title: Detection of convergent and parallel evolution at the amino acid sequence level.
abstract: 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.
authors.affiliation: Institute of Molecular Evolutionary Genetics, Pennsylvania State University, University Park 16802, USA. zhang@imeg.bio.psu.edu
authors.forename: J
authors.initials: J
authors.lastname: Zhang
authors.forename: S
authors.initials: S
authors.lastname: Kumar
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D019143:Evolution, Molecular; D006801:Humans; D008957:Models, Genetic; D009113:Muramidase / Q000235:genetics; D011506:Proteins / Q000235:genetics*; D012641:Selection, Genetic; D013270:Stomach / Q000201:enzymology
chemical_list: D011506:Proteins; D009113:Muramidase