Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites.
Maximum-likelihood methods based on models of codon substitution accounting for heterogeneous selective pressures across sites have proved to be powerful in detecting positive selection in protein-coding DNA sequences. Those methods are phylogeny based and do not account for the effects of recombination. When recombination occurs, such as in population data, no unique tree topology can describe the evolutionary history of the whole sequence. This violation of assumptions raises serious concerns about the likelihood method for detecting positive selection. Here we use computer simulation to evaluate the reliability of the likelihood-ratio test (LRT) for positive selection in the presence of recombination. We examine three tests based on different models of variable selective pressures among sites. Sequences are simulated using a coalescent model with recombination and analyzed using codon-based likelihood models ignoring recombination. We find that the LRT is robust to low levels of recombination (with fewer than three recombination events in the history of a sample of 10 sequences). However, at higher levels of recombination, the type I error rate can be as high as 90%, especially when the null model in the LRT is unrealistic, and the test often mistakes recombination as evidence for positive selection. The test that compares the more realistic models M7 (beta) against M8 (beta and omega) is more robust to recombination, where the null model M7 allows the positive selection pressure to vary between 0 and 1 (and so does not account for positive selection), and the alternative model M8 allows an additional discrete class with omega = d(N)/d(S) that could be estimated to be >1 (and thus accounts for positive selection). Identification of sites under positive selection by the empirical Bayes method appears to be less affected than the LRT by recombination.
Anisimova
Maria
M
Department of Biology, University College London, London WC1E 6BT, United Kingdom. m.anisimova@ucl.ac.uk
Nielsen
Rasmus
R
Yang
Ziheng
Z
D001499:Bayes Theorem; D003062:Codon; D003198:Computer Simulation; D035923:Hepatitis delta Antigens / Q000235:genetics; D016013:Likelihood Functions; D008957:Models, Genetic; D011995:Recombination, Genetic / Q000235:genetics*; D012641:Selection, Genetic
D003062:Codon; D035923:Hepatitis delta Antigens