Study populations

UK Biobank: The GWAS was conducted among 436,236 white European participants in UK Biobank with both genotype and risk-taking data. UKB is a population-based cohort study of volunteers aged between 40 and 69 years, who were registered with the National Health Service and living within ~25 miles of one of the 22 UKB assessment centres throughout the United Kingdom at the time of recruitment. Recruitment took place between 2006 and 2010. Overall, 503,325 participants were recruited to the cohort after sending invitations to ~9.2 million people61. All participants provided written informed consent. The study was approved by the National Research Ethics Service Committee North West–Haydock and all study procedures were performed in accordance with the World Medical Association Declaration of Helsinki ethical principles for medical research.

The Fenland cohort study: Eating behaviour, diet and food-related behaviour phenotypes among participants enrolled in the Fenland cohort study were used in the present analysis. The Fenland study is a population-based cohort study of volunteers recruited from participating General Practices in Ely, Wisbech and the surrounding Cambridgeshire region between 2004 and 201562. Eligible individuals were adults registered at a collaborating General Practice and residing in Cambridgeshire at the time of recruitment. Exclusion criteria were: clinically diagnosed diabetes mellitus, inability to walk unaided, terminal illness (life expectancy of ≤1 year at the time of recruitment), clinically diagnosed psychotic disorder, pregnancy or lactation. All participants attended a visit to an MRC Epidemiology Unit testing centre where eating behaviour data was collected. Written informed consent was attained from all participants and the study was approved by the Cambridge Local Research Ethics Committee.

Phenotypes

Risk-taking propensity : As part of the baseline assessment, UKB participants completed a touchscreen questionnaire that included the question Would you describe yourself as someone who takes risks? Possible responses were: Yes, No, Don’t know or Prefer not to say. A total of 482,173 participants responded either ‘Yes’ (n = 129,877) or ‘No’ (n = 352,296). Those who answered ‘Don’t know’ or ‘Prefer not to say’ (n = 19,538) were excluded from this analysis. During follow-up, the question was asked again to a sub-set of participants. As the sample sizes were substantially decreased between follow-ups, in order to maximise sample size and increase power, we used the baseline responses of all participants for the primary GWAS analysis.

Eating behaviour: Eating behaviour was measured in the Fenland cohort using the 18-item version of the Three-Factor Eating Questionnaire (TFEQ-R18)63. Three eating behaviours are measured by the questionnaire: CR (6 items), UE (9 items) and EE (3 items). Each item was scored on a 4-point scale (1–4), with higher value indicating more of the behaviour. The items for each of the eating behaviours were then added together and transformed to a 0–100 scale using the following equation: [((raw score − lowest possible raw score)/possible raw score range) × 100]64. Scores were generated on an individual basis, for all Fenland study participants who completed the TFEQ-R18. EE describes a tendency to eat in response to dysphoric emotions, UE indicates a tendency to overeat accompanied by a subjective sense of loss of control over consumption and CR describes the intention to limit food intake in order to influence shape or weight.

A total of 3515 individuals (53.2% women; 98.5% self-reported white ethnicity) aged 35–64 years with intersecting eating behaviour and genotype data were included in the present analysis. Individuals lacking data regarding eating behaviour, age or sex were excluded. The eating behaviour analyses were sex-stratified based on evidence that the behaviours are all significantly higher among women (P < 0.0001 for UE and EE; P < 0.01 for CR) and reported sex modification of the association between BMI-associated loci and CR65.

Food-related behaviour: Food-related behaviour was measured as part of the general questionnaire administered to Fenland participants at baseline. To assess snacking while watching television, participants answered: Apart from meals, how often do you snack on foods while watching television? Possible answers were: Never or rarely, Occasionally, Usually, Always. To assess frequency of eating home-cooked meals, participants answered the question: When you eat your main meal at home, how often do you usually eat home cooked meals? Possible answers were: Never or less than once a month, 1–2 time per week, 3–5 times per week, 5+ times per week. Finally, to assess the frequency of breakfast eating, participants answered: How often do you usually eat breakfast? Possible answers were: Never or less than once a month, 1–2 times per week, 3–5 times per week, 5+ times per week.

