Article ; Online: metGWAS 1.0: an R workflow for network-driven over-representation analysis between independent metabolomic and meta-genome-wide association studies.
Bioinformatics (Oxford, England)
2023 Volume 39, Issue 9
Abstract: Motivation: The method of genome-wide association studies (GWAS) and metabolomics combined provide an quantitative approach to pinpoint metabolic pathways and genes linked to specific diseases; however, such analyses require both genomics and ... ...
| Abstract | Motivation: The method of genome-wide association studies (GWAS) and metabolomics combined provide an quantitative approach to pinpoint metabolic pathways and genes linked to specific diseases; however, such analyses require both genomics and metabolomics datasets from the same individuals/samples. In most cases, this approach is not feasible due to high costs, lack of technical infrastructure, unavailability of samples, and other factors. Therefore, an unmet need exists for a bioinformatics tool that can identify gene loci-associated polymorphic variants for metabolite alterations seen in disease states using standalone metabolomics. Results: Here, we developed a bioinformatics tool, metGWAS 1.0, that integrates independent GWAS data from the GWAS database and standalone metabolomics data using a network-based systems biology approach to identify novel disease/trait-specific metabolite-gene associations. The tool was evaluated using standalone metabolomics datasets extracted from two metabolomics-GWAS case studies. It discovered both the observed and novel gene loci with known single nucleotide polymorphisms when compared to the original studies. Availability and implementation: The developed metGWAS 1.0 framework is implemented in an R pipeline and available at: https://github.com/saifurbd28/metGWAS-1.0. |
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| MeSH term(s) | Humans ; Genome-Wide Association Study ; Workflow ; Metabolomics ; Computational Biology ; Databases, Factual |
| Language | English |
| Publishing date | 2023-08-23 |
| Publishing country | England |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 1422668-6 |
| ISSN | 1367-4811 ; 1367-4803 |
| ISSN (online) | 1367-4811 |
| ISSN | 1367-4803 |
| DOI | 10.1093/bioinformatics/btad523 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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