Genome-wide association studies are enriched for interacting genes.

ABSTRACT:

Background

With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.

Results

We used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured prote

SUBMITTER: Nguyen PT 

PROVIDER: S-EPMC11734473 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature