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-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.
journal_name
Elifejournal_title
eLifeauthors
Wesolowska-Andersen A,Zhuo Yu G,Nylander V,Abaitua F,Thurner M,Torres JM,Mahajan A,Gloyn AL,McCarthy MIdoi
10.7554/eLife.51503subject
Has Abstractpub_date
2020-01-27 00:00:00issn
2050-084Xpii
51503journal_volume
9pub_type
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