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In silico prioritisation of microRNA-associated common variants in multiple sclerosis
BACKGROUND: Genome-wide association studies (GWAS) have highlighted over 200 autosomal variants associated with multiple sclerosis (MS). However, variants in non-coding regions such as those encoding microRNAs have not been explored thoroughly, despite strong evidence of microRNA dysregulation in MS...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061723/ https://www.ncbi.nlm.nih.gov/pubmed/36991503 http://dx.doi.org/10.1186/s40246-023-00478-4 |
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author | Fashina, Ifeolutembi A. McCoy, Claire E. Furney, Simon J. |
author_facet | Fashina, Ifeolutembi A. McCoy, Claire E. Furney, Simon J. |
author_sort | Fashina, Ifeolutembi A. |
collection | PubMed |
description | BACKGROUND: Genome-wide association studies (GWAS) have highlighted over 200 autosomal variants associated with multiple sclerosis (MS). However, variants in non-coding regions such as those encoding microRNAs have not been explored thoroughly, despite strong evidence of microRNA dysregulation in MS patients and model organisms. This study explores the effect of microRNA-associated variants in MS, through the largest publicly available GWAS, which involved 47,429 MS cases and 68,374 controls. METHODS: We identified SNPs within the coordinates of microRNAs, ± 5-kb microRNA flanking regions and predicted 3′UTR target-binding sites using miRBase v22, TargetScan 7.0 RNA22 v2.0 and dbSNP v151. We established the subset of microRNA-associated SNPs which were tested in the summary statistics of the largest MS GWAS by intersecting these datasets. Next, we prioritised those microRNA-associated SNPs which are among known MS susceptibility SNPs, are in strong linkage disequilibrium with the former or meet a microRNA-specific Bonferroni-corrected threshold. Finally, we predicted the effects of those prioritised SNPs on their microRNAs and 3′UTR target-binding sites using TargetScan v7.0, miRVaS and ADmiRE. RESULTS: We have identified 30 candidate microRNA-associated variants which meet at least one of our prioritisation criteria. Among these, we highlighted one microRNA variant rs1414273 (MIR548AC) and four 3′UTR microRNA-binding site variants within SLC2A4RG (rs6742), CD27 (rs1059501), MMEL1 (rs881640) and BCL2L13 (rs2587100). We determined changes to the predicted microRNA stability and binding site recognition of these microRNA and target sites. CONCLUSIONS: We have systematically examined the functional, structural and regulatory effects of candidate MS variants among microRNAs and 3′UTR targets. This analysis allowed us to identify candidate microRNA-associated MS SNPs and highlights the value of prioritising non-coding RNA variation in GWAS. These candidate SNPs could influence microRNA regulation in MS patients. Our study is the first thorough investigation of both microRNA and 3′UTR target-binding site variation in multiple sclerosis using GWAS summary statistics. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00478-4. |
format | Online Article Text |
id | pubmed-10061723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100617232023-03-31 In silico prioritisation of microRNA-associated common variants in multiple sclerosis Fashina, Ifeolutembi A. McCoy, Claire E. Furney, Simon J. Hum Genomics Research BACKGROUND: Genome-wide association studies (GWAS) have highlighted over 200 autosomal variants associated with multiple sclerosis (MS). However, variants in non-coding regions such as those encoding microRNAs have not been explored thoroughly, despite strong evidence of microRNA dysregulation in MS patients and model organisms. This study explores the effect of microRNA-associated variants in MS, through the largest publicly available GWAS, which involved 47,429 MS cases and 68,374 controls. METHODS: We identified SNPs within the coordinates of microRNAs, ± 5-kb microRNA flanking regions and predicted 3′UTR target-binding sites using miRBase v22, TargetScan 7.0 RNA22 v2.0 and dbSNP v151. We established the subset of microRNA-associated SNPs which were tested in the summary statistics of the largest MS GWAS by intersecting these datasets. Next, we prioritised those microRNA-associated SNPs which are among known MS susceptibility SNPs, are in strong linkage disequilibrium with the former or meet a microRNA-specific Bonferroni-corrected threshold. Finally, we predicted the effects of those prioritised SNPs on their microRNAs and 3′UTR target-binding sites using TargetScan v7.0, miRVaS and ADmiRE. RESULTS: We have identified 30 candidate microRNA-associated variants which meet at least one of our prioritisation criteria. Among these, we highlighted one microRNA variant rs1414273 (MIR548AC) and four 3′UTR microRNA-binding site variants within SLC2A4RG (rs6742), CD27 (rs1059501), MMEL1 (rs881640) and BCL2L13 (rs2587100). We determined changes to the predicted microRNA stability and binding site recognition of these microRNA and target sites. CONCLUSIONS: We have systematically examined the functional, structural and regulatory effects of candidate MS variants among microRNAs and 3′UTR targets. This analysis allowed us to identify candidate microRNA-associated MS SNPs and highlights the value of prioritising non-coding RNA variation in GWAS. These candidate SNPs could influence microRNA regulation in MS patients. Our study is the first thorough investigation of both microRNA and 3′UTR target-binding site variation in multiple sclerosis using GWAS summary statistics. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00478-4. BioMed Central 2023-03-30 /pmc/articles/PMC10061723/ /pubmed/36991503 http://dx.doi.org/10.1186/s40246-023-00478-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Fashina, Ifeolutembi A. McCoy, Claire E. Furney, Simon J. In silico prioritisation of microRNA-associated common variants in multiple sclerosis |
title | In silico prioritisation of microRNA-associated common variants in multiple sclerosis |
title_full | In silico prioritisation of microRNA-associated common variants in multiple sclerosis |
title_fullStr | In silico prioritisation of microRNA-associated common variants in multiple sclerosis |
title_full_unstemmed | In silico prioritisation of microRNA-associated common variants in multiple sclerosis |
title_short | In silico prioritisation of microRNA-associated common variants in multiple sclerosis |
title_sort | in silico prioritisation of microrna-associated common variants in multiple sclerosis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061723/ https://www.ncbi.nlm.nih.gov/pubmed/36991503 http://dx.doi.org/10.1186/s40246-023-00478-4 |
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