The impact of 22q11.2 copy-number variants on human traits in the general population.

While extensively studied in clinical cohorts, the phenotypic consequences of 22q11.2 copy-number variants (CNVs) in the general population remain understudied. To address this gap, we performed a phenome-wide association scan in 405,324 unrelated UK Biobank (UKBB) participants by using CNV calls from genotyping array. We mapped 236 Human Phenotype Ontology terms linked to any of the 90 genes encompassed by the region to 170 UKBB traits and assessed the association between these traits and the copy-number state of 504 genotyping array probes in the region. We found significant associations for eight continuous and nine binary traits associated under different models (duplication-only, deletion-only, U-shape, and mirror models). The causal effect of the expression level of 22q11.2 genes on associated traits was assessed through transcriptome-wide Mendelian randomization (TWMR), revealing that increased expression of ARVCF increased BMI. Similarly, increased DGCR6 expression causally reduced mean platelet volume, in line with the corresponding CNV effect. Furthermore, cross-trait multivariable Mendelian randomization (MVMR) suggested a predominant role of genuine (horizontal) pleiotropy in the CNV region. Our findings show that within the general population, 22q11.2 CNVs are associated with traits previously linked to genes in the region, and duplications and deletions act upon traits in different fashions. We also showed that gain or loss of distinct segments within 22q11.2 may impact a trait under different association models. Our results have provided new insights to help further the understanding of the complex 22q11.2 region.

Robust genetic nurture effects on education: A systematic review and meta-analysis based on 38,654 families across 8 cohorts.

Similarities between parents and offspring arise from nature and nurture. Beyond this simple dichotomy, recent genomic studies have uncovered "genetic nurture" effects, whereby parental genotypes influence offspring outcomes via environmental pathways rather than genetic transmission. Such genetic nurture effects also need to be accounted for to accurately estimate "direct" genetic effects (i.e., genetic effects on a trait originating in the offspring). Empirical studies have indicated that genetic nurture effects are particularly relevant to the intergenerational transmission of risk for child educational outcomes, which are, in turn, associated with major psychological and health milestones throughout the life course. These findings have yet to be systematically appraised across contexts. We conducted a systematic review and meta-analysis to quantify genetic nurture effects on educational outcomes. A total of 12 studies comprising 38,654 distinct parent(s)-offspring pairs or trios from 8 cohorts reported 22 estimates of genetic nurture effects. Genetic nurture effects on offspring's educational outcomes (ßgenetic nurture = 0.08, 95% CI [0.07, 0.09]) were smaller than direct genetic effects (ßdirect genetic = 0.17, 95% CI [0.13, 0.20]). Findings were largely consistent across studies. Genetic nurture effects originating from mothers and fathers were of similar magnitude, highlighting the need for a greater inclusion of fathers in educational research. Genetic nurture effects were largely explained by observed parental education and socioeconomic status, pointing to their role in environmental pathways shaping child educational outcomes. Findings provide consistent evidence that environmentally mediated parental genetic influences contribute to the intergenerational transmission of educational outcomes, in addition to effects due to genetic transmission.

Using genetic data to strengthen causal inference in observational research.

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations.

Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes - child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely.

Cannabis use and atherosclerotic cardiovascular disease: a Mendelian randomization study.

BACKGROUND: Association between cannabis use and development of atherosclerotic cardiovascular disease (ASCVD) is inconsistent and challenging to interpret, given existing study limitations. METHODS: Sixty five independent single-nucleotide polymorphisms (SNPs), obtained from a genome-wide association study on lifetime cannabis use, were employed as genetic instruments to estimate the effects of genetically indexed cannabis use on risk of coronary artery disease (CAD) and acute ischemic stroke (IS) using a two-sample Mendelian randomization (MR) approach. Summary statistics on CAD (CARDIoGRAMplusC4D; 60,801 cases and 123,504 controls) and IS (MEGASTROKE; 34,217 cases and 406,111 controls) were obtained separately. A comprehensive review of the observational literature on cannabis use and CAD or IS was also performed and contrasted with MR results. RESULTS: There was no causal effect of cannabis use on the risk of CAD (odds ratio (OR) per ever-users vs. never-users 0.93; 95% confidence interval (CI), 0.83 to 1.03) or IS (OR 1.05; 95%CI, 0.93 to 1.19). Sensitivity analyses yielded similar results, and no heterogeneity and directional pleiotropy was observed. Our meta-analysis of observational studies showed no significant association between ever use of cannabis with risk of CAD (k = 6 studies; ORpooled = 1.23, 95%CI 0.78 to 1.69), nor with IS (k = 6 studies; ORpooled = 1.22, 95%CI 0.95 to 1.50). CONCLUSION: Using a genetic approach approximating a clinical trial does not provide evidence consistent with a causal effect of genetic predisposition to cannabis use on CAD or IS development. Further studies are needed to replicate our findinds, an to investigate more precisely the risk of ASCVD in relation to the quantity, type, route of administration, or the age at exposure to cannabis.

