Systematic GWAS-assessment of disease modules reveals a multi-omic MS module strongly associated with risk factors

Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been methodically evaluated, and no guidelines for predictive multi-omics integration exist. We assessed eight module identification methods in 57 expression and methylation studies of 19 diseases using GWAS enrichment analysis, which showed modules inferred from clique-based methods being most enriched. Next, we applied the same strategy for multi-omics integration of 19 datasets of multiple sclerosis (MS), which allowed the robust identification of a module of 220 genes. Most genes of the module could be epigenetically validated (n = 217, P = 10-47) and were also independently validated for association with five different risk factors of MS, which further stressed the high relevance of the module for MS. We believe our analysis provides a workflow for improving disease module methods, but also for combining and assessing the performance of multi-omics approaches for complex diseases.