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

• Published in: BMC genomics Vol. 22; no. 1; pp. 1 - 631
• Main Authors: Badam, Tejaswi V. S, de Weerd, Hendrik A, Martínez-Enguita, David, Olsson, Tomas, Alfredsson, Lars, Kockum, Ingrid, Jagodic, Maja, Lubovac-Pilav, Zelmina, Gustafsson, Mika
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 30.08.2021; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Benchmark; Benchmarks; Bioinformatics; Bioinformatik; Cardiovascular disease; Care and treatment; Data integration; Datasets; Diabetes; Diagnosis; Disease modules; Diseases; DNA methylation; Environmental risk; Epigenetics; Epstein-Barr virus; Gene expression; Genes; Genome-wide association analysis; Genome-wide association studies; Genomes; Genomics; Health aspects; Identification and classification; Identification methods; Inflammatory diseases; Integration; Medicin och hälsovetenskap; Methods; Methylation; Methylomics; Modules; Multi-omics; Multiple sclerosis; Network analysis; Network modules; Observations; Prevention; Protein network analysis; Risk analysis; Risk factors; Single nucleotide polymorphisms; Transcriptomics; Workflow; Sweden; Methylomics; Network modules; Multiple sclerosis; Protein network analysis; Disease modules; Transcriptomics; Network analysis; Data integration; Genome-wide association analysis; Benchmark; Multi-omics; Risk factors
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-021-07935-1

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. 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.sup.- 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. 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.