This project aims to investigate gene-diet interactions and their role in metabolic disease risk using large-scale population-based cohort data from Korean Genome and Epidemiology Study (KoGES) and the UK Biobank. The key research questions are:
a) How do dietary factors influence genetic susceptibility to metabolic diseases?
b) What are the critical gene-diet interactions associated with metabolic disease outcomes?
c) Can these interactions be used to develop predictive models for metabolic disease risk?
Metabolic diseases such as obesity, diabetes, and cardiovascular disease pose major global health challenges. While both genetic predisposition and dietary habits are known contributors, the complex interplay between them remains insufficiently explored. Leveraging advancements in genome-wide association studies (GWAS) and multi-omics integration, this study will analyze how dietary patterns modify genetic risk.
We will harmonize genetic and dietary data from KoGES (~200,000 participants) and the UK Biobank (~500,000 participants). A multi-step methodology will be applied:
1. GWAS and Gene-Diet interaction analysis to identify genetic variants associated with metabolic traits and their interaction with dietary variables.
2. Network modeling using Association Weight Matrix (AWM) and Partial Correlation and Information Theory (PCIT) to model gene-diet interactions and their impact on metabolic disease pathways.
3. Machine learning approaches to develop and validate a prediction model for metabolic disease risk based on gene-diet interactions.
The findings will provide scientific evidence to support precision nutrition strategies by identifying individuals at high risk based on their genetic and dietary profiles. This study will contribute to early disease prediction and the development of personalized dietary guidelines, promoting targeted prevention and improving population health outcomes.