146/annurev.neuro.29.051605.113009 | — | — | — |
| Ibrahim‐Verbaas C , Bressler J , Debette S , et al. GWAS for executive function and processing speed suggests involvement of the CADM2 gene. Mol Psychiatry. 2016;21(2):189‐197.2586980410.1038/mp.2015.37PMC4722802 | — | — | — |
| Kwon JM , Goate AM . The candidate gene approach. Alcohol Res Health. 2000;24(3):164‐168.11199286PMC6709736 | — | — | — |
| Mitchell BL , Campos AI , Rentería ME , et al. Twenty‐five and up (25Up) study: a new wave of the Brisbane longitudinal twin study. Twin Res Hum Genet. 2019;1‐10.3119812610.1017/thg.2019.27 | — | — | — |
| Murray CJ , Barber RM , Foreman KJ , et al. Global, regional, and national disability‐adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. Lancet. 2015;386(10009):2145‐2191.2632126110.1016/S0140-6736(15)61340-XPMC4673910 | — | — | — |
| Pasman JA , Verweij KJ , Gerring Z , et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability. Nat Neurosci. 2018;21(9):1161‐1170.3015066310.1038/s41593-018-0206-1PMC6386176 | — | — | — |
| Rodgers A , Ezzati M , Vander Hoorn S , Lopez AD , Lin R‐B , Murray CJ . Distribution of major health risks: findings from the global burden of disease study. PLoS Med. 2004;1(1):e27.1552604910.1371/journal.pmed.0010027PMC523844 | — | — | — |
| Sanchez‐Roige S , Fontanillas P , Elson SL , et al. Genome‐wide association studies of impulsive personality traits (BIS‐11 and UPPS‐P) and drug experimentation in up to 22,861 adult research participants identify loci in the CACNA1I and CADM2 genes. J Neurosci. 2019;39(13):2562‐2572.3071832110.1523/JNEUROSCI.2662-18.2019PMC6435820 | — | — | — |
| Strawbridge RJ , Ward J , Cullen B , et al. Genome‐wide analysis of self‐reported risk‐taking behaviour and cross‐disorder genetic correlations in the UK Biobank cohort. Transl Psychiatry. 2018;8(39):1‐11.2939139510.1038/s41398-017-0079-1PMC5804026 | — | — | — |
| Stringer S , Minică C , Verweij KJ , et al. Genome‐wide association study of lifetime cannabis use based on a large meta‐analytic sample of 32 330 subjects from the international cannabis consortium. Transl Psychiatry. 2016;6(3):1‐9, e769.10.1038/tp.2016.36PMC487245927023175 | — | — | — |
| Sudlow C , Gallacher J , Allen N , et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.2582637910.1371/journal.pmed.1001779PMC4380465 | — | — | — |
| Team RC . R language definition. 2000. | — | — | — |
| The 1000 Genomes Project Consortium . A global reference for human genetic variation. Nature. 2015;526:68‐74.2643224510.1038/nature15393PMC4750478 | — | — | — |
| The Human Protein Atlas ‐ CADM2. https://www.proteinatlas.org/ENSG00000175161-CADM2/tissue. Accessed on 13th of November 2019. | — | — | — |
| Tyndale RF . Genetics of alcohol and tobacco use in humans. Ann Med. 2003;35(2):94‐121.1279533910.1080/07853890310010014 | — | — | — |
| UN . Transforming Our World: The 2030 Agenda for Sustainable Development. 2016. https://stg-wedocs.unep.org/bitstream/handle/20.500.11822/11125/unepswiosm1inf7sdg.pdf?sequence=1 | — | — | — |
| Van Leijenhorst L , Zanolie K , Van Meel CS , Westenberg PM , Rombouts SA , Crone EA . What motivates the adolescent? Brain regions mediating reward sensitivity across adolescence. Cereb Cortex. 2010;20(1):61‐69.1940690610.1093/cercor/bhp078 | — | — | — |
| Verdejo‐García A , Lawrence AJ , Clark L . Impulsivity as a vulnerability marker for substance‐use disorders: review of findings from high‐risk research, problem gamblers and genetic association studies. Neuroscience & Biobehavioral Reviews. 2008;32(4):777‐810.1829588410.1016/j.neubiorev.2007.11.003 | — | — | — |
| Verweij KJ , Zietsch BP , Lynskey MT , et al. Genetic and environmental influences on cannabis use initiation and problematic use: a meta‐analysis of twin studies. Addiction. 2010;105(3):417‐430.2040298510.1111/j.1360-0443.2009.02831.xPMC2858354 | — | — | — |
| Vink JM , Willemsen G , Boomsma DI . Heritability of smoking initiation and nicotine dependence. Behav Genet. 2005;35(4):397‐406.1597102110.1007/s10519-004-1327-8 | — | — | — |
