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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2023
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.
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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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