CAREER: Next-generation inference of evolutionary paramaters from genome-wide sequence data

Brown University, Providence RI

Investigators

Abstract

The goal of this project is to develop a suite of novel statistical and computational methods for inferring detailed evolutionary histories from whole-genomes of multiple individuals. While advances in sequencing technology have made DNA sequencing routine in most laboratories, methods to infer evolutionary histories from large sequencing datasets have had to make restrictive or biologically unrealistic assumptions to achieve computational tractability. This project will overcome the limitations of current methods by relaxing such restrictive assumptions and it will provide users with ways to assess the accuracy of estimates produced by the methods. This research will be integrated with an education plan that consists of multiple activities intended to introduce young women in high school to computer science and biology research: offering summer research experiences in the PI's lab, inviting high school students to collaborate with undergraduates in the PI's courses that teach programming skills to biology majors, and teaching programming to a variety of high school audiences. The objectives of this project are to develop methods that accurately infer (1) changes in population size over time, (2) rates of migration between populations over time, and (3) genomic targets of natural selection --- all from sequence data alone, taken from multiple individuals within a single species. The methods developed will model recombination, produce measures of uncertainty for reported estimates, allow for complex population histories, and identify regions of the genome under selection. The methods developed in this project will also be applied to test hypotheses regarding the evolutionary histories of a range of organisms. Software produced and data analyzed in publications resulting from the research will be made available to the public on the lab's data repository (http://ramachandran-data.brown.edu/) and through R packages deposited on the Comprehensive R Archive Network (http://cran.r-project.org/).