Applying a Novel Bioinformatic Method to Study Plant EvolutionPublic Deposited
The study of local adaptation in plants is critical for understanding the evolution of traits that contribute to survival in a dynamic environment, the genes underlying them, and the general process of adaptation. However, in the study of natural, non-model plant species, population-level whole-genome sampling is not always feasible and can be costly. Therefore, there is a need for methods based on population-differentiation that can take a reduced representation of whole-genome data to identify loci under selection within or among populations. Levels of Exclusively Shared Difference (LSD) is a method developed using human genomic data that can detect signatures of selection along the branches of a population tree (phylogeny). Here, I show how LSD can be used to identify candidate genes under selection within genomic, transcriptomic, and discrete gene data sets collected from multiple plant populations. I compare the candidate genes under selection identified by LSD to those identified by traditional methods and show how this novel method can be adapted for use plants to overcome some of the limitations of other selection detection methods. Using LSD on plant population genomic data will expand the ways in which adaptively evolving genes can be identified. Identifying adaptive candidate genes has a range of implications for plant research and LSD expands the types of datasets that can be used to elucidate patterns of plant evolution, inform the development of improved cultivars, and guide conservation efforts for endangered species.
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