Nearly all genetic variants associated with complex disease are noncoding. Many noncoding disease risk variants affect the amplitude of gene expression. However, we have identified mRNA splicing as an additional primary link between genetic variants and complex diseases. Thus, an understanding of how, and which, genetic variants affect RNA splicing can greatly aid our understanding of the impact of noncoding variants. Despite the importance of RNA splicing in mediating genetic risk for disease, the dominant assay to determine mRNA content in a cell or tissue, RNA-seq of polyadenylated mRNA, primarily captures steady-state mRNA isoforms, which reflect not only RNA splicing but also other processes such as RNA decay. Further, RNA-seq provides little information on the pathway of RNA isoform biogenesis. Yet, other assays beyond RNA-seq that report on the pathway of RNA splicing and in a manner independent of decay are sorely lacking, significantly compromising our ability to account for how, and which, genetic variants affect RNA splicing. We propose to first develop a battery of novel genomic assays to monitor the pathway of splicing and then exploit these assays to define the impact of genetic variation on splicing. We will optimize such approaches to yield datasets to study the mechanisms by which genetic variants affect mRNA splicing at unprecedented detail. Specifically, to achieve our goals, we propose i) to develop genome-wide assays to monitor splicing in novel ways, ii) to search for splicing quantitative trail loci using these assays, and iii) to account through an integrated approach for the functional mechanisms by which genetic variants affect splicing. At the conclusion of this project, we will have developed genomic assays and computational approaches that allow us to reach a deep understanding of the mechanisms that link sequence variation to variation in splicing and ultimately to disease.
This project will reveal links between human genetic variants, changes in the expression of our genes, and disease. These findings will contribute to both the interpretation of variations in our genome and the prediction of outcomes for such variations, both with respect to disease and to therapeutic efficacy.