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Background Identifying causal risk factors for self-harm is essential to inform preventive interventions. Epidemiological studies have identified risk factors associated with self-harm, but these associations can be subject to confounding. By implementing genetically informed methods to better account for confounding, this study aimed to better identify plausible causal risk factors for self-harm. Methods and findings Using summary statistics from 24 genome-wide association studies (GWASs) comprising 16,067 to 322,154 individuals, polygenic scores (PSs) were generated to index 24 possible individual risk factors for self-harm (i.e., mental health vulnerabilities, substance use, cognitive traits, personality traits, and physical traits) among a subset of UK Biobank participants (N = 125,925, 56.2% female) who completed an online mental health questionnaire in the period from 13 July 2016 to 27 July 2017. In total, 5,520 (4.4%) of these participants reported having self-harmed in their lifetime. In binomial regression models, PSs indexing 6 risk factors (major depressive disorder [MDD], attention deficit/hyperactivity disorder [ADHD], bipolar disorder, schizophrenia, alcohol dependence disorder, and lifetime cannabis use) predicted self-harm, with effect sizes ranging from odds ratio (OR) = 1.05 (95% CI 1.02 to 1.07, q = 0.008) for lifetime cannabis use to OR = 1.20 (95% CI 1.16 to 1.23, q = 1.33 × 10−35) for MDD. No systematic differences emerged between suicidal and non-suicidal self-harm. To further probe causal relationships, two-sample Mendelian randomisation (MR) analyses were conducted, with MDD, ADHD, and schizophrenia emerging as the most plausible causal risk factors for self-harm. The genetic liabilities for MDD and schizophrenia were associated with self-harm independently of diagnosis and medication. Main limitations include the lack of representativeness of the UK Biobank sample, that self-harm was self-reported, and the limited power of some of the included GWASs, potentially leading to possible type II error. Conclusions In addition to confirming the role of MDD, we demonstrate that ADHD and schizophrenia likely play a role in the aetiology of self-harm using multivariate genetic designs for causal inference. Among the many individual risk factors we simultaneously considered, our findings suggest that systematic detection and treatment of core psychiatric symptoms, including psychotic and impulsivity symptoms, may be beneficial among people at risk for self-harm., Kai Xiang Lim and co-workers use genetic instruments to investigate risk factors associated with self-harm., Author summary Why was this study done? Self-harm is an act of self-injury or self-poisoning carried out by an individual and can be subdivided into suicidal and non-suicidal self-harm. Self-harm has been found to be associated with a range of risk factors, but these associations can be subject to confounding. Using genetic information to help strengthen causal inference can help to identify plausibly causal risk factors for self-harm. What did the researchers do and find? We used data from UK Biobank participants, who provided their genetic information and answered questions about their engagement in self-harming. We investigated 24 potential individual risk factors for self-harm. Out of these 24 risk factors, major depressive disorder, attention-deficit hyperactivity disorder and schizophrenia appeared to be the most plausible causal risk factors for self-harm. No difference emerged between risk factors for non-suicidal and suicidal self-harm. What do these findings mean? Using a different design, with different sets of assumptions, than epidemiological studies that are not genetically informed, we showed that psychiatric symptoms related to major depressive disorder, attention deficit/hyperactivity disorder, and schizophrenia plausibly play a role as individual risk factors leading to an increased risk of self-harm. Detecting and treating psychiatric symptoms, such as impulsivity and psychotic symptoms, may be helpful for people at risk for self-harm.

Background As the scale and power of GWAS have increased to detect the small genetic effect sizes involved in complex polygenic traits, the results of GWAS, especially SNP effect size estimates, are increasingly utilised for prediction. A popular approach is the application of Polygenic Risk Scores (PRS). However, the effect size estimates are inherently prone to overfit the specific samples on which the GWAS were performed (e.g. “the Winner's Curse”). The inflation of effect size estimation reduces the out-of-sample accuracy of prediction based on GWAS results. Many shrinkage methods have been developed to correct for such inflation. As GWAS results and large individual genotype data sets become widely available, there is greater opportunity to accurately evaluate effect size inflation and compare the performance of different shrinkage methods. Methods We develop a novel permutation-based shrinkage method and compare its performance with previously developed methods based on local false discovery rate and the LASSO. We evaluate the performance of the methods across a wide range of phenotypes and investigate different factors (e.g. phenotype heritability) that influence their impact on the predictive power of polygenic risk scores. Results Using large-scale real genotype data, we show that SNP effect sizes are markedly overfit even in relatively large samples, but that our permutation-based shrinkage approach can improve PRS prediction dramatically. Discussion Our results suggest that GWAS results can be adjusted using an efficient empirical approach to provide more accurate effect size estimates and thus greater downstream predictive power. This approach could be applied to a wide variety of big data settings.

Background In 2015, we published PRSice (‘precise’), a software for the calculation, application and evaluation of polygenic risk scores (PRS). PRSice has been widely used in the field of psychiatric genetics since. Here we describe an updated version – PRSice 2 – which contains several important new features. While a key feature of the original version was its identification of the most predictive PRS via its ‘high resolution scoring’, the inherent over-fitting involved meant that we recommended using a significance threshold of P Methods We demonstrate the performance of PRSice 2 here with our latest cross-trait PRS analysis results, and, in particular, investigate how confounding typically associated with non-genetic studies can be re-introduced in such studies and give rise to misinterpretation of their results.