7 resultados para Evolutionary trees

em National Center for Biotechnology Information - NCBI


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The reconstruction of multitaxon trees from molecular sequences is confounded by the variety of algorithms and criteria used to evaluate trees, making it difficult to compare the results of different analyses. A global method of multitaxon phylogenetic reconstruction described here, Bootstrappers Gambit, can be used with any four-taxon algorithm, including distance, maximum likelihood, and parsimony methods. It incorporates a Bayesian-Jeffreys'-bootstrap analysis to provide a uniform probability-based criterion for comparing the results from diverse algorithms. To examine the usefulness of the method, the origin of the eukaryotes has been investigated by the analysis of ribosomal small subunit RNA sequences. Three common algorithms (paralinear distances, Jukes-Cantor distances, and Kimura distances) support the eocyte topology, whereas one (maximum parsimony) supports the archaebacterial topology, suggesting that the eocyte prokaryotes are the closest prokaryotic relatives of the eukaryotes.

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Evolutionary trees are often estimated from DNA or RNA sequence data. How much confidence should we have in the estimated trees? In 1985, Felsenstein [Felsenstein, J. (1985) Evolution 39, 783–791] suggested the use of the bootstrap to answer this question. Felsenstein’s method, which in concept is a straightforward application of the bootstrap, is widely used, but has been criticized as biased in the genetics literature. This paper concerns the use of the bootstrap in the tree problem. We show that Felsenstein’s method is not biased, but that it can be corrected to better agree with standard ideas of confidence levels and hypothesis testing. These corrections can be made by using the more elaborate bootstrap method presented here, at the expense of considerably more computation.

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Evolutionary trees are often estimated from DNA or RNA sequence data. How much confidence should we have in the estimated trees? In 1985, Felsenstein [Felsenstein, J. (1985) Evolution 39, 783-791] suggested the use of the bootstrap to answer this question. Felsenstein's method, which in concept is a straightforward application of the bootstrap, is widely used, but has been criticized as biased in the genetics literature. This paper concerns the use of the bootstrap in the tree problem. We show that Felsenstein's method is not biased, but that it can be corrected to better agree with standard ideas of confidence levels and hypothesis testing. These corrections can be made by using the more elaborate bootstrap method presented here, at the expense of considerably more computation.

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In the maximum parsimony (MP) and minimum evolution (ME) methods of phylogenetic inference, evolutionary trees are constructed by searching for the topology that shows the minimum number of mutational changes required (M) and the smallest sum of branch lengths (S), respectively, whereas in the maximum likelihood (ML) method the topology showing the highest maximum likelihood (A) of observing a given data set is chosen. However, the theoretical basis of the optimization principle remains unclear. We therefore examined the relationships of M, S, and A for the MP, ME, and ML trees with those for the true tree by using computer simulation. The results show that M and S are generally greater for the true tree than for the MP and ME trees when the number of nucleotides examined (n) is relatively small, whereas A is generally lower for the true tree than for the ML tree. This finding indicates that the optimization principle tends to give incorrect topologies when n is small. To deal with this disturbing property of the optimization principle, we suggest that more attention should be given to testing the statistical reliability of an estimated tree rather than to finding the optimal tree with excessive efforts. When a reliability test is conducted, simplified MP, ME, and ML algorithms such as the neighbor-joining method generally give conclusions about phylogenetic inference very similar to those obtained by the more extensive tree search algorithms.

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The genetic history of a group of populations is usually analyzed by reconstructing a tree of their origins. Reliability of the reconstruction depends on the validity of the hypothesis that genetic differentiation of the populations is mostly due to population fissions followed by independent evolution. If necessary, adjustment for major population admixtures can be made. Dating the fissions requires comparisons with paleoanthropological and paleontological dates, which are few and uncertain. A method of absolute genetic dating recently introduced uses mutation rates as molecular clocks; it was applied to human evolution using microsatellites, which have a sufficiently high mutation rate. Results are comparable with those of other methods and agree with a recent expansion of modern humans from Africa. An alternative method of analysis, useful when there is adequate geographic coverage of regions, is the geographic study of frequencies of alleles or haplotypes. As in the case of trees, it is necessary to summarize data from many loci for conclusions to be acceptable. Results must be independent from the loci used. Multivariate analyses like principal components or multidimensional scaling reveal a number of hidden patterns and evaluate their relative importance. Most patterns found in the analysis of human living populations are likely to be consequences of demographic expansions, determined by technological developments affecting food availability, transportation, or military power. During such expansions, both genes and languages are spread to potentially vast areas. In principle, this tends to create a correlation between the respective evolutionary trees. The correlation is usually positive and often remarkably high. It can be decreased or hidden by phenomena of language replacement and also of gene replacement, usually partial, due to gene flow.

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The study of development has relied primarily on the isolation of mutations in genes with specific functions in development and on the comparison of their expression patterns in normal and mutant phenotypes. Comparative evolutionary analyses can complement these approaches. Phylogenetic analyses of Sonic hedgehog (Shh) and Hoxd-10 genes from 18 cyprinid fish species closely related to the zebrafish provide novel insights into the functional constraints acting on Shh. Our results confirm and extend those gained from expression and crystalline structure analyses of this gene. Unexpectedly, exon 1 of Shh is found to be almost invariant even in third codon positions among these morphologically divergent species suggesting that this exon encodes for a functionally important domain of the hedgehog protein. This is surprising because the main functional domain of Shh had been thought to be that encoded by exon 2. Comparisons of Shh and Hoxd-10 gene sequences and of resulting gene trees document higher evolutionary constraints on the former than on the latter. This might be indicative of more general evolutionary patterns in networks of developmental regulatory genes interacting in a hierarchical fashion. The presence of four members of the hedgehog gene family in cyprinid fishes was documented and their homologies to known hedgehog genes in other vertebrates were established.

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Homologues of the human major histocompatibility complex (MHC) HLA-A, -B, -E, -F, and -G loci are present in all the Catarrhini (Old World primates, apes, and humans), and some of their allelic lineages have survived several speciation events. Analysis of 26 MHC class I cDNAs from seven different genera of New World primates revealed that the Callitrichinae (tamarins and marmosets) are an exception to these rules of MHC stability. In gene trees of primate MHC class I genes, sequences from the Callitrichinae cluster in a genus-specific fashion, whereas in the other genera of New World primates, as in the Catarrhini, they cluster in a transgeneric way. The genus-specific clustering of the Callitrichinae cDNAs indicates that there is no orthology between MHC class I loci in genera of this phyletic group. Additionally, the Callitrichinae genera exhibit limited variability of their MHC class I genes, in contrast to the high variability displayed by all other primates. Each Callitrichinae genus, therefore, expresses its own set of MHC class I genes, suggesting that an unusually high rate of turnover of loci occurs in this subfamily. The limited variability of MHC class I genes in the Callitrichinae is likely the result of the recent origin of these loci.