To empirically model the phenotypic and genetic relationships among AUDIT items, we used lavaan (18) and Genomic Structural Equation Modeling (9) to conduct phenotypic and genetic confirmatory factor analyses, respectively, using weighted least squares estimation. Further details are provided in the Supplementary Material 5.1 and described extensively elsewhere (20–23). We tested three models: (i) a parallel factor model (i.e., a sum score model), (ii) a common factor model, and (iii) a correlated factors model. The common and correlated-factors models were selected based on prior research (Table S1) while the parallel factor model served to test the restrictive assumptions of sum score approaches. We assessed model fit using conventional indices that were available in both the lavaan and Genomic Structural Equation Modeling software (9) (Supplementary Material 5). Only data from UK Biobank (the largest sample) was included in the phenotypic factor analyses. For the genetic factor analyses, GWAS summary statistics from the meta-analyses for each AUDIT item were subjected to standard quality control practices, as described above. Genomic Structural Equation Modeling’s multivariable version of LD score regression was then used to
