Karin J. H. Verweij

Substance use and psychiatric disorders frequently co-occur, but establishing causality of their relationships is difficult given the multifactorial nature of these traits. To assess these relationships, we applied two genetically informed methods using the largest available European-ancestry genome-wide association studies of six substance use behaviors and twelve psychiatric and personality phenotypes. Using Genomic Structural Equation Modelling, we modelled the latent genetic architecture of the psychiatric and personality traits and examined these factors’ correlations with substance use traits. A structure with three factors fit the data best: F1 (PTSD, MDD, GAD, ADHD, agreeableness), F2 (BD, SCZ, OCD, openness), and F3 (OCD, GAD, extraversion, neuroticism), which showed differential correlations with substance use. Smoking correlated significantly which each factor, however, strongest with F1, alcohol use showed weak but significant correlations with F1 and F2, and cannabis use showed moderate correlations with F1 and F2, but strongest with F2. Using Mendelian Randomization, we examined bidirectional causal relationships. We found strong evidence that smoking continuation was causally associated with increased bipolar disorder risk, cigarettes per day with increased schizophrenia, and smoking initiation with increased extraversion and decreased agreeableness. Genetic liability to schizophrenia and PTSD were causally associated with increased risk of cannabis use, and schizophrenia with increased alcohol use. Liability to neuroticism, openness and agreeableness was causally linked to smoking behaviors, and extraversion to cannabis use. Most remaining associations showed weak causal evidence, largely due to heterogeneity and pleiotropy. Together, these findings provide evidence for both shared genetic liability and, for a subset of relationships, direct causal effects between substance use behaviors, psychiatric disorders, and personality traits, which can help inform future intervention and prevention research.

Studying the genetics of intelligence provides insight into the neurobiology of cognitive function and rare neurodevelopmental conditions. Here we integrated fluid intelligence (FI) tests across different UK Biobank measures and imputed FI for unmeasured individuals, increasing the sample size from ~270,000 to >455,000. We show through extensive validation that the imputed phenotype genetically resembles measured FI, although some noncognitive signal likely remains. Combining measured and imputed FI increases the number of independent significant common SNP associations from 390 to 550 and reduces the ascertainment bias introduced by FI test participants being, on average, more educated. Rare variant analyses identify 26 significantly associated genes (false discovery rate < 1%), including 8 without strong prior evidence of involvement in intelligence or neurodevelopmental conditions, which replicate in aggregate in external cohorts. Our results demonstrate that imputed intelligence can boost power for genetic discovery and support its use to help discover new neurodevelopmental condition-associated genes.

Romantic (typically sexual) relationships are important to personal, physical, mental, social, and economic well-being, and to human evolution. Yet little is known about factors contributing to long-term lack of intimate relationships. We investigated phenotypic and genetic correlates of never having had sex in ~400,000 UK residents aged 39 to 73 and ~13,500 Australian residents aged 18 to 89. The strongest associations revealed that sexless individuals were more educated, less likely to use alcohol and smoke, more nervous, lonelier, and unhappier. Sexlessness was more strongly associated with physical characteristics (e.g., upper body strength) in men than in women. Sexless men tended to live in regions with fewer women, and sexlessness was more prevalent in regions with more income inequality. Common genetic variants explained 17% (SE = 4%) and 14% (SE = 3%) of variation in sexlessness in men and women, with a genetic correlation between sexes of 0.56 (SE = 0.17). Polygenic scores predicted a range of related outcomes in the Australian dataset. Our findings uncover multifaceted correlates of human intimacy and raise important lines of enquiry in the evolutionary and social sciences.

