The major challenge of studying RNA splicing in AUD is the scarcity of large-scale transcriptomic data with high sequencing depths in brains from individuals with and without AUD. Moreover, the contribution of an RNA splicing event to risk for AUD cannot be directly inferred using RNA sequencing (RNA-seq) alone [14], because the splicing changes contributing to the disorder cannot be distinguished from the splicing changes induced by alcohol exposure. Currently, methods are available to infer the causality of gene expression for a trait, such as PrediXcan [15], transcriptome-wide association study (TWAS) [16], and summary-based Mendelian randomization (SMR) [17]. These methods have been used in combination with splicing quantitative trait loci (sQTL) to study the causal effect of alternative splicing in the susceptibility to diseases such as Alzheimer’s disease [18], glioma [19], osteoporosis [20], and more recently, AUD [13]. Although these studies suggest that alternative splicing of genes is associated with complex diseases including AUD, identification of specific splicing events would provide not only stronger evidence that RNA alternative splicing impacts risk for AUD, but also the molecular basis for experimentally
