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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.

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.

OBJECTIVE: Genome-wide association studies (GWASs) of the Alcohol Use Disorders Identification Test (AUDIT), a 10-item screen for alcohol use disorder (AUD), have elucidated novel loci for alcohol consumption and misuse. However, these studies also revealed that GWASs can be influenced by numerous biases (e.g., measurement error, selection bias), which may have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders and/or medical conditions. The authors used genomic structural equation modeling to elucidate the genetics of alcohol consumption and problematic consequences of alcohol use as measured by AUDIT. METHODS: To explore these unexpected differences in genetic correlations, the authors conducted the first item-level and the largest GWAS of AUDIT items (N=160,824) and applied a multivariate framework to mitigate previous biases. RESULTS: The authors identified novel patterns of similarity (and dissimilarity) among the AUDIT items and found evidence of a correlated two-factor structure at the genetic level ("consumption" and "problems," rg=0.80). Moreover, by applying empirically derived weights to each of the AUDIT items, the authors constructed an aggregate measure of alcohol consumption that was strongly associated with alcohol dependence (rg=0.67), moderately associated with several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by conducting polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, the authors identified novel genetic associations between alcohol consumption, alcohol misuse, and health. CONCLUSIONS: This work further emphasizes the value of AUDIT for both clinical and genetic studies of AUD and the importance of using multivariate methods to study genetic associations that are more closely related to AUD.

BACKGROUND: Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. METHODS: We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, ξ, for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. RESULTS: We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N=11,586) and individual genetic variants in the UK Biobank (N=380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. DISCUSSION: We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether specific GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.

Contains fulltext : 237013.pdf (Publisher’s version ) (Closed access) Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have 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 loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of 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 trait with complex and far-reaching social and health correlates. 27 p.

Structured AbstractImportancePsychiatric 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.ObjectiveTo identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes.DesignThis genomic study applied a multivariate transcriptomic method, Transcriptome-wide Structural Equation Modeling (T-SEM), to investigate gene expression patterns associated with four genomic factors indexing shared risk across 11 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. Public databases describing drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk.Main Outcomes and MeasuresGene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes.ResultsIn total, T-SEM identified 451 genes whose expression was associated with the genomic factors and 41 genes with disorder-specific effects. We find the most hits for a Thought Disorders factor defined by bipolar disorder and schizophrenia. We identify 39 existing pharmacological interventions that could be repurposed to target gene expression hits for this same factor.Conclusions and RelevanceThe 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.

Structural similarity networks are a central focus of magnetic resonance imaging (MRI) research into human brain connectomes in health and disease. We present Morphometric INverse Divergence (MIND), a robust method to estimate within-subject structural similarity between cortical areas based on the Kullback-Leibler divergence between the multivariate distributions of their structural features. Compared to the prior approach of morphometric similarity networks (MSNs) on N>10,000 data from the ABCD cohort, MIND networks were more consistent with known cortical symmetry, cytoarchitecture, and (in N=19 macaques) gold-standard tract-tracing connectivity, and were more invariant to cortical parcellation. Importantly, MIND networks were remarkably coupled with cortical gene co-expression, providing fresh evidence for the unified architecture of brain structure and transcription. Using kinship (N=1282) and genetic data (N=4085), we characterized the heritability of MIND phenotypes, identifying stronger genetic influence on the relationship between structurally divergent regions compared to structurally similar regions. Overall, MIND presents a biologically-validated lens for analyzing the structural organization of the cortex using readily-available MRI measurements.

Eating disorders (EDs) are complex psychiatric conditions that often emerge during adolescence, and affected individuals frequently demonstrate high rates of psychiatric comorbidity, particularly with depressive and anxiety disorders. Although risk for EDs reflects both genetic and neurobiological factors, knowledge of how genetic risk for EDs relates to neurobiology and psychiatric symptoms during critical developmental periods remains limited. We therefore implemented a novel multivariate framework, which sought to advance knowledge of the etiology of EDs by simultaneously estimating associations between genetic risk, brain structure and ED-related psychopathology symptoms in over 4,500 adolescents of European ancestry from the Adolescent Brain and Cognitive Development study (M(SD)age=119.29(7.49) months). Polygenic scores for anorexia nervosa (AN PGS) and body mass index (BMI PGS) were generated and related to three morphometric brain features— cortical thickness, surface area and subcortical grey matter volume—and to latent psychopathology factors using structural equation modeling. We identified a three-factor structure of ED-related psychopathology symptoms: eating, distress and fear factors. Increased BMI PGS were uniquely associated with greater eating factor scores, whereas AN PGS were unrelated to psychopathology factors. Moreover, genetic risk for high BMI and for AN had distinct neural correlates, where greater BMI PGS predicted widespread increases in cortical thickness and reductions in surface area while AN PGS were nominally related to reduced caudate volume. Altered default mode and visual network thickness was associated with greater eating factor scores, whereas distress and fear factor scores reflected a shared reduction in somatomotor network thickness. Our novel findings indicate that greater genetic risk for high BMI and altered cortical thickness of canonical brain networks underpin ED symptomatology in early adolescence. As neurobiological factors appear to shape disordered eating earlier in the life course than previously thought, these results underscore the need for early detection and intervention efforts for EDs.

