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Background <p>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.</p> Results <p>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 protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground <i>vs.</i> matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies.</p> Conclusions <p>Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.</p>

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Genome-wide association studies are enriched for interacting genes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月15日

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  • Peter T. Nguyen,
  • Simon G. Coetzee,
  • Irina Silacheva,
  • Dennis J. Hazelett

摘要

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 protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs. matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies.

Conclusions

Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.