Protecting against researcher bias in secondary data analysis: challenges and potential solutions.

Analysis of secondary data sources (such as cohort studies, survey data, and administrative records) has the potential to provide answers to science and society's most pressing questions. However, researcher biases can lead to questionable research practices in secondary data analysis, which can distort the evidence base. While pre-registration can help to protect against researcher biases, it presents challenges for secondary data analysis. In this article, we describe these challenges and propose novel solutions and alternative approaches. Proposed solutions include approaches to (1) address bias linked to prior knowledge of the data, (2) enable pre-registration of non-hypothesis-driven research, (3) help ensure that pre-registered analyses will be appropriate for the data, and (4) address difficulties arising from reduced analytic flexibility in pre-registration. For each solution, we provide guidance on implementation for researchers and data guardians. The adoption of these practices can help to protect against researcher bias in secondary data analysis, to improve the robustness of research based on existing data.

Cyber-victimisation and mental health in young people: a co-twin control study.

BACKGROUND: The rise of social media use in young people has sparked concern about the impact of cyber-victimisation on mental health. Although cyber-victimisation is associated with mental health problems, it is not known whether such associations reflect genetic and environmental confounding. METHODS: We used the co-twin control design to test the direct association between cyber-victimisation and multiple domains of mental health in young people. Participants were 7708 twins drawn from the Twins Early Development Study, a UK-based population cohort followed from birth to age 22. RESULTS: Monozygotic twins exposed to greater levels of cyber-victimisation had more symptoms of internalising, externalising and psychotic disorders than their less victimised co-twins at age 22, even after accounting for face-to-face peer victimisation and prior mental health. However, effect sizes from the most stringent monozygotic co-twin control analyses were decreased by two thirds from associations at the individual level [pooled ß across all mental health problems = 0.06 (95% CI 0.03-0.10) v. 0.17 (95% CI 0.15-0.19) in individual-level analyses]. CONCLUSIONS: Cyber-victimisation has a small direct association with multiple mental health problems in young people. However, a large part of the association between cyber-victimisation and mental health is due to pre-existing genetic and environmental vulnerabilities and co-occurring face-to-face victimisation. Therefore, preventative interventions should target cyber-victimisation in conjunction with pre-existing mental health vulnerabilities and other forms of victimisation.

Combining multivariate genomic approaches to elucidate the comorbidity between autism spectrum disorder and attention deficit hyperactivity disorder.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are two highly heritable neurodevelopmental disorders. Several lines of evidence point towards the presence of shared genetic factors underlying ASD and ADHD. We conducted genomic analyses of common risk variants (i.e. single nucleotide polymorphisms, SNPs) shared by ASD and ADHD, and those specific to each disorder. METHODS: With the summary data from two GWAS, one on ASD (N = 46,350) and another on ADHD (N = 55,374) individuals, we used genomic structural equation modelling and colocalization analysis to identify SNPs shared by ASD and ADHD and SNPs specific to each disorder. Functional genomic analyses were then conducted on shared and specific common genetic variants. Finally, we performed a bidirectional Mendelian randomization analysis to test whether the shared genetic risk between ASD and ADHD was interpretable in terms of reciprocal relationships between ASD and ADHD. RESULTS: We found that 37.5% of the SNPs associated with ASD (at p < 1e-6) colocalized with ADHD SNPs and that 19.6% of the SNPs associated with ADHD colocalized with ASD SNPs. We identified genes mapped to SNPs that are specific to ASD or ADHD and that are shared by ASD and ADHD, including two novel genes INSM1 and PAX1. Our bidirectional Mendelian randomization analyses indicated that the risk of ASD was associated with an increased risk of ADHD and vice versa. CONCLUSIONS: Using multivariate genomic analyses, the present study uncovers shared and specific genetic variants associated with ASD and ADHD. Further functional investigation of genes mapped to those shared variants may help identify pathophysiological pathways and new targets for treatment.

