Skip to main content
SpringerLink
Log in

Polygenic risk for aggressive behavior from late childhood through early adulthood

  • Original Contribution
  • Published:
European Child & Adolescent Psychiatry Aims and scope Submit manuscript

Abstract

Twin studies suggest a substantial role for genes in explaining individual differences in aggressive behavior across development. It is unclear, however, how directly measured genetic risk is associated with aggressive behavior at different moments across adolescence and how genes might distinguish developmental trajectories of aggressive behavior. Here, a polygenic risk score derived from the EAGLE-Consortium genome-wide association study of aggressive behavior in children was tested as predictor of latent growth classes derived from those measures in an adolescent population (n = 2229, of which n = 1246 with genetic information) and a high-risk sample (n = 543, of which n = 335 with genetic information). In the population sample, the polygenic risk score explained variation in parent-reported aggressive behavior at all ages and distinguished between stable low aggressive behavior and moderate and high-decreasing trajectories based on parent–report. In contrast, the polygenic risk score was not associated with self- and teacher-reported aggressive behavior, and no associations were found in the high-risk sample. This pattern of results suggests that methodological choices made in genome-wide association studies impact the predictive strength of polygenic risk scores, not just with respect to power but likely also in terms of generalizability and specificity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Availability of data and material

GDPR and Dutch legislation preclude data from being shared in openly accessible repositories. TRAILS data are available after submitting a publication proposal; the procedure is described here https://www.trails.nl/en/hoofdmenu/data/data-use

Code availability

Syntax files for latent class growth models and subsequent multinomial logistic regressions are deposited on the Open Science Framework.

Notes

  1. We computed growth mixture models (GMM, which allow for within-class variance) and latent class growth models (LCGM, which restrict within-group variance of intercept and slope to zero) with the aim to compare model fit between both types. However, GMM did not converge in all cases, owing to non-positive covariance matrices. For this reason, we moved forward with the more restrictive yet easier to model latent class growth models. We also computed more parsimonious models with intercept and slope but without quadratic effect, however, quadratic means and variances were statistically significant in most models, thus for consistency we moved forward with quadratic models.

  2. The meta-analysis did not differentiate between parent- and teacher-report in moderation analyses but the vast majority of studies using “other” report were based on parent-reports.

References

  1. Vuoksimaa E, Rose RJ, Pulkkinen L et al (2021) Higher aggression is related to poorer academic performance in compulsory education. J Child Psychol Psychiatry 62:327–338. https://doi.org/10.1111/jcpp.13273

    Article  PubMed  Google Scholar 

  2. Schaeffer CM, Petras H, Ialongo N et al (2003) Modeling growth in boys’ aggressive behavior across elementary school: links to later criminal involvement, conduct disorder, and antisocial personality disorder. Dev Psychol 39:1020–1035. https://doi.org/10.1037/0012-1649.39.6.1020

    Article  PubMed  Google Scholar 

  3. Timmermans M, Van Lier PA, Koot HM (2008) Which forms of child/adolescent externalizing behaviors account for late adolescent risky sexual behavior and substance use? J Child Psychol Psychiatry 49:386–394. https://doi.org/10.1111/j.1469-7610.2007.01842.x

    Article  PubMed  Google Scholar 

  4. Vergunst F, Tremblay RE, Nagin D et al (2019) Association between childhood behaviors and adult employment earnings in Canada. JAMA Psychiat 76:1044–1051. https://doi.org/10.1001/jamapsychiatry.2019.1326

    Article  Google Scholar 

  5. Cleverley K, Szatmari P, Vaillancourt T et al (2012) Developmental trajectories of physical and indirect aggression from late childhood to adolescence: Sex differences and outcomes in emerging adulthood. J Am Acad Child Adolesc Psychiatry 51:1037–1051. https://doi.org/10.1016/j.jaac.2012.07.010

    Article  PubMed  Google Scholar 

  6. Reef J, Diamantopoulou S, van Meurs I et al (2011) Developmental trajectories of child to adolescent externalizing behavior and adult DSM-IV disorder: results of a 24-year longitudinal study. Soc Psychiatry Psychiatr Epidemiol 46:1233–1241. https://doi.org/10.1007/s00127-010-0297-9

