Association of the DSM-IV diagnosis of stimulant dependence was evaluated using logistic regression models that were solved with generalized estimating equations to correct for correlations among related individuals. Models included covariates for age, sex, and the first five PCs. Association tests were performed separately within each population group and within each genotyping platform to account for batch effects. The association test results were corrected for genomic inflation (λ) and combined across population and batch groups via inverse variance meta-analysis implemented in the program METAL23. We ignored results for variants whose heterogeneity p-values from the meta-analysis were less than 1.4 × 10−6 in AAs or 3.3 × 10−9 in EAs (different thresholds were used given the sample size difference across populations) implying inconsistency across datasets. The p -value threshold was set at 5.0 × 10−8 for GWS. A suggestive significance level was set at 5.0 × 10−6, and replication was sought for variants that passed this threshold. Association testing in the replication dataset was performed using the same covariates as in the discovery sample in regression models implemented in geepack (https://cran.r-project.org/web/). Results for the discovery and replication datasets were combined using the inverse variance meta-analysis as described above.
