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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.

Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of 1,025,550 individuals of European descent and 92,630 individuals of African descent. Nineteen independent SNPs were genome-wide significant (P < 5e-8) for the general addiction risk factor (addiction-rf), which showed high polygenicity. Across ancestries, PDE4B was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An addiction-rf polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targets.

Despite an estimated heritability of ~50%, genome-wide association studies of opioid use disorder (OUD) have revealed few genome-wide significant loci. We conducted a cross-ancestry meta-analysis of OUD in the Million Veteran Program (N = 425,944). In addition to known exonic variants in OPRM1 and FURIN, we identified intronic variants in RABEPK, FBXW4, NCAM1 and KCNN1. A meta-analysis including other datasets identified a locus in TSNARE1. In total, we identified 14 loci for OUD, 12 of which are novel. Significant genetic correlations were identified for 127 traits, including psychiatric disorders and other substance use-related traits. The only significantly enriched cell-type group was CNS, with gene expression enrichment in brain regions previously associated with substance use disorders. These findings increase our understanding of the biological basis of OUD and provide further evidence that it is a brain disease, which may help to reduce stigma and inform efforts to address the opioid epidemic.

Testing the association between genetic scores for Attention Deficit Hyperactivity Disorder (ADHD) and health conditions, can help us better understand its complex etiology. Electronic health records linked to genetic data provide an opportunity to test whether genetic scores for ADHD correlate with ADHD and additional health outcomes in a health care context across different age groups. We generated polygenic scores (ADHD-PGS) trained on summary statistics from the latest genome-wide association study of ADHD (N = 55,374) and applied them to genome-wide data from 12,383 unrelated individuals of African-American ancestry and 66,378 unrelated individuals of European ancestry from the Vanderbilt Biobank. Overall, only Tobacco use disorder (TUD) was associated with ADHD-PGS in the African-American ancestry group (Odds ratio [95% confidence intervals] = 1.23[1.16-1.31], p = 9.3 × 10-09 ). Eighty-six phenotypes were associated with ADHD-PGS in the European ancestry individuals, including ADHD (OR[95%CIs] = 1.22[1.16-1.29], p = 3.6 × 10-10 ), and TUD (OR[95%CIs] = 1.22[1.19-1.25], p = 2.8 × 10-46 ). We then stratified outcomes by age (ages 0-11, 12-18, 19-25, 26-40, 41-60, and 61-100). Our results suggest that ADHD polygenic scores are associated with ADHD diagnoses early in life and with an increasing number of health conditions throughout the lifespan (even in the absence of ADHD diagnosis). This study reinforces the utility of applying trait-specific PGSs to biobank data, and performing exploratory sensitivity analyses, to probe relationships among clinical conditions.

Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of psychopathology versus rarer forms of serious mental illness. Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest that the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind rather than merely in degree.

Posttraumatic stress disorder (PTSD) is a psychiatric disorder that may arise in response to severe traumatic event and is diagnosed based on three main symptom clusters (reexperiencing, avoidance, and hyperarousal) per the Diagnostic Manual of Mental Disorders (version DSM-IV-TR). In this study, we characterized the biological heterogeneity of PTSD symptom clusters by performing a multi-omics investigation integrating genetically regulated gene, splicing, and protein expression in dorsolateral prefrontal cortex tissue within a sample of US veterans enrolled in the Million Veteran Program (N total = 186,689). We identified 30 genes in 19 regions across the three PTSD symptom clusters. We found nine genes to have cell-type specific expression, and over-representation of miRNA-families - miR-148, 30, and 8. Gene-drug target prioritization approach highlighted cyclooxygenase and acetylcholine compounds. Next, we tested molecular-profile based phenome-wide impact of identified genes with respect to 1678 phenotypes derived from the Electronic Health Records of the Vanderbilt University biorepository (N = 70,439). Lastly, we tested for local genetic correlation across PTSD symptom clusters which highlighted metabolic (e.g., obesity, diabetes, vascular health) and laboratory traits (e.g., neutrophil, eosinophil, tau protein, creatinine kinase). Overall, this study finds comprehensive genomic evidence including clinical and regulatory profiles between PTSD, hematologic and cardiometabolic traits, that support comorbidities observed in epidemiologic studies of PTSD.

BackgroundIdentifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.MethodsHere, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.ResultsWe find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system.ConclusionsBy leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings.

Anorexia nervosa (AN) is a psychiatric disorder defined by anthropometric symptoms, such as low body weight, and cognitive-behavioral symptoms, such as restricted eating, fear of weight gain, and distorted body image. Recent studies have identified a genetic association between AN and metabolic/anthropometric factors, including body mass index (BMI). Although the reported associations may be under pleiotropic genetic influences, they may represent independent risk factors for AN. Here we examined the independent contributions of genetic predisposition to low body weight and polygenic risk (PRS) for AN in a clinical population (Vanderbilt University Medical Center biobank, BioVU). We fitted logistic and linear regression models in a retrospective case-control design (123 AN patients, 615 age-matched controls). We replicated the genetic correlations between PRSBMI and AN (p = 1.12 × 10-3, OR = 0.96), but this correlation disappeared when controlling for lowest BMI (p = 0.84, OR = 1.00). Additionally, we performed a phenome-wide association analysis of the PRSAN and found that the associations with metabolic phenotypes were attenuated when controlling for PRSBMI. These findings suggest that the genetic association between BMI and AN may be a consequence of the weight-related diagnostic criteria for AN and that genetically regulated anthropometric traits (like BMI) may be independent of AN psychopathology. If so, individuals with cognitive-behavioral symptomatology suggestive of AN, but with a higher PRSBMI, may be under-diagnosed given current diagnostic criteria. Furthermore, PRSBMI may serve as an independent risk factor for weight loss and weight gain during recovery.

ObjectiveGenome-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.MethodsTo 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.ResultsThe 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.ConclusionsThis 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.

The growing prevalence of opioid use disorder (OUD) constitutes an urgent health crisis. Ample evidence indicates that risk for OUD is heritable. As a surrogate (or proxy) for OUD, we explored the genetic basis of using prescription opioids 'not as prescribed'. We hypothesized that misuse of opiates might be a heritable risk factor for OUD. To test this hypothesis, we performed a genome-wide association study (GWAS) of problematic opioid use (POU) in 23andMe research participants of European ancestry (N = 132,113; 21% cases). We identified two genome-wide significant loci (rs3791033, an intronic variant of KDM4A; rs640561, an intergenic variant near LRRIQ3). POU showed positive genetic correlations with the two largest available GWAS of OUD and opioid dependence (rg = 0.64, 0.80, respectively). We also identified numerous additional genetic correlations with POU, including alcohol dependence (rg = 0.74), smoking initiation (rg = 0.63), pain relief medication intake (rg = 0.49), major depressive disorder (rg = 0.44), chronic pain (rg = 0.42), insomnia (rg = 0.39), and loneliness (rg = 0.28). Although POU was positively genetically correlated with risk-taking (rg = 0.38), conditioning POU on risk-taking did not substantially alter the magnitude or direction of these genetic correlations, suggesting that POU does not simply reflect a genetic tendency towards risky behavior. Lastly, we performed phenome- and lab-wide association analyses, which uncovered additional phenotypes that were associated with POU, including respiratory failure, insomnia, ischemic heart disease, and metabolic and blood-related biomarkers. We conclude that opioid misuse can be measured in population-based cohorts and provides a cost-effective complementary strategy for understanding the genetic basis of OUD.