As the food-related behaviour groups were not continuous, we coded the variables for analysis in logistic regression models. In general, 0 was coded as the more healthy, and 1 as the less healthy response. Frequency of eating home-cooked food was coded: 0 for 5+ times a week; 1 for <5 times a week. Snacking in front of the TV was coded: 0 for never or rarely; 1 for occasionally, usually or always. Frequency of eating breakfast was coded: 0 skips breakfast <2 times a week; 1 skips breakfast ≥2 times a week.

Dietary information: Average daily calorie, fat, protein, carbohydrate, fruit, vegetable and fibre intakes were measured using the food frequency questionnaire (FFQ). The FFQ is a validated 130-item semi-quantitative questionnaire that aims to measure self-reported habitual dietary intake over the previous year. Food intake frequency was converted to daily energy (kcal/day) and nutrient intakes (g/day) using FETA 2.53 software66. A total of 8981 participants (52.8% women; 98.6% self-reported white ethnicity) aged 30.5–64 years had intersecting genotype, dietary and food-related behaviour data and were included in the present analysis.

Statistical analysis

Genotyping, imputation and quality control procedures: We analysed data from the 2017 imputed genetic data, based on the Haplotype Reference Consortium (HRC) panel release from UKB, comprising 7,736,308 million SNPs. Genotyping, imputation, phasing and quality control are described in detail elsewhere67. Briefly, 487,409 of the UKB participants were genotyped using the Affymetrix Applied Biosystems UK Axiom array (Santa Clara, CA, USA), designed to optimise imputation performance in GWAS studies. A small number of participants (n = 49,950) were genotyped using the Affymetrix Applied Biosystems UL BiLEVE Axiom Array68. The arrays share 95% of their marker content67. SNPs were excluded prior to imputation if they were multi-allelic, had missing data or had a minor allele frequency (MAF) < 1%. Phasing was performed using a modified version of the SHAPEIT2 algorithm. Imputation was performed using IMPUTE 2 and a merged reference panel comprised of the 1000 Genomes Project Phase 3 and UK10K haplotype reference panels. In addition to quality control procedures employed by UKB, we defined a white European ancestry set based on a k-mean clustering using the first five genetic principle components.

Genome-wide association analyses: GWAS testing for associations between SNPs and self-reported risk-taking was performed using a linear mixed model (LMM) implemented in BOLT-LMM69. This approach minimises any effect of population structure and permits the inclusion of related individuals in the analysis, maximising statistical power. As all of the top 10 principal components were significantly, but minimally, associated with odds of risk-taking, this approach was appropriate (Supplementary Table 3). SNPs were established based on distance based clumping, using a distance of 1 Mb. Sex, age and genotyping array were included as covariates. SNPs were filtered based on info >0.5 and MAF >1%. Individuals were excluded based on ancestry, withdrawal from the UK Biobank study, mismatch between genetic sex and reported gender and failure of genetic quality control. A total of 436,236 individuals of white European ancestry and 7,736,308 variants were included in the analysis.

Heritability analyses were performed using restricted maximum likelihood implemented in BOLT-LMM, which computes heritability on the observed scale69. Genetic variance was calculated for all genotyped autosomal SNPs (N = 612,622) for which quality control was performed, adjusting for chip status, age, sex and the top 10 genetically determined principal components. Only unrelated individuals of white European ancestry were included in this analysis (N = 339,414).

In the absence of an appropriate data set in which to directly replicate our results, we compared our results in the baseline data set from UK Biobank, to those ascertained using data on risk-taking at the first follow-up assessment and, separately, at the second follow-up assessment. We also conducted a GWAS of a closely related phenotype, ‘ever smoking’, in the same European ancestry UK Biobank sample in order to look up our genome-wide significant SNPs for risk-taking. This sample consisted of 207,229 ever smokers (46%) and 243,177 never smokers.

Genetic correlations: Genetic correlations (r g ) were calculated using LD score regression70. Genetic correlations between risk-taking and 12 traits available in publicly available data sets were conducted.

Pathway and tissue enrichment analysis: We used MAGENTA to implement a gene set enrichment analysis-based approach to test the genome-wide discovery data for associations with biological pathways defined in Go Term, PANTHER, KEGG, Biocarta, Reactome and Ingenuity. MAGENTA maps each gene in the genome to a single index SNP with the lowest P value within the window ranging from 110 kb upstream to 40 kb downstream of the gene. This P value, representing a gene score, is then corrected in a regression model for confounding factors such as gene size, SNP density and LD-related properties. Each mapped gene in the genome is then ranked by its adjusted gene score. The observed number of gene scores in a given pathway with a ranked score above 75th percentile threshold was calculated. This observed statistic is then compared to one calculated from randomly permuted pathways of identical size. This comparison generates an empirical GSEA P value for the pathway. An individual pathway was defined as being significantly enriched when it reached FDR <0.05 in either analysis.