Identifying risk factors involved in the common versus specific liabilities to substance use: A genetically informed approach.

Individuals most often use several rather than one substance among alcohol, cigarettes or cannabis. This widespread co-occurring use of multiple substances is thought to stem from a common liability that is partly genetic in origin. Genetic risk may indirectly contribute to a common liability to substance use through genetically influenced mental health vulnerabilities and individual traits. To test this possibility, we used polygenic scores indexing mental health and individual traits and examined their association with the common versus specific liabilities to substance use. We used data from the Avon Longitudinal Study of Parents and Children (N = 4218) and applied trait-state-occasion models to delineate the common and substance-specific factors based on four classes of substances (alcohol, cigarettes, cannabis and other illicit substances) assessed over time (ages 17, 20 and 22). We generated 18 polygenic scores indexing genetically influenced mental health vulnerabilities and individual traits. In multivariable regression, we then tested the independent contribution of selected polygenic scores to the common and substance-specific factors. Our results implicated several genetically influenced traits and vulnerabilities in the common liability to substance use, most notably risk taking (bstandardised = 0.14; 95% confidence interval [CI] [0.10, 0.17]), followed by extraversion (bstandardised = -0.10; 95% CI [-0.13, -0.06]), and schizophrenia risk (bstandardised = 0.06; 95% CI [0.02, 0.09]). Educational attainment (EA) and body mass index (BMI) had opposite effects on substance-specific liabilities such as cigarette use (bstandardised-EA = -0.15; 95% CI [-0.19, -0.12]; bstandardised-BMI = 0.05; 95% CI [0.02, 0.09]) and alcohol use (bstandardised-EA = 0.07; 95% CI [0.03, 0.11]; bstandardised-BMI = -0.06; 95% CI [-0.10, -0.02]). These findings point towards largely distinct sets of genetic influences on the common versus specific liabilities.

Developmental sensitivity to cannabis use patterns and risk for major depressive disorder in mid-life: findings from 40 years of follow-up.

BACKGROUND: Evidence regarding the association between cannabis use and depression remain conflicting, especially as studies have not typically adopted a longitudinal design with a follow-up period that was long enough to adequately cover the risk period for onset of depression. METHOD: Males from the Cambridge Study in Delinquent Development (CSDD) (N = 285) were assessed seven times from age 8 to 48 years to prospectively investigate the association between cannabis use and risk of major depressive disorder (MDD). A combination of multiple analyses (logistic regression, Cox regression, fixed-effects analysis) was employed to explore the strength and direction of effect within different developmental stages. RESULTS: Multiple regression analyses revealed that early-onset cannabis use (before age 18) but not late-onset cannabis use (after age 27) was associated with a higher risk and shorter time until a subsequent MDD diagnosis. This effect was present in high-frequency [(odds ratio (OR) 8.83, 95% confidence interval (CI) 1.29-70.79]; [hazard ratio (HR) 8.69, 95% CI 2.07-36.52)] and low-frequency early-onset users (OR 2.41, 95% CI 1.22-4.76; HR 2.09, 95% CI 1.16-3.74). Effect of increased frequency of cannabis use on increased risk of subsequent MDD was observed only for use during adolescence (age 14-18) but not at later life stages, while controlling for observed and non-unobserved time-invariant factors. Conversely, MDD in adulthood (age 18-32) was linked to a reduction in subsequent cannabis use (age 32-48). CONCLUSIONS: The present findings provide evidence implicating frequent cannabis use during adolescence as a risk factor for later life depression. Future studies should further examine causality of effects in larger samples.

The role of birth weight on the causal pathway to child and adolescent ADHD symptomatology: a population-based twin differences longitudinal design.

