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Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits
Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469333/ https://www.ncbi.nlm.nih.gov/pubmed/37603758 http://dx.doi.org/10.1073/pnas.2206612120 |
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author | Hudaiberdiev, Sanjarbek Taylor, D. Leland Song, Wei Narisu, Narisu Bhuiyan, Redwan M. Taylor, Henry J. Tang, Xuming Yan, Tingfen Swift, Amy J. Bonnycastle, Lori L. Consortium, DIAMANTE Chen, Shuibing Stitzel, Michael L. Erdos, Michael R. Ovcharenko, Ivan Collins, Francis S. |
author_facet | Hudaiberdiev, Sanjarbek Taylor, D. Leland Song, Wei Narisu, Narisu Bhuiyan, Redwan M. Taylor, Henry J. Tang, Xuming Yan, Tingfen Swift, Amy J. Bonnycastle, Lori L. Consortium, DIAMANTE Chen, Shuibing Stitzel, Michael L. Erdos, Michael R. Ovcharenko, Ivan Collins, Francis S. |
author_sort | Hudaiberdiev, Sanjarbek |
collection | PubMed |
description | Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies. |
format | Online Article Text |
id | pubmed-10469333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-104693332023-09-01 Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits Hudaiberdiev, Sanjarbek Taylor, D. Leland Song, Wei Narisu, Narisu Bhuiyan, Redwan M. Taylor, Henry J. Tang, Xuming Yan, Tingfen Swift, Amy J. Bonnycastle, Lori L. Consortium, DIAMANTE Chen, Shuibing Stitzel, Michael L. Erdos, Michael R. Ovcharenko, Ivan Collins, Francis S. Proc Natl Acad Sci U S A Biological Sciences Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies. National Academy of Sciences 2023-08-21 2023-08-29 /pmc/articles/PMC10469333/ /pubmed/37603758 http://dx.doi.org/10.1073/pnas.2206612120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Biological Sciences Hudaiberdiev, Sanjarbek Taylor, D. Leland Song, Wei Narisu, Narisu Bhuiyan, Redwan M. Taylor, Henry J. Tang, Xuming Yan, Tingfen Swift, Amy J. Bonnycastle, Lori L. Consortium, DIAMANTE Chen, Shuibing Stitzel, Michael L. Erdos, Michael R. Ovcharenko, Ivan Collins, Francis S. Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits |
title | Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits |
title_full | Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits |
title_fullStr | Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits |
title_full_unstemmed | Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits |
title_short | Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits |
title_sort | modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with t2d and glycemic traits |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469333/ https://www.ncbi.nlm.nih.gov/pubmed/37603758 http://dx.doi.org/10.1073/pnas.2206612120 |
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