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

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 i...

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Detalles Bibliográficos
Autores principales: Wesolowska-Andersen, Agata, Zhuo Yu, Grace, Nylander, Vibe, Abaitua, Fernando, Thurner, Matthias, Torres, Jason M, Mahajan, Anubha, Gloyn, Anna L, McCarthy, Mark I
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007221/
https://www.ncbi.nlm.nih.gov/pubmed/31985400
http://dx.doi.org/10.7554/eLife.51503
Descripción
Sumario: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.