Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

Transcriptional cartography integrates multiscale biology of the human cortex.

The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.

Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort.

Importance: Many psychiatric outcomes share a common etiologic pathway reflecting behavioral disinhibition, generally referred to as externalizing (EXT) disorders. Recent genome-wide association studies (GWASs) have demonstrated the overlap between EXT disorders and important aspects of veterans' health, such as suicide-related behaviors and substance use disorders (SUDs). Objective: To explore correlates of risk for EXT disorders within the Veterans Health Administration (VA) Million Veteran Program (MVP). Design, Setting, and Participants: A series of phenome-wide association studies (PheWASs) of polygenic risk scores (PGSs) for EXT disorders was conducted using electronic health records. First, ancestry-specific PheWASs of EXT PGSs were conducted in the African, European, and Hispanic or Latin American ancestries. Next, a conditional PheWAS, covarying for PGSs of comorbid psychiatric problems (depression, schizophrenia, and suicide attempt; European ancestries only), was performed. Lastly, to adjust for unmeasured confounders, a within-family analysis of significant associations from the main PheWAS was performed in full siblings (European ancestries only). This study included the electronic health record data from US veterans from VA health care centers enrolled in MVP. Analyses took place from February 2022 to August 2023 covering a period from October 1999 to January 2020. Exposures: PGSs for EXT, depression, schizophrenia, and suicide attempt. Main Outcomes and Measures: Phecodes for diagnoses derived from the International Statistical Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification, codes from electronic health records. Results: Within the MVP (560 824 patients; mean [SD] age, 67.9 [14.3] years; 512 593 male [91.4%]), the EXT PGS was associated with 619 outcomes, of which 188 were independent of risk for comorbid problems or PGSs (from odds ratio [OR], 1.02; 95% CI, 1.01-1.03 for overweight/obesity to OR, 1.44; 95% CI, 1.42-1.47 for viral hepatitis C). Of the significant outcomes, 73 (11.9%) were significant in the African results and 26 (4.5%) were significant in the Hispanic or Latin American results. Within-family analyses uncovered robust associations between EXT PGS and consequences of SUDs, including liver disease, chronic airway obstruction, and viral hepatitis C. Conclusions and Relevance: Results of this cohort study suggest a shared polygenic basis of EXT disorders, independent of risk for other psychiatric problems. In addition, this study found associations between EXT PGS and diagnoses related to SUDs and their sequelae. Overall, this study highlighted the potential negative consequences of EXT disorders for health and functioning in the US veteran population.

Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals.

Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.

The molecular genetic landscape of human brain size variation.

Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.

Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics.

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, although down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci; the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses were found robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who generate and share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers' use of the summary statistics.

Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank.

Mental conditions exhibit a higher-order transdiagnostic factor structure which helps to explain the widespread comorbidity observed in psychopathology. However, the phenotypic and genetic structures of psychopathology may differ, raising questions about the validity and utility of these factors. Here, we study the phenotypic and genetic factor structures of ten psychiatric conditions using UK Biobank and public genomic data. Although the factor structure of psychopathology was generally genetically and phenotypically consistent, conditions related to externalizing (e.g., alcohol use disorder) and compulsivity (e.g., eating disorders) exhibited cross-level disparities in their relationships with other conditions, plausibly due to environmental influences. Domain-level factors, especially thought disorder and internalizing factors, were more informative than a general psychopathology factor in genome-wide association and polygenic index analyses. Collectively, our findings enhance the understanding of comorbidity and shared etiology, highlight the intricate interplay between genes and environment, and offer guidance for psychiatric research using polygenic indices.

Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes.

Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.

A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders.

Substance use disorders (SUDs) are phenotypically and genetically correlated with each other and with other psychological traits characterized by behavioural under-control, termed externalizing phenotypes. In this study, we used genomic structural equation modelling to explore the shared genetic architecture among six externalizing phenotypes and four SUDs used in two previous multivariate genome-wide association studies of an externalizing and an addiction risk factor, respectively. We first evaluated five confirmatory factor analytic models, including a common factor model, alternative parameterizations of two-factor structures and a bifactor model. We next explored the genetic correlations between factors identified in these models and other relevant psychological traits. Finally, we quantified the degree of polygenic overlap between externalizing and addiction risk using MiXeR. We found that the common and two-factor structures provided the best fit to the data, evidenced by high factor loadings, good factor reliability and no evidence of concerning model characteristics. The two-factor models yielded high genetic correlations between factors (rg s ≥ 0.87), and between the effect sizes of genetic correlations with external traits (rg  ≥ 0.95). Nevertheless, 21 of the 84 correlations with external criteria showed small, significant differences between externalizing and addiction risk factors. MiXer results showed that approximately 81% of influential externalizing variants were shared with addiction risk, whereas addiction risk shared 56% of its influential variants with externalizing. These results suggest that externalizing and addiction genetic risk are largely shared, though both constructs also retain meaningful unshared genetic variance. These results can inform future efforts to identify specific genetic influences on externalizing and SUDs.

