ehaviors 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 1,2 . Collectively, this group of correlated traits are classified as externalizing 3 . Twin studies have demonstrated that externalizing liability is highly heritable (~80%) 4,5 . 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 disorders 6 . For many high-cost, high-risk behaviors with an externalizing component-opioid use disorder and suicide attempts 7 being salient examples-there are limited genotyped cases available for gene discovery 8,9 .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

Behaviors and disorders related to self-regulation, such as substance use, antisocial conduct, and ADHD, are collectively referred to as externalizing and have a shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The identified loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results captures variation in a broad range of behavioral and medical outcomes that were not part of our genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions, and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental condition.

Genome‐wide association studies are rapidly advancing our understanding of the genetic architecture of complex disorders, including many psychiatric conditions such as major depression, schizophrenia, and substance use disorders. One common goal of genome‐wide association studies is to use findings for enhanced clinical prediction in the future, which can aid in identifying at‐risk individuals to enable more effective prevention screening and treatment strategies. In order to achieve this goal, we first need to gain a better understanding of the issues surrounding the return of complex genetic results. In this article, we summarize the current literature on: (a) genetic literacy in the general population, (b) the public's interest in receiving genetic test results for psychiatric conditions, (c) how individuals react to and interpret their genotypic information for specific psychiatric conditions, and (d) gaps in our knowledge that will be critical to address as we move toward returning genotypic information for psychiatric conditions in both research and clinical settings. By reviewing extant studies, we aim to increase awareness of the potential benefits and consequences of returning genotypic information for psychiatric conditions.

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Genome-wide association studies aim to identify genetic variants that are associated with a disease phenotype in order to enhance precision medicine efforts. Despite the excitement surrounding the promise of precision medicine and interest among the public in accessing personalized genetic information, there has been little effort dedicated to understanding how complex genetic risk information could be incorporated into clinical practice to inform prevention, screening, and treatment. In this article, we briefly summarize the literature on the impact of receiving genetic risk information on health-related behavior, discuss the limitations of these studies, and outline the challenges that will need to be overcome, along with suggested next steps for future studies, to understand the true promise of precision medicine. The current literature demonstrates that there is no consistent or strong evidence that receiving complex genetic risk information, such as polygenic risk scores, has an impact on behavior; however, there are a number of limitations that may impact the failure to find significant effects associated with receiving genetic feedback. Behavior change is a complex process and simply providing genetic risk information without incorporating a theoretical perspective on behavior change diminishes the potential impact of receiving genetic risk information on actual behavior change. Future studies and interventions which return genetic feedback should be designed using theoretical frameworks of behavior change models to improve the impact of receiving personalized genetic information.