Substance use disorders (SUDs) are phenotypically and genetically correlated with each other and with other psychological traits characterized by behavioral undercontrol, termed externalizing phenotypes. In this study, we used Genomic Structural Equation Modeling to explore the shared genetic architecture among six externalizing phenotypes and four SUDs used in two previous multivariate GWAS of an externalizing and an addiction risk factor, respectively. Using a preregistered set of criteria, we first evaluated the performance of five confirmatory factor analytic models, including a common factor model, alternative parameterizations of two-factor structures, and a bifactor model. We used a combination of model fit, factor reliability, and model characteristics to adjudicate among the models. We next explored the genetic correlations between factors identified in these models and other relevant psychological traits. We found that a common factor model, in which all externalizing phenotypes and SUDs were influenced by a single dimension of genetic risk best characterized the relationships among our phenotypes. Although two two-factor models also performed well, we found that the factors in those models were very highly correlated with each other (rgS > .87) and similarly genetically correlated with external criteria, suggesting they did not represent meaningfully distinct dimensions. Results from this study can be used to inform future efforts to characterize genetic liability for broad externalizing as well as specific externalizing phenotypes.

Our understanding of the genetic architecture of the human cerebral cortex is limited both in terms of the diversity of brain structural phenotypes and the anatomical granularity of their associations with genetic variants. Here, we conducted 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 36,843 individuals from the UK Biobank and the ABCD cohorts. These phenotypes include cortical thickness, surface area, grey matter volume, and measures of folding, neurite density, and water diffusion. We identified 4,349 experiment-wide significant loci associated with global and regional phenotypes. Multiple lines of analyses 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 surface area and volume specifically are associated with cephalic disorders. Finally, we identified complex inter-phenotype and inter-regional genetic relationships among the 13 phenotypes which reflect developmental differences among them. These analyses help refine the role of common genetic variants in human cortical development and organisation.One sentence summaryGWAS of 2,347 neuroimaging phenotypes shed light on the global and regional genetic organisation of the cortex, underlying cellular and developmental processes, and links to neurodevelopmental and cephalic disorders.

ImportanceCharacterizing whether genetic variants for psychiatric outcomes operate via specific versus general pathways provides more informative measures of genetic risk, and, potentially, allows us to design more targeted prevention and interventions.ObjectiveEmploy multivariate methods to tease apart variants associated with problematic alcohol use through either general or specific pathways and compare results to standard univariate genetic analysis of problematic alcohol use.DesignWe compared results from a univariate genome wide association study (GWAS) of problematic alcohol use to those from a previous multivariate GWAS of externalizing phenotypes. We identified genetic variants associated with problematic alcohol use through a broad liability to externalizing, and those that remain after removing shared variance with externalizing. We compared these results across SNP overlap, bioannotations, genetic correlations, and polygenic scores.SettingWe included GWAS summary statistics from existing GWAS, and two US based hold out samples: The National Longitudinal Study of Adolescent to Adult Health (Add Health) and the Collaborative Study on the Genetics of Alcoholism (COGA).ParticipantsPublicly available GWAS of externalizing behaviors and participants in Add Health (N=5,107) and COGA (N=7,483), limited to individuals of European ancestries.Exposure(s)N/AMain Outcome(s) and Measure(s)Outcomes included problematic alcohol use (ALCP-O), shared risk for externalizing (EXT), and problematic alcohol use-specific risk (ALCP-S) for the GWASs; a preregistered list of 99 available phenotypes for genetic correlations; and substance use, substance use disorder criteria, and alcohol misuse in the polygenic score analyses.ResultsThe analysis differentiated SNPs operating through common versus specific risk pathways. While ALCP-O was associated with multiple phenotypes, ALCP-S was predominantly associated with alcohol use and other forms of psychopathology. Polygenic scores for ALCP-O were associated with a variety of other forms of substance use and substance use disorders, polygenic scores for ALCP-S were only associated with alcohol phenotypes. Polygenic scores for both ALCP-S and EXT show differential patterns of associations with alcohol misuse across development.Conclusions and RelevanceFocusing on the differential impacts of shared and specific risk can better characterize pathways of risk for alcohol use disorders. Multivariate methods can be a useful tool for studying many psychiatric conditions. Parsing risk pathways will become increasingly relevant as genetic information is incorporated into clinical practice for psychiatric outcomes.