Major depressive disorder (MDD) and loneliness are phenotypically and genetically correlated with coronary artery disease (CAD), but whether these associations are explained by pleiotropic genetic variants or shared comorbidities is unclear. To tease apart these scenarios, we first assessed the medical morbidity pattern associated with genetic risk factors for MDD and loneliness by conducting a phenome-wide association study in 18,385 European-ancestry individuals in the Vanderbilt University Medical Center biobank, BioVU. Polygenic scores for MDD and loneliness were developed for each person using previously published meta-GWAS summary statistics, and were tested for association with 882 clinical diagnoses ascertained via billing codes in electronic health records. We discovered strong associations with heart disease diagnoses, and next embarked on targeted analyses of CAD in 3893 cases and 4197 controls. We found odds ratios of 1.11 (95% CI, 1.04-1.18; P 8.43 × 10-4) and 1.13 (95% CI, 1.07-1.20; P 4.51 × 10-6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Results were similar in patients without psychiatric symptoms, and the increased risk persisted in females even after adjusting for multiple conventional risk factors and a polygenic score for CAD. In a final sensitivity analysis, we statistically adjusted for the genetic correlation between MDD and loneliness and re-computed polygenic scores. The polygenic score unique to loneliness remained associated with CAD (OR 1.09, 95% CI 1.03-1.15; P 0.002), while the polygenic score unique to MDD did not (OR 1.00, 95% CI 0.95-1.06; P 0.97). Our replication sample was the Atherosclerosis Risk in Communities (ARIC) cohort of 7197 European-ancestry participants (1598 incident CAD cases). In ARIC, polygenic scores for MDD and loneliness were associated with hazard ratios of 1.07 (95% CI, 0.99-1.14; P = 0.07) and 1.07 (1.01-1.15; P = 0.03), respectively, and we replicated findings from the BioVU sensitivity analyses. We conclude that genetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in females.

Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.

BackgroundThe origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations, we tested for a sex-differentiated genetic architecture within and between traits.MethodsUsing European ancestry genome-wide association summary statistics for 20 neuropsychiatric and behavioral traits, we tested for sex differences in single nucleotide polymorphism (SNP)-based heritability and genetic correlation (rg < 1). For each trait, we computed per-SNP z scores from sex-stratified regression coefficients and identified genes with sex-differentiated effects using a gene-based approach. We calculated correlation coefficients between z scores to test for shared sex-differentiated effects. Finally, we tested for sex differences in across-trait genetic correlations.ResultsWe observed no consistent sex differences in SNP-based heritability. Between-sex, within-trait genetic correlations were high, although

PurposeTo investigate the role of consumption phenotypes as genetic proxies for alcohol misuse and nicotine dependence.MethodsWe leveraged GWAS data from well-powered studies of consumption, alcohol misuse, and nicotine dependence phenotypes measured in individuals of European ancestry from the UK Biobank (UKB) and other population-based cohorts (largest total N = 263,954), and performed genetic correlations within a medical-center cohort, BioVU (N = 66,915). For alcohol, we used quantitative measures of consumption and misuse via AUDIT from UKB. For smoking, we used cigarettes per day from UKB and non-UKB cohorts comprising the GSCAN consortium, and nicotine dependence via ICD codes from UKB and Fagerström Test for Nicotine Dependence from non-UKB cohorts.ResultsIn a large phenome-wide association study, we show that smoking consumption and dependence phenotypes show similar strongly negatively associations with a plethora of diseases, whereas alcohol consumption shows patterns of genetic association that diverge from those of alcohol misuse.ConclusionsOur study suggests that cigarette smoking consumption, which can be easily measured in the general population, may be good a genetic proxy for nicotine dependence, whereas alcohol consumption is not a direct genetic proxy of alcohol misuse.