BACKGROUND: Available evidence points towards lower birth weight as a risk factor for the development of attention deficit/hyperactivity disorder (ADHD) symptoms. We probed the causal nature of this putative effect of birth weight on ADHD symptoms using the twin differences design, which accounts for genetic and shared environmental confounds. METHOD: In a large population-based twin sample - 3,499 monozygotic (MZ) and 6,698 dizygotic (DZ) pairs - parents, teachers or twins rated the twins' ADHD symptoms at nine assessment waves (2-16 years). We implemented the twin differences design, which completely accounts for shared environmental and genetic confounding in MZ twins. We tested whether: (a) the lighter-born twins had elevated ADHD symptoms compared to the heavier-born twins, by regressing within-pair differences of ADHD symptoms on within-pair differences of birth weight among MZ twins; (b) the effect of birth weight on ADHD was moderated by gender, gestational age and low birth weight; (c) this effect changed with age at ADHD assessment using adapted latent growth curve models; and (d) results differed for inattention and hyperactivity/impulsivity. RESULTS: Birth weight significantly predicted ADHD symptoms from early childhood to late adolescence. The lighter-born twin had more ADHD symptoms than the heavier-born cotwin among MZ twins across assessment waves and raters. No moderation effect was detected. The magnitude of the effect of birth weight decreased significantly across time for hyperactivity/impulsivity, but the decrease failed to reach significance for inattention. Estimates for inattention were significantly larger than for hyperactivity/impulsivity at each time point, implying stronger effect of birth weight on inattention symptoms. CONCLUSIONS: Our findings provide stringent evidence for environmental effect of lower birth weight on the causal pathway to elevated ADHD symptoms. Effect of birth weight persists across a 14-year period from childhood into late adolescence, in particular for inattention symptoms.

Can cognitive insight predict symptom remission in a first episode psychosis cohort?

BACKGROUND: The outcome of first episode psychosis (FEP) is highly variable and difficult to predict. Cognitive insight measured at illness onset has previously been found to predict psychopathology 12-months later. The aims of this study were to examine whether the prospective relationship between cognitive insight and symptom severity is evident at four-years following FEP and to examine some psychological correlates of cognitive insight. METHODS: FEP participants (n = 90) completed the Beck Cognitive Insight Scale (BCIS) at illness onset, and associations between BCIS scores with symptom severity outcomes (4-years after FEP) were assessed. The BCIS scales (self-reflectiveness and self-certainty) were examined as a composite score, and individually compared to other cognitive measures (IQ and jumping to conclusions (JTC) bias). RESULTS: Regression analyses revealed that the cognitive insight composite did not predict 4-year symptom remission in this study while the self-reflection subscale of the BCIS predicted severity of symptoms at 4-years. Self-certainty items of the BCIS were not associated with symptom severity. Significant correlations between the JTC bias, self-certainty and IQ were found, but self-reflection did not correlate with these other cognitive measures. CONCLUSIONS: Self-reflective capacity is a more relevant and independent cognitive construct than self-certainty for predicting prospective symptom severity in psychosis. Improving self-reflection may be a useful target for early intervention research.

Assessing rates and predictors of cannabis-associated psychotic symptoms across observational, experimental and medical research.

Cannabis, one of the most widely used psychoactive substances worldwide, can give rise to acute cannabis-associated psychotic symptoms (CAPS). While distinct study designs have been used to examine CAPS, an overarching synthesis of the existing findings has not yet been carried forward. To that end, we quantitatively pooled the evidence on rates and predictors of CAPS (k = 162 studies, n = 210,283 cannabis-exposed individuals) as studied in (1) observational research, (2) experimental tetrahydrocannabinol (THC) studies, and (3) medicinal cannabis research. We found that rates of CAPS varied substantially across the study designs, given the high rates reported by observational and experimental research (19% and 21%, respectively) but not medicinal cannabis studies (2%). CAPS was predicted by THC administration (for example, single dose, Cohen's d = 0.7), mental health liabilities (for example, bipolar disorder, d = 0.8), dopamine activity (d = 0.4), younger age (d = -0.2), and female gender (d = -0.09). Neither candidate genes (for example, COMT, AKT1) nor other demographic variables (for example, education) predicted CAPS in meta-analytical models. The results reinforce the need to more closely monitor adverse cannabis-related outcomes in vulnerable individuals as these individuals may benefit most from harm-reduction efforts.

Understanding the relationship between loneliness, substance use traits and psychiatric disorders: A genetically informed approach.

Loneliness is a common, yet distressing experience associated with adverse outcomes including substance use problems and psychiatric disorders. To what extent these associations reflect genetic correlations and causal relationships is currently unclear. We applied Genomic Structural Equation Modelling (GSEM) to dissect the genetic architecture between loneliness and psychiatric-behavioural traits. Included were summary statistics from 12 genome-wide association analyses, including loneliness and 11 psychiatric phenotypes (range N: 9,537 - 807,553). We first modelled latent genetic factors amongst the psychiatric traits to then investigate potential causal effects between loneliness and the identified latent factors, using multivariate genome-wide association analyses and bidirectional Mendelian randomization. We identified three latent genetic factors, encompassing neurodevelopmental/mood conditions, substance use traits and disorders with psychotic features. GSEM provided evidence of a unique association between loneliness and the neurodevelopmental/mood conditions latent factor. Mendelian randomization results were suggestive of bidirectional causal effects between loneliness and the neurodevelopmental/mood conditions factor. These results imply that a genetic predisposition to loneliness may elevate the risk of neurodevelopmental/mood conditions, and vice versa. However, results may reflect the difficulty of distiguishing between loneliness and neurodevelopmental/mood conditions, which present in similar ways. We suggest, overall, the importance of addressing loneliness in mental health prevention and policy.

