Individuals <22 or > 90 years old and those with missing AUDIT-C scores were removed from the analyses, leaving 200,680 EAs; 56,495 AAs; 14,112 LAs; 1366 EAAs; and 189 SAAs in the AUDIT-C GWAS and 202,004 EAs (34,658 cases; 167,346 controls); 56,648 AAs (17,267 cases; 39,381 controls); 14,175 LAs (3449 cases; 10,726 controls); 1374 EAAs (164 cases; 1210 controls); and 190 SAs (44 cases; 144 controls) in the AUD GWAS. We used linear regression for the GWAS of age-adjusted mean AUDIT-C score and logistic regression for AUD diagnosis; in both cases age, sex, and the first 10 PCs were covariates. To evaluate the impact on AUD findings of controlling for AUDIT-C and the impact on AUDIT-C findings of controlling for AUD, we repeated the GWAS for AUD with AUDIT-C as a covariate and AUDIT-C with AUD as a covariate. For both phenotypes, following GWAS in each of the five populations, the summary statistics were combined within phenotype in trans-population meta-analyses. SNPs in EAs or those present in at least two populations were meta-analyzed. Sex-stratified GWAS for both phenotypes were then
