Genome-Wide Association Studies of a Broad Spectrum of Antisocial Behavior | Genetics and Genomics | JAMA Network
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Figure 1.  Miami Plot Showing P Values of the Single-Nucleotide Polymorphism Associations With Antisocial Behavior in Males and Females
Miami Plot Showing P Values of the Single-Nucleotide Polymorphism Associations With Antisocial Behavior in Males and Females

The threshold for genome-wide significance (P < 1.67 × 10−8) is indicated by the red dotted line, and the threshold for promising findings (P < 1.0 × 10−5) is indicated by the blue dotted line.

Figure 2.  Polygenic Risk Scores (PRSs) in the Finnish Crime Study, Michigan State University Twin Registry (MSUTR), and Yale-Penn Samples
Polygenic Risk Scores (PRSs) in the Finnish Crime Study, Michigan State University Twin Registry (MSUTR), and Yale-Penn Samples

The PRSs for antisocial personality disorder (ASPD) among patients with antisocial behavior (ASB) in the Finnish Crime Study using sex-combined (A) and male-only (B) samples. Summary-summary statistic–based results plotting the explained variance in ASB within the MSUTR (sex combined [C], males only [D], and females only [E]) and Yale-Penn (sex combined [F], males only [G], and females only [H]) samples. The proportion of variance explained (Nagelkerke R2) was computed by comparison of a full model (covariates plus PRS) score with a reduced model (covariates only). Seven different P value thresholds for selecting risk alleles are denoted by the color of each bar. The number of single-nucleotide polymorphisms (SNPs) per threshold is displayed below each bar.

aStatistical significance at P < .05.

bStatistical significance after correcting for multiple testing at P < .006.

Table 1.  Study Design, Sample Sizes, and Phenotypes for Genome-Wide Association Study Cohorts
Study Design, Sample Sizes, and Phenotypes for Genome-Wide Association Study Cohorts
Table 2.  Genetic Correlation Estimates for 9 Traits With Broad Antisocial Behavior
Genetic Correlation Estimates for 9 Traits With Broad Antisocial Behavior
Supplement.

eAppendix 1. Phenotypes and Cohort Description

eAppendix 2. Meta-analysis and Quality Control

eAppendix 3. Gene and Gene-Set Analyses

eAppendix 4. Enrichment of Signal in Previously Implicated Genes for Antisocial Behavior

eAppendix 5. Replication

eAppendix 6. Functional Annotation

eAppendix 7. Tissue Expression

eTable 1. Genotyping, Quality Control, and Imputation Information of the Participating Cohorts

eTable 2. Number of Duplicate Markers and Mismatches, the Genomic Control Values, and Number of Processed Polymorphisms That Were Available After QC

eTable 3. Genes Showing the Strongest Association (P < 10−<) With Antisocial Behavior in the Sex-Combined MAGMA Gene Analysis (N = 16 400)

eTable 4. Genes Showing the Strongest Association (P < 10−<) With Antisocial Behavior in the Female-Specific MAGMA Gene Analysis (N = 8535)

eTable 5. Genes Showing the Strongest Association (P < 10−<) With Antisocial Behavior in the Male-Specific MAGMA Gene Analysis (N = 7772)

eTable 6. The 50 Gene Sets Showing the Strongest Association in the Sex-Combined MAGMA Gene-Set Analysis

eTable 7. Combined (N = 16 400) Enrichment Results Regarding 29 Candidate Genes (Derived From Vassos et al) Previously Related to Antisocial Phenotypes

eTable 8. Female-Specific (N = 8535) Enrichment Results Regarding 29 Candidate Genes (Derived From Vassos et al) Previously Related to antisocial phenotypes

eTable 9. Male-Specific (N = 7772) Enrichment Results Regarding 29 Candidate Genes (Derived From Vassos et al) Previously Related to Antisocial Phenotypes

eTable 10. Replication Test Regarding SNPs in High LD (r2>0.6) With the Top 3 SNPs (Chr1: rs2764450, Chr11: rs11215217, ChrX: rs41456347) Available in the Finnish Crime Study and MSUTR

eTable 11. Results of the Combined GWAS Meta-analysis for the Independent* Signals Reaching P < 10−< in the Discovery Stage

eTable 12. Results of the Female-Specific GWAS Meta-analysis for the Independent Signals Reaching P < 10−< in the Discovery Stage

eTable 13. Results of the Male-Specific GWAS Meta-analysis for the Independent Signals Reaching P < 10−< in the Discovery Stage

eTable 14. Cohort-Specific P Values, β Values, and Allele Frequencies for the Three 3 SNPs

eTable 15. Sign and Fisher Exact Test of Directional Effects Among SNPs of ASB Females With Males (Clumped) and ASB and Educational Attainment (clumped) for Different P Value Thresholds

eFigure 1. Regional Plot of Chrosomsome 1:180242092 and Heatmap of Gene Expression of Mapped Genes for ASB

eFigure 3. Quantile-Quantile Plots for Antisocial Behavior Results

eFigure 4. Manhattan Plot for Antisocial Behavior Results for the Combined Meta-Analyses

eFigure 5. Forest Plots of the Harmonized z Statistics and Meta-analysis Estimates for the 3 Top SNPs