Tissue enrichment analysis was performed using the genotype-tissue expression (GTEx) database71. This approach uses stratified LD score regression, a method for partitioning heritability from GWAS summary statistics, to test whether trait heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue72. Significance thresholds were established using Bonferroni correction for the number of tests performed.

Mendelian randomisation: We conducted a bi-directional MR analysis of risk-taking to BMI using all genome-wide significant variants for risk-taking from the present GWAS. An unpublished GWAS meta-analysis of BMI using UKB plus GIANT data and comprising a total of 772,825 individuals provided effect estimates for BMI. For the risk-taking to BMI analyses, SNPs were aligned to the risk-taking increasing allele. For the BMI to risk-taking MR, SNPs were aligned to the BMI-increasing allele. We used conventional inverse-weighted variance (IVW) MR, by regressing the SNP effect estimates for risk-taking on the SNP effect estimates of the outcome of interest. This analysis was conducted in R version 3.3.1.

As IVW MR assumes the absence of horizontal pleiotropy (heterogeneity) and may be biased by weak instruments, MR Egger and weighted median MR were also performed. The MR Egger method is similar to that of conventional IVW MR. However, unlike IVW MR, the regression is not constrained to pass through the origin. Significant departure of the y intercept from zero indicates pleiotropy73. The drawback of this method is low statistical power, and susceptibility to bias from weak instruments, which tend to bias results toward the null59. Weighted median MR complements MR Egger and allows up to 50% of the information in the MR analysis to come from SNPs that are invalid instruments, including those that are invalid as a result of pleitropy59, and yields more precise results than MR Egger if all genetic variants have similar magnitudes of association with the exposure74. MR is also limited by factors beyond pleiotropy that cannot be controlled but should be considered. For example, canalisation and compensation might mitigate the effects of genetic changes on outcomes and heterogeneity in exposures may make causal inferences about the dimensions of a trait that are important difficult to infer without biological knowledge.

In order to identify specific SNPs associated with risk-taking that might drive overall effects on BMI, we performed a ‘leave-one-out’ analysis. For this analysis, we repeated the MR of risk-taking to BMI with each of the genome-wide significant SNPs for risk-taking removed, in turn.

PRS analysis: A weighted PRS for risk-taking was constructed for Fenland participants (n = 11,249) using the summary statistics from the present UKB GWAS. The 26 loci showing genome-wide significant associations with risk-taking were included in the score. At each locus, the number of risk increasing alleles were summed and multiplied by the effect estimate on risk-taking from our UKB GWAS. The results across all 26 SNPs were summed for each participant. The association between the PRS and eating behaviour was examined in the Fenland study using sex-stratified regression models, adjusted for age. The association between the PRS and both the diet and food-related behaviour variables was analysed in Fenland using linear or logistic regression models, as appropriate, adjusted for age and sex. Outcome variables were log-transformed if they were not normally distributed, in order to improve the normality of the residuals.

The following 12 traits were analysed using the PRS: EE, UE, CR, total calorie intake per day, fat intake (g/day), fibre intake (g/day), protein intake (g/day), carbohydrate intake (g/day), fruit and vegetable intake (g/day), snacking while watching TV, frequency of skipping breakfast (times per week) and number of home cooked meals (times per week). This analysis was conducted in Stata version 14.

Code availability

Code is available upon request from the corresponding authors.

Data availability

GWAS summary statistics are available at https://doi.org/10.22025/2018.20.202.00002. Individual-level data are available from UK Biobank but restrictions apply to the availability of this data, which was used under license for the current study. Approved researchers may apply for access under the UK Biobank access framework (details can be found here: http://www.ukbiobank.ac.uk/wp-content/uploads/2012/09/Access-Procedures-2011.pdf). Data from the Fenland study are governed in accordance with the MRC Policy and Guidance on Sharing of Research Data from Population and Patient Studies and the terms of the participants’ consent and study ethical approvals. Approved researchers wishing to access these data should contact the Fenland Study team (http://epi-meta.medschl.cam.ac.uk/overview.html).