Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks

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Molecular evolution has informed our understanding of gene function; however, classical methods have largely been static in their implementation, focusing on single genes. Here, we present and prove the utility of a dynamic, network-based understanding of molecular evolution to infer relationships between genes associated with human diseases. We have shown previously that groups of genes within functional niches tend to share similar evolutionary histories. Exploiting the availability of whole genomes from multiple species, these histories can be numerically scored and dynamically compared to one another using a sequence-based signature termed Evolutionary Rate Covariation (ERC). To explore potential applications, we characterized ERC amongst disease genes and found that many diseases contain significant ERC signatures between their contributing genes. We show that ERC can also prioritize “true” disease genes amongst unrelated gene candidates. Lastly, these signatures can serve as a foundation for creating instructive gene-based networks, unveiling novel relationships between diseases thought to be clinically distinct. Our hope is that this study will add to the increasing evidence that advancing our understanding of molecular evolution can be a crucial asset in large-scale gene discovery pursuits (Link to our webserver that provides intuitive ERC analysis tools: http://csb.pitt.edu/erc_analysis/).

Vyšlo v časopise: Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks. PLoS Genet 11(2): e32767. doi:10.1371/journal.pgen.1004967
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004967

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