NSF Postdoctoral Fellowship in Biology FY 2021: Comparative methods and model clades for evolutionary developmental biology

Church, Samuel H, Cambridge MA

Investigators

Abstract

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2021, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. The funded research will use large datasets to investigate how evolution leads to biodiversity. Methods for collecting large amounts of data with thousands of measured biological traits are becoming increasingly common and inexpensive to generate. Examples of such data include developmental assays of gene expression. As this data becomes more common, so do comparisons of these data across species. However, evolutionary biologists have warned for years that comparisons across species must be performed using mathematical models that account for how related species are, or they risk misidentifying patterns across species. This fellowship will focus on developing new methods for comparing data across species. This research is important given that, in this era of rapid global change, robust models of evolution are needed to accurately predict how biodiversity is likely to change in the future. Furthermore, this research will broaden participation in science by recruiting and mentoring undergraduate students from underrepresented groups in computational biological analysis. This research project will result in a new comparative framework for analyzing high-dimensional data on gene expression across species. Gene expression data are common in assays of developmental biology, yet current comparisons of these data across taxa often fail to consider the evolutionary non-independence of observations. There exists a rich literature of comparative methods designed to statistically compare traits across taxa, but their implementation for high-dimensional continuous traits faces technical limitations. To overcome these limitations, first the fellow will identify and extend evolutionary models to describe the distribution of expression data across taxa. This analysis will require the fellow to achieve specific training objectives, including seeking out advanced knowledge in mathematical modeling, to overcome common challenges in analyzing datasets that have many thousands of parameters. Second, the fellow will apply these methods to real-world data on gene expression across tissues and species, testing hypotheses about the degree to which phylogeny, gene pathway, and gene product type explain variation. The results of this study will be a comparative framework that can be immediately applied to datasets of gene expression across species, which will be released as free, publicly accessible, open-source software. 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.