The degree to which the genetic associations documented here for AAB are also associated with child or adolescent antisocial behavior is not clear. The results from this study provide an empirical starting point for subsequent developmental analyses to examine these questions. Fifth, there are likely to be aspects of the environment that moderate genetic influences on AAB that we did not explicitly examine here but that may be valuable to pursue in subsequent studies. Finally, our genome-wide association approach examined only common genetic variation. There is suggestive evidence that rare nonsynonymous exonic SNPs account for 14% of the variance in a behavioral disinhibition phenotype.As rare variant-genotyping arrays and whole-genome sequencing become more widely available and cost effective, our understanding of the genetics of AAB will improve. In summary, our goal in this study was to take an atheoretical approach to investigate the molecular genetic basis of AAB in a high-risk sample. The heritability of AAB was 25%, although this estimate did not differ significantly from zero. No SNP reached strict genome-wide significance, but gene-based tests identified an association between ABCB1 and AAB. Expression analyses further indicated that ABCB1 is robustly expressed in the brain, providing some evidence that variation in this gene could be related to a behavioral outcome. Previously documented associations between variants in ABCB1 and other drugs of abuse suggest that ABCB1 may confer general risk across a range of externalizing behaviors,cannabis growing rather than risk that is unique to AAB. This was consistent with post hoc analyses in our sample, where we found that variation in ABCB1 was associated with DSM-IV alcohol and cocaine dependence criteria.
These pieces of evidence suggest that ABCB1 may be a gene of interest for further study. We also found enrichment of several immune-related canonical pathways and gene ontologies, which is consistent with previous suggestions that immune and inflammatory pathways are associated with externalizing spectrum behaviors. As a whole, our study goes beyond the candidate gene approach typically taken in studies of AAB, and implicates a gene and gene sets for which there is convergent evidence from other lines of research. These findings, although novel and promising, would benefit from direct replication.Behaviors related to self-regulation, such as substance use disorders or antisocial behaviors, have far-reaching consequences for affected individuals, their families, communities and society at large. Collectively, this group of correlated traits are classified as externalizing. Twin studies have demonstrated that externalizing liability is highly heritable. To date, however, no large-scale molecular genetic studies have utilized the extensive degree of genetic overlap among externalizing traits to aid gene discovery, as most studies have focused on individual disorders6 . For many high-cost, high-risk behaviors with an externalizing component—opioid use disorder and suicide attempts being salient examples—there are limited genotyped cases available for gene discovery. A complementary strategy to the single-disease approach is to study the shared genetic architecture across traits in multivariate analyses, which boosts statistical power by pooling data across genetically correlated traits. Multivariate approaches can use summary statistics from genome-wide association studies to discover connections between phenotypes not typically studied together because they span different domains, fields of study or life stages.
New statistical methods can increase the effective sample size by adjusting for sample overlap. Elucidating the shared genetic basis of externalizing liability can advance our understanding of the developmental etiology of self-regulation and enables mapping the pathways by which genetic risk and socio-environmental factors contribute to the development of externalizing outcomes. We applied genomic structural equation modeling to summary statistics from GWAS on multiple forms of externalizing for which large samples were available. We posited that applying this multivariate approach would lead to identification of genetic variants associated with a broad array of externalizing phenotypes, and with related behavioral, social and medical outcomes that were not directly included in our GWAS. This approach was grounded in the literature showing shared genetic liability across numerous externalizing disorders and with nonpsychiatric variation in externalizing behavior.For a complete description of the model selection procedure, see the Supplementary Methods. In summary, before genomic SEM, we first applied hierarchical clustering to a matrix of LD) score genetic correlations, which identified three clusters . An exploratory factor analysis benchmarked four factor models, specifying one to four latent factors, with the aim to best explain the genetic correlations among the phenotypes . The three-factor solution was determined to be the best-fitting exploratory model, which aligned with the hierarchical clustering. We proceeded with confirmatory factor analysis to formally model genetic covariances with genomic SEM, which is unbiased by sample overlap and sample-size imbalances. As indicated by its model fit indices =8007.35; Akaike information criterion =8051.35; comparative fit index =0.662; standardized root mean square residual =0.161, we found that a common factor model with phenotypes did not satisfy our preregistered criteria .
Two more complicated specifications were tested, a correlated three-factor model and a bifactor model , but neither of these two models met the criteria or provided a parsimonious interpretation. Finally, we estimated a revised and less complex common factor model with the phenotypes that displayed moderate-to-large loadings on the single factor estimated in the first common factor model with 11 phenotypes. The revised common factor model with 7 externalizing phenotypes provided the best fit across all specifications,vertical grow system and it closely approximated the observed genetic covariance matrix =390.234, AIC=422.234, CFI=0.957 and SRMR=0.079. This model was selected as our final factor model because it identified a genetic factor of externalizing that was suitable for genome-wide association analysis, offered an easily interpretable factor solution and satisfied the model fit criteria. We hereafter refer to it as ‘the externalizing factor’.The common factor captures a shared genetic liability to the final seven externalizing traits , and genetic variants associated with EXT predict central externalizing disorders and a range of behavioral and medical outcomes that were not in the model . We performed a leave-one-phenotype-out genomic SEM analysis to ensure that no single phenotype, for example, the phenotype with the largest N, was unduly influencing the genetic architecture estimated for EXT . We found that the genetic correlations between EXT and each of seven leave-one-phenotype-out models were not distinguishable from unity , which suggests that none of the phenotypes are driving the genetic architecture of EXT. We extended genomic SEM to estimate genetic correlations between EXT and 91 preregistered phenotypes with GWAS summary statistics that were not among the seven discovery phenotypes.
