Changing Population Size in McDonald-Kreitman Style Analyses: Artifactual Correlations and Adaptive Evolution between Humans and Chimpanzees

Abstract

It is known that methods to estimate the rate of adaptive evolution, which are based on the McDonald-Kreitman test, can be biased by changes in effective population size. Here, we demonstrate theoretically that changes in population size can also generate an artifactual correlation between the rate of adaptive evolution and any factor that is correlated to the strength of selection acting against deleterious mutations. In this context, we have investigated whether several site-level factors influence the rate of adaptive evolution in the divergence of humans and chimpanzees, two species that have been inferred to have undergone population size contraction since they diverged. We find that the rate of adaptive evolution, relative to the rate of mutation, is higher for more exposed amino acids, lower for amino acid pairs that are more dissimilar in terms of their polarity, volume, and lower for amino acid pairs that are subject to stronger purifying selection, as measured by the ratio of the numbers of nonsynonymous to synonymous polymorphisms (pN/pS). All of these correlations are opposite to the artifactual correlations expected under contracting population size. We therefore conclude that these correlations are genuine.

Keywords: McDonald–Kreitman; adaptive evolution; chimpanzee; human.

Fig. 1.

Estimates of ω_a and ω_na plotted against mean relative solvent accessibility. Data binned into 20 RSA bins of roughly equal number of sites. For each analysis, a weighted linear regression is fitted to the data. 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). Regression is weighted by the reciprocal of the variance for each estimate of ω_a and ω_na, which were estimated by bootstrapping the data by gene 100 times for each data point.

Fig. 2.

The adaptive and nonadaptive substitution rate plotted against the difference in (a) volume, (b) polarity, and (c) the ratio of nonsynonymous to synonymous polymorphisms, p_N2/p_S2, for 75 pairs of amino acids In (c), the polymorphisms are split by sampling from a hypergeometric distribution, with one set used to calculate rates of adaptive and nonadaptive substitution and the other to estimate the polymorphism statistics. A weighted linear regression is fitted to the data, weighted by the variance of each estimate. The respective significance of each correlation is shown in the legend (*P < 0.05; **P < 0.01; ***P < 0.001; “.” 0.05 ≤ P < 0.10).

Fig. 3.

Log(meanS) plotted against (a) volume difference, (b) polarity difference, (c) p_N2/p_S2, (d) mean RSA. 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) based on an unweighted regression fit to the data.