Structural brain changes along the lineage leading to modern Homo sapiens contributed to our distinctive cognitive and social abilities. However, the evolutionarily relevant molecular variants impacting key aspects of neuroanatomy are largely unknown. Here, we integrate evolutionary annotations of the genome at diverse timescales with common variant associations from large-scale neuroimaging genetic screens. We find that alleles with evidence of recent positive polygenic selection over the past 2000-3000 years are associated with increased surface area (SA) of the entire cortex, as well as specific regions, including those involved in spoken language and visual processing. Therefore, polygenic selective pressures impact the structure of specific cortical areas even over relatively recent timescales. Moreover, common sequence variation within human gained enhancers active in the prenatal cortex is associated with postnatal global SA. We show that such variation modulates the function of a regulatory element of the developmentally relevant transcription factor HEY2 in human neural progenitor cells and is associated with structural changes in the inferior frontal cortex. These results indicate that non-coding genomic regions active during prenatal cortical development are involved in the evolution of human brain structure and identify novel regulatory elements and genes impacting modern human brain structure.

Tourette syndrome (TS) is a neuropsychiatric disorder of complex genetic architecture involving multiple interacting genes. Here, we sought to elucidate the pathways that underlie the neurobiology of the disorder through genome-wide analysis. We analyzed genome-wide genotypic data of 3581 individuals with TS and 7682 ancestry-matched controls and investigated associations of TS with sets of genes that are expressed in particular cell types and operate in specific neuronal and glial functions. We employed a self-contained, set-based association method (SBA) as well as a competitive gene set method (MAGMA) using individual-level genotype data to perform a comprehensive investigation of the biological background of TS. Our SBA analysis identified three significant gene sets after Bonferroni correction, implicating ligand-gated ion channel signaling, lymphocytic, and cell adhesion and transsynaptic signaling processes. MAGMA analysis further supported the involvement of the cell adhesion and trans-synaptic signaling gene set. The lymphocytic gene set was driven by variants in FLT3, raising an intriguing hypothesis for the involvement of a neuroinflammatory element in TS pathogenesis. The indications of involvement of ligand-gated ion channel signaling reinforce the role of GABA in TS, while the association of cell adhesion and trans-synaptic signaling gene set provides additional support for the role of adhesion molecules in neuropsychiatric disorders. This study reinforces previous findings but also provides new insights into the neurobiology of TS.

BackgroundVariation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder.MethodsTo conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations.FindingsWe identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia.InterpretationThese findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder.FundingNational Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

Anorexia nervosa (AN) and obsessive-compulsive disorder (OCD) are often comorbid and likely to share genetic risk factors. Hence, we examine their shared genetic background using a cross-disorder GWAS meta-analysis of 3495 AN cases, 2688 OCD cases, and 18,013 controls. We confirmed a high genetic correlation between AN and OCD (rg = 0.49 ± 0.13, p = 9.07 × 10-7) and a sizable SNP heritability (SNP h2 = 0.21 ± 0.02) for the cross-disorder phenotype. Although no individual loci reached genome-wide significance, the cross-disorder phenotype showed strong positive genetic correlations with other psychiatric phenotypes (e.g., rg = 0.36 with bipolar disorder and 0.34 with neuroticism) and negative genetic correlations with metabolic phenotypes (e.g., rg = -0.25 with body mass index and -0.20 with triglycerides). Follow-up analyses revealed that although AN and OCD overlap heavily in their shared risk with other psychiatric phenotypes, the relationship with metabolic and anthropometric traits is markedly stronger for AN than for OCD. We further tested whether shared genetic risk for AN/OCD was associated with particular tissue or cell-type gene expression patterns and found that the basal ganglia and medium spiny neurons were most enriched for AN-OCD risk, consistent with neurobiological findings for both disorders. Our results confirm and extend genetic epidemiological findings of shared risk between AN and OCD and suggest that larger GWASs are warranted.