Genetic predispositions and environmental influences both play an important role in adolescent externalizing behavior; however, they are not always independent. To elucidate gene-environment interplay, we examined the interrelationships between externalizing polygenic risk scores, parental knowledge, and peer substance use in impacting adolescent externalizing behavior across two time-points in a high-risk longitudinal sample of 1,200 adolescents (764 European and 436 African ancestry; M(age) = 12.99) from the Collaborative Study on the Genetics of Alcoholism. Results from multivariate path analysis indicated that externalizing polygenic scores were directly associated with adolescent externalizing behavior but also indirectly via peer substance use, in the European ancestry sample. No significant polygenic association nor indirect effects of genetic risk were observed in the African ancestry group, likely due to more limited power. Our findings underscore the importance of gene-environment interplay and suggest peer substance use may be a mechanism through which genetic risk influences adolescent externalizing behavior.

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used Genomic Structural Equation Modeling and prior genome-wide association studies (GWAS) of educational attainment (n = 1,131,881) and cognitive test performance (n = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability.We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Non-cognitive genetics were enriched in the same brain tissues and cell types as cognitive performance but showed different associations with gray-matter brain volumes. Non-cognitive genetics were further distinguished by associations with personality traits, less risky behavior,and increased risk for certain psychiatric disorders.For socioeconomic success and longevity, non-cognitive and cognitive-performance genetics demonstrated similar-magnitude associations. By conducting a GWAS of a phenotype that was not directly measured, we offer a first view of genetic architecture of non-cognitive skills influencing educational success.

Polygenic scores offer developmental psychologists new methods for integrating genetic information into research on how people change and develop across the life span. Indeed, polygenic scores have correlations with developmental outcomes that rival correlations with traditional developmental psychology variables, such as family income. Yet linking people's genetics with differences between them in socially valued developmental outcomes, such as educational attainment, has historically been used to justify acts of state-sponsored violence. In this review, we emphasize that an interdisciplinary understanding of the environmental and structural determinants of social inequality, in conjunction with a transactional developmental perspective on how people interact with their environments, is critical to interpreting associations between polygenic measures and phenotypes. While there is a risk of misuse, early applications of polygenic scores to developmental psychology have already provided novel findings that identify environmental mechanisms of life course processes that can be used to diagnose inequalities in social opportunity.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.

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.

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 geneCADM2, we next performed single-SNP phenome-wide studies (PheWAS) of several of the implicated variants inCADM2in a multi-ancestral 23andMe cohort (N=3,229,317, European; N=579,623, Latin American; N=199,663, African American). Finally, we producedCadm2mutant 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=.20-.50) with other personality traits, and various psychiatric and medical traits. We identified significant associations proximal to genes such asTCF4andPTPRF, and also identified nominal associations proximal toDRD2andCRHR1. PheWAS forCADM2variants 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 ofCADM2in impulsivity and numerous other psychiatric and somatic traits across ancestries and species.

