Collaborative Research: Bayesian Model Checking for Phylogenetics in the Post-Genomic Era

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Diagrams of evolutionary relationships (phylogenetic trees) for species and genes are widely employed in biological research, including the fields of medicine, epidemiology, forensics, conservation, evolutionary biology and agriculture. This research project will explore new ideas and develop new software tools to improve the accuracy by which phylogenetic relationships are determined; in this way the research will contribute to improved understanding and decision-making for a broad range of scientific disciplines and practical applications. Results from this research will be broadly disseminated, including in-person and online training opportunities to familiarize researchers in the relevant disciplines with these newly developed computer-based analytical tools. Further, the research activities will involve the participation and training of a postdoctoral scholar, a graduate student, and several undergraduates at Louisiana State University (LSU) and the University of Hawaii at Manoa. This project will be incorporated into a seminar series at LSU focused on increasing awareness of computational biology among undergraduate students.Phylogenetic trees are now routinely inferred from enormous genome-scale data sets, revealing extensive variation in apparent phylogenetic signal across loci. However, no general tools currently exist to objectively and quantitatively assess how much of this variation is due to biological processes and how much is caused by methodological error. Distinguishing between true variation and error is the problem to be studied in this project, as resolving this issue is essential for robustly resolving the Tree of Life and for understanding genomic evolution. The goal of this work is to give researchers the tools to identify and avoid situations where phylogenetic inferences are unreliable. These tools will be implemented in open-source software (RevBayes and R), and will be easily extensible to many types of phylogenetic inference beyond those in this project. This research will implement suites of existing, alternative statistical approaches employing Bayesian posterior prediction to rigorously assess absolute fit of phylogenetic models to evolutionary data, and how this fit impacts the reliability of inference. Simulations comparing performance of alternative models will focus on three types of inferences: (i) estimation of individual gene trees, (ii) estimation of species trees from many genes, and (iii) comparative analysis of continuous traits. These approaches will be applied to exemplar empirical questions, including the placement of turtles among amniotes using several recently published genome-scale data sets. These data contain surprising and massive heterogeneity in phylogenetic signal regarding the placement of turtles, and thus form an excellent case study.