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...

Descripción completa

Detalles Bibliográficos
Autores principales: Thiecke, Michiel J., Yang, Emma J., Burren, Oliver S., Ray-Jones, Helen, Spivakov, Mikhail
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