We used a generalized linear mixed effect model (GLMM) to model the number of RNA-seq reads for each allele based on its allelic type (reference or alternative allele), study group (AUD or social/non-drinkers), and the interaction terms between the two variables. A random variable was used to account for the reads from the two alleles within the same individual. To adjust for the over-dispersion effects of the RNA-seq reads, a negative binomial distribution was used in the GLMM model.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{{log}}}(\mu ) = \beta _0 + \beta _1X_1 + \beta _2X_2 + \beta _{12}X_1X_2 + bX_{\mathrm{{S}}}$$\end{document}log(μ)=β0+β1X1+β2X2+β12X1X2+bXSwhere μ is the expected number of sequencing reads for one allele (reference or alternative) in one specific subject, X1 is the allele type (0: reference allele and 1: alternative allele), X2 is the subject study group (0: control group and 1: AUD group), and XS is the subject ID. In this model, β0, β1, β2, and β12 are coefficients of fixed effects, while b is the coefficient for random effect that models the differences between subjects. Our
