Collaborative Research: OPUS: CRS: A Synthetic View of Evolutionary Heterogeneity and the Tree of Life

University Of Hawaii, Honolulu

Investigators

Abstract

The advent of inexpensive genome-scale DNA sequencing has led to major changes in the study of evolutionary history. Researchers now routinely assemble and analyze enormous genetic datasets in an effort to robustly resolve the patterns of evolutionary relatedness that together make up the global tree of life. While this shift has resulted in dramatic improvements in humanity’s understanding of evolutionary history, it brings important challenges. Chief among these is that evolution acts in heterogeneous ways across different regions of the genome and across the tree of life. This heterogeneity can be strong enough to frustrate analyses and, as a result, has led to a growing number of studies that arrive at fundamentally conflicting conclusions about evolutionary history, despite sampling large fractions of the genome and analyzing these with the field’s best available methods. This project will detect and quantify patterns of evolutionary heterogeneity in a large collection of genome-scale empirical datasets, mitigate the impacts of this heterogeneity, and develop a more accurate understanding of evolutionary history in the process. The research team will accomplish these goals by integrating a series of statistical tools that they have previously developed into a cohesive analytical strategy that can identify, explain, and resolve conflicting results. These tools include Bayesian techniques for measuring the fit between evolutionary models and data, as well as unbounded measures of statistical support. By integrating these tools and deploying them across a large collection of empirical datasets, the research team will develop a synthetic understanding of the most important drivers of evolutionary heterogeneity and conflicting results across the tree of life. This synthesis will lead to better techniques for identifying and mitigating the effects of evolutionary heterogeneity in massive datasets, enabling the field to develop a more accurate understanding of the history of life on earth. The research project will also provide new tools for visualizing evolutionary heterogeneity in phylogenetic trees, train graduate student researchers in both Hawaii and Louisiana, and develop a novel structured mentoring experience that will train a diverse group of beginning phylogenetic investigators in statistical phylogenomic methods. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.