Participation bias in the UK Biobank distorts genetic associations and downstream analyses.

While volunteer-based studies such as the UK Biobank have become the cornerstone of genetic epidemiology, the participating individuals are rarely representative of their target population. To evaluate the impact of selective participation, here we derived UK Biobank participation probabilities on the basis of 14 variables harmonized across the UK Biobank and a representative sample. We then conducted weighted genome-wide association analyses on 19 traits. Comparing the output from weighted genome-wide association analyses (neffective = 94,643 to 102,215) with that from standard genome-wide association analyses (n = 263,464 to 283,749), we found that increasing representativeness led to changes in SNP effect sizes and identified novel SNP associations for 12 traits. While heritability estimates were less impacted by weighting (maximum change in h2, 5%), we found substantial discrepancies for genetic correlations (maximum change in rg, 0.31) and Mendelian randomization estimates (maximum change in ßSTD, 0.15) for socio-behavioural traits. We urge the field to increase representativeness in biobank samples, especially when studying genetic correlates of behaviour, lifestyles and social outcomes.

Stressful life events and relapse of psychosis: analysis of causal association in a 2-year prospective observational cohort of individuals with first-episode psychosis in the UK.

BACKGROUND: Despite accumulating evidence of an association between stressful life events and psychosis relapse, the extent to which this is a causal relationship remains unclear. We aimed to examine the association between exposure to, and number of, stressful life events after initial psychosis onset and psychosis relapse. METHODS: In this 2-year prospective observational study, we recruited individuals with first-episode psychosis, aged 18-65 years, who presented to psychiatric services in south London, UK. Participants were assessed via interview, with additional data obtained from electronic clinical records. Stressful life events were recorded at psychosis onset and during the 2-year follow-up using a brief questionnaire that assesses 12 major life events. Psychosis relapse was defined as inpatient admission because of symptom exacerbation within 2 years from psychosis onset. We examined the time to first psychosis relapse and the number and length of relapses using survival and binomial regression analyses. We used fixed-effects regression and cross-lagged path analysis to examine the directionality of effects and control for unmeasured confounders. FINDINGS: Between April 12, 2002, and July 26, 2013, 256 individuals with first-episode psychosis (100 [39%] female and 156 [61%] male; 16 [6%] Asian, 140 [55%] Black African or Caribbean, 86 [34%] White, and 14 [6%] mixed ethnicity) were recruited, with a mean age of onset of psychosis of 28·06 years (SD 8·03; range 17·21-56·03). 93 (36%) participants experienced at least one relapse during the 2-year follow-up. 253 individuals had all relevant data and were included in analyses. For people exposed to stressful life events after the onset of psychosis, the adjusted hazard (hazard ratio [HR] 2·60, 95% CI 1·63-4·16, p<0·0001), incidence (incidence rate ratio [IRR] 1·87, 1·24-2·80, p=0·0026), and length (IRR 2·53, 1·40-4·67, p=0·0011) of relapse were greater than for those who were unexposed. These relationships were dose dependent (HR 1·36; 1·09-1·69, p=0·0054; incidence IRR 1·26, 1·02-1·53, p=0·023; length IRR 1·52, 1·12-2·12, p=0·0028). Adjusted fixed-effects models showed a higher (odds ratio [OR] 3·82, 1·82-8·00, p=0·0004) and dose-dependent (OR 1·62, 1·18-2·21, p=0·0028) risk of relapse when stressful life events preceded relapse compared with the period when they did not. Cross-lagged path analysis confirmed an effect of stressful life events on the number of subsequent relapses (ß=0·66, p=0·0055) that was dose dependent (ß=0·29, p=0·029), but it did not show an effect of relapses on subsequent risk or number of stressful life events. INTERPRETATION: These results provide converging evidence of a causal effect of stressful life events on the risk of relapse in psychosis. They suggest that there is a need to develop interventions at the individual and health-service level that could mitigate the harmful effects of stressful life events. FUNDING: National Institute for Health Research, UK.

