Here we present a multi-omics systems approach to identify causal variants and genes associated with AUD and DPW. Using Mendelian Randomization-based methods on the largest available transcriptomic and epigenomic data for brain tissues (Supplementary Data 1) and myeloid cells, we prioritized regulatory variants that influence AUD and DPW (Fig. 1). The current manuscript explored the multi-omic integration results in AUD and DPW separately and subsequently focused on the overlapping genes between these two traits. Overlapping genes prioritized in current analysis are primarily driven by individual GWASs. Therefore, these signals are minimally influenced by the sample size bias that might arise due to the integration of two correlated traits with extreme differences in power. Results of predicted mRNA expression (integration analyses) were compared with the mRNA expression from the brains of individuals diagnosed with AUD and controls (N = 138) to validate the differential expression of genes prioritized in the GWAS integration analyses. To the best of our knowledge, this is the largest systematic multi-omics integration analysis to identify the functional impact of variants and genes associated with two correlated but etiologically distinct aspects of alcohol involvement.
