Cargando…
Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology
Genetic variants showing associations with specific biological traits and diseases detected by genome-wide association studies (GWAS) commonly map to non-coding DNA regulatory regions. Many of these regions are located considerable distances away from the genes they regulate and come into their prox...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573080/ https://www.ncbi.nlm.nih.gov/pubmed/34759959 http://dx.doi.org/10.3389/fgene.2021.745672 |
_version_ | 1784595343710617600 |
---|---|
author | Thiecke, Michiel J. Yang, Emma J. Burren, Oliver S. Ray-Jones, Helen Spivakov, Mikhail |
author_facet | Thiecke, Michiel J. Yang, Emma J. Burren, Oliver S. Ray-Jones, Helen Spivakov, Mikhail |
author_sort | Thiecke, Michiel J. |
collection | PubMed |
description | Genetic variants showing associations with specific biological traits and diseases detected by genome-wide association studies (GWAS) commonly map to non-coding DNA regulatory regions. Many of these regions are located considerable distances away from the genes they regulate and come into their proximity through 3D chromosomal interactions. We previously developed COGS, a statistical pipeline for linking GWAS variants with their putative target genes based on 3D chromosomal interaction data arising from high-resolution assays such as Promoter Capture Hi-C (PCHi-C). Here, we applied COGS to COVID-19 Host Genetic Consortium (HGI) GWAS meta-analysis data on COVID-19 susceptibility and severity using our previously generated PCHi-C results in 17 human primary cell types and SARS-CoV-2-infected lung carcinoma cells. We prioritise 251 genes putatively associated with these traits, including 16 out of 47 genes highlighted by the GWAS meta-analysis authors. The prioritised genes are expressed in a broad array of tissues, including, but not limited to, blood and brain cells, and are enriched for genes involved in the inflammatory response to viral infection. Our prioritised genes and pathways, in conjunction with results from other prioritisation approaches and targeted validation experiments, will aid in the understanding of COVID-19 pathology, paving the way for novel treatments. |
format | Online Article Text |
id | pubmed-8573080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85730802021-11-09 Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology Thiecke, Michiel J. Yang, Emma J. Burren, Oliver S. Ray-Jones, Helen Spivakov, Mikhail Front Genet Genetics Genetic variants showing associations with specific biological traits and diseases detected by genome-wide association studies (GWAS) commonly map to non-coding DNA regulatory regions. Many of these regions are located considerable distances away from the genes they regulate and come into their proximity through 3D chromosomal interactions. We previously developed COGS, a statistical pipeline for linking GWAS variants with their putative target genes based on 3D chromosomal interaction data arising from high-resolution assays such as Promoter Capture Hi-C (PCHi-C). Here, we applied COGS to COVID-19 Host Genetic Consortium (HGI) GWAS meta-analysis data on COVID-19 susceptibility and severity using our previously generated PCHi-C results in 17 human primary cell types and SARS-CoV-2-infected lung carcinoma cells. We prioritise 251 genes putatively associated with these traits, including 16 out of 47 genes highlighted by the GWAS meta-analysis authors. The prioritised genes are expressed in a broad array of tissues, including, but not limited to, blood and brain cells, and are enriched for genes involved in the inflammatory response to viral infection. Our prioritised genes and pathways, in conjunction with results from other prioritisation approaches and targeted validation experiments, will aid in the understanding of COVID-19 pathology, paving the way for novel treatments. Frontiers Media S.A. 2021-10-25 /pmc/articles/PMC8573080/ /pubmed/34759959 http://dx.doi.org/10.3389/fgene.2021.745672 Text en Copyright © 2021 Thiecke, Yang, Burren, Ray-Jones and Spivakov. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Thiecke, Michiel J. Yang, Emma J. Burren, Oliver S. Ray-Jones, Helen Spivakov, Mikhail Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology |
title | Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology |
title_full | Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology |
title_fullStr | Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology |
title_full_unstemmed | Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology |
title_short | Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology |
title_sort | prioritisation of candidate genes underpinning covid-19 host genetic traits based on high-resolution 3d chromosomal topology |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573080/ https://www.ncbi.nlm.nih.gov/pubmed/34759959 http://dx.doi.org/10.3389/fgene.2021.745672 |
work_keys_str_mv | AT thieckemichielj prioritisationofcandidategenesunderpinningcovid19hostgenetictraitsbasedonhighresolution3dchromosomaltopology AT yangemmaj prioritisationofcandidategenesunderpinningcovid19hostgenetictraitsbasedonhighresolution3dchromosomaltopology AT burrenolivers prioritisationofcandidategenesunderpinningcovid19hostgenetictraitsbasedonhighresolution3dchromosomaltopology AT rayjoneshelen prioritisationofcandidategenesunderpinningcovid19hostgenetictraitsbasedonhighresolution3dchromosomaltopology AT spivakovmikhail prioritisationofcandidategenesunderpinningcovid19hostgenetictraitsbasedonhighresolution3dchromosomaltopology |