by demographic characteristics such as the socio-economic status, age or sex (45). It remains to be determined how generalizable the genetics of AUDIT are across different populations, especially in samples of different ancestries (as we have only included individuals of European ancestry in the present study) or cultures (UK vs US). A similar point also applies to sex-stratified samples, considering that AUDIT scores differ in men and women. Finally, it is important to note that the Problems PRS exhibited weaker associations with AUD and other alcohol phenotypes in comparison to its AUDIT-P counterpart. Although the two predictors generally had similar effects in single PRS models, the Problems PRS was rendered redundant in the cross-method analyses when both of the highly correlated AUDIT-P and Problems PRSs (e.g., r = .84 in UK Biobank) were included in the regression models. However, we caution against the interpretation that the univariate GWAS approach is preferable. The multivariate GWAS function of Genomic Structural Equation Modeling is not only more flexible than traditional univariate GWAS, but its results may also be more robust to confounding, as the software automatically applies a correction for population stratification (20). Furthermore, Genomic Structural Equation Modeling is better suited to investigate
