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      Approximate Likelihood-Ratio Test for Branches: A Fast, Accurate, and Powerful Alternative

      1 , 1
      Systematic Biology
      Informa UK Limited

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          Abstract

          We revisit statistical tests for branches of evolutionary trees reconstructed upon molecular data. A new, fast, approximate likelihood-ratio test (aLRT) for branches is presented here as a competitive alternative to nonparametric bootstrap and Bayesian estimation of branch support. The aLRT is based on the idea of the conventional LRT, with the null hypothesis corresponding to the assumption that the inferred branch has length 0. We show that the LRT statistic is asymptotically distributed as a maximum of three random variables drawn from the chi(0)2 + chi(1)2 distribution. The new aLRT of interior branch uses this distribution for significance testing, but the test statistic is approximated in a slightly conservative but practical way as 2(l1- l2), i.e., double the difference between the maximum log-likelihood values corresponding to the best tree and the second best topological arrangement around the branch of interest. Such a test is fast because the log-likelihood value l2 is computed by optimizing only over the branch of interest and the four adjacent branches, whereas other parameters are fixed at their optimal values corresponding to the best ML tree. The performance of the new test was studied on simulated 4-, 12-, and 100-taxon data sets with sequences of different lengths. The aLRT is shown to be accurate, powerful, and robust to certain violations of model assumptions. The aLRT is implemented within the algorithm used by the recent fast maximum likelihood tree estimation program PHYML (Guindon and Gascuel, 2003).

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          Author and article information

          Journal
          Systematic Biology
          Informa UK Limited
          1076-836X
          1063-5157
          August 2006
          August 01 2006
          August 2006
          August 2006
          August 01 2006
          August 2006
          : 55
          : 4
          : 539-552
          Affiliations
          [1 ]Equipe Méthodes et Algorithmes pour la Bioinformatique LIRMM-CNRS, Université Montpellier II Montpellier, 34392, France E-mail: manisimova@hotmail.com (M.A.); gascuel@lirmm.fr (O.G.)
          Article
          10.1080/10635150600755453
          16785212
          b1ea1aa9-1588-48ff-b0c6-ff057a3feb88
          © 2006
          History

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