Childhood Maltreatment and Mental Health Problems: A Systematic Review and Meta-Analysis of Quasi-Experimental Studies.

OBJECTIVE: Childhood maltreatment is associated with mental health problems, but the extent to which this relationship is causal remains unclear. To strengthen causal inference, the authors conducted a systematic review and meta-analysis of quasi-experimental studies examining the relationship between childhood maltreatment and mental health problems. METHODS: A search of PubMed, PsycINFO, and Embase was conducted for peer-reviewed, English-language articles from database inception until January 1, 2022. Studies were included if they examined the association between childhood maltreatment and mental health problems using a quasi-experimental method (e.g., twin/sibling differences design, children of twins design, adoption design, fixed-effects design, random-intercept cross-lagged panel model, natural experiment, propensity score matching, or inverse probability weighting). RESULTS: Thirty-four quasi-experimental studies were identified, comprising 54,646 independent participants. Before quasi-experimental adjustment for confounding, childhood maltreatment was moderately associated with mental health problems (Cohen's d=0.56, 95% CI=0.41, 0.71). After quasi-experimental adjustment, a small association between childhood maltreatment and mental health problems remained (Cohen's d=0.31, 95% CI=0.24, 0.37). This adjusted association between childhood maltreatment and mental health was consistent across different quasi-experimental methods, and generalized across different psychiatric disorders. CONCLUSIONS: These findings are consistent with a small, causal contribution of childhood maltreatment to mental health problems. Furthermore, the findings suggest that part of the overall risk of mental health problems in individuals exposed to maltreatment is due to wider genetic and environmental risk factors. Therefore, preventing childhood maltreatment and addressing wider psychiatric risk factors in individuals exposed to maltreatment could help to prevent psychopathology.

Resting heart rate and antisocial behaviour: a Mendelian randomisation study.

Observational studies frequently report phenotypic associations between low resting heart rate (RHR) and higher levels of antisocial behaviour (ASB), although it remains unclear whether this relationship reflects causality. To triangulate evidence, we conducted two-sample univariable Mendelian randomisation (MR), multivariable MR and linkage disequilibrium score regression (LDSC) analyses. Genetic data were accessed from published genome-wide association studies (GWAS) for RHR (n = 458,835) and ASB (n = 85,359) for the univariable analyses, along with a third GWAS for heart rate variability (HRV; n = 53,174) for all other analyses. Genome-wide significant (p < 5 × 10-8) single-nucleotide polymorphisms associated with RHR (n = 278) were selected as instrumental variables and the outcome was a composite measure of ASB. No causal association was observed between RHR and ASB (BIVW = - 0.0004, p = 0.841). The multivariable MR analyses including RHR and HRV also suggested no causal associations (BIVW = 0.016, p = 0.914) and no genetic correlations between the heart rate measures and ASB were observed using LDSC (rg = 0.057, p = 0.169). Sensitivity analyses suggested that our results are not likely to be affected by heterogeneity, pleiotropic effects, or reverse causation. These findings suggest that individual differences in autonomic nervous system functioning indexed by RHR are not likely to directly contribute to the development of ASB. Therefore, previously observed associations between RHR and ASB may arise from confounding, reverse causation, and/or additional study characteristics. Further causally informative longitudinal research is required to confirm our findings, and caution should be applied when using measures of RHR in interventions targeting ASB.

A genetically informed Registered Report on adverse childhood experiences and mental health.

Children who experience adversities have an elevated risk of mental health problems. However, the extent to which adverse childhood experiences (ACEs) cause mental health problems remains unclear, as previous associations may partly reflect genetic confounding. In this Registered Report, we used DNA from 11,407 children from the United Kingdom and the United States to investigate gene-environment correlations and genetic confounding of the associations between ACEs and mental health. Regarding gene-environment correlations, children with higher polygenic scores for mental health problems had a small increase in odds of ACEs. Regarding genetic confounding, elevated risk of mental health problems in children exposed to ACEs was at least partially due to pre-existing genetic risk. However, some ACEs (such as childhood maltreatment and parental mental illness) remained associated with mental health problems independent of genetic confounding. These findings suggest that interventions addressing heritable psychiatric vulnerabilities in children exposed to ACEs may help reduce their risk of mental health problems.