Browsing by Author "Peery, Rhiannon M."
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Item Ancient hybridization patterns between bighorn and thinhorn sheep(2021) Santos, Sarah H. D.; Peery, Rhiannon M.; Miller, Joshua M.; Dao, Anh; Lyu, Feng-Hua; Li, Xin; Li, Meng-Hua; Coltman, David W.Whole-genome sequencing has advanced the study of species evolution, including the detection of genealogical discordant events such as ancient hybridization and incomplete lineage sorting (ILS). The evolutionary history of bighorn (Ovis canadensis) and thinhorn (Ovis dalli) sheep present an ideal system to investigate evolutionary discordance due to their recent and rapid radiation and putative secondary contact between bighorn and thinhorn sheep subspecies, specifically the dark pelage Stone sheep (O. dalli stonei) and predominately white Dall sheep (O. dalli dalli), during the last ice age. Here, we used multiple genomes of bighorn and thinhorn sheep, together with snow (O. nivicola) and the domestic sheep (O. aries) as outgroups, to assess their phylogenomic history, potential introgression patterns and their adaptive consequences. Among the Pachyceriforms (snow, bighorn and thinhorn sheep) a consistent monophyletic species tree was retrieved; however, many genealogical discordance patterns were observed. Alternative phylogenies frequently placed Stone and bighorn as sister clades. This relationship occurred more often and was less divergent than that between Dall and bighorn. We also observed many blocks containing introgression signal between Stone and bighorn genomes in which coat colour genes were present. Introgression signals observed between Dall and bighorn were more random and less frequent, and therefore probably due to ILS or intermediary secondary contact. These results strongly suggest that Stone sheep originated from a complex series of events, characterized by multiple, ancient periods of secondary contact with bighorn sheep.Item A roadmap to robust discriminant analysis of principal components(2023) Cullingham, Catherine; Peery, Rhiannon M.; Miller, Joshua M.Identification of population structure is a common goal for a variety of applications, including conservation, wildlife management, and medical genetics. The outcome of these analyses can have far reaching implications; therefore, it is important to ensure an understanding of the strengths and weaknesses of the methodologies used. Increasing in popularity, the discriminant analysis of principal components (DAPC) method incorporates combinations of genetic variables (alleles) into a model that differentiates individuals into genetic clusters. However, users may not have a full understanding of how to best parameterize the model. In this issue of Thia (Molecular Ecology Resources, 2022) looks under the hood of the DAPC. Using simulated data, he demonstrates the importance of careful parameter selection in developing a DAPC model, what the implications are for over-fitting the model, and finally, how best to evaluate the results of DAPC models. This work highlights the issues that can arise when over-parameterizing the DAPC model when gene flow is high among clusters and provides important guidelines to ensure researchers are making conclusions that are biologically relevant.