Studying the genetics of measures of intelligence can help us understand the neurobiology of cognitive function and the aetiology of rare neurodevelopmental conditions. The largest previous genetic studies of measures of intelligence have used ~270k individuals who completed the fluid intelligence (FI) test in UK Biobank. Here, we integrate additional FI measures in this cohort and leverage eighty-two correlated variables to impute FI values for unmeasured individuals, increasing the sample size to >450k. Through population-based and within-family genome-wide association studies and downstream analyses, we show that this imputation produces a phenotype that genetically resembles measured FI and reduces ascertainment bias within the cohort. We further show that combining measured and imputed FI scores increases the number of independent SNP associations (p-8) from 385 to 608 and increases polygenic score accuracy in external cohorts by 15% on average. Additionally, incorporating imputed FI scores increases the number of gene-level associations with rare variants from five to twenty-six (FDR-8); for several of these, our results suggest that loss-of-function variants in the gene impact neurodevelopment, in addition to the previously documented altered-function variants. We also implicate twelve genes without strong prior evidence of association developmental disorders, of which eight have not been previously linked to intelligence (ROBO2, RB1CC1, ANK3, CHD9, TLK1, PCLO, DPP8, IPO9). These twelve genes were significantly enriched for de novo loss-of-function mutations in a set of >31k patients with developmental disorders (p=6.8x10^-4). We further identify three genes showing significant rare variant associations with educational attainment but not with FI, including CADPS2 in which, unusually, protein-truncating variants show a positive association. Our results demonstrate the power of phenotype imputation for genetic studies and suggest that incorporating genetic association results for cognitive phenotypes in the general population could help discover new developmental disorder genes.

Objective Observational studies consistently link physical activity (PA) to better brain health; psychiatric, neurocognitive, and mental well-being outcomes. While PA intervention studies generally find positive mental health effects, it is unclear whether day-to-day PA, not manipulated through an intervention, is beneficial. Design/Data Systematic review of Mendelian Randomization (MR) studies using genetic instrumental variables to assess causal effects of PA on brain health. In Embase and medRxiv, 557 articles were identified, of which 35 met inclusion criteria. Study quality was determined based on a MR-specific scoring system. Eligibility criteria MR studies exploring any PA exposure (self-reported PA, sedentary behaviours, strength-related traits) on mental health, cognition, or cognition-based neurodegenerative outcomes (e.g., Alzheimer’s). Results Of 35 studies, 43% received a low quality score and 57% a moderate score. There was no consistent evidence for causal effects of day-to-day PA. For sedentary behaviours (e.g., TV-watching) there was consistent evidence for increasing effects on depression. For strength-related traits (e.g., grip-strength) there was consistent evidence for protective effects, particularly on cognitive outcomes. For depression specifically, device-measured PA more often showed protective effects on depression than self-reported PA. Conclusion MR studies on PA and brain health are generally lacking in quality, due to low sample sizes and/or poorly measured variables. Results are mixed with the most consistent evidence indicating that better physical strength and less sedentary behaviour are beneficial for brain health. Since core MR assumptions are difficult to fulfill for highly complex PA traits, triangulation with other methods and improved phenotyping is needed in future work. What is already known? The prevalence of the most common mental health disorders (i.e., depression, anxiety) has been increasing in the last 20 years in the United States and Europe. bservational studies report a consistent association between physical activity and brain health outcomes, but the high risk of confounding precludes a causal interpretation. Physical activity intervention studies find positive mental health effects, but do not show whether day-to-day physical activity, not manipulated through an intervention, is beneficial. What are the new findings? verall, the Mendelian randomization literature offers limited support for a broadly protective effect of physical activity. Self-reported physical activity showed inconsistent associations with depression, schizophrenia, and bipolar disorder, possibly due to misclassification and confounding. behaviours were more consistently associated with negative mental health outcomes, particularly depression. Strength-related traits, such as handgrip strength and appendicular lean mass, showed the most consistent and strong, protective effects, especially on cognitive outcomes. Randomization relies on core assumptions that are difficult to fulfill for highly complex traits, like physical activity. Triangulation with other methods and improved phenotyping is needed in future work.

