Computational strategies in nutrigenetics: Constructing a reference dataset of nutrition-associated genetic polymorphisms

Abstract Objective: This study aims to create a comprehensive dataset of human genetic polymorphisms associated with nutrition by integrating data from multiple sources, including the LitVar database, PubMed, and the GWAS catalog. This consolidated resource is intended to facilitate research in nutrigenetics by providing a reliable foundation to explore genetic polymorphisms linked to nutrition-related traits. Methods: We developed a data integration pipeline to assemble and analyze the dataset. It performs data retrieval from LitVar and PubMed and merges the data to produce a unified dataset. Comprehensive MeSH queries are defined to extract relevant genetic associations, which are then cross-referenced with the GWAS data. Results: The resulting dataset aggregates extensive information on genetic polymorphisms and nutrition-related traits. Through MeSH query, we identified key genes and SNPs associated with nutrition-related traits. Cross-referencing with GWAS data provided insights on potential effects or risk alleles associated with this genetic polymorphisms. The co-occurrence analysis revealed meaningful gene-diet interactions, advancing personalized nutrition and nutrigenomics research. Conclusion: The dataset presented in this study consolidates and organizes information on genetic polymorphisms associated with nutrition, facilitating detailed exploration of gene-diet interactions. This resource advances personalized nutrition interventions and nutrigenomics research. The dataset is publicly accessible at https://zenodo.org/records/14052302, its adaptable structure ensures applicability in a broad range of genetic investigations.