Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals

We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization – genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.
Source: eLife - Category: Biomedical Science Tags: Computational and Systems Biology Genetics and Genomics Source Type: research