eFigure 2. Gene-Based Genome Wide Analyses

eAcknowledgments

eReferences

1.
McCollister  KE, French  MT, Fang  H.  The cost of crime to society: new crime-specific estimates for policy and program evaluation.  Drug Alcohol Depend. 2010;108(1-2):98-109.PubMedGoogle ScholarCrossref
2.
Wright  JP, Tibbetts  SG, Daigle  LE.  Criminals in the Making: Criminality Across the Life Course. London, England: SAGE Publications; 2014.
3.
Brewin  CR, Andrews  B, Rose  S, Kirk  M.  Acute stress disorder and posttraumatic stress disorder in victims of violent crime.  Am J Psychiatry. 1999;156(3):360-366.PubMedGoogle Scholar
4.
Abram  KM, Zwecker  NA, Welty  LJ, Hershfield  JA, Dulcan  MK, Teplin  LA.  Comorbidity and continuity of psychiatric disorders in youth after detention: a prospective longitudinal study.  JAMA Psychiatry. 2015;72(1):84-93.PubMedGoogle ScholarCrossref
5.
Goldstein  RB, Chou  SP, Saha  TD,  et al.  The Epidemiology of antisocial behavioral syndromes in adulthood: results from the National Epidemiologic Survey on Alcohol and Related Conditions–III.  J Clin Psychiatry. 2017;78(1):90-98.PubMedGoogle ScholarCrossref
6.
Polderman  TJC, Benyamin  B, de Leeuw  CA,  et al.  Meta-analysis of the heritability of human traits based on fifty years of twin studies.  Nat Genet. 2015;47(7):702-709.PubMedGoogle ScholarCrossref
7.
Rhee  SH, Waldman  ID.  Genetic and environmental influences on antisocial behavior: a meta-analysis of twin and adoption studies.  Psychol Bull. 2002;128(3):490-529.PubMedGoogle ScholarCrossref
8.
Burt  SA.  Are there meaningful etiological differences within antisocial behavior? results of a meta-analysis.  Clin Psychol Rev. 2009;29(2):163-178.PubMedGoogle ScholarCrossref
9.
Gunter  TD, Vaughn  MG, Philibert  RA.  Behavioral genetics in antisocial spectrum disorders and psychopathy: a review of the recent literature.  Behav Sci Law. 2010;28(2):148-173.PubMedGoogle ScholarCrossref
10.
Beaver  KM, DeLisi  M, Vaughn  MG, Barnes  JC.  Monoamine oxidase A genotype is associated with gang membership and weapon use.  Compr Psychiatry. 2010;51(2):130-134.PubMedGoogle ScholarCrossref
11.
Douglas  K, Chan  G, Gelernter  J,  et al.  5-HTTLPR as a potential moderator of the effects of adverse childhood experiences on risk of antisocial personality disorder.  Psychiatr Genet. 2011;21(5):240-248.PubMedGoogle ScholarCrossref
12.
Vassos  E, Collier  DA, Fazel  S.  Systematic meta-analyses and field synopsis of genetic association studies of violence and aggression.  Mol Psychiatry. 2014;19(4):471-477.PubMedGoogle ScholarCrossref
13.
Kendler  KS.  What psychiatric genetics has taught us about the nature of psychiatric illness and what is left to learn.  Mol Psychiatry. 2013;18(10):1058-1066.PubMedGoogle ScholarCrossref
14.
Dick  DM, Agrawal  A, Keller  MC,  et al.  Candidate gene-environment interaction research: reflections and recommendations.  Perspect Psychol Sci. 2015;10(1):37-59.PubMedGoogle ScholarCrossref
15.
Dick  DM, Aliev  F, Krueger  RF,  et al.  Genome-wide association study of conduct disorder symptomatology.  Mol Psychiatry. 2011;16(8):800-808.PubMedGoogle ScholarCrossref
16.
Tielbeek  JJ, Medland  SE, Benyamin  B,  et al.  Unraveling the genetic etiology of adult antisocial behavior: a genome-wide association study.  PLoS One. 2012;7(10):e45086.PubMedGoogle ScholarCrossref
17.
Salvatore  JE, Edwards  AC, McClintick  JN,  et al.  Genome-wide association data suggest ABCB1 and immune-related gene sets may be involved in adult antisocial behavior.  Transl Psychiatry. 2015;5:e558.PubMedGoogle ScholarCrossref
18.
Viding  E, Hanscombe  KB, Curtis  CJC, Davis  OSP, Meaburn  EL, Plomin  R.  In search of genes associated with risk for psychopathic tendencies in children: a two-stage genome-wide association study of pooled DNA.  J Child Psychol Psychiatry. 2010;51(7):780-788.PubMedGoogle ScholarCrossref
19.
Derringer  J, Corley  RP, Haberstick  BC,  et al.  Genome-wide association study of behavioral disinhibition in a selected adolescent sample.  Behav Genet. 2015;45(4):375-381.PubMedGoogle ScholarCrossref
20.
Visscher  PM, Brown  MA, McCarthy  MI, Yang  J.  Five years of GWAS discovery.  Am J Hum Genet. 2012;90(1):7-24.PubMedGoogle ScholarCrossref
21.
Schizophrenia Working Group of the Psychiatric Genomics Consortium.  Biological insights from 108 schizophrenia-associated genetic loci.  Nature. 2014;511(7510):421-427.PubMedGoogle ScholarCrossref
22.
Purcell  SM, Wray  NR, Stone  JL,  et al; International Schizophrenia Consortium.  Common polygenic variation contributes to risk of schizophrenia and bipolar disorder.  Nature. 2009;460(7256):748-752.PubMedGoogle Scholar
23.
Ripke  S, O’Dushlaine  C, Chambert  K,  et al; Multicenter Genetic Studies of Schizophrenia Consortium; Psychosis Endophenotypes International Consortium; Wellcome Trust Case Control Consortium 2.  Genome-wide association analysis identifies 13 new risk loci for schizophrenia.  Nat Genet. 2013;45(10):1150-1159.PubMedGoogle ScholarCrossref
24.
van Beijsterveldt  CEM, Bartels  M, Hudziak  JJ, Boomsma  DI.  Causes of stability of aggression from early childhood to adolescence: a longitudinal genetic analysis in Dutch twins.  Behav Genet. 2003;33(5):591-605.PubMedGoogle ScholarCrossref
25.
Krueger  RF, Hicks  BM, Patrick  CJ, Carlson  SR, Iacono  WG, McGue  M.  Etiologic connections among substance dependence, antisocial behavior, and personality: modeling the externalizing spectrum.  J Abnorm Psychol. 2002;111(3):411-424.PubMedGoogle ScholarCrossref
26.
Okbay  A, Baselmans  BML, De Neve  J-E,  et al; LifeLines Cohort Study.  Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses.  Nat Genet. 2016;48(6):624-633.PubMedGoogle ScholarCrossref
27.
Tiihonen  J, Rautiainen  M-R, Ollila  HM,  et al.  Genetic background of extreme violent behavior.  Mol Psychiatry. 2015;20(6):786-792.PubMedGoogle ScholarCrossref
28.
van der Sluis  S, Posthuma  D, Nivard  MG, Verhage  M, Dolan  CV.  Power in GWAS: lifting the curse of the clinical cut-off.  Mol Psychiatry. 2013;18(1):2-3.PubMedGoogle ScholarCrossref
29.
Li  Y, Willer  CJ, Ding  J, Scheet  P, Abecasis  GR.  MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes.  Genet Epidemiol. 2010;34(8):816-834.PubMedGoogle ScholarCrossref
30.
Howie  BN, Donnelly  P, Marchini  J.  A flexible and accurate genotype imputation method for the next generation of genome-wide association studies.  PLoS Genet. 2009;5(6):e1000529.PubMedGoogle ScholarCrossref
31.
Willer  CJ, Li  Y, Abecasis  GR.  METAL: fast and efficient meta-analysis of genomewide association scans.  Bioinformatics. 2010;26(17):2190-2191.PubMedGoogle ScholarCrossref
32.
Bulik-Sullivan  BK, Loh  P-R, Finucane  HK,  et al; Schizophrenia Working Group of the Psychiatric Genomics Consortium.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.  Nat Genet. 2015;47(3):291-295.PubMedGoogle ScholarCrossref
33.
Bulik-Sullivan  B, Finucane  HK, Anttila  V,  et al; ReproGen Consortium; Psychiatric Genomics Consortium; Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3.  An atlas of genetic correlations across human diseases and traits.  Nat Genet. 2015;47(11):1236-1241.PubMedGoogle ScholarCrossref
34.
Neale  BM, Medland  SE, Ripke  S,  et al; Psychiatric GWAS Consortium: ADHD Subgroup.  Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder.  J Am Acad Child Adolesc Psychiatry. 2010;49(9):884-897.PubMedGoogle ScholarCrossref
35.
Psychiatric GWAS Consortium Bipolar Disorder Working Group.  Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4.  Nat Genet. 2011;43(10):977-983.PubMedGoogle ScholarCrossref
36.
Okbay  A, Beauchamp  JP, Fontana  MA,  et al; LifeLines Cohort Study.  Genome-wide association study identifies 74 loci associated with educational attainment.  Nature. 2016;533(7604):539-542.PubMedGoogle ScholarCrossref
37.
Zheng  J, Erzurumluoglu  AM, Elsworth  BL,  et al; Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium.  LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.  Bioinformatics. 2017;33(2):272-279.PubMedGoogle ScholarCrossref
38.
Finucane  HK, Bulik-Sullivan  B, Gusev  A,  et al; ReproGen Consortium; Schizophrenia Working Group of the Psychiatric Genomics Consortium; RACI Consortium.  Partitioning heritability by functional annotation using genome-wide association summary statistics.  Nat Genet. 2015;47(11):1228-1235.PubMedGoogle ScholarCrossref
39.
Cale  EM, Lilienfeld  SO.  Sex differences in psychopathy and antisocial personality disorder. A review and integration.  Clin Psychol Rev. 2002;22(8):1179-1207.PubMedGoogle ScholarCrossref
40.
Ober  C, Loisel  DA, Gilad  Y.  Sex-specific genetic architecture of human disease.  Nat Rev Genet. 2008;9(12):911-922.PubMedGoogle ScholarCrossref
41.
Singh  AL, Waldman  ID.  The etiology of associations between negative emotionality and childhood externalizing disorders.  J Abnorm Psychol. 2010;119(2):376-388.PubMedGoogle ScholarCrossref
42.
Maguin  E, Loeber  R.  Academic performance and delinquency.  Crime Justice. 1996:145-264.Google Scholar
43.
McEvoy  A, Welker  R.  Antisocial behavior, academic failure, and school climate A critical review.  J Emot Behav Disord. 2000;8(3):130-140.Google ScholarCrossref
44.
Boutwell  BB, Barnes  JC, Beaver  KM, Haynes  RD, Nedelec  JL, Gibson  CL. A unified crime theory: the evolutionary taxonomy. Aggress Violent Behav. 2015;25(pt B):343-353.
45.
Lewis  GJ, Plomin  R.  Heritable influences on behavioural problems from early childhood to mid-adolescence: evidence for genetic stability and innovation.  Psychol Med. 2015;45(10):2171-2179.PubMedGoogle ScholarCrossref
46.
Wichers  M, Gardner  C, Maes  HH, Lichtenstein  P, Larsson  H, Kendler  KS.  Genetic innovation and stability in externalizing problem behavior across development: a multi-informant twin study.  Behav Genet. 2013;43(3):191-201.PubMedGoogle ScholarCrossref
47.
Pingault  J-B, Rijsdijk  F, Zheng  Y, Plomin  R, Viding  E.  Developmentally dynamic genome: Evidence of genetic influences on increases and decreases in conduct problems from early childhood to adolescence.  Sci Rep. 2015;5:10053.PubMedGoogle ScholarCrossref
48.
Peyrot  WJ, Milaneschi  Y, Abdellaoui  A,  et al.  Effect of polygenic risk scores on depression in childhood trauma.  Br J Psychiatry. 2014;205(2):113-119.PubMedGoogle ScholarCrossref
49.
Thomas  D.  Gene–environment-wide association studies: emerging approaches.  Nat Rev Genet. 2010;11(4):259-272.PubMedGoogle ScholarCrossref
Original Investigation
December 2017

Genome-Wide Association Studies of a Broad Spectrum of Antisocial Behavior

Author Affiliations
  • 1Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
  • 2Department of Child and Adolescent Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
  • 3QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
  • 4Department of Psychology and Speech-Language Pathology, University of Turku, Turku, Finland
  • 5Department of Pharmacology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
  • 6Department of Psychology, Faculty of Arts, Psychology, and Theology, Åbo Akademi University, Turku, Finland
  • 7National Institute for Health and Welfare, Helsinki, Finland
  • 8Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio
  • 9Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
  • 10Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
  • 11Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, England
  • 12Department of Epidemiology and Biostatistics, Michigan State University, East Lansing
  • 13Department of Psychology, Michigan State University, East Lansing
  • 14Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Center–Sophia Children’s Hospital, Rotterdam, the Netherlands
  • 15Department of Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands
  • 16Division of Psychology and Language Sciences, University College London, London, England
  • 17Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri
  • 18College of Criminology and Criminal Justice, Florida State University, Tallahassee
  • 19Center for Social and Humanities Research, King Abdulaziz University, Jeddah, Saudi Arabia
  • 20Psychology Department, Emory University, Atlanta, Georgia
  • 21Department of Psychology and the Virginia Institute for Psychiatric and Behavioural Genetics, Virginia Commonwealth University, Richmond
  • 22Department of African American Studies, Virginia Commonwealth University, Richmond
  • 23Faculty of Business, Karabuk University, Karabuk, Turkey
  • 24Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond
  • 25Department of Psychology, African American Studies, and Human & Molecular Genetics, Virginia Commonwealth University, Richmond
  • 26Department of Psychiatry and Behavioral Sciences, Psychiatric Genetic Epidemiology and Neurobiology Laboratory, SUNY Upstate Medical University, Syracuse, New York
  • 27Department of Neuroscience and Physiology, Psychiatric Genetic Epidemiology and Neurobiology Laboratory, SUNY Upstate Medical University, Syracuse, New York
  • 28Department of Clinical Genetics, Neuroscience Campus Amsterdam, Vrije Universiteit Medical Center, Amsterdam, the Netherlands
  • 29Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
  • 30Veterans Affairs (VA) Connecticut Healthcare Center, New Haven
  • 31Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia
  • 32Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
  • 33Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
  • 34MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, England
  • 35Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
  • 36Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland
JAMA Psychiatry. 2017;74(12):1242-1250. doi:10.1001/jamapsychiatry.2017.3069
Key Points

Questions  Which genetic variants are associated with antisocial behavior, are they sex specific, and do they correlate with other traits?

Findings  In this study of genome-wide association data from 5 population-based cohorts and 3 target samples, antisocial behavior was associated with polygenic traits, demonstrating pleiotropic genetic associations with educational attainment and distinct genetic effects across sex.

Meaning  Larger samples, divided by sex, are needed to validly identify genetic variants associated with antisocial behavior.

Abstract

Importance  Antisocial behavior (ASB) places a large burden on perpetrators, survivors, and society. Twin studies indicate that half of the variation in this trait is genetic. Specific causal genetic variants have, however, not been identified.

Objectives  To estimate the single-nucleotide polymorphism–based heritability of ASB; to identify novel genetic risk variants, genes, or biological pathways; to test for pleiotropic associations with other psychiatric traits; and to reevaluate the candidate gene era data through the Broad Antisocial Behavior Consortium.

Design, Setting, and Participants  Genome-wide association data from 5 large population-based cohorts and 3 target samples with genome-wide genotype and ASB data were used for meta-analysis from March 1, 2014, to May 1, 2016. All data sets used quantitative phenotypes, except for the Finnish Crime Study, which applied a case-control design (370 patients and 5850 control individuals).

Main Outcome and Measures  This study adopted relatively broad inclusion criteria to achieve a quantitative measure of ASB derived from multiple measures, maximizing the sample size over different age ranges.

