Sex and Alternative Splicing in Disease: a meta-analytic approach to identify interactions

How cell type, sex and disease interact and affect gene expression and splicing is an important, but complicated question. Visualizing and testing specific hypotheses around these complex interactions is an important first step to identifying molecular components underpinning complex disease. Using a meta-analytical framework, we develop an analytical path for identifying testable molecular hypotheses of complex interactions between splicing, sex, disease and cell type. We focus on type 1 diabetes (T1D) but the approach is generalizable to any complex disease with defined candidate loci. Previous studies report T1D-associated splicing in candidate genes, differences in disease effects across immune cell types, sex effects on splicing and cell-type-specific splicing. However, identifying and interpreting complex interactions between sex, splicing and disease are challenging. Here we demonstrate how a gene expression study of T1D, designed to evaluate these interactions can be analyzed in a straightforward manner. We find that sex-dependent T1D-associated splicing is markedly more prevalent in CD4 T cells than in CD8 T cells, affecting 72% of T1D candidate genes in CD4 cells compared to 30% in CD8 cells. We pinpoint exons whose rate of inclusion is affected by the interaction of sex and disease. We use long-read RNAseq to identify novel intron retention events and splice sites which are quantified with short-reads leading to a richer description of the regulatory impact of T1D on alternative splicing. We identify a set of candidate isoforms for follow-up molecular studies in BACH2, a transcription factor known to be relevant in disease prevalence.