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies have identified PAU risk genes, the genetic architecture of this trait is not fully understood. We conducted a proxy-phenotype meta-analysis of PAU, combining alcohol use disorder and problematic drinking, in 435,563 European-ancestry individuals. We identified 29 independent risk variants, 19 of them novel. PAU was genetically correlated with 138 phenotypes, including substance use and psychiatric traits. Phenome-wide polygenic risk score analysis in an independent biobank sample (BioVU, n = 67,589) confirmed the genetic correlations between PAU and substance use and psychiatric disorders. Genetic heritability of PAU was enriched in brain and in conserved and regulatory genomic regions. Mendelian randomization suggested causal effects on liability to PAU of substance use, psychiatric status, risk-taking behavior and cognitive performance. In summary, this large PAU meta-analysis identified novel risk loci and revealed genetic relationships with numerous other traits.

Humans are social animals that experience intense suffering when they perceive a lack of social connection. Modern societies are experiencing an epidemic of loneliness. Although the experience of loneliness is universally human, some people report experiencing greater loneliness than others. Loneliness is more strongly associated with mortality than obesity, emphasizing the need to understand the nature of the relationship between loneliness and health. Although it is intuitive that circumstantial factors such as marital status and age influence loneliness, there is also compelling evidence of a genetic predisposition toward loneliness. To better understand the genetic architecture of loneliness and its relationship with associated outcomes, we extended the genome-wide association study meta-analysis of loneliness to 511 280 subjects, and detect 19 significant genetic variants from 16 loci, including four novel loci, as well as 58 significantly associated genes. We investigated the genetic overlap with a wide range of physical and mental health traits by computing genetic correlations and by building loneliness polygenic scores in an independent sample of 18 498 individuals with EHR data to conduct a PheWAS with. A genetic predisposition toward loneliness was associated with cardiovascular, psychiatric, and metabolic disorders and triglycerides and high-density lipoproteins. Mendelian randomization analyses showed evidence of a causal, increasing, the effect of both BMI and body fat on loneliness. Our results provide a framework for future studies of the genetic basis of loneliness and its relationship to mental and physical health.

Rare genetic variants can cause epilepsy, and genetic testing has been widely adopted for severe, paediatric-onset epilepsies. The phenotypic consequences of common genetic risk burden for epilepsies and their potential future clinical applications have not yet been determined. Using polygenic risk scores (PRS) from a European-ancestry genome-wide association study in generalized and focal epilepsy, we quantified common genetic burden in patients with generalized epilepsy (GE-PRS) or focal epilepsy (FE-PRS) from two independent non-Finnish European cohorts (Epi25 Consortium, n = 5705; Cleveland Clinic Epilepsy Center, n = 620; both compared to 20 435 controls). One Finnish-ancestry population isolate (Finnish-ancestry Epi25, n = 449; compared to 1559 controls), two European-ancestry biobanks (UK Biobank, n = 383 656; Vanderbilt biorepository, n = 49 494), and one Japanese-ancestry biobank (BioBank Japan, n = 168 680) were used for additional replications. Across 8386 patients with epilepsy and 622 212 population controls, we found and replicated significantly higher GE-PRS in patients with generalized epilepsy of European-ancestry compared to patients with focal epilepsy (Epi25: P = 1.64×10-15; Cleveland: P = 2.85×10-4; Finnish-ancestry Epi25: P = 1.80×10-4) or population controls (Epi25: P = 2.35×10-70; Cleveland: P = 1.43×10-7; Finnish-ancestry Epi25: P = 3.11×10-4; UK Biobank and Vanderbilt biorepository meta-analysis: P = 7.99×10-4). FE-PRS were significantly higher in patients with focal epilepsy compared to controls in the non-Finnish, non-biobank cohorts (Epi25: P = 5.74×10-19; Cleveland: P = 1.69×10-6). European ancestry-derived PRS did not predict generalized epilepsy or focal epilepsy in Japanese-ancestry individuals. Finally, we observed a significant 4.6-fold and a 4.5-fold enrichment of patients with generalized epilepsy compared to controls in the top 0.5% highest GE-PRS of the two non-Finnish European cohorts (Epi25: P = 2.60×10-15; Cleveland: P = 1.39×10-2). We conclude that common variant risk associated with epilepsy is significantly enriched in multiple cohorts of patients with epilepsy compared to controls-in particular for generalized epilepsy. As sample sizes and PRS accuracy continue to increase with further common variant discovery, PRS could complement established clinical biomarkers and augment genetic testing for patient classification, comorbidity research, and potentially targeted treatment.

