Genome-wide association studies are enriched for interacting genes

Area of Science:

• Genomics
• Computational Biology
• Systems Biology

Background:

• Single-cell technologies offer insights into disease mechanisms and cell type origins.
• Genome-wide association studies (GWAS) identify genetic variants associated with diseases.
• Integrating multi-omics data is crucial for understanding genetic influences on disease development.

Purpose of the Study:

• To develop a method for understanding how genetic variants from GWAS influence disease development.
• To utilize genetic algorithms with multi-omics data to model gene and cell type contributions to disease risk.
• To explore the collective impact of genes and cell types on increased disease susceptibility.

Main Methods:

• Employed genetic algorithms with paired single-nucleus RNA-seq and ATAC-seq data.
• Integrated genome annotations and protein-protein interaction data.
• Assessed gene-cell set proposals using objective functions, including protein-protein interactions.

Main Results:

• Genetic algorithms identified gene-cell sets with significantly higher fitness scores compared to control sets.
• The model successfully identified known gene targets and ligand-receptor interactions.
• Analysis revealed that disease-associated variants exhibit more physical interactions than expected by chance, exemplified in breast cancer.

Conclusions:

• Genetic algorithms can generate coherent cellular models of disease risk from susceptibility variants.
• This computational approach enhances the interpretation of GWAS findings by linking variants to cellular mechanisms.
• The study demonstrates the utility of multi-omics integration for dissecting complex disease etiologies.