Julia Chifman1, Laura Kubatko2. 1. Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, Department of Statistics, The Ohio State University, Columbus, OH 43210 and Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43210, USA. 2. Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, Department of Statistics, The Ohio State University, Columbus, OH 43210 and Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43210, USA Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, Department of Statistics, The Ohio State University, Columbus, OH 43210 and Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43210, USA.
Abstract
MOTIVATION: Increasing attention has been devoted to estimation of species-level phylogenetic relationships under the coalescent model. However, existing methods either use summary statistics (gene trees) to carry out estimation, ignoring an important source of variability in the estimates, or involve computationally intensive Bayesian Markov chain Monte Carlo algorithms that do not scale well to whole-genome datasets. RESULTS: We develop a method to infer relationships among quartets of taxa under the coalescent model using techniques from algebraic statistics. Uncertainty in the estimated relationships is quantified using the nonparametric bootstrap. The performance of our method is assessed with simulated data. We then describe how our method could be used for species tree inference in larger taxon samples, and demonstrate its utility using datasets for Sistrurus rattlesnakes and for soybeans. AVAILABILITY AND IMPLEMENTATION: The method to infer the phylogenetic relationship among quartets is implemented in the software SVDquartets, available at www.stat.osu.edu/∼lkubatko/software/SVDquartets.
MOTIVATION: Increasing attention has been devoted to estimation of species-level phylogenetic relationships under the coalescent model. However, existing methods either use summary statistics (gene trees) to carry out estimation, ignoring an important source of variability in the estimates, or involve computationally intensive Bayesian Markov chain Monte Carlo algorithms that do not scale well to whole-genome datasets. RESULTS: We develop a method to infer relationships among quartets of taxa under the coalescent model using techniques from algebraic statistics. Uncertainty in the estimated relationships is quantified using the nonparametric bootstrap. The performance of our method is assessed with simulated data. We then describe how our method could be used for species tree inference in larger taxon samples, and demonstrate its utility using datasets for Sistrurus rattlesnakes and for soybeans. AVAILABILITY AND IMPLEMENTATION: The method to infer the phylogenetic relationship among quartets is implemented in the software SVDquartets, available at www.stat.osu.edu/∼lkubatko/software/SVDquartets.
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