Objective: To investigate the genetic architecture of internalizing symptoms in childhood and adolescence. Method: In 22 cohorts, multiple univariate genome-wide association studies (GWASs) were performed using repeated assessments of internalizing symptoms, in a total of 64,561 children and adolescents between 3 and 18 years of age. Results were aggregated in meta-analyses that accounted for sample overlap, first using all available data, and then using subsets of measurements grouped by rater, age, and instrument. Results: The meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide significant hits and showed low single nucleotide polymorphism (SNP) heritability (1.66%, 95% CI = 0.84-2.48%, neffective = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% CI = 3.08%-8.18%). The contribution of additive genetic effects on internalizing symptoms appeared to be stable over age, with overlapping estimates of SNP heritability from early childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the well-being spectrum (|rg| > 0.70), as well as with insomnia, loneliness, attention-deficit/hyperactivity disorder, autism, and childhood aggression (range |rg| = 0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. Conclusion: Genetic correlations indicate that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity among childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success. Dr. Border has received support from the National Institutes of Health (NIH; MH100141 and MH016880 ). Dr. Peterson has received support from NIH (K01MH113848) and the Brain & Behavior Research Foundation Young Investigator Grant (28632). Drs. Sallis and Munafò have served as members of the MRC Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/7). Dr. Shabalin has received support from a NARSAD Young Investigator Award. Dr. Vilor-Tejedor has received support from a post-doctoral grant, Juan de la Cierva Programme (FJC2018-038085-I), Ministerio de Ciencia, Innovación y Universidades – Spanish State Research Agency. Her research has received additional support of “la Caixa” Foundation (LCF/PR/GN17/10300004) and the Health Department of the Catalan Government (Health Research and Innovation Strategic Plan (PERIS) 2016-2020 grant# SLT002/16/00201). She has acknowledged the support of the Spanish Ministry of Science, Innovation and Universities to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Programme/Generalitat de Catalunya. Dr. Alemany has received support from a Juan de la Cierva – Incorporación Postdoctoral Contract from Ministerio de Economía, Industria y Competitividad (IJCI-2017-34068). Dr. Corley has received support from NIH (DA011015, AG046938, and DA035804). Dr. Evans has received support from NIH (MH100141, DA044283, and AG046938). Dr. Havdahl has received support from the South-Eastern Norway Regional Health Authority (2018059) at Nic Waals Institute, Lovisenberg Diaconal Hospital. Dr. Rimfeld has received support from a Sir Henry Wellcome Postdoctoral Fellowship. Dr. Andreassen has served as a consultant to HealthLytix and has received funding from the Research Council of Norway (223273 and 273291) and KG Jebsen Stiftelsen. Dr. Boomsma has received support from the Royal Netherlands Academy of Science Professor Award (PAH/5535). Dr. Brown has received support from NIH (DA035804). Dr. Copeland has received support from NIH (MH117559 and HD093651). Dr. Dick has received support from a mid-career award NIH K02 AA018755, NIH R01 AA015416 (Finnish Twin Study), P50 AA022537 (Alcohol Research Center), R25 AA027402 (VCU GREAT), and U10 AA008401 (COGA) from the National Institute on Alcohol Abuse and Alcoholism. Drs. Hewitt, Krauter, Smolen, and Stallings have received support from NIH (DA011015 and DA035804). Dr. Hopfer has received support from NIH (DA042755, DA035804, and DA032555). Dr. Kaprio has received support from the Academy of Finland (grants 308248 and 312073). Dr. Larsson has served as a speaker for Evolan Pharma and Shire/Takeda and has received research grants from Shire/Takeda, all outside the submitted work. Dr. Lichtenstein has received support from the Swedish Research Council for Health, Working Life and Welfare (project 2012-1678) and the Swedish Research Council (2016-1989). Dr. Lundström has received support from the Swedish Research Council ( 2017-02552 ). Dr. Magnus has received support from the Research Council of Norway through its Centers of Excellence scheme, project no 262700. Dr. Njølstad has received support from the European Research Council (AdG #293574). Stiftelsen Kristian Gerhard Jebsen has received support from the Research Council of Norway (FRIPRO 240413), the Western Norway Regional Health Authority (Strategic Fund “Personalized Medicine for Children and Adults”) and the Novo Nordisk Foundation (54741). Dr. Plomin has received support from a MRC Professorship award (G19/2). Dr. Reichborn-Kjennerud has received support from the Research Council of Norway grant 274611 . Drs. Reynolds and Wadsworth have received support from NIH (AG046938). Dr. Rose has received support from a NIH Research Scientist Award (AA-000145). Dr. Wall has received support from NIH (DA03580 and DA021905). Dr. Whitehouse has received support from an Investigator Grant from the National Health and Medical Research Council. Dr. Ystrøm has received support from the Research Council of Norway (262177 and 288083). Dr. Nivard has received support from ZonMW (849200011), the Netherlands Organisation for Health Research and Development (531003014 937), a Jacobs Foundation Research Fellowship, and a VENI grant awarded by NWO (VI.Veni.191G.030). Dr. Bartels has received support from an ERC Consolidator Grant (WELL-BEING 771057). Ms. Hagenbeek and Mr. Ip have received support from the Aggression in Children: Unraveling gene-environment interplay to inform Treatment and Intervention strategies project (ACTION). ACTION received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement no 602768

BackgroundElectronic health records (EHR) are useful tools for understanding complex medical phenotypes, but they have been underutilized for opioid use disorders (OUD). Patterns of prescription opioid use might provide an objective measure of OUD risk.MethodsWe extracted data 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 Prescription, Minimal Exposure (2 prescriptions within 90 days at least once, but never 3 prescriptions ResultsThe prevalence of substance (alcohol, tobacco, cannabis) use disorders was higher in patients with OUD and Chronic Exposure than those with No Prescription or Minimal Exposure. Patients in the OUD and Chronic Exposure groups had more psychiatric (anxiety, depression, schizophrenia, bipolar disorder) and medical comorbidities (pain, hepatitis C, HIV) than those in the Minimal Exposure group. Notably, patients in the Minimal Exposure group had different comorbidity profiles (higher rates of substance use and psychiatric disorders, more pain conditions) than those in the Unscreened or No Prescription groups, highlighting the value of including opioid exposure in studies of OUD.ConclusionsLong-term opioid prescription use may serve as an additional tool to characterize OUD risk.

