Grant: Discovery Grants Program - Individual

Nearly all life on Earth, especially eukaryotes, has undergone ancient whole-genome duplications (WGDs). This creates multiple copies of a gene, causes genomic divergence, and creates genetic complexity and novelty over evolutionary time. My research program focuses on developing mathematical and probability models of WGDs and tools to understand genome evolution and its implications for various fields, such as oncology and agriculture. My program's goals are repositioning ancient WGD analysis from the gene level to the syntenic block level, evaluating various divergence measures and investigating gene retention tendencies following multiple WGDs. I have used a branching process model to account for individual gene pairs, and to infer fractionation rate and polyploidy events in plant evolution. This approach leads to several new problems (shown in the research proposal) that could be studied using sets of gene pairs in syntenic blocks. The research will develop a model of syntenic block similarity (D) to analyze plant evolution, as well as to identify and overcome the original model's limitations. Synonymous substitutions per synonymous site (Ks) and fourfold degeneracy transversion rates (4DTv) are two prevalent measures of sequence divergence but may not effectively capture genomic structural variations like block shrinkage and movement along chromosome arms. These limitations can significantly impact the analysis of genome evolution, potentially introducing substantial underestimations in the count of genes involved in early polyploidization events and inaccuracies in fractionation rate estimations. My research project seeks to address these challenges by holistically integrating evolutionary metrics, including D, Ks, and 4DTv, leveraging powerful tools like SynMap to infer fractionation rates and structural variations. Moreover, the project will develop an open toolkit incorporating these metrics and observations. Furthermore, while gene duplication is recognized as vital for evolutionary innovation, the reasons some duplicate genes are retained while others are lost across lineages remain under-researched. This project will integrate a range of biological attributes, such as duplication mechanism and genomic context into a structured framework. Utilize methods like the variance inflation factor to detect multicollinearity, and advanced statistical methods, including machine learning and kernel methods, to discern genuine retention patterns (co-linear and non-linear) across species. Overall, this program has the potential to significantly improve our understanding of genome evolution and biodiversity. The WGD-focused methodologies might help breeders pick desirable features, benefiting Canadian agriculture. Development and sharing of computer tools could catalyze progress in evolutionary and comparative genomic research. Concurrently, research-oriented training endeavours to mould highly competent individuals.