title: Statistical properties of the branch-site test of positive selection.
abstract: The branch-site test is a likelihood ratio test to detect positive selection along prespecified lineages on a phylogeny that affects only a subset of codons in a protein-coding gene, with positive selection indicated by accelerated nonsynonymous substitutions (with ω = d(N)/d(S) > 1). This test may have more power than earlier methods, which average nucleotide substitution rates over sites in the protein and/or over branches on the tree. However, a few recent studies questioned the statistical basis of the test and claimed that the test generated too many false positives. In this paper, we examine the null distribution of the test and conduct a computer simulation to examine the false-positive rate and the power of the test. The results suggest that the asymptotic theory is reliable for typical data sets, and indeed in our simulations, the large-sample null distribution was reliable with as few as 20-50 codons in the alignment. We examined the impact of sequence length, the strength of positive selection, and the proportion of sites under positive selection on the power of the branch-site test. We found that the test was far more powerful in detecting episodic positive selection than branch-based tests, which average substitution rates over all codons in the gene and thus miss the signal when most codons are under strong selective constraint. Recent claims of statistical problems with the branch-site test are due to misinterpretations of simulation results. Our results, as well as previous simulation studies that have demonstrated the robustness of the test, suggest that the branch-site test may be a useful tool for detecting episodic positive selection and for generating biological hypotheses for mutation studies and functional analyses. The test is sensitive to sequence and alignment errors and caution should be exercised concerning its use when data quality is in doubt.
authors.affiliation: Department of Genetics, Evolution and Environment, University College London, United Kingdom. z.yang@ucl.ac.uk
authors.forename: Ziheng
authors.initials: Z
authors.lastname: Yang
authors.forename: Mario
authors.initials: M
authors.lastname: dos Reis
mesh_terms: D000818:Animals; D016477:Artifacts; D003062:Codon; D003198:Computer Simulation / Q000706:statistics & numerical data; D019143:Evolution, Molecular; D016678:Genome; D023281:Genomics / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D009154:Mutation; D016366:Open Reading Frames / Q000235:genetics*; D010802:Phylogeny; D012641:Selection, Genetic; D016415:Sequence Alignment
chemical_list: D003062:Codon