Genome wide association studies are enriched for interacting genes.

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

• Recent single-cell technologies offer insights into disease mechanisms and cell type origins.
• Multi-omics data, including single-nucleus RNA and ATAC sequencing, are crucial for understanding genetic variant influence on disease.
• Genome-wide association studies (GWAS) identify genetic variants associated with diseases.

Purpose of the Study:

• To develop a method using genetic algorithms to link genetic variants from GWAS to specific cell types and genes involved in disease risk.
• To demonstrate how multi-omics data, genome annotations, and protein-protein interaction networks can be integrated to build a cellular model of disease risk.

Main Methods:

• Utilized genetic algorithms to evaluate gene-cell set proposals based on objective functions incorporating multi-omics data and annotations.
• Employed protein-protein interaction data as a key objective function to identify biologically relevant gene-cell associations.
• Compared fitness scores and subgraph sizes between sets of disease-associated variants and control variants.

Main Results:

• Genetic algorithms identified gene-cell sets with significantly higher fitness scores and subgraph sizes for disease-associated variants compared to controls.
• The model successfully identified known gene targets and ligand-receptor interactions, validating its predictive capability.
• Demonstrated that susceptibility variants in diseases like breast cancer exhibit a higher degree of physical interactions than expected by chance.

Conclusions:

• Application of genetic algorithms to GWAS provides a robust method for generating coherent cellular models of disease risk.
• This approach effectively integrates diverse biological data to elucidate the functional roles of genetic variants in disease pathogenesis.
• The findings highlight the importance of considering cell type-specific effects and molecular interactions in understanding genetic contributions to disease.