Fig. 1.

Schematic of the MK regression. The MK regression consists of two components: a generalized linear model and a McDonald–Kreitman-based likelihood function. First, I assume that, in a site-wise manner, the rate of adaptive evolution (${\omega }_{\mathrm{a}}$) at a functional site is a linear combination of local genomic features followed by an exponential transformation, in which regression coefficient β_i indicates the effect of the ith feature on adaptive evolution. Similarly, I assume that the probability of observing a SNP ($P_{\text{func}}$) at the same functional site is another linear combination of the same set of genomic features, followed by a logistic transformation. Second, in the McDonald–Kreitman-based likelihood function, I combine ${\omega }_{\mathrm{a}}$ and $P_{\text{func}}$ at every functional site with two neutral parameters, $D_{\text{neut}}$ and $P_{\text{neut}}$, to calculate the probability of observed divergence and polymorphism data given model parameters. $D_{\text{neut}}$ and $P_{\text{neut}}$ denote the expected number of substitutions and the probability of observing a SNP at a neutral site, respectively. $D_{\text{func}}$ denotes the expected number of substitutions at a functional site.