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An Empirical Test for Branch-Specific Positive Selection
Author(s) -
Gabrielle Nickel,
David L. Tefft,
Karrie Goglin,
Mark D. Adams
Publication year - 2008
Publication title -
genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.792
H-Index - 246
eISSN - 1943-2631
pISSN - 0016-6731
DOI - 10.1534/genetics.108.090548
Subject(s) - biology , phylogenetic tree , evolutionary biology , selection (genetic algorithm) , null model , lineage (genetic) , context (archaeology) , genetics , most recent common ancestor , phylogenetics , extant taxon , divergence (linguistics) , gene , ecology , machine learning , paleontology , linguistics , philosophy , computer science
The use of phylogenetic analysis to predict positive selection specific to human genes is complicated by the very close evolutionary relationship with our nearest extant primate relatives, chimpanzees. To assess the power and limitations inherent in use of maximum-likelihood (ML) analysis of codon substitution patterns in such recently diverged species, a series of simulations was performed to assess the impact of several parameters of the evolutionary model on prediction of human-specific positive selection, including branch length and d(N)/d(S) ratio. Parameters were varied across a range of values observed in alignments of 175 transcription factor (TF) genes that were sequenced in 12 primate species. The ML method largely lacks the power to detect positive selection that has occurred since the most recent common ancestor between humans and chimpanzees. An alternative null model was developed on the basis of gene-specific evaluation of the empirical distribution of ML results, using simulated neutrally evolving sequences. This empirical test provides greater sensitivity to detect lineage-specific positive selection in the context of recent evolutionary divergence.

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