All of the above analyses were conducted in R43. 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 model43. We use the conditional R2 to select the most predictive pT (see Supplemental Materials), but report both statistics for the most predictive PRS threshold. The proportion of variance attributable to the PRS (Δconditional R2) was estimated as the difference between the conditional R2 for a model with covariates alone and the model that included covariates and the PRS (i.e. conditional R2(full model) - conditional R2(model without PRS)). The use of ΔR2 (typically Nagelkerke’s pseudo-R2 for binary traits20) as an index of the most predictive PRS relates to its role as an index of predictor efficacy38,39,44, such that the addition of the PRS to a model improves the fit of the model, thus indicating unique variance attributable to the PRS, over and above covariates. As peer use was restricted to recall at age 12–17, we did not test whether trajectory
