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 developm...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-024-00421-w |
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| author | Peter T. Nguyen Simon G. Coetzee Irina Silacheva Dennis J. Hazelett |
| author_facet | Peter T. Nguyen Simon G. Coetzee Irina Silacheva Dennis J. Hazelett |
| author_sort | Peter T. Nguyen |
| collection | DOAJ |
| description | 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 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. |
| format | Article |
| id | doaj-art-b6fa1c6555e24fb2a87ab260ac69f5c6 |
| institution | Kabale University |
| issn | 1756-0381 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-b6fa1c6555e24fb2a87ab260ac69f5c62025-01-19T12:12:45ZengBMCBioData Mining1756-03812025-01-0118111810.1186/s13040-024-00421-wGenome-wide association studies are enriched for interacting genesPeter T. Nguyen0Simon G. Coetzee1Irina Silacheva2Dennis J. Hazelett3The Department of Biomedical and Translational Sciences, Cedars-Sinai Medical CenterThe Department of Computational Biomedicine, Cedars-Sinai Medical CenterThe Department of Biomedical and Translational Sciences, Cedars-Sinai Medical CenterThe Department of Computational Biomedicine, Cedars-Sinai Medical CenterAbstract 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.https://doi.org/10.1186/s13040-024-00421-wGWASGenetic algorithmsVariant prioritizationMulti-omicsBreast cancerComplex disease |
| spellingShingle | Peter T. Nguyen Simon G. Coetzee Irina Silacheva Dennis J. Hazelett Genome-wide association studies are enriched for interacting genes BioData Mining GWAS Genetic algorithms Variant prioritization Multi-omics Breast cancer Complex disease |
| title | Genome-wide association studies are enriched for interacting genes |
| title_full | Genome-wide association studies are enriched for interacting genes |
| title_fullStr | Genome-wide association studies are enriched for interacting genes |
| title_full_unstemmed | Genome-wide association studies are enriched for interacting genes |
| title_short | Genome-wide association studies are enriched for interacting genes |
| title_sort | genome wide association studies are enriched for interacting genes |
| topic | GWAS Genetic algorithms Variant prioritization Multi-omics Breast cancer Complex disease |
| url | https://doi.org/10.1186/s13040-024-00421-w |
| work_keys_str_mv | AT petertnguyen genomewideassociationstudiesareenrichedforinteractinggenes AT simongcoetzee genomewideassociationstudiesareenrichedforinteractinggenes AT irinasilacheva genomewideassociationstudiesareenrichedforinteractinggenes AT dennisjhazelett genomewideassociationstudiesareenrichedforinteractinggenes |