Results  The discovery samples comprised 16 400 individuals, whereas the target samples consisted of 9381 individuals (all individuals were of European descent), including child and adult samples (mean age range, 6.7-56.1 years). Three promising loci with sex-discordant associations were found (8535 female individuals, chromosome 1: rs2764450, chromosome 11: rs11215217; 7772 male individuals, chromosome X, rs41456347). Polygenic risk score analyses showed prognostication of antisocial phenotypes in an independent Finnish Crime Study (2536 male individuals and 3684 female individuals) and shared genetic origin with conduct problems in a population-based sample (394 male individuals and 431 female individuals) but not with conduct disorder in a substance-dependent sample (950 male individuals and 1386 female individuals) (R2 = 0.0017 in the most optimal model, P = 0.03). Significant inverse genetic correlation of ASB with educational attainment (r = –0.52, P = .005) was detected.

Conclusions and Relevance  The Broad Antisocial Behavior Consortium entails the largest collaboration to date on the genetic architecture of ASB, and the first results suggest that ASB may be highly polygenic and has potential heterogeneous genetic effects across sex.

Introduction

Antisocial behavior (ASB) covers a range of inappropriate behaviors that cause harm to others, the community, and the environment. These behaviors include aggression, hostility, theft, deceitfulness, and violent felonies. In addition to the monetary effects,1 violent criminal behavior also has significant social and emotional costs. Communities with high rates of crime often face high rates of unemployment, drug and alcohol abuse, poverty, and other social pathologic conditions.2 Survivors of crime often experience emotional trauma and can develop serious mental health problems, such as posttraumatic stress disorder.3 In addition, ASB has high comorbidity with other psychiatric traits and maladaptive behaviors.4,5 Therefore, identification of the causal mechanisms that underlie ASB is important to identify prevention and treatment modalities. Accumulated evidence from quantitative and molecular genetic studies6,7 reveals the substantial influence of genetic factors in the etiology of ASB. Most evidence of a role of genetics is derived from twin studies and, to a lesser extent, adoption studies7,8 and indicates that approximately half of the variance in ASB can be explained by genetic factors, whereas the remainder can be explained by unique and common environmental factors.6-8 A twin study9 further determined that the association between ASB and cognitive and psychiatric traits is in part attributable to common genetic factors, indicating there may be shared biological mechanisms that underlie these behaviors. Early candidate gene studies9-11 identified a number of genetic polymorphisms involved in serotonergic and catecholaminergic function, among others, that may be involved in ASB. However, a systematic review and meta-analysis12 of most published genetic association studies on aggression and violence failed to reveal a significant overall association between any of the previously reported candidate genes and aggression. The lack of replication of candidate genes for ASB is consistent with other candidate gene findings in psychiatry, which for the most part have failed to identify reproducible and clinically useful genetic variants.13 This is attributable in part to the a priori inferences of the classic candidate gene approach, which increases the chances of false-positive findings in the typically small sample sizes of the individual studies.14

Genome-wide association studies (GWASs) can overcome these limitations. To date, relatively few GWASs have focused on antisocial phenotypes. One study,15 performed on childhood conduct disorder in an American sample (872 patients and 3091 control individuals), detected 3 genome-wide significant loci. However, none of the other published GWASs16-19 reported evidence of a genome-wide association with any genetic variants.

This lack of positive results from GWASs is most likely attributable to low statistical power to detect small effects.20 For example, recent work of the Schizophrenia Working Group of the Psychiatric Genomics Consortium revealed the direct association between sample size and success in detecting genetic variants. Their latest GWASs, including 36 989 patients and 113 075 controls, identified 108 genome-wide significant independent genomic loci, providing new insights in the pathology of schizophrenia,21 whereas earlier studies by Purcell et al22 (N = 6909 [3322 patients]) and Ripke et al23 (N = 59 318 [21 246 patients]) detected 1 and 13 genome-wide significant single-nucleotide polymorphisms (SNPs), respectively.

To increase sample sizes for gene finding for ASB, we initiated the Broad Antisocial Behavior Consortium (BroadABC). BroadABC represents a collaborative research initiative to conduct genetic analyses on a larger scale to identify biological mechanisms that underlie the course of ASB. In designing BroadABC’s gene-discovery strategies, we weighed the benefits and costs of outcome measure heterogeneity in relation to the total sample size. We chose to maximize sample size by pooling the heterogeneous measures of the individual cohorts, including different age ranges, and jointly analyzing their data. Our rationale is supported by a genetically informative longitudinal study24 demonstrating evidence of genetic continuity (the continuity in ASB during childhood and adolescence is explained largely by genetic factors). Moreover, a prior study25 examining the etiologic connections between the externalizing spectrum found that additive genetic factors account for 81% of the variance in externalizing behavior. Lastly, previous meta-analytical GWASs have successfully applied this joint analysis approach by identifying additional loci associated with depressive symptoms and neuroticism.26

Methods

BroadABC focuses on the broad spectrum of ASB and currently consists of 5 discovery cohorts (a combined 16 400 individuals) and 3 independent prediction and replication samples: (1) a population-based sample (n = 825), (2) a forensic sample (n = 6220), and (3) and a substance-dependent sample (n = 2336). In total, BroadABC has genotypic and phenotypic data from 25 781 individuals across 8 unique samples and, to our knowledge, is the largest collective sample available to estimate the effects of genome-wide genetic variants for ASB and testing for genetic overlap with other traits. All participants provided written informed consent. All data were deidentified. Because of the extra perceived vulnerability of the Finnish Crime Study participants, multiple committees, including the Ethics Committee for Pediatrics, Adolescent Medicine, and Psychiatry, Hospital District of Helsinki and Uusimaa, and Criminal Sanctions Agency, approved this study.27

Cohorts and Phenotypes

Except for the Finnish Crime Study, which used a dichotomized outcome measure, all studies used a continuous scale to increase statistical power.28 To maximize sample size, we included studies with a broad range of antisocial measures, including aggressive and nonaggressive domains of ASB and using study-specific scales in different age groups (Table 1 and eAppendix 1 in the Supplement). Five large population-based discovery cohorts and 3 target samples (all participants were of European descent) were included in this study from March 1, 2014, to May 1, 2016 (see Table 1 for cohort-specific details). The discovery samples comprised 16 400 individuals, whereas the target samples consisted of 9381 individuals. All participants were recruited from different regions; thus, sample overlap was highly unlikely.

Genotyping

Genome-wide genotyping was performed independently in the cohorts using commercially available genotyping arrays. All cohorts imputed their genotype data to the 1000 Genomes phase 1 version 3 (build 37, hg19) reference panel using the standard software package MACH29 or IMPUTE230 except for the Finnish Crime Study and Michigan State University Twin Registry (MSUTR), which were not imputed. Additional details and cohort-specific procedures concerning the genotyping process, imputation, and quality control are provided in eTable 1 and eTable 2 in the Supplement.

Statistical Analysis
GWASs at the Cohort Level

Analyses of the GWASs were performed at the cohort level according to a prespecified analysis plan (standard operating procedures). Each cohort uploaded sex-specific and combined GWAS results to the BroadABC server as input for the meta-analyses. All analyses were restricted to samples of European ancestry. For sex-pooled analysis of the X chromosome, males were treated as homozygous females. Quality control and meta-analysis of the GWAS summary results were performed by 2 of us (J.J.T. and A.J.) following a strict analysis protocol. Additional details on the standard operating procedure analysis plan and quality control are provided in eAppendix 2 in the Supplement and on the BroadABC website (http://broadabc.ctglab.nl/).