Obsessive-compulsive disorder (OCD) is a highly heritable complex phenotype that demonstrates sex differences in age of onset and clinical presentation, suggesting a possible sex difference in underlying genetic architecture. We present the first genome-wide characterization of the sex-specific genetic architecture of OCD, utilizing the largest set of OCD cases and controls available from the Psychiatric Genomics Consortium. We assessed evidence for several mechanisms that may contribute to sex differences including a sex-dependent liability threshold, the presence of individual sex-specific risk variants on the autosomes and the X chromosome, and sex-specific pleiotropic effects. Furthermore, we tested the hypothesis that genetic heterogeneity between the sexes may obscure associations in a sex-combined genome-wide association study. We observed a strong genetic correlation between male and female OCD and no evidence for a sex-dependent liability threshold model, suggesting that sex-combined analysis does not suffer from widespread loss of power because of genetic heterogeneity between the sexes. While we did not detect any significant sex-specific genome-wide single nucleotide polymorphisms (SNP) associations, we did identify two significant gene-based associations in females: GRID2 and GRP135, which showed no association in males. We observed that the SNPs with sexually differentiated effects showed an enrichment of regulatory variants influencing expression of genes in brain and immune tissues. These findings suggest that future studies with larger sample sizes hold great promise for the identification of sex-specific genetic risk factors for OCD.

Several occurrences of the word 'schizophrenia' have been re-worded as 'liability to schizophrenia' or 'schizophrenia risk', including in the title, which should have been "GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability," as well as in Supplementary Figures 1-10 and Supplementary Tables 7-10, to more accurately reflect the findings of the work.

ObjectiveTourette's syndrome is polygenic and highly heritable. Genome-wide association study (GWAS) approaches are useful for interrogating the genetic architecture and determinants of Tourette's syndrome and other tic disorders. The authors conducted a GWAS meta-analysis and probed aggregated Tourette's syndrome polygenic risk to test whether Tourette's and related tic disorders have an underlying shared genetic etiology and whether Tourette's polygenic risk scores correlate with worst-ever tic severity and may represent a potential predictor of disease severity.MethodsGWAS meta-analysis, gene-based association, and genetic enrichment analyses were conducted in 4,819 Tourette's syndrome case subjects and 9,488 control subjects. Replication of top loci was conducted in an independent population-based sample (706 case subjects, 6,068 control subjects). Relationships between Tourette's polygenic risk scores (PRSs), other tic disorders, ascertainment, and tic severity were examined.ResultsGWAS and gene-based analyses identified one genome-wide significant locus within FLT3 on chromosome 13, rs2504235, although this association was not replicated in the population-based sample. Genetic variants spanning evolutionarily conserved regions significantly explained 92.4% of Tourette's syndrome heritability. Tourette's-associated genes were significantly preferentially expressed in dorsolateral prefrontal cortex. Tourette's PRS significantly predicted both Tourette's syndrome and tic spectrum disorders status in the population-based sample. Tourette's PRS also significantly correlated with worst-ever tic severity and was higher in case subjects with a family history of tics than in simplex case subjects.ConclusionsModulation of gene expression through noncoding variants, particularly within cortico-striatal circuits, is implicated as a fundamental mechanism in Tourette's syndrome pathogenesis. At a genetic level, tic disorders represent a continuous spectrum of disease, supporting the unification of Tourette's syndrome and other tic disorders in future diagnostic schemata. Tourette's PRSs derived from sufficiently large samples may be useful in the future for predicting conversion of transient tics to chronic tic disorders, as well as tic persistence and lifetime tic severity.

