We first performed a linear regression with alcohol intake as a dependent variable to identify possible covariates (e.g. sex, age, post-mortem interval [PMI], RNA integrity number (RIN)). Gene-level analyses started with the featureCounts-derivedsample-by-gene read count matrix. The basic normalization and adjustment pipeline for the expression data matrix consisted of: (i) removal of low expression genes (<1 CPM in >50% of the individuals); (ii) differential gene expression analysis based upon adjustment for the chosen covariates. We filtered out all genes with lower expression in a substantial fraction of the cohort, with 18,463 genes with at least 1 CPM in at least 50% of the individuals; note that only these genes were carried forward in all subsequent analyses. The log10 normalized alcohol consumption (from NSWBB brains) was used for differential expression analysis using the DeSeq2 program. The analysis was controlled for sex, age, PMI, Body mass index (BMI), RIN, batch and severity of alcoholism (AUDIT scores).