Meta-analysis of Discovery Cohorts

The meta-analysis across discovery cohorts was run for the pooled male-female GWAS results (N = 16 400), as well as separately for the sexes (8535 female individuals and 7772 male individuals), using a fixed-effects model with z scores weighted by sample size as implemented in the software METAL.31 We only reported and interpreted the results of polymorphisms with a total sample size greater than 10 000 (across all samples) and 5000 (sex specific). The genome-wide significance threshold was set at 1.67 × 10−8 because we performed 3 meta-analyses; polymorphisms with P<10−6 were considered to be promising findings.

Polygenic Risk Scores

We performed a polygenic risk score (PRS) analysis in the Finnish Crime Study to test whether a genetic risk for ASB could significantly discriminate between prisoners and matched controls. We used the software package PRSice to estimate the best-fit PRS at a broad range of P value thresholds. For clumping, the linkage disequilibrium (LD) threshold was set to an R2 of 0.25 and a 500-kb distance. The PRS analyses were conducted based on the sex-combined samples and the male-specific samples (given the overrepresentation of male prisoners), and sex, age, and 4 principal components were included as covariates. In addition, to evaluate evidence for shared genetic origin, we used the summary-summary statistic–based analysis as implemented in PRSice, using the sex-combined, male-specific, and female-specific samples in MSUTR and Yale-Penn samples after applying more stringent clumping thresholds (R2 = 0.05, 300-kb distance).

LD Regression Score Heritability and Correlation Analyses

To calculate the SNP heritability and estimate the genetic correlation between ASB and a range of cognitive, psychiatric, and reproductive traits, we used the (cross-trait) LD score regression method. The LD score method disentangles the contribution of true polygenic signal and bias caused by population stratification to the inflated test statistics in GWASs and optionally calculates a genetic correlation (rg) among different traits.32 This method is particularly useful because it only requires GWAS summary statistics and is not biased by sample overlap.33 Genetic correlations of ASB were calculated with cognitive and psychiatric traits previously reported to be comorbid with ASB using summary results from attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder21,34,35 that are publicly available on the Psychiatric Genomics Consortium webpage (https://www.med.unc.edu/pgc/results-and-downloads). The summary statistics of neuroticism and educational attainment (defined as number of years in the educational system) were provided by the Social Science Genetic Association Consortium.26,36 The genetic correlations of ASB with reproductive traits were computed from a centralized database of summary-level GWASs.37

The methods and results regarding the functional annotation, gene analysis, gene-set analyses, replication analysis, and tests for enrichment in loci previously related to antisocial phenotypes are reported in eAppendixes 3 through 6, eTables 3 through 10, and eFigure 1 and eFigure 2 in the Supplement).

Results

The discovery samples comprised 16 400 individuals, whereas the target samples consisted of 9381 individuals (all individuals were of European descent), including child and adult samples (mean age range, 6.7-56.1 years). We removed 2 134 049 SNPs because of insufficient total sample size (N<10 000), resulting in 7 392 849 SNPs available for analyses. There were no discrepancies between the results files of the 2 analysts at the cohort or meta-analysis level. The genomic inflation factors were 1.015 for the combined analysis, 1.012 for the male analysis, and 1.001 for the female analysis, which are as expected under a polygenic model given the sample size, prevalence, and heritability of ASB (eFigure 3 in the Supplement).

Meta-analysis of GWAS

The combined discovery meta-analysis incorporating both sexes did not identify genetic variants of genome-wide significance (n = 16 400, lowest P = 6.1 × 10−7). The strongest associations were located on chromosome 20, followed by chromosomes 1, 19, 22, and 6 (eFigure 4 in the Supplement). The SNPs yielding P values smaller than 1.0 × 10−6 were considered promising (eTables 11, 12, and 13 in the Supplement).

The GWAS meta-analysis for females only (n = 8535) (eTable 12 in the Supplement) revealed 2 promising loci on chromosome 1 (rs2764450, R2 = 0.35%, P = 4.8 × 10−8) and chromosome 11 (rs11215217, R2 = 0.37%, P = 2.1 × 10−8), whereas the meta-analysis for males (n = 7772) (eTable 13 in the Supplement) identified a near genome-wide signal on chromosome X (rs41456347, R2 = 0.41%, P = 2.0 × 10−8). We found no evidence of heterogeneity (I2 = 0) across discovery samples in the association of rs276445023 = 2.62, P = .45), rs1121521724 = 3.01, P = .54), and rs4145634724 = 2.72, P = .60) with ASB (eFigure 5 in the Supplement). Functional annotation was performed for the top 3 loci to gain insight into possible causal genes (eAppendix 6 and eAppendix 7 and eFigure 1 in the Supplement). Top signals were located differently across sex (Figure 1 and eTable 14 in the Supplement). We tested whether the signs of the regression coefficients were consistently in the same direction between the SNPs for males and females. The sign tests showed no consistent directions of effect (proportions were 0.51, 0.50, and 0.50) for SNPs selected for different P value thresholds (0.05, 0.001, and 0.0001, respectively). Moreover, Fisher exact tests found no evidence for enrichment of SNPs with low P values across sex regardless of sign (odds ratio [OR], 0.9; P = .05 for males; OR, 1.1; P = .001 for females) (eTable 15 in the Supplement).

The sex-specific signals were supported by a large number of promising SNPs, which were in incomplete LD with the lead SNP (eFigure 4). Imputation quality for the lead SNPs was high for rs41456347 (mean R2 = 99.7), rs2764450 (mean R2 = 93.8), and rs11215217 (mean R2 =  86.8). Gene-based and gene-set analyses yielded no significant genes (top gene was centromeric protein I [OMIM 300065], P = 3.2 × 10−5) (eTables 3-9, and 14 and eFigure 1 in the Supplement) or gene sets (top gene-set was Reactome cell communication, P = 3.6 × 10−4) (eTable 6 in the Supplement). None of the traditional candidate genes on ASB were significantly associated with ASB (top gene was tyrosine hydroxylase [OMIM 191290], P = 0.08 for correlation) (eTables 7, 8, and 9 in the Supplement).

Polygenic Risk Scores

The BroadABC antisocial genetic risk scores were associated with case-control status of antisocial personality disorder in the Finnish Crime Study (sex combined, R2 = 0.0017, P = .03; male-specific R2 = 0.0018, P = .05) (Figure 2A and B). Nevertheless, the analyses revealed low Nagelkerke R2 estimates (R2 = 0.0019 in the most optimal model) not exceeding the Bonferroni-corrected threshold for significance. Using summary statistics in PRSice software, we found that the genetic effect from the females-only ASB analysis significantly overlapped with genetic effects in the expected direction on conduct problems in MSUTR (R2 = 0.021 for the most optimal model, P = .004) but not with the sex-combined and males-only analyses (Figure 2C-E). No significant genetic overlap was found with conduct disorder in Yale-Penn sample, although a nominal significant effect (R2 = 0.0022, P = .04) in the expected direction was found in the males-only analysis (Figure 2E-H).

