A Maximum Likelihood Method for Detecting Functional Divergence at Individual Codon Sites, with Application to Gene Family Evolution

Abstract

Abstract. The tailoring of existing genetic systems to new uses is called genetic co-option. Mechanisms of genetic co-option have been difficult to study because of difficulties in identifying functionally important changes. One way to study genetic co-option in protein-coding genes is to identify those amino acid sites that have experienced changes in selective pressure following a genetic co-option event. In this paper we present a maximum likelihood method useful for measuring divergent selective pressures and identifying the amino acid sites affected by divergent selection. The method is based on a codon model of evolution and uses the nonsynonymous-to-synonymous rate ratio (x) as a measure of selection on the protein, with x = 1, <1, and >1 indicating neutral evolution, purifying selection, and positive selection, respectively. The model allows variation in x among sites, with a fraction of sites evolving under divergent selective pressures. Divergent selection is indicated by different x's between clades, such as between paralogous clades of a gene family. We applied the codon model to duplication followed by functional divergence of (i) the e and c globin genes and (ii) the eosinophil cationic protein (ECP) and eosinophilderived neurotoxin (EDN) genes. In both cases likelihood ratio tests suggested the presence of sites evolving under divergent selective pressures. Results of the e and c globin analysis suggested that divergent selective pressures might be a consequence of a weakened relationship between fetal hemoglobin and 2,3-diphosphoglycerate. We suggest that empirical Bayesian identification of sites evolving under divergent selective pressures, combined with structural and functional information, can provide a valuable framework for identifying and studying mechanisms of genetic co-option. Limitations of the new method are discussed.

Parallel signatures of sequence evolution among hearing genes in echolocating mammals: an emerging model of genetic convergence

“…To test for divergent selection acting on echolocating versus non-echolocating taxa, we compared o averaged across branches within focal clades (foreground) with o estimated for the rest of the tree (background) (Bielawski and Yang, 2004). These analyses were conducted on the partial sequences that included more echolocating species.…”

Relaxed functional constraints on triplicate α-globin gene in the bank vole suggest a different evolutionary history from other rodents

“…The clade model C (Bielawski and Yang, 2004), which allowed a subset of codon sites along each of the three HBA-T3 branches to evolve under positive selection, with different o estimated for each branch, was a significantly better fit than the Nearly Neutral Model M1a (Po0.001; Table 1). However, only in the HBA-T3 branches of Peromyscus and Rattus was there a class of sites with o41 (1.54 and 2.89, respectively), whereas for the bank vole HBA-T3 branch, o ¼ 0.41 was estimated for the same site class (Table 1).…”

“…A likelihood ratio test was used to compare the model A that assumed four site classes (class 0 of codons under stringent functional constraints with 0oo 0 o1; class 1 of unconstrained codons with o 1 ¼ 1; and classes 2a and 2b of codons conserved or neutral on the background branches, but that could be under positive selection with o 2 on the foreground branch), with the null model fixing o 2 at 1. In addition, we compared clade model C (Bielawski and Yang, 2004) having four free o parameters (o 2 , o 3 , o 4 and o 5 ) against the M1a (Nearly Neutral) model considering only two types of sites for the entire tree with 0oo 0 o1 and o 1 ¼ 1 (Nielsen and Yang, 1998;Yang et al, 2005) that allowed us to specify the HBA-T3 branches of Rattus, Peromyscus and Clethrionomys as independent foreground branches.…”

Distinct Evolutionary Patterns Between Two Duplicated Color Vision Genes Within Cyprinid Fishes

“…A test for functional divergence between the two copies of LWS-1 and SWS2 was performed using the clade Model C (Bielawski and Yang 2004) implemented in PAML. This model is based on a branch-site model and allows variation in the x ratio among sites with a proportion of sites evolving under different selective constraints between a pair of clades.…”

“…The models mentioned above were performed for both of the large and small data set of these two color vision genes, respectively. Because the models implemented in PAML are noted to be prone to the problem of multiple local optima (Bielawski and Yang 2004;Yang et al 2000), starting x values both above and below 1 were used. The results corresponding to the highest likelihood were used.…”