    Article  PubMed  Google Scholar 

  7. Luningham JM, Hendriks AM, Krapohl E et al (2020) Harmonizing behavioral outcomes across studies, raters, and countries: application to the genetic analysis of aggression in the ACTION Consortium. J Child Psychol Psychiatry 61:807–817. https://doi.org/10.1111/jcpp.13188

    Article  PubMed  PubMed Central  Google Scholar 

  8. Rhee SH, Waldman ID (2002) Genetic and environmental influences on antisocial behavior: a meta-analysis of twin and adoption studies. Psychol Bull 128:490–529. https://doi.org/10.1037/0033-2909.128.3.490

    Article  PubMed  Google Scholar 

  9. Porsch RM, Middeldorp CM, Cherny SS et al (2016) Longitudinal heritability of childhood aggression. Am J Med Genet B Neuropsychiatr Genet 171:697–707. https://doi.org/10.1002/ajmg.b.32420

    Article  PubMed  Google Scholar 

  10. Van Beijsterveldt C, Bartels M, Hudziak J, Boomsma D (2003) Causes of stability of aggression from early childhood to adolescence: a longitudinal genetic analysis in Dutch twins. Behav Genet 33:591–605. https://doi.org/10.1023/a:1025735002864

    Article  PubMed  Google Scholar 

  11. Wesseldijk LW, Bartels M, Vink JM et al (2018) Genetic and environmental influences on conduct and antisocial personality problems in childhood, adolescence, and adulthood. Eur Child Adolesc Psychiatry 27:1123–1132. https://doi.org/10.1007/s00787-017-1014-y

    Article  PubMed  Google Scholar 

  12. Raine A (2018) Antisocial personality as a neurodevelopmental disorder. Annu Rev Clin Psychol 14:259–289. https://doi.org/10.1146/annurev-clinpsy-050817-084819

    Article  PubMed  Google Scholar 

  13. Pappa I, St Pourcain B, Benke K et al (2016) A genome-wide approach to children’s aggressive behavior: The EAGLE consortium. Am J Med Genet B Neuropsychiatr Genet 171:562–572. https://doi.org/10.1002/ajmg.b.32333

    Article  CAS  PubMed  Google Scholar 

  14. Wang FL, Galán CA, Lemery-Chalfant K et al (2020) Evidence for two genetically distinct pathways to co-occurring internalizing and externalizing problems in adolescence characterized by negative affectivity or behavioral inhibition. J Abnorm Psychol 129:633–645. https://doi.org/10.1037/abn0000525

    Article  PubMed  PubMed Central  Google Scholar 

  15. Elam KK, Chassin L, Pandika D (2018) Polygenic risk, family cohesion, and adolescent aggression in Mexican American and European American families: Developmental pathways to alcohol use. Dev Psychopathol 30:1715–1728. https://doi.org/10.1017/S0954579418000901

    Article  PubMed  PubMed Central  Google Scholar 

  16. Barnes J, Liu H, Motz RT et al (2019) The propensity for aggressive behavior and lifetime incarceration risk: A test for gene-environment interaction (GxE) using whole-genome data. Aggress Violent Behav 49:101307. https://doi.org/10.1016/j.avb.2019.07.002shaw

    Article  Google Scholar 

  17. Shaw DS, Galán CA, Lemery-Chalfant K, et al (2019) Trajectories and Predictors of Children’s Early-Starting Conduct Problems: Child, Family, Genetic, and Intervention Effects. Dev Psychopathol 31(5):1911–1921. https://doi.org/10.1017/S0954579419000828

  18. Burt SA (2009) Are there meaningful etiological differences within antisocial behavior? Results of a meta-analysis. Clin Psychol Rev 29:163–178. https://doi.org/10.1016/j.cpr.2008.12.004

