SnIPRE: selection inference using a Poisson random effects model

PLoS Comput Biol. 2012;8(12):e1002806. doi: 10.1371/journal.pcbi.1002806. Epub 2012 Dec 6.


We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes. A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional consequence. We demonstrate how the model's estimated fixed and random effects can be used to identify genes under selection. The parameter estimates from our generalized linear model can be transformed to yield population genetic parameter estimates for quantities including the average selection coefficient for new mutations at a locus, the synonymous and non-synynomous mutation rates, and species divergence times. Furthermore, our approach incorporates stochastic variation due to the evolutionary process and can be fit using standard statistical software. The model is fit in both the empirical Bayes and Bayesian settings using the lme4 package in R, and Markov chain Monte Carlo methods in WinBUGS. Using simulated data we compare our method to existing approaches for detecting genes under selection: the McDonald-Kreitman test, and two versions of the Poisson random field based method MKprf. Overall, we find our method universally outperforms existing methods for detecting genes subject to selection using polymorphism and divergence data.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Computer Simulation
  • Humans
  • Mutation
  • Poisson Distribution*
  • Polymorphism, Genetic
  • Selection, Genetic*
  • Stochastic Processes

Grant support

This work has been funded by the National Science Foundation under grant NSF0516310. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.