In the present study, we sought to elucidate the genetics of alcohol consumption and problematic consequences of alcohol use measured via AUDIT using Genomic Structural Equation Modeling (9), a novel multivariate framework that allows for structural equation modeling techniques to be applied to genetic covariance matrices based on GWAS results. Accordingly, we undertook the first item-level and largest to-date GWAS meta-analyses of AUDIT (N=160,824), using data from three population-based cohorts of European ancestry. We then used Genomic Structural Equation Modeling (9) to analyze the item-level GWAS results with the aims of (i) investigating the latent genetic factor structure of AUDIT, based on prior knowledge (Table S1), and (ii) conducting multivariate GWASs of the resulting latent genetic factor(s). We posited that applying this approach would lead to more nuanced, empirically-derived weights to each of the AUDIT items when constructing our aggregate measures (as opposed to giving each item equivalent weight), which is a novel approach for GWASs of AUD phenotypes. Finally, to characterize the biology and liability associated with each latent genetic factor, we used a variety of in-silico tools and polygenic analyses spanning three independent cohorts that varied in their method of ascertainment and prevalence of AUD.
