To keep the excitement going over John Hawks, et al, new paper about the Acceleration of Human Evolution, here’s a link to his “Acceleration Rarely-Asked Questions” about the topic. ( I also blogged about his further explanations of the paper over at The Lyceum.)
Here’s a Sample Question:
Methods to detect recent selection all have biases of one kind or another. How can we be sure that one or more of these biases haven’t really exaggerated the number of alleles in your dataset?
The start of his answer:
We are working with a tremendous advantage that previous studies of recent selection have lacked: Mathematics. Unquestionably, there are biases in the data, and as described below we have minimized these to the extent possible. But unlike every other study, we actually describe the theoretical reasons why selection should have accelerated in the human genome.
At one level, the mathematical answer is as simple as “more people means more mutations.” But more deeply, we can predict a linear response of new selected alleles to population size, and we can model this response with respect to a particular frequency range. The genome is a complicated place — with different mutations originating at different times, selected at different strengths, consequently with different fixation probabilities and different current frequencies. For some reason, nobody really tried to describe this mathematically before.