To test whether these skipped exon events play a causal role in the development of AUD, we designed a MR-based approach. First, considering the genetic variants as the instrumental variable, we imputed the splicing outcome \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\hat{\mathrm \Psi }}}}$$\end{document}Ψ^ for each of the 1093 SE using our predictive models, based on the genotypes of 8038 EA subjects from 1127 independent families in COGA [24]. Second, we examined the associations between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\hat{\mathrm \Psi }}}}$$\end{document}Ψ^ and alcohol dependence diagnosis (DSM-IV, n = 2348 control and 2412 AUD subjects) and symptom count (SXCT, n = 7421; 67% had one or more symptoms). The analysis workflow is depicted in Fig. 2A.