Do polygenic indices capture "direct" effects on child externalizing behavior? Within-family analyses in two longitudinal birth cohorts.

Behaviors and disorders characterized by difficulties with self-regulation, such as problematic substance use, antisocial behavior, and symptoms of attention-deficit/hyperactivity disorder (ADHD), incur high costs for individuals, families, and communities. These externalizing behaviors often appear early in the life course and can have far-reaching consequences. Researchers have long been interested in direct measurements of genetic risk for externalizing behaviors, which can be incorporated alongside other known risk factors to improve efforts at early identification and intervention. In a preregistered analysis drawing on data from the Environmental Risk (E-Risk) Longitudinal Twin Study (N=862 twins) and the Millennium Cohort Study (MCS; N=2,824 parent-child trios), two longitudinal cohorts from the UK, we leveraged molecular genetic data and within-family designs to test for genetic effects on externalizing behavior that are unbiased by the common sources of environmental confounding. Results are consistent with the conclusion that an externalizing polygenic index (PGI) captures causal effects of genetic variants on externalizing problems in children and adolescents, with an effect size that is comparable to those observed for other established risk factors in the research literature on externalizing behavior. Additionally, we find that polygenic associations vary across development (peaking from age 5-10 years), that parental genetics (assortment and parent-specific effects) and family-level covariates affect prediction little, and that sex differences in polygenic prediction are present but only detectable using within-family comparisons. Based on these findings, we believe that the PGI for externalizing behavior is a promising means for studying the development of disruptive behaviors across child development.

Robust estimation of cortical similarity networks from brain MRI.

Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n > 11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically validated lens for cortical connectomics using readily available MRI data.

Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing.

Importance: Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy. Objective: To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes. Design, Setting, and Participants: This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023. Main Outcomes and Measures: Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes. Results: In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders. Conclusions and Relevance: The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.

CADM2 is implicated in impulsive personality and numerous other traits by genome- and phenome-wide association studies in humans and mice.

Impulsivity is a multidimensional heritable phenotype that broadly refers to the tendency to act prematurely and is associated with multiple forms of psychopathology, including substance use disorders. We performed genome-wide association studies (GWAS) of eight impulsive personality traits from the Barratt Impulsiveness Scale and the short UPPS-P Impulsive Personality Scale (N = 123,509-133,517 23andMe research participants of European ancestry), and a measure of Drug Experimentation (N = 130,684). Because these GWAS implicated the gene CADM2, we next performed single-SNP phenome-wide studies (PheWAS) of several of the implicated variants in CADM2 in a multi-ancestral 23andMe cohort (N = 3,229,317, European; N = 579,623, Latin American; N = 199,663, African American). Finally, we produced Cadm2 mutant mice and used them to perform a Mouse-PheWAS ("MouseWAS") by testing them with a battery of relevant behavioral tasks. In humans, impulsive personality traits showed modest chip-heritability (~6-11%), and moderate genetic correlations (rg = 0.20-0.50) with other personality traits, and various psychiatric and medical traits. We identified significant associations proximal to genes such as TCF4 and PTPRF, and also identified nominal associations proximal to DRD2 and CRHR1. PheWAS for CADM2 variants identified associations with 378 traits in European participants, and 47 traits in Latin American participants, replicating associations with risky behaviors, cognition and BMI, and revealing novel associations including allergies, anxiety, irritable bowel syndrome, and migraine. Our MouseWAS recapitulated some of the associations found in humans, including impulsivity, cognition, and BMI. Our results further delineate the role of CADM2 in impulsivity and numerous other psychiatric and somatic traits across ancestries and species.

Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.

Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort.

