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Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes

Several disease risk variants reside on non-coding regions of DNA, particularly in open chromatin regions of specific cell types. Identifying the cell types relevant to complex traits through the integration of chromatin accessibility data and genome-wide association studies (GWAS) data can help to...

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Autores principales: Das, Akash Chandra, Foroutan, Aidin, Qian, Brian, Hosseini Naghavi, Nader, Shabani, Kayvan, Shooshtari, Parisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570273/
https://www.ncbi.nlm.nih.gov/pubmed/36232752
http://dx.doi.org/10.3390/ijms231911456
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author Das, Akash Chandra
Foroutan, Aidin
Qian, Brian
Hosseini Naghavi, Nader
Shabani, Kayvan
Shooshtari, Parisa
author_facet Das, Akash Chandra
Foroutan, Aidin
Qian, Brian
Hosseini Naghavi, Nader
Shabani, Kayvan
Shooshtari, Parisa
author_sort Das, Akash Chandra
collection PubMed
description Several disease risk variants reside on non-coding regions of DNA, particularly in open chromatin regions of specific cell types. Identifying the cell types relevant to complex traits through the integration of chromatin accessibility data and genome-wide association studies (GWAS) data can help to elucidate the mechanisms of these traits. In this study, we created a collection of associations between the combinations of chromatin accessibility data (bulk and single-cell) with an array of 201 complex phenotypes. We integrated the GWAS data of these 201 phenotypes with bulk chromatin accessibility data from 137 cell types measured by DNase-I hypersensitive sequencing and found significant results (FDR adjusted p-value ≤ 0.05) for at least one cell type in 21 complex phenotypes, such as atopic dermatitis, Graves’ disease, and body mass index. With the integration of single-cell chromatin accessibility data measured by an assay for transposase-accessible chromatin with high-throughput sequencing (scATAC-seq), taken from 111 adult and 111 fetal cell types, the resolution of association was magnified, enabling the identification of further cell types. This resulted in the identification of significant correlations (FDR adjusted p-value ≤ 0.05) between 15 categories of single-cell subtypes and 59 phenotypes ranging from autoimmune diseases like Graves’ disease to cardiovascular traits like diastolic/systolic blood pressure.
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spelling pubmed-95702732022-10-17 Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes Das, Akash Chandra Foroutan, Aidin Qian, Brian Hosseini Naghavi, Nader Shabani, Kayvan Shooshtari, Parisa Int J Mol Sci Article Several disease risk variants reside on non-coding regions of DNA, particularly in open chromatin regions of specific cell types. Identifying the cell types relevant to complex traits through the integration of chromatin accessibility data and genome-wide association studies (GWAS) data can help to elucidate the mechanisms of these traits. In this study, we created a collection of associations between the combinations of chromatin accessibility data (bulk and single-cell) with an array of 201 complex phenotypes. We integrated the GWAS data of these 201 phenotypes with bulk chromatin accessibility data from 137 cell types measured by DNase-I hypersensitive sequencing and found significant results (FDR adjusted p-value ≤ 0.05) for at least one cell type in 21 complex phenotypes, such as atopic dermatitis, Graves’ disease, and body mass index. With the integration of single-cell chromatin accessibility data measured by an assay for transposase-accessible chromatin with high-throughput sequencing (scATAC-seq), taken from 111 adult and 111 fetal cell types, the resolution of association was magnified, enabling the identification of further cell types. This resulted in the identification of significant correlations (FDR adjusted p-value ≤ 0.05) between 15 categories of single-cell subtypes and 59 phenotypes ranging from autoimmune diseases like Graves’ disease to cardiovascular traits like diastolic/systolic blood pressure. MDPI 2022-09-28 /pmc/articles/PMC9570273/ /pubmed/36232752 http://dx.doi.org/10.3390/ijms231911456 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Das, Akash Chandra
Foroutan, Aidin
Qian, Brian
Hosseini Naghavi, Nader
Shabani, Kayvan
Shooshtari, Parisa
Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes
title Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes
title_full Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes
title_fullStr Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes
title_full_unstemmed Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes
title_short Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes
title_sort single-cell chromatin accessibility data combined with gwas improves detection of relevant cell types in 59 complex phenotypes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570273/
https://www.ncbi.nlm.nih.gov/pubmed/36232752
http://dx.doi.org/10.3390/ijms231911456
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