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

ABSTRACT: Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue - pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization - genetic fine-map

SUBMITTER: Wesolowska-Andersen A 

PROVIDER: S-EPMC7007221 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature