Molecular Biology and Evolution, Vol 14, 527-536, Copyright © 1997 by Society for Molecular Biology and Evolution
J Zhang and S Kumar
Adaptive evolution at the molecular level can be studied by detecting
convergent and parallel evolution at the amino acid sequence level. For a
set of homologous protein sequences, the ancestral amino acids at all
interior nodes of the phylogenetic tree of the proteins can be
statistically inferred. The amino acid sites that have experienced
convergent or parallel changes on independent evolutionary lineages can
then be identified by comparing the amino acids at the beginning and end of
each lineage. At present, the efficiency of the methods of ancestral
sequence inference in identifying convergent and parallel changes is
unknown. More seriously, when we identify convergent or parallel changes,
it is unclear whether these changes are attributable to random chance. For
these reasons, claims of convergent and parallel evolution at the amino
acid sequence level have been disputed. We have conducted computer
simulations to assess the efficiencies, of the parsimony and Bayesian
methods of ancestral sequence inference in identifying convergent and
parallel-change sites. Our results showed that the Bayesian method performs
better than the parsimony method in identifying parallel changes, and both
methods are inefficient in identifying convergent changes. However, the
Bayesian method is recommended for estimating the number of
convergent-change sites because it gives a conservative estimate. We have
developed statistical tests for examining whether the observed numbers of
convergent and parallel changes are due to random chance. As an example, we
reanalyzed the stomach lysozyme sequences of foregut fermenters and found
that parallel evolution is statistically significant, whereas convergent
evolution is not well supported.
ORIGINAL ARTICLE
Detection of convergent and parallel evolution at the amino acid sequence level
Institute of Molecular Evolutionary Genetics, Pennsylvania State University, University Park 16802, USA. zhang@imeg.bio.psu.edu
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