Studying the genetics of intelligence can help us understand the neurobiology of cognitive function and the aetiology of rare neurodevelopmental conditions. The largest previous genetic studies of intelligence have used ~270k individuals who completed the fluid intelligence (FI) tests in UK Biobank. Here, we integrate additional FI measures in this cohort and leverage 82 correlated variables to impute FI values for unmeasured individuals, increasing the sample size to >450k. Through population-based and within-family genome-wide association studies and genetic correlation analyses, we show that this imputation produces a phenotype that genetically resembles measured FI, as well as reduces ascertainment bias within the cohort. We further show that combining measured and imputed FI scores increases the number of independent SNP associations (p<5x10-8) from 385 to 608 and increases polygenic score accuracy in external cohorts by 15% on average. Additionally, incorporating imputed FI scores increases the number of gene-level associations with rare variants from five to twenty-six (FDR31k patients with developmental disorders (p=6.8x10-4). We further identify three genes showing significant rare variant associations with educational attainment but not with FI in UK Biobank, including CADPS2 in which, unusually, protein-truncating variants show a positive association. Our results demonstrate the power of phenotype imputation for genetic studies and suggest that incorporating genetic association results for cognitive phenotypes in the general population could help discover new developmental disorder genes.

Obsessive–compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D₁ and D₂ type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index.

In civilizations, individuals are born into or sorted into different levels of socio-economic status (SES). SES clusters in families and geographically, and is robustly associated with genetic effects. Here we first review the history of scientific research on the relationship between SES and heredity. We then discuss recent findings in genomics research in light of the hypothesis that SES is a dynamic social construct that involves genetically influenced traits that help in achieving or retaining a socio-economic position, and can affect the distribution of genes associated with such traits. Social stratification results in people with differing traits being sorted into strata with different environmental exposures, which can result in evolutionary selection pressures through differences in mortality, reproduction and non-random mating. Genomics research is revealing previously concealed genetic consequences of the way society is organized, yielding insights that should be approached with caution in pursuit of a fair and functional society.

Experiencing a traumatic event may lead to Posttraumatic Stress Disorder (PTSD), including symptoms such as flashbacks and hyperarousal. Individuals suffering from PTSD are at increased risk of cardiovascular disease (CVD), but it is unclear why. This study assesses shared genetic liability and potential causal pathways between PTSD and CVD. We leveraged summary-level data of genome-wide association studies (PTSD: N = 1,222,882; atrial fibrillation (AF): N = 482,409; coronary artery disease (CAD): N = 1,165,690; hypertension (HT): N = 458,554; heart failure (HF): N = 977,323). First, we estimated genetic correlations and utilized genomic structural equation modeling to identify a common genetic factor for PTSD and CVD. Next, we assessed biological, behavioural, and psychosocial factors as potential mediators. Finally, we employed multivariable Mendelian randomization to examine causal pathways between PTSD and CVD, incorporating the same potential mediators. Significant genetic correlations were found between PTSD and CAD, HT, and HF (r_g = 0.21-0.32, p ≤ 3.08 · 10⁻¹⁶), but not between PTSD and AF. Insomnia, smoking, alcohol dependence, waist-to-hip ratio, and inflammation (IL6, C-reactive protein) partly mediated these associations. Mendelian randomization indicated that PTSD causally increases CAD (IVW OR = 1.53, 95% CIs = 1.19-1.96, p = 0.001), HF (OR = 1.44, CIs = 1.08-1.92, p = 0.012), and to a lesser degree HT (OR = 1.25, CIs = 1.05-1.49, p = 0.012). While insomnia, smoking, alcohol, and inflammation were important mediators, independent causal effects also remained. In addition to shared genetic liability between PTSD and CVD, we present strong evidence for causal effects of PTSD on CVD. Crucially, we implicate specific lifestyle and biological mediators (insomnia, substance use, inflammation) which has important implications for interventions to prevent CVD in PTSD patients.