The genetic correlations indicate convergent and discriminant validity of the common EXT factor : As anticipated, EXT showed strong positive genetic correlations with drug exposure , antisocial behavior and impulsivity measures, including motor impulsivity and failures to plan . We estimated similar genetic correlations with personality domains as to those reported in twin studies, that is, positive correlation with extraversion , and negative with conscientiousness and agreeableness. However, prior work has found neuroticism but not openness to be correlated with externalizing, while we found a positive correlation with openness but not with neuroticism . Notably, EXT was also correlated with suicide attempts and post-traumatic stress disorder . EXT showed more modest inverse correlations with educational attainment and intelligence , indicating that EXT is not simply reflecting genetic influences on cognitive ability. Finally, there was a significant correlation with the Townsend index,a measure of neighborhood deprivation that reflects high concentrations of unemployment, household overcrowding and lower home ownership and car ownership. Genetic correlations can reflect correlated social processes or variables that are nonrandomly distributed with respect to genotypes, such as genetic nurture or neighborhood conditions, and we return to this topic in within-family analyses below.Externalizing disorders and behaviors are a widely prevalent cause of human suffering, but an understanding of the molecular genetic underpinnings of externalizing has lagged behind progress made in other areas of medical and psychiatric genetics. For example, dozens of genetic loci have been discovered for schizophrenia, bipolar disorder and major depressive disorders, whereas for antisocial behavior, AUDs and opioid use disorders, only a very small number of loci have been discovered.
We used multivariate genomic analyses to accelerate genetic discovery, identifying 579 genome-wide significant loci associated with a liability toward externalizing outcomes, 121 of which are entirely new discoveries for any of the seven phenotypes analyzed. Follow-up bio-informatic analyses suggest the implicated genes have early neurodevelopmental effects, which are then associated with behavioral patterns that have repercussions across the lifespan. Our results demonstrate that moving beyond traditional disease classification categories can enhance gene discovery, improve polygenic scores, and provide information about the underlying pathways by which genetic variants impact clinical outcomes. GWAS efforts find almost ubiquitous genetic correlations across psychiatric disorders; new analytic methods now allow us to capitalize on these genetic correlations. Pragmatically, non-disease phenotypes such as the ones we use here are often easier to measure in the general population than diagnostic status, making it easier to achieve large sample sizes. Expanding beyond individual diagnoses increases our ability to detect genes underlying human behavioral and medical outcomes of consequence. Our polygenic score for externalizing has one of the largest effect sizes of any polygenic score in psychiatric and behavioral genetics, accounting for ~10% of the variance in a phenotypic EXT. These effect sizes rival the associations observed with ‘traditional’ covariates used in social science research. Polygenic scores created using our GWAS results were associated not just with psychiatric and substance use disorders, but also with correlated social outcomes, such as lower employment and greater criminal justice system involvement, as well as with biomedical conditions affecting nearly every system in the body. These results highlight again that there is no distinct line between the genetic study of biomedical conditions and the genetic study of social and behavioral traits.Modern genetics research is routinely appropriated by white supremacist movements to argue that racialized disparities in health, employment and criminal justice system involvement are due to the genetic inferiority of people of color rather than environmental and historical disadvantages. At the same time, failing to understand how individual genetic differences contribute to vulnerability to externalizing can increase stigma and blame for these behaviors. Given the horrific legacy of eugenics, the ongoing reality of racism in the medical and criminal justice systems and the importance of combatting stigma in psychiatric disorders, the scientific results we report here must be interpreted with great care. Our results are not evidence that some people are genetically determined to experience certain life outcomes or are ‘innately’ antisocial. Genetic differences are probabilistically associated with psychiatric, medical and social outcomes, in part via environmental mechanisms that might differ across historical, political and economic contexts.In conclusion, our analyses demonstrate the far-reaching toll of human suffering borne by people with high genetic liabilities to externalizing. Future work will be needed to tease apart the pathways by which biological and social risks unfold within and across generations, and our findings can contribute to that effort.Descriptions of the Cannabis sativa plant and its medicinal properties were already accessible to Greek and Roman physicians in the first century AD, when Dioscorides included the plant in his classic textbook of pharmacology, entitled Materia Medica . Ancient Indian and Chinese medical writers were even more accurate than their European colleagues in describing the remarkable physiological and psychological effects of this plant . We know now that these effects, which in humans include a variable combination of euphoria, relaxation, reflflex tachycardia, and hypothermia, are primarily produced by the dibenzopyrane derivative, delta-9-tetrahydrocannabinol , present in the yellow resin that covers the leaves and flower clusters of the ripe female plant. The chemical structure of delta-9-THC was elucidated by the pioneering studies of R. Adams and Gaoni and Mechoulam . Unlike morphine, cocaine, and other alkaloids of plant origin, delta-9-THC is a highly hydrophobic compound, a property that, curiously enough, has slowed the progress on the mode of action of this compound for nearly three decades. The affinity of delta-9-THC for lipid membranes erroneously suggested, indeed, that the drug’s main effect was to modify in a non-selective manner the fluidity of cell membranes rather than to activate a selective cell-surface receptor .