eLife (Jan 2020)

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

  • Agata Wesolowska-Andersen,
  • Grace Zhuo Yu,
  • Vibe Nylander,
  • Fernando Abaitua,
  • Matthias Thurner,
  • Jason M Torres,
  • Anubha Mahajan,
  • Anna L Gloyn,
  • Mark I McCarthy

DOI
https://doi.org/10.7554/eLife.51503
Journal volume & issue
Vol. 9

Abstract

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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-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.

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