SNP Heritability and Genetic Correlation of ASB With Other Traits

The estimated proportion of the phenotypic variance in ASB explained by all SNPs was 5.2%, with an SE of 2.7% (P = .03). Sample sizes were too small in the sex-specific meta-analyses to be used to estimate SNP heritability (h2) for the male and female samples separately. We found a significant (corrected α = .006) and moderate negative genetic correlation between ASB and educational attainment (r = −0.52, P = .005). Follow-up analyses using the Fisher exact test revealed evidence of enrichment of low P (P < .001) in the same SNPs for ASB and educational attainment (OR = 3.26, P = .001). Moreover, we found a promising positive genetic correlation with neuroticism (r = 0.29, P = .02) and support for a negative genetic correlation between ASB and age at menopause (r = −0.49, P = .01), age of first birth (r = −0.43, P = .008), and a positive genetic correlation with number of children ever born (r = 0.42, P = .03)38 (Table 2). There was no evidence for genetic overlap between ASB and schizophrenia, bipolar disorder, ADHD, or age at menarche.

Discussion

To our knowledge, this study represents the largest investigation on the genetic architecture of ASB to date. Our meta-analyses of diverse continuous measures of ASB found that ASB is heritable and highly polygenic and suggests that part of the genetic architecture is sex specific. This finding is not surprising in view of the sex-influenced phenotypic expression. We also found a strong inverse correlation of ASB with genetic variants for educational attainment and some reproductive traits and a positive genetic correlation with neuroticism but not with schizophrenia, bipolar disorder, or ADHD.

The SNP heritability analyses found that the collective effect of the measured SNPs accounted for 5% of the variance or 10% of the heritability of approximately 50%, as estimated from family-based studies. Recent GWASs on other complex traits, such as height, body mass index, and schizophrenia, demonstrated that with greater sample sizes, the SNP h2 increases. The relatively small total GWAS discovery sample size yielded limited power to detect small genetic effects, which could explain in part the high missing heritability in our study, although we cannot rule out that most of the genetic variance in ASB is attributable to rare alleles. Taken together, we suspect that with greater sample sizes and better imputation and coverage of the common and rare allele spectrum, over time, SNP heritability in ASB could approach the family-based estimates.

Polygenic risk score analysis, based on a broad conceptualization of ASB, could reliably determine some of the variation in antisocial personality disorder in a forensic cohort, demonstrating that population-based genetic association studies can also be informative for samples that are at risk. Nevertheless, effect sizes were small, indicating limited prognostication accuracy and clinical utility for the current GWAS outcomes.

Despite the small but significant collective genetic effect on ASB, none of the individual genetic variants exceeded the significance threshold in our overall meta-analysis. The sex-specific meta-analyses, however, revealed 3 promising loci. Moreover, stronger polygenic risk effects were found for the sex-specific analyses. Given the substantial differences in prevalence, age at onset, and severity of ASB between males and females,39 which might in part reflect sex differences in genetic architecture, it is important to account for those effects in genetic research designs.40 Our current results suggest the presence of at least some sex-specific genetic effects. Even though sample sizes were smaller, the sex-specific analyses yielded increased specificity because potential noise attributable to different genetic loci driving the genetic component of ASB in male and female individuals was removed.

Our genetic correlation analyses revealed a promising positive genetic correlation of ASB with neuroticism, a finding that is concordant with previous twin research demonstrating a shared genetic origin of externalizing behavior and negative emotionality.41 Moreover, we found significant genetic overlap between ASB and educational attainment, indicating a common underlying genetic architecture that influenced both phenotypes. The negative genetic correlation with educational attainment is consistent with a previous epidemiologic study42 that reported a negative association between academic performance and delinquency. This finding is important because it may provide insight into the developmental pathways that underlie the association between academic failure and ASB.43 Of interest, ASB also correlated with reproductive traits, thus fitting to the unified evolutionary theory that Boutwell and colleagues proposed.44 Their theory suggests that increased criminality represents a faster life history approach—one that would be significantly calibrated by genes.

Limitations

Given the nature of ASB with no accepted gold standard for measuring the trait, our attempt to bring together large historic collections is possibly burdened by measurement diversity. The consortium includes adult and child samples, and within the adult samples, some focus on lifetime antisocial behavior and others focus on retrospective reports of child behavior. Nevertheless, previous studies45-47 have demonstrated stable and unique genetic influences on ASB during the lifespan. Moreover, despite the broad conceptualization of ASB in the current study, the sample size is relatively small for gene-finding purposes. Still, we found a polygenic signal, and our correlation analyses further reflect the validity and usefulness of our approach. To identify individual SNPs or genes associated with ASB, we found that even larger samples are needed.

Conclusions

Our study suggests that ASB may be highly polygenic and has potential heterogeneous genetic associations across sex. As large-scale initiatives, such as the BroadABC, continue to increase, these collaborative efforts will also facilitate the conduct of epidemiologic studies that incorporate genome-wide data and environmental factors in a joint analysis.48 Discoveries obtained from such gene-environment–wide interaction studies may contribute to more advanced explanatory models of the complex origin of ASB, thereby ultimately aiding prevention and intervention strategies.49

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Article Information

Corresponding Author: Jorim J. Tielbeek, MSc, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, and Department of Clinical Genetics, VUMC, W&N Building, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands (j.tielbeek@vumc.nl).

Accepted for Publication: August 3, 2017.

Correction: This article was corrected on January 24, 2018, to fix an incorrect degree in the byline.

Published Online: October 4, 2017. doi:10.1001/jamapsychiatry.2017.3069

Author Contributions: Dr Medland and Ms Posthuma contributed equally to this work. Mr Tielbeek 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.

Study concept and design: Tielbeek, Polderman, Jansen, Tiemeier, Aliev, Popma, Medland, Posthuma.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Tielbeek, Johansson, Polderman, Jansen, Viding, Beaver, LoParo, Paunio, Pappa, Aliev, Popma, Posthuma.

Critical revision of the manuscript for important intellectual content: Tielbeek, Johansson, Polderman, Rautiainen, Jansen, Taylor, Burt, Tong, Lu, Tiemeier, Viding, Plomin, Martin, Heath, Madden, Montgomery, Waldman, Gelernter, Kranzler, Farrer, Perry, Munafò, Tiihonen, Mous, Leeuw, Watanabe, Hammerschlag, Salvatore, Bigdeli, Dick, Faraone, Popma, Medland, Posthuma.

Statistical analysis: Tielbeek, Johansson, Rautiainen, Jansen, Taylor, Burt, Tong, Lu, Plomin, Beaver, Waldman, Perry, LoParo, Pappa, Leeuw, Hammerschlag, Salvatore, Aliev, Bigdeli, Faraone.

Obtained funding: Tielbeek, Burt, Tiemeier, Plomin, Martin, Heath, Madden, Montgomery, Gelernter, Dick, Popma, Posthuma.

Administrative, technical, or material support: Tielbeek, Jansen, Farrer, Paunio, Pappa, Watanabe, Aliev, Medland.

Study supervision: Polderman, Tiemeier, Martin, Montgomery, Waldman, Munafò, Paunio, Popma, Medland, Posthuma.