ImportanceEconomic and racial inequality is linked to disparities in children’s mental health. Biomarkers that reflect these social disparities are lacking.ObjectiveWe examined the hypothesis that salivary DNA-methylation patterns of higher inflammation and faster pace of biological aging are economically, racially and ethnically stratified and are associated with child mental health.DesignThe Texas Twin Project is an on-going, observational, longitudinal study that began in May 2012. Analyses were preregistered on May 7, 2021, and completed on August 23, 2021.SettingThe population-based study identified and recruited participants from public school rosters in the greater Austin area.ParticipantsParticipants in the analytic data set included all participants that agreed to contribute DNA samples and whose samples were assayed by January 2021.ExposuresFamily- and neighborhood-level socioeconomic inequality, racial and ethnic identities (White, Latinx, Black, Asian).Main Measure(s)Environmental exposures were analyzed in relation to salivary DNA- methylation profiles of higher inflammation (DNAm-CRP) and faster pace of biological aging (DunedinPoAm). Child internalizing problems, attention problems, aggression, rule-breaking, ADHD, oppositional defiant disorder, and conduct disorder were measured using parent-reports and self-reports on abbreviated versions of the Achenbach Child Behavior Checklist and Conners 3.The hypotheses being tested were formulated after data collection of the present data freeze and were pre-registered prior to analyses being conducted.ResultsIn a sample of N=1,183 8-to-19-year-olds (609 female, age M=13.38y), children’s salivary DNA-methylation profiles and psychiatric symptoms differed by socioeconomic conditions, race and ethnicity. Children with more parent-reported internalizing symptoms had higher DNAm-CRP (r=0.15, 95% CI=0.05 to 0.25, P=0.004) and DunedinPoAm (r=0.15, CI=0.05 to 0.25, P=0.002), and children with more parent-reported aggression problems had higher DNAm-CRP (r=0.17, CI=0.04 to 0.31, P=0.013). DNAm-CRP partially mediated advantage of higher family socioeconomic status (16% of total effect) and White racial identity (12% of total effect) on reduced internalizing symptoms. DunedinPoAm also partially mediated advantage of White racial identity on internalizing (19% of total effect).Conclusions and RelevanceSocioeconomic and racial inequality are visible in children’s epigenetic profiles of inflammation and the rate of biological aging in a manner that is tied to social disparities in mental health.Key PointsQuestionWe examined whether salivary DNA-methylation profiles are socially stratified and associated with child mental health.FindingsIn this preregistered, cross-sectional observational study of 1,183 children and adolescents, socioeconomic, racial, and ethnic disparities in mental health were associated with salivary DNA-methylation profiles of inflammation and the pace of biological aging.MeaningDNA-methylation biomarkers hold promise as tools to quantify the biological impact of socioeconomic inequality and being racially minoritized in a manner that is tied to social disparities in mental health.

Children’s cognitive functioning and educational performance are socially stratified. Social inequality, including classism and racism, may operate partly via epigenetic mechanisms that modulate neurocognitive development. Following preregistered analyses of data from 1,183 8-to 19-year-olds from the Texas Twin Project, we examined whether salivary DNA-methylation measures of inflammation (DNAm-CRP), cognitive functioning (Epigenetic-g), and pace of biological aging (DunedinPoAm) are socially stratified and associated with performance on tests of cognitive functions. We find that children growing up in more disadvantaged families and neighborhoods and children from marginalized racial/ethnic groups exhibit DNA-methylation profiles associated with higher chronic inflammation, lower cognitive functioning, and faster pace of biological aging. These salivary DNA-methylation profiles were associated with processing speed, general executive function, perceptual reasoning, verbal comprehension, reading, and math. Given that the DNA-methylation measures we examined were originally developed in adults, our results suggest that social inequalities may produce in children molecular signatures that, when observed in adults, are associated with chronic inflammation, advanced aging, and reduced cognitive function. Salivary DNA-methylation profiles might be useful as a surrogate endpoint in assessing the effectiveness of psychological and economic interventions that aim to reduce negative effects of childhood social inequality on lifespan development.Significance StatementChildren’s cognitive functioning differs by dimensions of social inequality, such as class and race. Epigenetic mechanisms that regulate gene expression might be critically involved in the biological embedding of environmental privilege and adversity. We find that children growing up in more disadvantaged families and neighborhoods and from marginalized racial/ethnic groups exhibit higher chronic inflammation, lower cognitive functioning, and a faster pace of biological aging, as indicated by novel salivary DNA-methylation measures. These DNA-methylation measures of higher inflammation, lower cognitive functioning, and a faster pace of biological aging were, in turn, associated with performance on multiple cognitive tests. DNA-methylation measures might be useful as a surrogate endpoint in evaluation of programs to address the childhood social determinants of lifelong cognitive disparities.