Background: Many psychiatric outcomes are thought to share a common etiological pathway reflecting behavioral disinhibition, generally referred to as externalizing disorders (EXT). Recent genome-wide association studies (GWAS) have demonstrated the overlap between EXT and important aspects of veterans' health, such as suicide-related behaviors, substance use disorders, and other medical conditions. Methods: We conducted a series of phenome-wide association studies (PheWAS) of polygenic scores (PGS) for EXT, and comorbid psychiatric problems (depression, schizophrenia, and suicide attempt) in an ancestrally diverse cohort of U.S. veterans (N = 560,824), using diagnostic codes from electronic health records. We conducted ancestry-specific PheWASs of EXT PGS in the European, African, and Hispanic/Latin American ancestries. To determine if associations were driven by risk for other comorbid problems, we performed a conditional PheWAS, covarying for comorbid psychiatric problems (European ancestries only). Lastly, to adjust for unmeasured confounders we performed a within-family analysis of significant associations from the main PheWAS in full-siblings (N = 12,127, European ancestries only). Results: The EXT PGS was associated with 619 outcomes across all bodily systems, of which, 188 were independent of risk for comorbid problems of PGS. Effect sizes ranged from OR = 1.02 (95% CI = 1.01, 1.03) for overweight/obesity to OR = 1.44 (95% CI = 1.42, 1.47) for viral hepatitis C. Of the significant outcomes 73 (11.9%) and 26 (4.5%) were significant in the African and Hispanic/Latin American results, respectively. Within-family analyses uncovered robust associations between EXT and consequences of substance use disorders, including liver disease, chronic airway obstruction, and viral hepatitis C. Conclusion: Our results demonstrate a shared polygenic basis of EXT across populations of diverse ancestries and independent of risk for other psychiatric problems. The strongest associations with EXT were for diagnoses related to substance use disorders and their sequelae. Overall, we highlight the potential negative consequences of EXT for health and functioning in the US veteran population.

Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics.

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci, while the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses are robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers' use of the summary statistics.

Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis.

Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA). We model this multivariate system of genetic relationships by applying Genomic Structural Equation Modeling (Genomic SEM) and parsimoniously define five genomic brain factors underlying both CT and SA along with a general factor capturing genetic overlap across all brain regions. We validate these factors by demonstrating the generalizability of the model to a semi-independent sample and show that the factors align with biologically and functionally relevant parcellations of the cortex. We apply Stratified Genomic SEM to identify specific categories of genes (e.g., neuronal cell types) that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. Finally, we examine genetic associations with psychiatric and cognitive correlates, finding that broad aspects of cognitive function are associated with a general factor for SA and that psychiatric associations are null. These analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex.

Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals.

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. To improve our understanding of the genetics of PAU, we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals. We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine-mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and/or chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by drug repurposing analysis. Cross-ancestry polygenic risk scores (PRS) showed better performance in independent sample than single-ancestry PRS. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. The analysis of diverse ancestries contributed significantly to the findings, and fills an important gap in the literature.

Examining the shared etiology of psychopathology with genome-wide association studies.

Genome-wide association studies (GWASs) have ushered in a new era of reproducible discovery in psychiatric genetics. The field has now identified hundreds of common genetic variants that are associated with mental disorders, and many of them influence more than one disorder. By advancing the understanding of causal biology underlying psychopathology, GWAS results are poised to inform the development of novel therapeutics, stratification of at-risk patients, and perhaps even the revision of top-down classification systems in psychiatry. Here, we provide a concise review of GWAS findings with an emphasis on findings that have elucidated the shared genetic etiology of psychopathology, summarizing insights at three levels of analysis: 1) genome-wide architecture; 2) networks, pathways, and gene sets; and 3) individual variants/genes. Three themes emerge from these efforts. First, all psychiatric phenotypes are heritable, highly polygenic, and influenced by many pleiotropic variants with incomplete penetrance. Second, GWAS results highlight the broad etiological roles of neuronal biology, system-wide effects over localized effects, and early neurodevelopment as a critical period. Third, many loci that are robustly associated with multiple forms of psychopathology harbor genes that are involved in synaptic structure and function. Finally, we conclude our review by discussing the implications that GWAS results hold for the field of psychiatry, as well as expected challenges and future directions in the next stage of psychiatric genetics.

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data.

Introduction: Opioid use disorders (OUDs) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHRs) are useful tools for understanding complex medical phenotypes but have been underutilized for OUD because of challenges related to underdiagnosis, binary diagnostic frameworks, and minimally characterized reference groups. As a first step in addressing these challenges, a new paradigm is warranted that characterizes risk for opioid prescription misuse on a continuous scale of severity, i.e., as a continuum. Methods: Across sites within the PsycheMERGE network, we extracted prescription opioid data and diagnoses that co-occur with OUD (including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C) for over 2.6 million patients across three health registries (Vanderbilt University Medical Center, Mass General Brigham, Geisinger) between 2005 and 2018. We defined three groups based on levels of opioid exposure: no prescriptions, minimal exposure, and chronic exposure and then compared the comorbidity profiles of these groups to the full registries and to those with OUD diagnostic codes. Results: Our results confirm that EHR data reflects known higher prevalence of substance use disorders, psychiatric disorders, medical, and pain diagnoses in patients with OUD diagnoses and chronic opioid use. Comorbidity profiles that distinguish opioid exposure are strikingly consistent across large health systems, indicating the phenotypes described in this new quantitative framework are robust to health systems differences. Conclusion: This work indicates that EHR prescription opioid data can serve as a platform to characterize complex risk markers for OUD using existing data.