Conflict of Interest Disclosures: Dr Kranzler reported serving as a paid consultant, advisory board member, or continuing medical education speaker for Indivior and Lundbeck and is a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the past 3 years by AbbVie, Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Ethypharm, Indivior, Eli Lilly and Company, Lundbeck, Otsuka, and Pfizer. With his institution, Dr Faraone reported having US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of attention-deficit/hyperactivity disorder and has received income, potential income, travel expenses, and/or research support from Rhodes Pharmaceuticals, Arbor Pharmaceuticals, Pfizer, Ironshore, Shire, Akili Interactive Labs, CogCubed, Alcobra, VAYA Pharma, NeuroLifeSciences, and NACE. No other disclosures were reported.

Funding/Support: This project was funded by grants 406-12-131 and 453-14-005 from the Netherlands Organization for Scientific Research, award K01DA033346 from the National Institute on Drug Abuse, an F32AA022269 fellowship, grant K01AA024152 from the National Institutes of Health, the Foundation “De Drie Lichten” in the Netherlands, the Waldemar von Frenckell Foundation, the Finnish Cultural Foundation, the Finnish Ministry of Health and Social Affairs through the development fund for Niuvanniemi Hospital, Kuopio, Finland, and the Society of Swedish Literature in Finland.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Group Information: The Broad Antisocial Behavior Consortium collaborators consist of all the authors of this article.

Additional Contributions: Meta-analyses and statistical analyses for the Twins Early Development Study were performed on the Genetic Cluster Computer (http://www.geneticcluster.org), which is financially supported by grant NWO 480-05-003 from the Netherlands Organization for Scientific Research. Dr Mous was funded by National Health and Medical Research Council Senior Research Fellowship APP1103623. Gabriel Cellular Partida, PhD, University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, Queensland, Australia, provided the script for the Miami plot; J.C. Barnes, PhD, School of Criminal Justice, University of Cincinnati, Cincinnati, Ohio, and Philipp Koellinger, PhD, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam, the Netherlands, provided helpful comments on the manuscript; and Richard Karlsson Linnér, MSc, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam, the Netherlands, provided advice on statistical power. None of these individuals were compensated for their work. This research was facilitated by the Social Science Genetic Association Consortium. We thank the individuals who participated in the studies. A full list of cohort acknowledgments is provided in the Supplement.

