2D GWAS in Complex Disease Research at Bordeaux University Hospital

In collaboration with Bordeaux University Hospital

At Bordeaux University Hospital, research is being conducted on complex diseases, including intellectual disability, using cutting-edge genomic analysis.

The primary goal is to analyze sequencing data from patients to identify genetic variants associated with these conditions, improving understanding of their genetic basis and informing diagnostic and therapeutic approaches.

From Classical to 2D GWAS

A central tool in this research is 2D GWAS (Genome-Wide Association Study), an approach that extends traditional GWAS methods.

• Classical GWAS (univariate) examines the association between a single genetic variant (SNP) and a specific phenotype, identifying variants that increase or decrease disease risk.

• However, complex diseases are often influenced by interactions between multiple variants, which classical GWAS cannot detect — leaving part of the “missing heritability” unexplained.

2D GWAS (epistatic or bivariate) overcomes this limitation by analyzing interactions between pairs of genetic variants and their combined effects on the phenotype.

This approach can:

• Detect complex, non-linear correlations invisible in univariate analyses

• Explore millions of potential interactions using advanced algorithms and AI

• Provide a more comprehensive explanation of heritability and improve phenotype prediction for complex traits

AI-Powered Analysis with BionomeeX

Through collaboration with Bordeaux University Hospital, BionomeeX applies AI-powered algorithms to:

• Process sequencing data

• Identify interacting genetic variants

• Generate detailed maps of genetic interactions

This enables researchers to:

• 🔍 Reveal previously hidden epistatic relationships contributing to intellectual disability

• 📊 Enhance the accuracy of genotype-to-phenotype predictions

• 💡 Inform potential therapeutic strategies and support precision medicine initiatives

Toward Precision Medicine

This work demonstrates the potential of AI-driven 2D GWAS to transform the study of complex genetic diseases, providing:

• A deeper understanding of genetic architecture

• Improved diagnostics

• Pathways toward personalized treatment approaches