  19. Piotrowska PJ, Stride CB, Croft SE, Rowe R (2015) Socioeconomic status and antisocial behaviour among children and adolescents: A systematic review and meta-analysis. Clin Psychol Rev 35:47–55. https://doi.org/10.1016/j.cpr.2014.11.003

  20. Loeber R, Capaldi DM, Costello E (2013) Gender and the development of aggression, disruptive behavior, and delinquency from childhood to early adulthood. In: Tolan PH, Leventhal BL (eds) Advances in development and psychopathology: Brain Research Foundation symposium series. Springer, Disruptive behavior disorders, pp 137–160

    Google Scholar 

  21. Rescorla LA, Bochicchio L, Achenbach TM et al (2014) Parent–teacher agreement on children’s problems in 21 societies. J Clin Child Adolesc Psychol 43:627–642. https://doi.org/10.1080/15374416.2014.900719

    Article  PubMed  Google Scholar 

  22. Huisman M, Oldehinkel AJ, Winter AD et al (2008) Cohort profile: the Dutch ‘TRacking adolescents’ individual lives’ survey’; TRAILS. Int J Epidemiol 37:1227–1235. https://doi.org/10.1093/ije/dym273

    Article  PubMed  Google Scholar 

  23. Nederhof E, Jorg F, Raven D et al (2012) Benefits of extensive recruitment effort persist during follow-ups and are consistent across age group and survey method. The TRAILS study. BMC Med Res Methodol 12:93–108. https://doi.org/10.1186/1471-2288-12-93

    Article  PubMed  PubMed Central  Google Scholar 

  24. Oldehinkel AJ, Rosmalen JG, Buitelaar JK, et al (2015) Cohort Profile Update: The TRacking Adolescents’ Individual Lives Survey (TRAILS). Int J Epidemiol 76–76n. https://doi.org/10.1093/ije/dyu225

  25. McCarthy S, Das S, Kretzschmar W et al (2016) A 860 reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48:1279–1283. https://doi.org/10.1038/ng.3643

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Das S, Forer L, Schönherr S et al (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284–1287. https://doi.org/10.1038/ng.3656

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Vilhjálmsson BJ, Yang J, Finucane HK et al (2015) Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet 97:576–592. https://doi.org/10.1016/j.ajhg.2015.09.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Boyle EA, Li YI, Pritchard JK (2017) An expanded view of complex traits: from polygenic to omnigenic. Cell 169:1177–1186

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Achenbach TM, Rescorla LA (2001) Manual for the ASEBA school-age forms & profiles: child behavior checklist for ages 6–18, teacher’s report form, youth self-report: an integrated system of multi-informant assessment. University of Vermont, Research Center for children youth & families

  30. Achenbach TM (1991) Manual for the Teacher’s Report Form and 1991 profile. Univ Vermont/Department Psychiatry

  31. de Winter AF, Oldehinkel AJ, Veenstra R et al (2005) Evaluation of non-response bias in mental health determinants and outcomes in a large sample of pre-adolescents. Eur J Epidemiol 20:173–181. https://doi.org/10.1007/s10654-004-4948-6

    Article  PubMed  Google Scholar 

  32. Ganzeboom HB, Treiman DJ (1996) Internationally comparable measures of occupational status for the 1988 International Standard Classification of Occupations. Soc Sci Res 25:201–239. https://doi.org/10.1006/ssre.1996.0010

    Article  Google Scholar 

  33. Veenstra R, Lindenberg S, Oldehinkel AJ et al (2006) Temperament, environment, and antisocial behavior in a population sample of preadolescent boys and girls. Int J Behav Dev 30:422–432. https://doi.org/10.1177/0165025406071490

    Article  Google Scholar 

  34. Karlsson Linner R, Mallard TT, Barr PB, et al (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

  35. Cai N, Revez JA, Adams MJ et al (2020) Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat Genet 52:437–447. https://doi.org/10.1038/s41588-020-0594-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Sijbrandij JJ, Hoekstra T, Almansa J et al (2019) Identification of developmental trajectory classes: Comparing three latent class methods using simulated and real data. Adv Life Course Res 42:100288. https://doi.org/10.1016/j.alcr.2019.04.018

