In subsequent analyses, we first considered covariance among the AUDIT-C metrics (mean and trajectory) and AUD diagnostic codes. We then evaluated the association of each of these measures (age-adjusted mean AUDIT-C, AUDIT-C trajectories, and AUD diagnostic codes) with rs2066702 (in AAs) and rs1229984 (in EAs) using ordered logit models, in which the allele count, i.e., 0, 1, or 2 minor alleles, was used as the outcome variable and the phenotype as the predictor in regression analyses. Of note, this approach is opposite to convention in which the phenotype is the outcome. Our goal was to compare the association between several candidate phenotype variables with an established genotype, adjusting for covariates. We are modeling association, not causation. In this context the role of dependent and independent variables is irrelevant. By modeling allele count as the outcome and candidate phenotypes as predictors adjusting for age, sex, and 10 PCs we were able to more directly compare independent associations between the candidate phenotypes and the established AHD1B variants relevant to each ancestry in a standardized manner. We used different SNPs for EAs and
