Thursday, December 26, 2024
12/26/2024
Project Summary/Abstract The growing abundance of population genomic data creates a critical need for inference approaches that can reveal evolutionary history. The PI's long-term goal is to understand how natural selection shapes the evolution and function of the molecular networks that comprise life. Toward that goal, the PI's group develops and applies methods for inferring the evolutionary past from population genomic data. The objectives of this application are to understand how context affects mutation fitness effects, to develop improved inference methods, and to support the population genomics research community. The rationale is that this research program will both reveal new insights into evolution and enhance the ability of colleagues to reveal complementary insights. The PI's research group has expanded the concept of a distribution of fitness effects to multiple dimensions, focusing on differences in mutation fitness effects among populations. The PI proposes to apply this approach to numerous systems, to elucidate the relative roles of genetic and environmental context in creating differences in fitness effects. The group will also extend this approach to consider differences in fitness effects over time. The PI developed and maintains the software dadi, among the most popular approaches for fitting population genomic models to data. The PI will continue to support and enhance dadi, while developing complementary inference approaches. These will include new diffusion methods based on pairs of loci and the linkage among them and a novel deep learning approach for inferring the distribution of fitness effects. The PI helped found the PopSim consortium, which aims to expand the rigor and transparency of population ge- nomic models for the scientific community. The PI's group will continue to be active in the consortium, particularly leading a new initiative to facilitate rigorous testing of population genomic methods via open competition. The proposed research program is innovative both conceptually and methodologically. The novel concept of a multidimensional distribution of fitness effects has many applications, and the group will develop novel method- ology for several population genomics inferences. The expected outcomes of the proposed research are new insights into the ecology and biology of mutation fitness effects, new population genomic inference tools, and a framework for blinded evaluation of such tools. These outcomes are expected to have important positive impact on the filed of population genomics. The methods will be widely applicable and well-supported, and the inferences will feed into approaches for inferring the evolutionary past and predicting the evolutionary future.
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