| Wagner WE III . Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics. Sage Publications; 2019. | — | — | — |
| Willemsen G , Vink JM , Abdellaoui A , et al. The adult Netherlands twin register: twenty‐five years of survey and biological data collection. Twin Res Hum Genet. 2013;16(1):271‐281.2329864810.1017/thg.2012.140PMC3739974 | — | — | — |
| Willer CJ , Li Y , Abecasis GR . METAL: fast and efficient meta‐analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190‐2191.2061638210.1093/bioinformatics/btq340PMC2922887 | — | — | — |
| Zietsch B , Verweij K , Bailey J , Wright M , Martin N . Genetic and environmental influences on risky sexual behaviour and its relationship with personality. Behav Genet. 2010;40(1):12‐21.1981308410.1007/s10519-009-9300-1 | — | — | — |
In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Parsing genetically influenced risk pathways: genetic loci impact problematic alcohol use via externalizing and specific risk. | 2022 | 36180423 |
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| The genetics of cannabis lifetime use. | Bright U et al. | — | 2026 | → |
| Alcohol use disorder and body mass index show genetic pleiotropy and shared neural associations. | Malone SG et al. | — | 2025 | → |
| A Systematic Investigation of the Common Genetic Architecture of Substance Use Traits and the Relationship with Mental Health. | Pasman JA et al. | — | 2025 | → |
| Differences and similarities between the genetic architecture of lifetime substance use across different substances. | Bright U et al. | — | 2025 | → |
| Genetic influences and causal pathways shared between cannabis use disorder and other substance use traits. | Galimberti M et al. | — | 2024 | → |
| Investigating the shared genetic architecture between attention-deficit/hyperactivity disorder and risk taking behavior: A large-scale genomewide cross-trait analysis. | Chen Y et al. | — | 2024 | → |
| "Weeding out" violence? Translational perspectives on the neuropsychobiological links between cannabis and aggression. | Bortolato M et al. | — | 2024 | → |
| CADM2 is implicated in impulsive personality and numerous other traits by genome- and phenome-wide association studies in humans and mice. | Sanchez-Roige S et al. | — | 2023 | → |
| Dose response of running on blood biomarkers of wellness in generally healthy individuals. | Nogal B et al. | — | 2023 | → |
| Genetic correlation for alcohol consumption between Europeans and East Asians. | Liu X et al. | — | 2023 | → |
| Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response. | Carreras-Torres R et al. | — | 2023 | → |
| Leveraging related health phenotypes for polygenic prediction of impulsive choice, impulsive action, and impulsive personality traits in 1534 European ancestry community adults. | Deng WQ et al. | — | 2023 | → |
| The association between the <i>CCDC88A</i> gene polymorphism at rs1437396 and alcohol use disorder, with or without major depression disorder. | Bonea M et al. | — | 2023 | → |
| The Genetically Informed Neurobiology of Addiction (GINA) model. | Bogdan R et al. | — | 2023 | → |
| Genetics and neurobiology of eating disorders. | Bulik CM et al. | — | 2022 | → |
| Parsing genetically influenced risk pathways: genetic loci impact problematic alcohol use via externalizing and specific risk. | Barr PB et al. | — | 2022 | → |
| The CADM2 Gene and Behavior: A Phenome-Wide Scan in UK-Biobank. | Pasman JA et al. | — | 2022 | → |
| The genetic aetiology of cannabis use: from twin models to genome-wide association studies and beyond. | Verweij KJH et al. | — | 2022 | → |
| Associations between the CADM2 gene, substance use, risky sexual behavior, and self-control: A phenome-wide association study. | Arends RM et al. | — | 2021 | → |
| Parsing Genetically Influenced Risk Pathways: Genetic Loci Impact Problematic Alcohol Use Via Externalizing and Specific Risk | Barr PB et al. | — | 2021 | — |