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is rapidly growing. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some of which are novel—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the “additive SNP factor.” Regressions in which the true regressor is the additive SNP factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Brain structural covariance norms capture the coordination of neurodevelopmental programs between different brain regions. We develop and apply anatomical imbalance mapping (AIM), a method to measure and model individual deviations from these norms, to provide a lifespan map of morphological integration in the human cortex. In cross-sectional and longitudinal data, analysis of whole-brain average anatomical imbalance reveals a reproducible tightening of structural covariance by age 25 y, which loosens after the seventh decade of life. Anatomical imbalance change in development and in aging is greatest in the association cortex and least in the sensorimotor cortex. Finally, we show that interindividual variation in whole-brain average anatomical imbalance is positively correlated with a marker of human prenatal stress (birthweight disparity between monozygotic twins) and negatively correlated with general cognitive ability. This work provides methods and empirical insights to advance our understanding of coordinated anatomical organization of the human brain and its interindividual variation.

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STAR*D treatment guidelines was acceptable, AUC = .659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were .662, 95% CI [0.632, 0.692] and .655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample.

Behaviors and disorders related to self-regulation, such as substance use, antisocial conduct, and ADHD, are collectively referred to asexternalizingand 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.

We systematically interrogate the joint genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic, and molecular genetic levels of analysis. We identify four broad factors (Neurodevelopmental, Compulsive, Psychotic, and Internalizing) that underlie genetic correlations among the disorders, and test whether these factors adequately explain their genetic correlations with biobehavioral traits. We introduce Stratified Genomic Structural Equation Modelling, which we use to identify gene sets and genomic regions that disproportionately contribute to pleiotropy, including protein-truncating variant intolerant genes expressed in excitatory and GABAergic brain cells that are enriched for pleiotropy between disorders with psychotic features. Multivariate association analyses detect a total of 152 (20 novel) independent loci which act on the four factors, and identify nine loci that act heterogeneously across disorders within a factor. Despite moderate to high genetic correlations across all 11 disorders, we find very little utility of, or evidence for, a single dimension of genetic risk across psychiatric disorders.

Genome-wide association studies (GWASs) of the Alcohol Use Disorder Identification Test (AUDIT), a ten-item screener for alcohol use disorder (AUD), have elucidated novel loci for alcohol consumption and misuse. However, these studies also revealed that GWASs can be influenced by numerous biases (e.g., measurement error, selection bias), which have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders/medical conditions. To explore these unexpected differences in genetic correlations, we conducted the first item-level and largest GWAS of AUDIT items (N=160,824), and applied a multivariate framework to mitigate previous biases. In doing so, we identified novel patterns of similarity (and dissimilarity) among the AUDIT items, and found evidence of a correlated two-factor structure at the genetic level (Consumption and Problems, rg=.80). Moreover, by applying empirically-derived weights to each of the AUDIT items, we constructed an aggregate measure of alcohol consumption that is strongly associated with alcohol dependence (rg=.67) and several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by performing polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, we identified novel genetic associations between alcohol consumption, alcohol misuse, and human health. Our work further emphasizes the value of AUDIT for both clinical and genetic studies of AUD, and the importance of using multivariate methods to study genetic associations that are more closely related to AUD.

Internalising symptoms in childhood and adolescence are as heritable as adult depression and anxiety, yet little is known of their molecular basis. This genome-wide association meta-analysis of internalising symptoms included repeated observations from 64,641 individuals, aged between 3 and 18. The N-weighted meta-analysis of overall internalising symptoms (INToverall) detected no genome-wide significant hits and showed low SNP heritability (1.66%, 95% confidence intervals 0.84-2.48%, Neffective=132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalising symptoms showing the highest heritability (5.63%, 95% confidence intervals 3.08-8.18%). Additive genetic effects on internalising symptoms appeared stable over age, with overlapping estimates of SNP heritability from early-childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the wellbeing spectrum (|rg|> 0.70), as well as with insomnia, loneliness, attention-deficit hyperactivity disorder, autism, and childhood aggression (range |rg|=0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. The pattern of genetic correlations suggests that childhood and adolescent internalising symptoms share substantial genetic vulnerabilities with adult internalising disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalising symptoms over time and the high comorbidity amongst childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

