ArticleBMC genomics2021
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 sclerosis.
Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Epstein-Barr Virus, Lower Vitamin D, Low Sun Exposure, and HLA-DRB1*1501 Risk Variant Share Common Epigenetic Pathways Leading to Multiple Sclerosis Onset.Annals of neurology · 2026Article
- DNA methylation mediates the multiple sclerosis onset risk associated with HHV-6 DNA positivity.Environmental epigenetics · 2026Article
- Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules.Patterns (New York, N.Y.) · 2024Article
- The lung-brain axis in multiple sclerosis: Mechanistic insights and future directions.Brain, behavior, & immunity - health · 2024Article
- TOPAS, a network-based approach to detect disease modules in a top-down fashion.NAR genomics and bioinformatics · 2022Article
- MODalyseR-a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data.Bioinformatics advances · 2022Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThere 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.
resultWe 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
conclusionsWe 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.
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