A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosisShow others and affiliations
2021 (English)In: BMC Genomics, E-ISSN 1471-2164, Vol. 22, no 1, article id 631
Article in journal (Refereed) Published
Abstract [en]
Background: There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result: We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10− 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions: We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.
Place, publisher, year, edition, pages
BioMed Central, 2021. Vol. 22, no 1, article id 631
Keywords [en]
Benchmark, Data integration, Disease modules, Genome-wide association analysis, Methylomics, Multi-omics, Multiple sclerosis, Network analysis, Network modules, Protein network analysis, Risk factors, Transcriptomics
National Category
Bioinformatics and Systems Biology Immunology in the medical area Medical Genetics
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:his:diva-20535DOI: 10.1186/s12864-021-07935-1ISI: 000692402600002PubMedID: 34461822Scopus ID: 2-s2.0-85113734842OAI: oai:DiVA.org:his-20535DiVA, id: diva2:1592478
Funder
Swedish Research Council, 2015–03807Swedish Research Council, 2018–02638EU, Horizon 2020, grant 818170Knut and Alice Wallenberg Foundation, 2019.0089Knowledge Foundation, 20170298Swedish Foundation for Strategic Research , SB16–0095Swedish National Infrastructure for Computing (SNIC), SNIC 2020/5–177, LiU-2018-12 and LiU-2019-25
Note
CC BY 4.0
© 2021, The Author(s)
This 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 giveappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtainpermission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/.The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.
Correspondence: mika.gustafsson@liu.se
This work was supported by the Swedish Research Council (grant 2015–03807(M.G.), grant 2018–02638(M.J.)), the Swedish foundation for strategic research (grant SB16–0095(M.G.)), the Center for Industrial IT (CENIIT)(M.G.), European Union Horizon 2020/European Research Council Consolidator grant (Epi4MS, grant 818170(M.J.)), Knut and Alice Wallenberg Foundation (grant 2019.0089(M.J.)) and the Knowledge Foundation (grant 20170298(Z.L.)). Computational resources were granted by Swedish National Infrastructure for Computing (SNIC; SNIC 2020/5–177, LiU-2018-12 and LiU-2019-25). The funding bodies had no role in the study and collection, ana-lysis, and interpretation of data and in writing the manuscript. Open Accessfunding provided by Linköping University.
2021-09-092021-09-092024-01-17Bibliographically approved