metGWAS 1.0: an R workflow for network-driven over-representation analysis between independent metabolomic and...

Area of Science:

• Bioinformatics
• Systems Biology
• Genetics

Background:

• Genome-wide association studies (GWAS) and metabolomics are powerful for identifying disease-linked genes and pathways.
• Combined genomic and metabolomic datasets from the same individuals are often infeasible due to cost and logistical challenges.
• A need exists for tools analyzing standalone metabolomics data to find gene associations with metabolite alterations.

Purpose of the Study:

• To develop a bioinformatics tool, metGWAS 1.0, for identifying gene loci-associated polymorphic variants linked to metabolite alterations using independent GWAS and metabolomics data.
• To enable the discovery of novel metabolite-gene associations in disease states without requiring paired datasets.

Main Methods:

• Developed metGWAS 1.0, a bioinformatics tool integrating independent GWAS data and standalone metabolomics data.
• Employed a network-based systems biology approach within the metGWAS 1.0 framework.
• Evaluated the tool using metabolomics datasets from two case studies.

Main Results:

• metGWAS 1.0 successfully identified gene loci associated with metabolite alterations using independent datasets.
• The tool discovered both previously known and novel gene loci and single nucleotide polymorphisms.
• Performance was validated against existing metabolomics-GWAS case studies.

Conclusions:

• metGWAS 1.0 provides a feasible computational approach to identify gene-metabolite associations from standalone metabolomics data.
• The tool facilitates novel discoveries in disease-trait research by leveraging independent GWAS and metabolomics resources.
• This framework advances the integration of multi-omics data for biological discovery.