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Uncovering the genetic architecture of broad antisocial behavior through a genome-wide association study meta-analysis

Abstract

Despite the substantial heritability of antisocial behavior (ASB), specific genetic variants robustly associated with the trait have not been identified. The present study by the Broad Antisocial Behavior Consortium (BroadABC) meta-analyzed data from 28 discovery samples (N = 85,359) and five independent replication samples (N = 8058) with genotypic data and broad measures of ASB. We identified the first significant genetic associations with broad ASB, involving common intronic variants in the forkhead box protein P2 (FOXP2) gene (lead SNP rs12536335, p = 6.32 × 10−10). Furthermore, we observed intronic variation in Foxp2 and one of its targets (Cntnap2) distinguishing a mouse model of pathological aggression (BALB/cJ strain) from controls (BALB/cByJ strain). Polygenic risk score (PRS) analyses in independent samples revealed that the genetic risk for ASB was associated with several antisocial outcomes across the lifespan, including diagnosis of conduct disorder, official criminal convictions, and trajectories of antisocial development. We found substantial genetic correlations of ASB with mental health (depression rg = 0.63, insomnia rg = 0.47), physical health (overweight rg = 0.19, waist-to-hip ratio rg = 0.32), smoking (rg = 0.54), cognitive ability (intelligence rg = −0.40), educational attainment (years of schooling rg = −0.46) and reproductive traits (age at first birth rg = −0.58, father’s age at death rg = −0.54). Our findings provide a starting point toward identifying critical biosocial risk mechanisms for the development of ASB.

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Fig. 1: SNP-based results from the GWAS meta-analysis (N = 85,359) on broad antisocial behavior.
Fig. 2: Bar charts illustrating the proportion of variance (incremental R2, or ΔR2) explained by the PRSs.
Fig. 3: Significant genetic correlations of ASB with previously published results of other traits and diseases, computed using cross-trait LD score regression in LDHub, Bonferroni-corrected p value: 0.00068 (bars represent 95% confidence intervals).

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Acknowledgements

GM-V was supported by a Doctoral Research Scholarship from the FRQSC. MB, IO-M, and J-PG are supported by the Canada Research Chair Program. KR is supported by a Sir Henry Wellcome Postdoctoral Fellowship. ADB and DD were supported by grants from the Lundbeck Foundation (R102-A9118, R155-2014-1724 and R248-2017-2003), the EU FP7 Program (Grant No. 602805, “Aggressotype”) and H2020 Program (Grant No. 667302, “CoCA”), NIMH (1U01MH109514-01. JL, AM, and KR acknowledge the following funders: Academy of Finland, the Signe and Ane Gyllenberg foundation, Juho Vainio foundation, Yrjö Jahnsson foundation, Jalmari and Rauha Ahokas foundation, Sigrid Juselius Foundation and The Finnish Society of Sciences and Letters. DD is supported by NIH R01 AA015416 (Finnish Twin Study), P50 AA022537 (Alcohol Research Center), R25 AA027402 (VCU GREAT), R34 AA027347 (Personalized Risk Assessment), R01 AA028064 (Parental Marital Discord, PI: JS), and U10 AA008401 (COGA) from the National Institute on Alcohol Abuse and Alcoholism (NIAAA), and by R01 DA050721 (Externalizing Consortium) from the National Institute on Drug Abuse (NIDA). BF, MK, and NRM were also supported by funding from the European Community’s Horizon 2020 Programme (H2020/2014–2020) under grant agreements no. 728018 (Eat2beNICE) and no. 847879 (PRIME). They also received relevant funding from the Netherlands Organization for Scientific Research (NWO) for the Dutch National Science Agenda NeurolabNL project (grant 400-17-602). HMS and MRM are members of the Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/7). This work is also supported by the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. AW has been supported by funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 602768 for the ACTION consortium. JK has been supported by the Academy of Finland Academy professorship program (grants 265240 and 263278). LC-C was supported by a QIMR Berghofer Research Fellowship. JJM was supported by a QIMR Berghofer International PhD Scholarship. ML was funded by an NHMRC Boosting Dementia Leadership Fellowship (APP1140441). SEM and NGM were funded by NHMRC investigator grants (APP1172917 and APP1172990). PJ has been supported by Academy of Finland (Grants no. 319403, 284385 and 274521) and the Stiftelsen för Åbo Akademi Foundation. MK was supported by a personal Rubicon grant (no. 45219212) from the Dutch Research Council. Additional acknowledgements for each study cohort are described in Supplementary Table 1. We acknowledge the work of all members of the Spit for Science Working Group. Spit for Science Director: Danielle M. Dick. Registry management: Kimberly Pedersen, Zoe Neale, Nathaniel Thomas. Data cleaning and management: Amy E. Adkins, Nathaniel Thomas, Zoe Neale, Kimberly Pedersen, Thomas Bannard and Seung B. Cho. Data collection: Amy E. Adkins, Peter Barr, Holly Byers, Erin C. Berenz, Erin Caraway, Seung B. Cho, James S. Clifford, Megan Cooke, Elizabeth Do, Alexis C. Edwards, Neeru Goyal, Laura M. Hack, Lisa J. Halberstadt, Sage Hawn, Sally Kuo, Emily Lasko, Jennifer Lend, Mackenzie Lind, Elizabeth Long, Alexandra Martelli, Jacquelyn L. Meyers, Kerry Mitchell, Ashlee Moore, Arden Moscati, Aashir Nasim, Zoe Neale, Jill Opalesky, Cassie Overstreet, A. Christian Pais, Kimberly Pedersen, Tarah Raldiris, Jessica Salvatore, Jeanne Savage, Rebecca Smith, David Sosnowski, Jinni Su, Nathaniel Thomas, Chloe Walker, Marcie Walsh, Teresa Willoughby, Madison Woodroof and Jia Yan. Genotypical data processing and cleaning: Cuie Sun, Brandon Wormley, Brien Riley, Fazil Aliev, Roseann Peterson and Bradley T. Webb.

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Study concept and design: JJT, TJCP, and DP. Acquisition, analysis, or interpretation of data: EU, BSW, LC-C, EG, TTM, BEL, PRJ, AJ, HMS, GP, GRBS, AGA, KR, BK, MK, AMH, JES, IMN, DD, ALKM, SAB, JES, KS, RP, KMH, SV, MM, WGI, NRM, JM, JFV, BLM, JJM, TFMA, IO-M, RET, SMC, JPG, MRB, GD, FV, and MKL. Drafting of the manuscript: JJT, TJCP, PRJ, BBB, TEM, AC, and DP. Study supervision: TJCP, NGM, EC, KR, JGE, JL, CAH, AJO, HS, HL, MP, AW, EV, PJ, YL, DR, IG, TP, JK, KPH, MRM, GM-V, RP, EV, BBB, FA, DD, AP, SVF, ADB, SEM, BF, MB, JBP, JCG, JCB, SEF, TEM, AC, and DP. All authors critically evaluated and revised the manuscript. JJT had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Jorim J. Tielbeek.

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BF has received educational speaking fees from Medice.

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Tielbeek, J.J., Uffelmann, E., Williams, B.S. et al. Uncovering the genetic architecture of broad antisocial behavior through a genome-wide association study meta-analysis. Mol Psychiatry 27, 4453–4463 (2022). https://doi.org/10.1038/s41380-022-01793-3

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