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Socrates, A.

Publications and source records attributed to Socrates, A..

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Identifying Potential Causal Risk Factors for Self-Harm: A Polygenic Risk Scoring and Mendelian Randomisation Approach

BackgroundMultiple individual vulnerabilities and traits are phenotypically associated with suicidal and non-suicidal self-harm. However, associations between these risk factors and self-harm are subject to confounding. We implemented genetically informed methods to better identify individual risk factors for self-harm.\n\nMethodsUsing genotype data and online Mental Health Questionnaire responses in the UK Biobank sample (N = 125,925), polygenic risk scores (PRS) were generated to index 24 plausible individual risk factors for self-harm in the following domains: mental health vulnerabilities, substance use phenotypes, cognitive traits, personality traits and physical traits. PRS were entered as predictors in binomial regression models to predict self-harm. Multinomial regressions were used to model suicidal and non-suicidal self-harm. To further probe the causal nature of these relationships, two-sample Mendelian Randomisation (MR) analyses were conducted for significant risk factors identified in PRS analyses.\n\nOutcomesSelf-harm was predicted by PRS indexing six individual risk factors, which are major depressive disorder (MDD), attention deficit/hyperactivity disorder (ADHD), bipolar disorder, schizophrenia, alcohol dependence disorder (ALC) and lifetime cannabis use. Effect sizes ranged from {beta} = 0.044 (95% CI: 0.016 to 0.152) for PRS for lifetime cannabis use, to {beta} = 0.179 (95% CI: 0.152 to 0.207) for PRS for MDD. No systematic distinctions emerged between suicidal and non-suicidal self-harm. In follow-up MR analyses, MDD, ADHD and schizophrenia emerged as plausible causal risk factors for self-harm.\n\nInterpretationAmong a range of potential risk factors leading to self-harm, core predictors were found among psychiatric disorders. In addition to MDD, liabilities for schizophrenia and ADHD increased the risk for self-harm. Detection and treatment of core symptoms of these conditions, such as psychotic or impulsivity symptoms, may benefit self-harming patients.\n\nFundingLim is funded by Kings International Postgraduate Research Scholarship. Dr Pingault is funded by grant MQ16IP16 from MQ: Transforming Mental Health. Dr Coleman is supported by the UK National Institute of Health Research Maudsley Biomedical Research Centre. MRC grant MR/N015746/1 to CML and PFOR. Dr Hagenaars is funded by the Medical Research Council (MR/S0151132). Kylie P. Glanville is funded by the UK Medical Research Council (PhD studentship; grant MR/N015746/1). This paper represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and Kings College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.\n\nResearch in ContextO_ST_ABSEvidence before this studyC_ST_ABSA search was conducted on PubMed for literature from inception until 1st May 2019 using terms related to suicidal self-harm (SSH) and non-suicidal self-harm (NSSH), as well as polygenic risk scores (PRS), (\"self-harm\"[All Fields] OR \"self-injurious\"[All Fields] OR \"self-mutilation\"[All Fields] OR \"suicide\"[All Fields]) AND (\"polygenic\"[All Fields] OR \"multifactorial inheritance\"[All Fields]). Similar search was done for Mendelian Randomisation (MR), replacing \"multifactorial inheritance\" and \"polygenic\" with \"Mendelian Randomisation/Randomization\". Evidence was included only if the study had used PRS or MR method to predict self-harm phenotypes using risk factors of self-harm. Ten papers for PRS and no paper for MR were identified.\n\nThere were mixed results for PRS studies. PRS for MDD predicted SSH in two studies but not in another two studies. PRS for depressive symptoms predicted SSH but not NSSH. PRS for schizophrenia predicted SSH in one but not in another two studies. PRS for bipolar disorder predicted SSH in one study but did not predict SSH nor NSSH in another two studies.\n\nAdded value of this studyBy using a large population-based sample, we systematically studied individual vulnerabilities and traits that can potentially lead to self-harm, including mental health vulnerabilities, substance use phenotypes, cognitive traits, personality traits and physical traits, summing up to 24 PRS as genetic proxies for 24 risk factors. We conducted MR to strengthen causal inference. We further distinguished non-suicidal self-harm (NSSH) and suicidal self-harm (SSH).\n\nApart from PRS for schizophrenia, MDD and bipolar disorder, novel PRS were also identified to be associated with self-harm, which are PRS for attention-deficit hyperactivity disorder (ADHD), cannabis use and alcohol dependence. A larger sample size allowed us to confirm positive findings from the previously mixed literature regarding the associations between PRS for MDD, bipolar disorder, and schizophrenia with self-harm. Multivariate analyses and MR analyses strengthened the evidence implicating MDD, ADHD and schizophrenia as plausible causal risk factors for self-harm.\n\nImplications of all the available evidenceAmong the 24 risk factors considered, plausible causal risk factors for self-harm were identified among psychiatric conditions. Using PRS and MR methods and a number of complementary analyses provided higher confidence to infer causality and nuanced insights into the aetiology of self-harm. From a clinical perspective, detection and treatment of core symptoms of these conditions, such as psychotic or impulsivity symptoms, may prevent individuals from self-harming.

