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      Phylogenetic Trees and Networks Can Serve as Powerful and Complementary Approaches for Analysis of Genomic Data

      1 , 2 , 3 , 4
      Systematic Biology
      Oxford University Press (OUP)

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          Abstract

          Genomic data have had a profound impact on nearly every biological discipline. In systematics and phylogenetics, the thousands of loci that are now being sequenced can be analyzed under the multispecies coalescent model (MSC) to explicitly account for gene tree discordance due to incomplete lineage sorting (ILS). However, the MSC assumes no gene flow post divergence, calling for additional methods that can accommodate this limitation. Explicit phylogenetic network methods have emerged, which can simultaneously account for ILS and gene flow by representing evolutionary history as a directed acyclic graph. In this point of view, we highlight some of the strengths and limitations of phylogenetic networks and argue that tree-based inference should not be blindly abandoned in favor of networks simply because they represent more parameter rich models. Attention should be given to model selection of reticulation complexity, and the most robust conclusions regarding evolutionary history are likely obtained when combining tree- and network-based inference.

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          Most cited references51

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          Gene Trees in Species Trees

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            Testing for ancient admixture between closely related populations.

            One enduring question in evolutionary biology is the extent of archaic admixture in the genomes of present-day populations. In this paper, we present a test for ancient admixture that exploits the asymmetry in the frequencies of the two nonconcordant gene trees in a three-population tree. This test was first applied to detect interbreeding between Neandertals and modern humans. We derive the analytic expectation of a test statistic, called the D statistic, which is sensitive to asymmetry under alternative demographic scenarios. We show that the D statistic is insensitive to some demographic assumptions such as ancestral population sizes and requires only the assumption that the ancestral populations were randomly mating. An important aspect of D statistics is that they can be used to detect archaic admixture even when no archaic sample is available. We explore the effect of sequencing error on the false-positive rate of the test for admixture, and we show how to estimate the proportion of archaic ancestry in the genomes of present-day populations. We also investigate a model of subdivision in ancestral populations that can result in D statistics that indicate recent admixture.
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              Is Open Access

              ASTRAL: genome-scale coalescent-based species tree estimation

              Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions. Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees. Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact: warnow@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Systematic Biology
                Oxford University Press (OUP)
                1063-5157
                1076-836X
                May 2020
                May 01 2020
                September 21 2019
                May 2020
                May 01 2020
                September 21 2019
                : 69
                : 3
                : 593-601
                Affiliations
                [1 ]Department of Biological Sciences, New York City College of Technology, The City University of New York, 285 Jay Street, Brooklyn, NY 11201, USA
                [2 ]Biology PhD Program, CUNY Graduate Center, 365 5th Ave., New York, NY 10016, USA
                [3 ]Department of Botany, University of Wisconsin – Madison, 1300 University Ave, Madison, WI 53706, USA
                [4 ]Department of Statistics, University of Wisconsin – Madison, 1300 University Ave, Madison, WI 53706, USA
                Article
                10.1093/sysbio/syz056
                31432090
                505e9983-d259-43e1-8a59-2f92c74357db
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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