Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort

Abstract

Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome wide association study in 116 255 UK Biobank participants who responded yes/no to the question “Would you consider yourself a risk-taker?” Risk-takers (compared to controls) were more likely to be men, smokers and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait which has a major impact on a range of common physical and mental health disorders.

Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE

“…The previously identified BMI-associated variant (rs13078960) 60 , 61 is not in LD (r 2 <0.07) with the PA–associated variants that we identified, except for the SSOE-increasing allele at rs62253088 being positively, but weakly, correlated with the BMI-increasing allele at rs13078960 (r 2 =0.2). The previously identified G alleles at both rs13084531 64 and rs57401290 63 associated with risk taking are weakly to moderately correlated (r 2 =0.52 and 0.23, respectively) with the SSOE-increasing allele that we identified at rs62253088 (see Supplementary Figure 5 ). It thus appears that this locus may be important for several personality, cognitive, and behavioral traits, and may potentially be involved in reward systems.…”

Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores

“…PRSs were included in the analysis if: (1) there was evidence of significant genetic correlation of the trait with BD and (2) we had at least 80% power to detect PRS association in a general case-only analysis of our data assuming 50% prevalence of the sub-phenotype. We began by considering PRSs for major psychiatric disorders (BD 33 , SCZ 34 , MDD 35 , ADHD 36 , anxiety 37 , PTSD 19 , OCD 38 , anorexia nervosa 39 , alcohol use disorder 40 , and insomnia 41 ) and personality and lifestyle traits related to BD (alcohol consumption 40 , educational attainment (EA) 42 , risk-taking 43 , subjective well-being 44 , neuroticism 45 , anhedonia 46 , and body mass index (BMI) 47 ). The GWAS summary statistics were restricted to wellimputed variants (INFO > 0.9) when information on imputation quality was available.…”

Translational Potential of Neuroimaging Genomic Analyses to Diagnosis and Treatment in Mental Disorders

“…While aforementioned methods need individual SNP data to estimate the genetic similarity matrix, another set of methods use only GWAS summary results and genetic data from a reference population (public data) to achieve the same goal of heritability or genetic correlation estimation. Among others [69,70], the LD score regression (LDSC) model [71] has been implemented in numerous studies [27,[72][73][74], and has also been extended to estimate genetic correlation [75] and partitioned effects for cell-specificity or annotation pathways [76]. In sum this line of research provides reliable estimation of total or partitioned genetic effects on phenotypes of interest, which can be very valuable for validity check of imaging phenotypes or selection of proper cohorts for detailed studies.…”