Factors That Affect the Rates of Adaptive and Nonadaptive Evolution at the Gene Level in Humans and Chimpanzees

Abstract

The rate of amino acid substitution has been shown to be correlated to a number of factors including the rate of recombination, the age of the gene, the length of the protein, mean expression level, and gene function. However, the extent to which these correlations are due to adaptive and nonadaptive evolution has not been studied in detail, at least not in hominids. We find that the rate of adaptive evolution is significantly positively correlated to the rate of recombination, protein length and gene expression level, and negatively correlated to gene age. These correlations remain significant when each factor is controlled for in turn, except when controlling for expression in an analysis of protein length; and they also generally remain significant when biased gene conversion is taken into account. However, the positive correlations could be an artifact of population size contraction. We also find that the rate of nonadaptive evolution is negatively correlated to each factor, and all these correlations survive controlling for each other and biased gene conversion. Finally, we examine the effect of gene function on rates of adaptive and nonadaptive evolution; we confirm that virus-interacting proteins (VIPs) have higher rates of adaptive and lower rates of nonadaptive evolution, but we also demonstrate that there is significant variation in the rate of adaptive and nonadaptive evolution between GO categories when removing VIPs. We estimate that the VIP/non-VIP axis explains about 5-8 fold more of the variance in evolutionary rate than GO categories.

Keywords: adaptive evolution; chimpanzees; gene age; humans; recombination rate.

Fig. 1.

Estimates of ω_a and ω_na plotted against the (a) mean RR, (b) gene age, (c) mean gene expression, and (d) mean protein length. The respective significance of each correlation is shown in the plot legend, (*P < 0.05; **P < 0.01; ***P < 0.001; “.” 0.05 ≤ P < 0.10 for ω_a and ω_na). Also shown is the line of best fit through the data. An unweighted regression is fitted to the estimates of ω_a and ω_na.

Fig. 2.

Estimates of ω_a (top) and ω_na (bottom) for GO categories that contain >100 VIP and non-VIP genes.

Fig. 3.

Correlation between the log of the mean strength of selection against deleterious mutations and (a) gene age, (b) RR, (c) gene length, and (d) gene expression. A linear regression has been fitted to each data set.