The integration of multi-omics data is essential for understanding complex biological systems, providing insights beyond single-omics approaches. However, challenges related to data heterogeneity, standardization, and computational scalability persist. This study explores the interdisciplinary application of semantic technologies to enhance data integration, standardization, and analysis in multi-omics research. We performed a systematic mapping study assessing literature from 2014 to 2024, focusing on the utilization of ontologies, knowledge graphs, and graph-based methods for multi-omics integration. Our findings indicate a growing number of publications in this field, predominantly appearing in high-impact journals. The deployment of semantic technologies has notably improved data visualization, querying, and management, thus enhancing gene and pathway discovery, and providing deeper disease insights and more accurate predictive modeling. The study underscores the significance of semantic technologies in overcoming multi-omics integration challenges. Future research should focus on integrating diverse data types, developing advanced computational tools, and incorporating AI and machine learning to foster personalized medicine applications.

Objective This study aimed to build 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. Such a resource could facilitate the exploration of genetic polymorphisms associated with nutrition-related traits. Methods We developed a Python pipeline to streamline the integration and analysis of genetic polymorphism data associated with nutrition. We employed the MeSH ontology as a framework to aggregate relevant genetic data. The pipeline comprises five distinct modules that go through the following steps: data extraction from LitVar and PubMed articles, generation of a joint dataset by data merging, generation of comprehensive MeSH term lists, filtering of the joint dataset using the selected MeSH sets, lexical analysis and augmentation of the dataset with data from of the GWAS catalog dataset. Results We successfully aggregated a wide range of papers and data on genetic polymorphism and nutrition-related traits into a single dataset. Cross-referencing with the GWAS catalog dataset provided information about possible effects or risk alleles associated with the identified genetic polymorphisms. The nutrigenetic dataset we developed is a tool for nutritionists and researchers, serving as a preliminary benchmark for personalized nutrition interventions based on genetic testing. Conclusion The pipeline presented here consolidates and organizes information on genetic polymorphisms associated with nutrition, enabling comprehensive analysis and exploration of gene-diet interactions. Overall, the method contributes to advancing personalized nutrition interventions and nutrigenomics research. The flexible nature of the system allows its application to other investigations related to genetic polymorphisms.

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. 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. 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. 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.

Genetic variants in nutrition-related genes exhibit variable functional consequences; however, systematic characterization across different nutritional domains remains limited. This highlights the need for detailed exploration of variant distribution and functional effects across nutritional gene categories. Therefore, the main objective of this computational study is to delve deeper into the distribution and functional impact of missense variants in nutrition-related genes. We analyzed Genetic polymoRphism variants using Personalized Medicine (GRPM) dataset, focusing on ten groups of nutrition-related genes. Missense variants were characterized using ProtVar for functional/structural impact, Pharos for functional classification, network analysis for pathway identification, and Gene Ontology enrichment for biological process annotation. The analysis of 63,581 Single Nucleotide Polymorphisms (SNP) revealed 27,683 missense variants across 1589 genes. Food intolerance (0.23) and food allergy (0.15) groups showed the highest missense/SNP ratio, while obesity-related genes showed the lowest (0.04). Enzymes predominated in xenobiotic and vitamin metabolism groups, while G-protein-coupled receptors were enriched in eating behavior genes. The vitamin metabolism group had the highest proportion of pathogenic variants. Network analysis identified apolipoproteins as central hubs in metabolic groups and inflammatory proteins in allergy-related groups. These findings offer insights into personalized nutrition approaches and underscore the utility of computational variant analysis in elucidating gene-diet interactions.