The author list was in the wrong order in the HTML version of the original article and in the HTML version of the original correction notice. This has been corrected to show the 23andMe Research Team as the fourth author and Abraham A. Palmer as the last author in both places.

Genetic factors contribute to the risk for developing alcohol use disorder (AUD). In collaboration with the genetics company 23andMe, Inc., we performed a genome-wide association study of the alcohol use disorder identification test (AUDIT), an instrument designed to screen for alcohol misuse over the past year. Our final sample consisted of 20 328 research participants of European ancestry (55.3% females; mean age = 53.8, SD = 16.1) who reported ever using alcohol. Our results showed that the 'chip-heritability' of AUDIT score, when treated as a continuous phenotype, was 12%. No loci reached genome-wide significance. The gene ADH1C, which has been previously implicated in AUD, was among our most significant associations (4.4 × 10-7 ; rs141973904). We also detected a suggestive association on chromosome 1 (2.1 × 10-7 ; rs182344113) near the gene KCNJ9, which has been implicated in mouse models of high ethanol drinking. Using linkage disequilibrium score regression, we identified positive genetic correlations between AUDIT score, high alcohol consumption and cigarette smoking. We also observed an unexpected positive genetic correlation between AUDIT and educational attainment and additional unexpected negative correlations with body mass index/obesity and attention-deficit/hyperactivity disorder. We conclude that conducting a genetic study using responses to an online questionnaire in a population not ascertained for AUD may represent a cost-effective strategy for elucidating aspects of the etiology of AUD.

Cerebrospinal fluid (CSF) levels of amyloid-β 42 (Aβ42) and tau have been evaluated as endophenotypes in Alzheimer's disease (AD) genetic studies. Although there are sex differences in AD risk, sex differences have not been evaluated in genetic studies of AD endophenotypes. We performed sex-stratified and sex interaction genetic analyses of CSF biomarkers to identify sex-specific associations. Data came from a previous genome-wide association study (GWAS) of CSF Aβ42 and tau (1527 males, 1509 females). We evaluated sex interactions at previous loci, performed sex-stratified GWAS to identify sex-specific associations, and evaluated sex interactions at sex-specific GWAS loci. We then evaluated sex-specific associations between prefrontal cortex (PFC) gene expression at relevant loci and autopsy measures of plaques and tangles using data from the Religious Orders Study and Rush Memory and Aging Project. In Aβ42, we observed sex interactions at one previous and one novel locus: rs316341 within SERPINB1 (p = 0.04) and rs13115400 near LINC00290 (p = 0.002). These loci showed stronger associations among females (β = - 0.03, p = 4.25 × 10-8; β = 0.03, p = 3.97 × 10-8) than males (β = - 0.02, p = 0.009; β = 0.01, p = 0.20). Higher levels of expression of SERPINB1, SERPINB6, and SERPINB9 in PFC was associated with higher levels of amyloidosis among females (corrected p values  0.38). In total tau, we observed a sex interaction at a previous locus, rs1393060 proximal to GMNC (p = 0.004), driven by a stronger association among females (β = 0.05, p = 4.57 × 10-10) compared to males (β = 0.02, p = 0.03). There was also a sex-specific association between rs1393060 and tangle density at autopsy (pfemale = 0.047; pmale = 0.96), and higher levels of expression of two genes within this locus were associated with lower tangle density among females (OSTN p = 0.006; CLDN16 p = 0.002) but not males (p ≥ 0.32). Results suggest a female-specific role for SERPINB1 in amyloidosis and for OSTN and CLDN16 in tau pathology. Sex-specific genetic analyses may improve understanding of AD's genetic architecture.

Purpose: Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities (Casey et al., 2016). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method: We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample (N = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N = 13,652 EHRs. Results: In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions: APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders.

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.