Recombination detection under evolutionary scenarios relevant to functional divergence.

Recombination can negatively impact methods designed to detect divergent gene function that rely on explicit knowledge of a gene tree. However, we know little about how recombination detection methods perform under evolutionary scenarios encountered in studies of functional molecular divergence. We use simulation to evaluate false positive rates for six recombination detection methods (GENECONV, MaxChi, Chimera, RDP, GARD-SBP, GARD-MBP) under evolutionary scenarios that might increase false positives. Broadly, these scenarios address: (i) asymmetric tree topology and sequence divergence, (ii) non-stationary codon bias and selection pressure, and (iii) positive selection. We also evaluate power to detect recombination under truly recombinant history. As with previous studies, we find that power increases with sequence divergence. However, we also find that accuracy to correctly infer the number of breakpoints is extremely low. When recombination is absent, increased sequence divergence leads to increased false positives. Furthermore, one method (GARD-SBP) is sensitive to tree shape, with higher false positive rates under an asymmetric tree topology. Somewhat surprisingly, all methods are robust to the simulated heterogeneity in codon bias, shifts in selection pressure and presence of positive selection. Based on these findings, we recommend that studies of functional divergence in systems where recombination is plausible can, and should, include a pre-test for recombination. Application of all methods to the core genome of Prochlorococcus reveals a substantial lack of concordance among results. Based on analysis of both real and simulated datasets we present some guidelines for the investigation of recombination in genes that may have experienced functional divergence.