Abstract
While the inference of species trees from molecular sequences has become a common type of analysis in studies of species diversification, few programs so far allow for the use of single-nucleotide polymorphisms (SNPs) for the same purpose. In this book chapter, I discuss the use of the Bayesian program SNAPP, which infers the species tree by mathematically integrating over all possible genealogies at each SNP. In particular, I focus on a molecular clock model developed for SNAPP, allowing the inference of divergence times together with the species tree topology and the population size, directly from SNP datasets in variant call format. With the growing availability of SNP datasets for multiple closely related species, this approach is becoming increasingly relevant for the reconstruction of the temporal framework of recent species diversification.
Key words
- Genomics
- Phylogeny
- Species tree
- SNPs
- Divergence times
- SNAPP
- BEAST
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Acknowledgments
I thank Julie Lee-Yaw, Amanda Haponski, Livia Loureiro, Sue Sherman-Broyles, Bohao Fang, Yayan Kusuma, Daniel Poveda-Martínez, Xiaoxi Yang, Cecilia Fiorini, Kristen Finch, Armel Donkpegan, Marta Liber, Jie Gao, and Julia Canitz for testing the snapp_prep.rb script. Funding was provided by the Research Council of Norway (FRIPRO 275869).
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Matschiner, M. (2022). Species Tree Inference with SNP Data. In: Pereira-Santana, A., Gamboa-Tuz, S.D., Rodríguez-Zapata, L.C. (eds) Plant Comparative Genomics. Methods in Molecular Biology, vol 2512. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2429-6_2
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DOI: https://doi.org/10.1007/978-1-0716-2429-6_2
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