    Article  PubMed  Google Scholar 

  37. Lee JJ, Wedow R, Okbay A et al (2018) Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet 50:1112–1121. https://doi.org/10.1038/s41588-018-0147-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jansen PR, Polderman TJC, Bolhuis K et al (2018) Polygenic scores for schizophrenia and educational attainment are associated with behavioural problems in early childhood in the general population. J Child Psychol Psychiatry 59:39–47. https://doi.org/10.1111/jcpp.12759

    Article  PubMed  Google Scholar 

  39. Hannigan LJ, Pingault J-B, Krapohl E et al (2018) Genetics of co-developing conduct and emotional problems during childhood and adolescence. Nat Hum Behav 2:514. https://doi.org/10.1038/s41562-018-0373-9

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This research is part of the TRacking Adolescents' Individual Lives Survey (TRAILS). Participating centers of TRAILS include various departments of the University Medical Center and University of Groningen, the University of Utrecht, the Radboud Medical Center Nijmegen, and the Parnassia Group, all in the Netherlands. TRAILS has been financially supported by various grants from the Netherlands Organization for Scientific Research (NWO), ZonMW, GB-MaGW, the Dutch Ministry of Justice, the European Science Foundation, the European Research Council, BBMRI-NL, and the participating universities. We are grateful to everyone who participated in this research or worked on this project to and make it possible. Preparation of this manuscript has been supported by the European Research Council (ERC) Starting Grant awarded to Tina Kretschmer under the Horizon 2020 Research and Innovation program (Grant Agreement Number 757364, Title: Ghosts from the Past—Consequences of Adolescent Peer Relations Across Contexts and Generations), by a NWO Visitor Travel Grant for Isabelle Ouellet-Morin to the University of Groningen (040.11.704) and a Canada Research Chair in the Developmental Origins of Vulnerability and Resilience awarded to Isabelle Ouellet-Morin.

Funding

Preparation of this manuscript has been supported by the European Research Council (ERC) Starting Grant awarded to Tina Kretschmer under the Horizon 2020 Research and Innovation program (Grant Agreement Number 757364, Title: Ghosts from the Past—Consequences of Adolescent Peer Relations Across Contexts and Generations) and by a NWO Visitor Travel Grant for Isabelle Ouellet-Morin to the University of Groningen (040.11.704). TRAILS has been financially supported by various grants from the Netherlands Organization for Scientific Research (NWO), ZonMW, GB-MaGW, the Dutch Ministry of Justice, the European Science Foundation, the European Research Council, BBMRI-NL, and the participating universities.

Author information

Authors and Affiliations

  1. Faculty of Behavioral and Social Sciences, Department of Pedagogy and Educational Sciences, University of Groningen, Grote Rozenstraat 38, 9712TJ, Groningen, Netherlands

    Tina Kretschmer

  2. University of Montreal & Research Center of the Montreal Mental Health University Institute, Montreal, Canada

    Isabelle Ouellet-Morin

  3. Faculty of Behavioral and Social Sciences, Department of Pedagogy and Educational Sciences, University of Groningen, Groningen, Netherlands

    Charlotte Vrijen

  4. Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands

    Ilja Maria Nolte

  5. Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, Netherlands

    Catharina Hartman

Authors
  1. Tina Kretschmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isabelle Ouellet-Morin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Charlotte Vrijen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ilja Maria Nolte
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Catharina Hartman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: TK, IOM, and CH; methodology: TK, IOM, CH, and IMN; formal analysis and investigation: TK; writing, reviewing, and editing: TK, IOM, CH, and CV.

Corresponding author

Correspondence to Tina Kretschmer.

Ethics declarations

Conflict of interest

None.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kretschmer, T., Ouellet-Morin, I., Vrijen, C. et al. Polygenic risk for aggressive behavior from late childhood through early adulthood. Eur Child Adolesc Psychiatry 32, 651–660 (2023). https://doi.org/10.1007/s00787-021-01906-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00787-021-01906-3

Keywords

Search

Navigation