All analyses were conducted in R (R Core Team, 2017). First, mixed effect regressions were used to examine whether alcohol consumption PRS predicted (a) alcohol use, (b) MAXD, (c) DSM5AUDSX, (d) DSM4AD, (e) SRE-T, and (f) cessation/reduction of problem drinking, and to determine which PRS threshold (i.e., pT) was most predictive of the drinking measure based on the p-value and R2. For the cessation/reduction measure, groups were contrasted with each other using binary comparisons (e.g., Group B vs. Group C). All regressions controlled for the covariates mentioned above as fixed effects, while the family identifier and recruitment site were included in the models as random effects (family nested within site). To assess model fit and the relative amount of variance explained by the PRS, we used the ‘MuMIn’ package in R to calculate both marginal and conditional R2 for each mixed model (Barton, 2011; Nakagawa & Schielzeth, 2012). We use the marginal R2 to select the most predictive pT. The proportion of variance attributable to the PRS (Δmarginal R2) was estimated as the difference between the marginal R2 (i.e., Δmarginal)
