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Genetic risk factors for ME/CFS identified using combinatorial analysis

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to hel...

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Autores principales: Das, Sayoni, Taylor, Krystyna, Kozubek, James, Sardell, Jason, Gardner, Steve
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749644/
https://www.ncbi.nlm.nih.gov/pubmed/36517845
http://dx.doi.org/10.1186/s12967-022-03815-8
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author Das, Sayoni
Taylor, Krystyna
Kozubek, James
Sardell, Jason
Gardner, Steve
author_facet Das, Sayoni
Taylor, Krystyna
Kozubek, James
Sardell, Jason
Gardner, Steve
author_sort Das, Sayoni
collection PubMed
description BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to help to alleviate some of these issues for patients. METHODS: We applied both GWAS and the PrecisionLife combinatorial analytics platform to analyze ME/CFS cohorts from UK Biobank, including the Pain Questionnaire cohort, in a case–control design with 1000 cycles of fully random permutation. Results from this study were supported by a series of replication and cohort comparison experiments, including use of disjoint Verbal Interview CFS, post-viral fatigue syndrome and fibromyalgia cohorts also derived from UK Biobank, and compared results for overlap and reproducibility. RESULTS: Combinatorial analysis revealed 199 SNPs mapping to 14 genes that were significantly associated with 91% of the cases in the ME/CFS population. These SNPs were found to stratify by shared cases into 15 clusters (communities) made up of 84 high-order combinations of between 3 and 5 SNPs. p-values for these communities range from 2.3 × 10(–10) to 1.6 × 10(–72). Many of the genes identified are linked to the key cellular mechanisms hypothesized to underpin ME/CFS, including vulnerabilities to stress and/or infection, mitochondrial dysfunction, sleep disturbance and autoimmune development. We identified 3 of the critical SNPs replicated in the post-viral fatigue syndrome cohort and 2 SNPs replicated in the fibromyalgia cohort. We also noted similarities with genes associated with multiple sclerosis and long COVID, which share some symptoms and potentially a viral infection trigger with ME/CFS. CONCLUSIONS: This study provides the first detailed genetic insights into the pathophysiological mechanisms underpinning ME/CFS and offers new approaches for better diagnosis and treatment of patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03815-8.
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spelling pubmed-97496442022-12-14 Genetic risk factors for ME/CFS identified using combinatorial analysis Das, Sayoni Taylor, Krystyna Kozubek, James Sardell, Jason Gardner, Steve J Transl Med Research BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to help to alleviate some of these issues for patients. METHODS: We applied both GWAS and the PrecisionLife combinatorial analytics platform to analyze ME/CFS cohorts from UK Biobank, including the Pain Questionnaire cohort, in a case–control design with 1000 cycles of fully random permutation. Results from this study were supported by a series of replication and cohort comparison experiments, including use of disjoint Verbal Interview CFS, post-viral fatigue syndrome and fibromyalgia cohorts also derived from UK Biobank, and compared results for overlap and reproducibility. RESULTS: Combinatorial analysis revealed 199 SNPs mapping to 14 genes that were significantly associated with 91% of the cases in the ME/CFS population. These SNPs were found to stratify by shared cases into 15 clusters (communities) made up of 84 high-order combinations of between 3 and 5 SNPs. p-values for these communities range from 2.3 × 10(–10) to 1.6 × 10(–72). Many of the genes identified are linked to the key cellular mechanisms hypothesized to underpin ME/CFS, including vulnerabilities to stress and/or infection, mitochondrial dysfunction, sleep disturbance and autoimmune development. We identified 3 of the critical SNPs replicated in the post-viral fatigue syndrome cohort and 2 SNPs replicated in the fibromyalgia cohort. We also noted similarities with genes associated with multiple sclerosis and long COVID, which share some symptoms and potentially a viral infection trigger with ME/CFS. CONCLUSIONS: This study provides the first detailed genetic insights into the pathophysiological mechanisms underpinning ME/CFS and offers new approaches for better diagnosis and treatment of patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03815-8. BioMed Central 2022-12-14 /pmc/articles/PMC9749644/ /pubmed/36517845 http://dx.doi.org/10.1186/s12967-022-03815-8 Text en © The Author(s) 2022 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
Das, Sayoni
Taylor, Krystyna
Kozubek, James
Sardell, Jason
Gardner, Steve
Genetic risk factors for ME/CFS identified using combinatorial analysis
title Genetic risk factors for ME/CFS identified using combinatorial analysis
title_full Genetic risk factors for ME/CFS identified using combinatorial analysis
title_fullStr Genetic risk factors for ME/CFS identified using combinatorial analysis
title_full_unstemmed Genetic risk factors for ME/CFS identified using combinatorial analysis
title_short Genetic risk factors for ME/CFS identified using combinatorial analysis
title_sort genetic risk factors for me/cfs identified using combinatorial analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749644/
https://www.ncbi.nlm.nih.gov/pubmed/36517845
http://dx.doi.org/10.1186/s12967-022-03815-8
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