Detecting Directional Selection from the Polymorphism Frequency Spectrum

TL;DR

This paper identifies biases in the Poisson Random Field method for detecting natural selection from genetic data and introduces three new inference techniques that improve accuracy using finite-site and diffusion models.

Contribution

The paper presents three novel methods to correct biases in the PRF approach, enabling more accurate inference of selection and mutation rates from polymorphism data.

Findings

The original PRF method tends to underestimate selection pressures.

New methods reduce biases and improve inference accuracy.

Evaluation shows the new methods outperform the original PRF in simulations.

Abstract

The distribution of genetic polymorphisms in a population contains information about the mutation rate and the strength of natural selection at a locus. Here, we show that the Poisson Random Field (PRF) method of population-genetic inference suffers from systematic biases that tend to underestimate selection pressures and mutation rates, and that erroneously infer positive selection. These problems arise from the infinite-sites approximation inherent in the PRF method. We introduce three new inference techniques that correct these problems. We present a finite-site modification of the PRF method, as well as two new methods for inferring selection pressures and mutation rates based on diffusion models. Our methods can be used to infer not only a "weighted average" of selection pressures acting on a gene sequence, but also the distribution of selection pressures across sites. We evaluate the accuracy of our methods, as well that of the original PRF approach, by comparison with Wright-Fisher simulations.