In the present study, we report the first item-level and largest GWAS of AUDIT to date (N=160,824), and we used Genomic Structural Equation Modeling to elucidate the genetic etiology of alcohol consumption and problematic alcohol use. By conducting phenotypic and genetic factor analyses of the individual AUDIT items, we provide evidence that two correlated latent factors (Consumption and Problems) parsimoniously explained the covariance in measures of alcohol consumption and problematic alcohol use across both levels of analysis. Moreover, by applying empirically-derived weights to the AUDIT items in a Genomic Structural Equation Modeling framework, we demonstrated that our method can ameliorate confounding biases that have complicated previous work with consumption phenotypes (in particular, the bias present in item 1). Notably, both Consumption and Problems share a strong, positive genetic correlation with alcohol dependence (both rg~0.7), and we show, for the first time, that the polygenic signal of Consumption is strongly associated with several AUD phenotypes in three independent cohorts. Finally, the results of our bioinformatic analyses further illustrate that Consumption and Problems have unique components of their genetic etiology. Collectively, our
