The possible evolutionary trajectories a population can follow is determined by the fitness effects of new mutations. Their relative frequencies are best specified through a distribution of fitness effects (DFE) that spans deleterious, neutral, and beneficial mutations. As such, the DFE is key to several aspects of the evolution of a population, and particularly the rate of adaptive molecular evolution (α). Inference of DFE from patterns of polymorphism and divergence has been a longstanding goal of evolutionary genetics.polyDFE provides a flexible statistical framework to estimate the DFE and α from site frequency spectrum (SFS) data. Several probability distributions can be fitted to the data to model the DFE. The method also jointly estimates a series of nuisance parameters that model the effect of unknown demography as well data imperfections, in particular possible errors in polarizing SNPs. This chapter is organized as a tutorial for polyDFE. We start by briefly reviewing the concept of DFE, α, and the principles underlying the method, and then provide an example using central chimpanzees data (Tataru et al., Genetics 207(3):1103-1119, 2017; Bataillon et al., Genome Biol Evol 7(4):1122-1132, 2015) to guide the user through the different steps of an analysis: formatting the data as input to polyDFE, fitting different models, obtaining estimates of parameters uncertainty and performing statistical tests, as well as model averaging procedures to obtain robust estimates of model parameters.
Keywords: Beneficial mutations; Distribution of fitness effects; Polymorphism and divergence data; Rate of adaptive molecular evolution.