epidemiology

Investigating the role of behaviour in the genetic risk for schizophrenia

The notion that behaviour may be on a causal path from genetics to psychiatric disorders, such as schizophrenia, highlights a potential for practical interventions. Motivated by this, we test the association between schizophrenia (SCZ) polygenic risk scores (PRS) and 420 behavioural traits (personality, psychological, lifestyle, nutritional) in a psychiatrically healthy sub-cohort of the UK Biobank. Higher schizophrenia PRS was associated with a range of traits, including lower verbal-numerical reasoning (P = 6x10-61), higher nervous feelings (P = 2x10-51) and higher self-reported risk-taking (P = 2x10-41). We follow-up the risk-taking association, hypothesising that the association may be due to a genetic propensity for risk-taking leading to greater migration, urbanicity or drug-taking - reported environmental risk factors for schizophrenia, and all positively associated with risk-taking in these data. However, schizophrenia PRS was also associated with traits, such as tea drinking (P = 2x10-34), that are highly unlikely to be on a causal path to schizophrenia. We depict four causal relationships that may in theory underlie such PRS-trait associations and illustrate ways of testing for each. For example, we contrast PRS-trait trends in the healthy sub-cohort to the corresponding trait values of medicated and non-medicated individuals diagnosed with schizophrenia, allowing some differentiation of mediation-by-behaviour, disease-onset effects and treatment effects. However, dedicated follow-up studies and new methods are required to fully disentangle these relationships. Thus, while we urge caution in interpretation of simple PRS cross-trait associations, we propose that well-designed PRS analyses can contribute to identifying behaviours on the causal path from genetics to disease.

genetics

Polygenic risk scores applied to a single cohort reveal pleiotropy among hundreds of human phenotypes

BackgroundThere is now convincing evidence that pleiotropy across the genome contributes to the correlation between human traits and comorbidity of diseases. The recent availability of genome-wide association study (GWAS) results have made the polygenic risk score (PRS) approach a powerful way to perform genetic prediction and identify genetic overlap among phenotypes.\n\nMethods and findingsHere we use the PRS method to assess evidence for shared genetic aetiology across hundreds of traits within a single epidemiological study - the Northern Finland Birth Cohort 1966 (NFBC1966). We replicate numerous recent findings, such as a genetic association between Alzheimers disease and lipid levels, while the depth of phenotyping in the NFBC1966 highlights a range of novel significant genetic associations between traits.\n\nConclusionsThis study illustrates the power in taking a hypothesis-free approach to the study of shared genetic aetiology between human traits and diseases. It also demonstrates the potential of the PRS method to provide important biological insights using only a single well-phenotyped epidemiological study of moderate sample size (~5k), with important advantages over evaluating genetic correlations from GWAS summary statistics only.

genetics