| 英文摘要: | A recent technological revolution in DNA sequencing has fundamentally changed the amount of genetic information that researchers can collect. This has led to the development of a new field in biology called population genomics. This field examines the processes of genetic evolution in natural populations of many living things at a remarkably fine scale, scanning across the genome - the entire collection of genetic information for each individual studied. Population genomics promises remarkable insights into population dynamics, adaptation, the genetics of important traits, and other areas of biology. However, researchers lack adequate tools for analyzing the rapidly growing amount of data, limiting the insights that they are able to draw. This project addresses this gap with an experimental approach to population genomics, using laboratory populations of yeast, and by developing sophisticated new analytical tools for population genomic data. The experimental approach provides a controlled setting for testing hypotheses about how evolution and adaptation affect variation across the genome, and it produces data to evaluate the performance of new analytical tools. These analytical tools will be disseminated to the scientific community, which will improve the ability of biologists to test and predict responses of populations to environmental change, gene flow, and evolution. Undergraduates, graduate students, and a post-doctoral scholar will be trained, and the research team will participate in outreach and training efforts to facilitate the use of these new tools in the population genomics community.
This project will focus on the scenario of populations under divergent selection with or without gene flow, using replicate laboratory populations of budding yeast (Saccharomyces cerevisiae). Populations will be grown under various levels of selection, migration, and recombination, and subjected to high-throughput sequencing to assess the impact of these factors on genetic differentiation at two scales: (1) the chromosomal region around single loci under divergent selection, and (2) the entire genome, under polygenic adaptation and divergence. The project will also develop novel analytical tools, based on Approximate Bayesian Computation (ABC), to determine which statistics calculated from population genomic data are most informative about underlying evolutionary processes, and to infer evolutionary parameters under complex models of divergent selection. The experimental data from yeast will provide test cases for the novel ABC-based methods, when the important factors, such as migration and selection, are known and experimentally controlled. |