Genotype-by-environment interaction (GxE) occurs when the size of a genetic effect varies systematically across levels of the environment and when the size of an environmental effect varies systematically across levels of the genotype. However, total variance in the phenotype may shift as a function of the moderator irrespective of its etiology such that the proportional effect of the predictor is constant. We expand the traditional GxE regression model to directly account for heteroscedasticity associated with both the genotype and the measured environment. We then derive a test statistic, ξ, for inferring whether GxE can be attributed to an effect of the moderator on the dispersion of the phenotype. We apply this method to identify genotype-by-birth year interactions for Body Mass Index (BMI) that are distinguishable from general secular increases in the variance of BMI or associations of the genetic predictors (both PGS and individual loci) with BMI variance. We provide software for analyzing such models.

The X-chromosome has long been hypothesized to have a disproportionate influence on the brain based on its enrichment for genes that are expressed in the brain and associated with intellectual disability. Here, we verify this hypothesis through partitioned heritability analysis of X-chromosome influences (XIs) on human brain anatomy in 32,256 individuals from the UK Biobank. We first establish evidence for dosage compensation in XIs on brain anatomy-reflecting larger XIs in males compared to females, which correlate with regional sex-biases in neuroanatomical variance. XIs are significantly larger than would be predicted from X-chromosome size for the relative surface area of cortical systems supporting attention, decision-making and motor control. Follow-up association analyses implicate X-linked genes with pleiotropic effects on cognition. Our study reveals a privileged role for the X-chromosome in human neurodevelopment and urges greater inclusion of this chromosome in future genome-wide association studies.

Educational attainment (EA) is influenced by cognitive abilities and by other characteristics and traits. However little is known about the genetic architecture of these “non-cognitive” contributions to EA. Here, we use Genomic Structural Equation Modelling and results of prior genome-wide association studies (GWASs) of EA (N = 1,131,881) and cognitive test performance (N = 257,841) to estimate SNP associations with variation in EA that is independent of cognitive ability. We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in EA. Phenotypic and biological annotation revealed that (1) both cognitive and non-cognitive contributions to EA were genetically correlated with socioeconomic success and longevity; and (2) non-cognitive contributions to EA were related to personality, decision making, risk-behavior, and increased risk for psychiatric disorders; (3) non-cognitive and cognitive contributions to EA were enriched in the same tissues and cell types, but (4) showed different associations with gray-matter neuroimaging phenotypes.

Sexual assault is a troubling epidemic that plagues college campuses across the United States, and is often proceeded by drinking by the perpetrator and/or victim. The goal of this study was to examine the effect of level of intoxication, history of alcohol-related blackouts, and childhood sexual abuse (CSA) on the likelihood of being a victim or perpetrator of coercive sexual activities. Participants (N = 2,244) were part of a 6-year longitudinal study which explored alcohol use and associated behavioral risks during college. A subsample (N = 1,423) completed 30 days of daily diary surveys across four years of college. Participants provided daily reports of their alcohol consumption, sexual coercion perpetration, and sexual coercion victimization. Using hierarchical linear models, results indicated that increases in daily estimated blood alcohol concentration (eBAC) were associated with a greater likelihood of being a victim and a perpetrator of sexual coercion. In addition, main effects of CSA and history of blackouts predicted a greater likelihood of being coerced into sexual activity, but blackouts were not associated with being a perpetrator. A significant interaction between blackouts and event-level eBAC indicated that individuals with a history of blackouts had a greater likelihood of sexual coercion victimization relative to those without prior blackouts. Finally, having a history of blackouts and CSA was predictive of a lower likelihood of being a perpetrator of sexual coercion at higher eBACs relative to those without a history of blackouts. Thus, prevention efforts should integrate the impact of blackouts and CSA on sexual coercion victimization and perpetration. (PsycINFO Database Record

