# Missense Variants in Nutrition-Related Genes: A Computational Study

## TL;DR

This study explores how missense genetic variants affect nutrition-related genes and their roles in food intolerance, allergies, and metabolism.

## Contribution

The study provides a systematic computational analysis of missense variants in nutrition-related genes across multiple domains.

## Key findings

- Food intolerance and allergy genes had the highest missense variant ratios.
- Vitamin metabolism genes showed the highest proportion of pathogenic variants.
- Apolipoproteins and inflammatory proteins were identified as key hubs in metabolic and allergy-related networks.

## Abstract

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.