Because the genetic correlation of AUDIT total score between the UKB and 23andMe cohorts was high (rg = 0.77, SE = 0.12, p = 7.15 × 10−11), we performed a sample-size based meta-analysis of AUDIT total score from the UKB and 23andMe cohorts using METAL (version 2011–03-25)(20). This meta-analysis comprises a total of 141,932 research participants of European ancestry and 9,519,872 genetic variants that passed quality control. We used clump-based pruning (see ‘Discovery GWAS’) to identify independently-associated variants. For each GWAS signal we defined a set of credible variants using a Bayesian refinement method developed by Maller et al. (21). These credible sets are considered to have a 99% probability of containing the ‘causal’ variant at each locus. Credible set analyses were performed in R (https://github.com/hailianghuang/FM-summary) for each of the index variants associated with AUDIT total score in the GWAS meta-analysis using SNPs within 1 Mb with an r2 >0.4 to the index variant. All downstream genetic analyses of AUDIT total score were performed using the GWAS meta-analysis summary statistics. The 23andMe AUDIT GWAS has previously been published (10) and
