Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1783495284747141120 |
---|---|
author | Wesolowska-Andersen, Agata Zhuo Yu, Grace Nylander, Vibe Abaitua, Fernando Thurner, Matthias Torres, Jason M Mahajan, Anubha Gloyn, Anna L McCarthy, Mark I |
author_facet | Wesolowska-Andersen, Agata Zhuo Yu, Grace Nylander, Vibe Abaitua, Fernando Thurner, Matthias Torres, Jason M Mahajan, Anubha Gloyn, Anna L McCarthy, Mark I |
author_sort | Wesolowska-Andersen, Agata |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7007221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-70072212020-02-10 Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals Wesolowska-Andersen, Agata Zhuo Yu, Grace Nylander, Vibe Abaitua, Fernando Thurner, Matthias Torres, Jason M Mahajan, Anubha Gloyn, Anna L McCarthy, Mark I eLife Computational and Systems Biology 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. eLife Sciences Publications, Ltd 2020-01-27 /pmc/articles/PMC7007221/ /pubmed/31985400 http://dx.doi.org/10.7554/eLife.51503 Text en © 2020, Wesolowska-Andersen et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Wesolowska-Andersen, Agata Zhuo Yu, Grace Nylander, Vibe Abaitua, Fernando Thurner, Matthias Torres, Jason M Mahajan, Anubha Gloyn, Anna L McCarthy, Mark I Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals |
title | Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals |
title_full | Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals |
title_fullStr | Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals |
title_full_unstemmed | Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals |
title_short | Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals |
title_sort | deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals |
topic | Computational and Systems Biology |
url | 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 |
work_keys_str_mv | AT wesolowskaandersenagata deeplearningmodelspredictregulatoryvariantsinpancreaticisletsandrefinetype2diabetesassociationsignals AT zhuoyugrace deeplearningmodelspredictregulatoryvariantsinpancreaticisletsandrefinetype2diabetesassociationsignals AT nylandervibe deeplearningmodelspredictregulatoryvariantsinpancreaticisletsandrefinetype2diabetesassociationsignals AT abaituafernando deeplearningmodelspredictregulatoryvariantsinpancreaticisletsandrefinetype2diabetesassociationsignals AT thurnermatthias deeplearningmodelspredictregulatoryvariantsinpancreaticisletsandrefinetype2diabetesassociationsignals AT torresjasonm deeplearningmodelspredictregulatoryvariantsinpancreaticisletsandrefinetype2diabetesassociationsignals AT mahajananubha deeplearningmodelspredictregulatoryvariantsinpancreaticisletsandrefinetype2diabetesassociationsignals AT gloynannal deeplearningmodelspredictregulatoryvariantsinpancreaticisletsandrefinetype2diabetesassociationsignals AT mccarthymarki deeplearningmodelspredictregulatoryvariantsinpancreaticisletsandrefinetype2diabetesassociationsignals |