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      Bias in phylogenetic tree reconciliation methods: implications for vertebrate genome evolution

      research-article
      1 ,
      Genome Biology
      BioMed Central

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          Abstract

          Background

          Comparative genomic studies are revealing frequent gains and losses of whole genes via duplication and pseudogenization. One commonly used method for inferring the number and timing of gene gains and losses reconciles the gene tree for each gene family with the species tree of the taxa considered. Recent studies using this approach have found a large number of ancient duplications and recent losses among vertebrate genomes.

          Results

          I show that tree reconciliation methods are biased when the inferred gene tree is not correct. This bias places duplicates towards the root of the tree and losses towards the tips of the tree. I demonstrate that this bias is present when tree reconciliation is conducted on both multiple mammal and Drosophila genomes, and that lower bootstrap cut-off values on gene trees lead to more extreme bias. I also suggest a method for dealing with reconciliation bias, although this method only corrects for the number of gene gains on some branches of the species tree.

          Conclusion

          Based on the results presented, it is likely that most tree reconciliation analyses show biases, unless the gene trees used are exceptionally well-resolved and well-supported. These results cast doubt upon previous conclusions that vertebrate genome history has been marked by many ancient duplications and many recent gene losses.

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          Most cited references26

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          Gene Trees in Species Trees

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            Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous.

            All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniques represent a parametric approach to statistical phylogenetics, because their criterion for evaluating a topology--the probability of the data, given the tree--is calculated with reference to an explicit evolutionary model from which the data are assumed to be identically distributed. Maximum parsimony can be considered nonparametric, because trees are evaluated on the basis of a general metric--the minimum number of character state changes required to generate the data on a given tree--without assuming a specific distribution. The shift to parametric methods was spurred, in large part, by studies showing that although both approaches perform well most of the time, maximum parsimony is strongly biased towards recovering an incorrect tree under certain combinations of branch lengths, whereas maximum likelihood is not. All these evaluations simulated sequences by a largely homogeneous evolutionary process in which data are identically distributed. There is ample evidence, however, that real-world gene sequences evolve heterogeneously and are not identically distributed. Here we show that maximum likelihood and BMCMC can become strongly biased and statistically inconsistent when the rates at which sequence sites evolve change non-identically over time. Maximum parsimony performs substantially better than current parametric methods over a wide range of conditions tested, including moderate heterogeneity and phylogenetic problems not normally considered difficult.
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              When less is more: gene loss as an engine of evolutionary change.

              M Olson (1999)
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2007
                16 July 2007
                : 8
                : 7
                : R141
                Affiliations
                [1 ]Department of Biology and School of Informatics, E. 3rd Street, Indiana University, Bloomington, IN 47405, USA
                Article
                gb-2007-8-7-r141
                10.1186/gb-2007-8-7-r141
                2323230
                17634151
                9dd35038-5a52-4052-ab50-9ed431a35773
                Copyright © 2007 Hahn; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 May 2007
                : 16 July 2007
                Categories
                Research

                Genetics
                Genetics

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