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The distribution of genetic polymorphisms in a population containsinformation about the mutation rate and the strength of natural selection at alocus. Here, we show that the Poisson Random Field (PRF) method ofpopulation-genetic inference suffers from systematic biases that tend tounderestimate selection pressures and mutation rates, and that erroneouslyinfer positive selection. These problems arise from the infinite-sitesapproximation inherent in the PRF method. We introduce three new inferencetechniques that correct these problems. We present a finite-site modificationof the PRF method, as well as two new methods for inferring selection pressuresand mutation rates based on diffusion models. Our methods can be used to infernot only a "weighted average" of selection pressures acting on a gene sequence,but also the distribution of selection pressures across sites. We evaluate theaccuracy of our methods, as well that of the original PRF approach, bycomparison with Wright-Fisher simulations.

Detecting Directional Selection from the Polymorphism Frequency Spectrum