Large DNA sequencing projects have revealed that there is not just one way that species are related. Genes and pieces of the same genome can have different relationships. Sometimes these relationships differ because of ancient mating between species called hybridization. When hybridization occurs, important traits can be shared between unrelated species. This project will develop new methods to determine the history of traits shared between unrelated species. It also will develop methods to more accurately determine the relationships among species. The research will allow researchers to identify cases where key traits, such as insecticide resistance, arise once and are passed between species. The research combines mathematical modeling with new statistical approaches. These will be combined to produce open-source software for carrying out the analyses. The software will ensure that all scientists will be able to take advantage of the tools developed by the research, adding to the national infrastructure by enabling new biological discoveries. This work also will improve and accelerate biological research with multiple societal benefits, including helping to identify genes controlling important traits. This project will support the training of a postdoctoral researcher, graduate students, and undergraduates from groups that are underrepresented in science and technology careers. Workshops will be developed to provide training in the use of the software developed by this project.

Species trees make it possible to understand the evolutionary history of traits. Previous work has documented how gene trees that are discordant with the species tree lead to inaccurate inferences about the timing and direction of trait transitions, the number of trait transitions, how often such changes are thought to be driven by adaptive evolution, and even the species tree itself. This research goes beyond documenting the problems of gene tree discordance, providing solutions to these problems. The theoretical approach used will make it possible to incorporate the multiple causes of discordance—including introgression—into a single framework, allowing researchers to make statistically rigorous inferences about evolutionary processes in the presence of discordance. Integrating these results into a single software package enables these insights and methods to be applied widely, for both binary and continuous traits.

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.

National Science Foundation (NSF)
Division of Environmental Biology (DEB)
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Leslie J. Rissler
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Indiana University
United States
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