References
1.
McCollister  KE, French  MT, Fang  H.  The cost of crime to society: new crime-specific estimates for policy and program evaluation.  Drug Alcohol Depend. 2010;108(1-2):98-109.PubMedGoogle ScholarCrossref
2.
Wright  JP, Tibbetts  SG, Daigle  LE.  Criminals in the Making: Criminality Across the Life Course. London, England: SAGE Publications; 2014.
3.
Brewin  CR, Andrews  B, Rose  S, Kirk  M.  Acute stress disorder and posttraumatic stress disorder in victims of violent crime.  Am J Psychiatry. 1999;156(3):360-366.PubMedGoogle Scholar
4.
Abram  KM, Zwecker  NA, Welty  LJ, Hershfield  JA, Dulcan  MK, Teplin  LA.  Comorbidity and continuity of psychiatric disorders in youth after detention: a prospective longitudinal study.  JAMA Psychiatry. 2015;72(1):84-93.PubMedGoogle ScholarCrossref
5.
Goldstein  RB, Chou  SP, Saha  TD,  et al.  The Epidemiology of antisocial behavioral syndromes in adulthood: results from the National Epidemiologic Survey on Alcohol and Related Conditions–III.  J Clin Psychiatry. 2017;78(1):90-98.PubMedGoogle ScholarCrossref
6.
Polderman  TJC, Benyamin  B, de Leeuw  CA,  et al.  Meta-analysis of the heritability of human traits based on fifty years of twin studies.  Nat Genet. 2015;47(7):702-709.PubMedGoogle ScholarCrossref
7.
Rhee  SH, Waldman  ID.  Genetic and environmental influences on antisocial behavior: a meta-analysis of twin and adoption studies.  Psychol Bull. 2002;128(3):490-529.PubMedGoogle ScholarCrossref
8.
Burt  SA.  Are there meaningful etiological differences within antisocial behavior? results of a meta-analysis.  Clin Psychol Rev. 2009;29(2):163-178.PubMedGoogle ScholarCrossref
9.
Gunter  TD, Vaughn  MG, Philibert  RA.  Behavioral genetics in antisocial spectrum disorders and psychopathy: a review of the recent literature.  Behav Sci Law. 2010;28(2):148-173.PubMedGoogle ScholarCrossref
10.
Beaver  KM, DeLisi  M, Vaughn  MG, Barnes  JC.  Monoamine oxidase A genotype is associated with gang membership and weapon use.  Compr Psychiatry. 2010;51(2):130-134.PubMedGoogle ScholarCrossref
11.
Douglas  K, Chan  G, Gelernter  J,  et al.  5-HTTLPR as a potential moderator of the effects of adverse childhood experiences on risk of antisocial personality disorder.  Psychiatr Genet. 2011;21(5):240-248.PubMedGoogle ScholarCrossref
12.
Vassos  E, Collier  DA, Fazel  S.  Systematic meta-analyses and field synopsis of genetic association studies of violence and aggression.  Mol Psychiatry. 2014;19(4):471-477.PubMedGoogle ScholarCrossref
13.
Kendler  KS.  What psychiatric genetics has taught us about the nature of psychiatric illness and what is left to learn.  Mol Psychiatry. 2013;18(10):1058-1066.PubMedGoogle ScholarCrossref
14.
Dick  DM, Agrawal  A, Keller  MC,  et al.  Candidate gene-environment interaction research: reflections and recommendations.  Perspect Psychol Sci. 2015;10(1):37-59.PubMedGoogle ScholarCrossref
15.
Dick  DM, Aliev  F, Krueger  RF,  et al.  Genome-wide association study of conduct disorder symptomatology.  Mol Psychiatry. 2011;16(8):800-808.PubMedGoogle ScholarCrossref
16.
Tielbeek  JJ, Medland  SE, Benyamin  B,  et al.  Unraveling the genetic etiology of adult antisocial behavior: a genome-wide association study.  PLoS One. 2012;7(10):e45086.PubMedGoogle ScholarCrossref
17.
Salvatore  JE, Edwards  AC, McClintick  JN,  et al.  Genome-wide association data suggest ABCB1 and immune-related gene sets may be involved in adult antisocial behavior.  Transl Psychiatry. 2015;5:e558.PubMedGoogle ScholarCrossref
18.
Viding  E, Hanscombe  KB, Curtis  CJC, Davis  OSP, Meaburn  EL, Plomin  R.  In search of genes associated with risk for psychopathic tendencies in children: a two-stage genome-wide association study of pooled DNA.  J Child Psychol Psychiatry. 2010;51(7):780-788.PubMedGoogle ScholarCrossref
19.
Derringer  J, Corley  RP, Haberstick  BC,  et al.  Genome-wide association study of behavioral disinhibition in a selected adolescent sample.  Behav Genet. 2015;45(4):375-381.PubMedGoogle ScholarCrossref
20.
Visscher  PM, Brown  MA, McCarthy  MI, Yang  J.  Five years of GWAS discovery.  Am J Hum Genet. 2012;90(1):7-24.PubMedGoogle ScholarCrossref
21.
Schizophrenia Working Group of the Psychiatric Genomics Consortium.  Biological insights from 108 schizophrenia-associated genetic loci.  Nature. 2014;511(7510):421-427.PubMedGoogle ScholarCrossref
22.
Purcell  SM, Wray  NR, Stone  JL,  et al; International Schizophrenia Consortium.  Common polygenic variation contributes to risk of schizophrenia and bipolar disorder.  Nature. 2009;460(7256):748-752.PubMedGoogle Scholar
23.
Ripke  S, O’Dushlaine  C, Chambert  K,  et al; Multicenter Genetic Studies of Schizophrenia Consortium; Psychosis Endophenotypes International Consortium; Wellcome Trust Case Control Consortium 2.  Genome-wide association analysis identifies 13 new risk loci for schizophrenia.  Nat Genet. 2013;45(10):1150-1159.PubMedGoogle ScholarCrossref
24.
van Beijsterveldt  CEM, Bartels  M, Hudziak  JJ, Boomsma  DI.  Causes of stability of aggression from early childhood to adolescence: a longitudinal genetic analysis in Dutch twins.  Behav Genet. 2003;33(5):591-605.PubMedGoogle ScholarCrossref
25.
Krueger  RF, Hicks  BM, Patrick  CJ, Carlson  SR, Iacono  WG, McGue  M.  Etiologic connections among substance dependence, antisocial behavior, and personality: modeling the externalizing spectrum.  J Abnorm Psychol. 2002;111(3):411-424.PubMedGoogle ScholarCrossref
26.
Okbay  A, Baselmans  BML, De Neve  J-E,  et al; LifeLines Cohort Study.  Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses.  Nat Genet. 2016;48(6):624-633.PubMedGoogle ScholarCrossref
27.
Tiihonen  J, Rautiainen  M-R, Ollila  HM,  et al.  Genetic background of extreme violent behavior.  Mol Psychiatry. 2015;20(6):786-792.PubMedGoogle ScholarCrossref
28.
van der Sluis  S, Posthuma  D, Nivard  MG, Verhage  M, Dolan  CV.  Power in GWAS: lifting the curse of the clinical cut-off.  Mol Psychiatry. 2013;18(1):2-3.PubMedGoogle ScholarCrossref
29.
Li  Y, Willer  CJ, Ding  J, Scheet  P, Abecasis  GR.  MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes.  Genet Epidemiol. 2010;34(8):816-834.PubMedGoogle ScholarCrossref
30.
Howie  BN, Donnelly  P, Marchini  J.  A flexible and accurate genotype imputation method for the next generation of genome-wide association studies.  PLoS Genet. 2009;5(6):e1000529.PubMedGoogle ScholarCrossref
31.
Willer  CJ, Li  Y, Abecasis  GR.  METAL: fast and efficient meta-analysis of genomewide association scans.  Bioinformatics. 2010;26(17):2190-2191.PubMedGoogle ScholarCrossref
32.
Bulik-Sullivan  BK, Loh  P-R, Finucane  HK,  et al; Schizophrenia Working Group of the Psychiatric Genomics Consortium.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.  Nat Genet. 2015;47(3):291-295.PubMedGoogle ScholarCrossref
33.
Bulik-Sullivan  B, Finucane  HK, Anttila  V,  et al; ReproGen Consortium; Psychiatric Genomics Consortium; Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3.  An atlas of genetic correlations across human diseases and traits.  Nat Genet. 2015;47(11):1236-1241.PubMedGoogle ScholarCrossref
34.
Neale  BM, Medland  SE, Ripke  S,  et al; Psychiatric GWAS Consortium: ADHD Subgroup.  Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder.  J Am Acad Child Adolesc Psychiatry. 2010;49(9):884-897.PubMedGoogle ScholarCrossref
35.
Psychiatric GWAS Consortium Bipolar Disorder Working Group.  Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4.  Nat Genet. 2011;43(10):977-983.PubMedGoogle ScholarCrossref
36.
Okbay  A, Beauchamp  JP, Fontana  MA,  et al; LifeLines Cohort Study.  Genome-wide association study identifies 74 loci associated with educational attainment.  Nature. 2016;533(7604):539-542.PubMedGoogle ScholarCrossref
37.
Zheng  J, Erzurumluoglu  AM, Elsworth  BL,  et al; Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium.  LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.  Bioinformatics. 2017;33(2):272-279.PubMedGoogle ScholarCrossref
38.
Finucane  HK, Bulik-Sullivan  B, Gusev  A,  et al; ReproGen Consortium; Schizophrenia Working Group of the Psychiatric Genomics Consortium; RACI Consortium.  Partitioning heritability by functional annotation using genome-wide association summary statistics.  Nat Genet. 2015;47(11):1228-1235.PubMedGoogle ScholarCrossref
39.
Cale  EM, Lilienfeld  SO.  Sex differences in psychopathy and antisocial personality disorder. A review and integration.  Clin Psychol Rev. 2002;22(8):1179-1207.PubMedGoogle ScholarCrossref
40.
Ober  C, Loisel  DA, Gilad  Y.  Sex-specific genetic architecture of human disease.  Nat Rev Genet. 2008;9(12):911-922.PubMedGoogle ScholarCrossref
41.
Singh  AL, Waldman  ID.  The etiology of associations between negative emotionality and childhood externalizing disorders.  J Abnorm Psychol. 2010;119(2):376-388.PubMedGoogle ScholarCrossref
42.
Maguin  E, Loeber  R.  Academic performance and delinquency.  Crime Justice. 1996:145-264.Google Scholar
43.
McEvoy  A, Welker  R.  Antisocial behavior, academic failure, and school climate A critical review.  J Emot Behav Disord. 2000;8(3):130-140.Google ScholarCrossref
44.
Boutwell  BB, Barnes  JC, Beaver  KM, Haynes  RD, Nedelec  JL, Gibson  CL. A unified crime theory: the evolutionary taxonomy. Aggress Violent Behav. 2015;25(pt B):343-353.
45.
Lewis  GJ, Plomin  R.  Heritable influences on behavioural problems from early childhood to mid-adolescence: evidence for genetic stability and innovation.  Psychol Med. 2015;45(10):2171-2179.PubMedGoogle ScholarCrossref
46.
Wichers  M, Gardner  C, Maes  HH, Lichtenstein  P, Larsson  H, Kendler  KS.  Genetic innovation and stability in externalizing problem behavior across development: a multi-informant twin study.  Behav Genet. 2013;43(3):191-201.PubMedGoogle ScholarCrossref
47.
Pingault  J-B, Rijsdijk  F, Zheng  Y, Plomin  R, Viding  E.  Developmentally dynamic genome: Evidence of genetic influences on increases and decreases in conduct problems from early childhood to adolescence.  Sci Rep. 2015;5:10053.PubMedGoogle ScholarCrossref
48.
Peyrot  WJ, Milaneschi  Y, Abdellaoui  A,  et al.  Effect of polygenic risk scores on depression in childhood trauma.  Br J Psychiatry. 2014;205(2):113-119.PubMedGoogle ScholarCrossref
49.
Thomas  D.  Gene–environment-wide association studies: emerging approaches.  Nat Rev Genet. 2010;11(4):259-272.PubMedGoogle ScholarCrossref
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