GABRA2, the gene encoding the α2 subunit of the GABAA receptor, potentially plays a role in the etiology of problematic drinking, as GABRA2 genotype has been associated with subjective response to alcohol and other alcohol-related reward processes. The GABRA2 gene has also been associated with illicit drug use, but the extent to which associations with drug use are independent of associations with alcohol use remains unclear, partly because most previous research has used a cross-sectional design that cannot discriminate comorbidity at the between-person level and co-occurrence within-persons. The present study used a daily monitoring method that assessed the effects of GABRA2 variation on substance use as it occurred in the natural environment during emerging adulthood. Non-Hispanic European participants provided DNA samples and completed daily reports of alcohol and drug use for 1 month per year across 4 years (N = 28,263 unique observations of N = 318 participants). GABRA2 variants were associated with illicit drug use in both sober and intoxicated conditions. Moreover, the effect of GABRA2 variation on drug use was moderated by an individual's degree of intoxication. These findings are consistent with recent genetic and neuroscience research, and they suggest GABRA2 variation influences drug-seeking behavior through both alcohol-related and alcohol-independent pathways. (PsycINFO Database Record

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is rapidly growing. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the “additive SNP factor.” Regressions in which the true regressor is the additive SNP factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Previous studies have found preliminary evidence for associations between common single-nucleotide polymorphisms (SNPs) in the cannabinoid receptor gene CNR1 and cannabis use and dependence. The present study examined a set of eight independent SNPs in or near CNR1 in relation to cannabis use measured longitudinally across emerging adulthood.Using latent growth curve modeling of 10 waves of longitudinal data spanning mean ages 18.4-23.8 years in a sample of non-Hispanic White individuals (n = 334), we tested if genotype at each CNR1 SNP was associated with both level and growth of cannabis use over time. Peer group drug use, a known correlate of individual use, was evaluated as a time-varying predictor of cannabis use and as a moderator of the relationship between SNPs and individual use.After correction for multiple comparisons, one SNP, rs806374, was significantly associated with individual differences in level-but not growth-of cannabis use over time, such that C carriers were more likely to use cannabis more frequently at study onset (around age 18). Peer drug use was a predictor of individual cannabis use that grew in terms of effect size with time, but did not significantly moderate the effect of rs806374 genotype.C carriers at rs806374 may be at specific risk for increased odds of use during the transition out of high school (around age 18). Future studies should investigate potential mechanisms at this developmental stage, including individual differences in subjective response, innate tolerance, reinforcement mechanisms, or general liability for substance misuse.

Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores and identify loci that cause divergence between traits. We demonstrate several applications of genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs. Polygenic scores from genomic SEM consistently outperform those from univariate GWASs. Genomic SEM is flexible and open ended, and allows for continuous innovation in multivariate genetic analysis.

Though an association exists between sexual trauma and adolescent suicidal thoughts and behaviors (STBs), not all adolescents with a sexual assault history develop STBs. The present study examined whether carrier status of the DRD4 VNTR polymorphism (DRD4 L vs. DRD4S) moderated the relationship between sexual trauma and STBs in a sample of psychiatrically hospitalized adolescents. A total of 76 psychiatrically hospitalized adolescents provided saliva samples for genotyping procedures and completed assessment measures. Using hierarchical regression analyses, results indicated that DRD4 L carriers with a sexual trauma history reported significantly more severe suicidal ideation than DRD4S homozygotes. There was no interaction evident for the prediction of suicide attempts. To our knowledge, this is the first study to provide preliminary support for synergistic effects of sexual trauma, DRD4, and severity of SI in adolescents. Further studies of the relationship between sexual trauma, DRD4, and STBs utilizing larger samples are needed.

Background and objectives The variable number tandem repeats (VNTR) polymorphism of the dopamine D4 receptor gene (DRD4) has received considerable attention as a potential genetic contributor to addiction. However, is unclear whether the polymorphism is involved in developing general traits that lead to risky behavior or an intermediate phenotype more specific to substance use disorders. Association studies have produced equivocal results. To control for potential confounds, the present study examined whether the long variant of the DRD4 VNTR polymorphism (DRD4L) is associated with greater substance misuse in a homogenous clinical sample of youth with a disruptive behavior disorder (DBD). Methods Fifty-one psychiatrically hospitalized adolescents (mean age = 14.86 years) with a DBD diagnosis were recruited as part of a larger study. Participants provided saliva samples for genotyping procedures after completing a diagnostic interview and an assessment battery. Results The odds of a substance use disorder diagnosis were significantly greater among DRD4L than DRD4S carriers (OR = 5.20, 95%CI:1.42-19.04, p = .01). Relative to DRD4S homozygotes, DRD4L carriers also reported greater marijuana use (t = -2.68, p = .01) and hard drug use (t = -2.26, p = .03). Discussion and conclusions Although adolescents with DBDs are already at heightened risk for substance misuse, the present findings suggest that DRD4L further increases those odds. Scientific significance As differences persisted even among a psychiatrically homogenous sample of impulsive and risk-prone adolescents, the present findings suggest that DRD4L may be involved in the development of an intermediate phenotype specific to substance abuse (eg, cue-elicited craving).