Population genomic analysis reveals no evidence for GC-biased gene conversion in Drosophila melanogaster.

Gene conversion is the nonreciprocal exchange of genetic material between homologous chromosomes. Multiple lines of evidence from a variety of taxa strongly suggest that gene conversion events are biased toward GC-bearing alleles. However, in Drosophila, the data have largely been indirect and unclear, with some studies supporting the predictions of a GC-biased gene conversion model and other data showing contradictory findings. Here, we test whether gene conversion events are GC-biased in Drosophila melanogaster using whole-genome polymorphism and divergence data. Our results provide no support for GC-biased gene conversion and thus suggest that this process is unlikely to significantly contribute to patterns of polymorphism and divergence in this system.

polyDFE: Inferring the Distribution of Fitness Effects and Properties of Beneficial Mutations from Polymorphism Data.

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.

Inference of Distribution of Fitness Effects and Proportion of Adaptive Substitutions from Polymorphism Data.

The distribution of fitness effects (DFE) encompasses the fraction of deleterious, neutral, and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (α). Inferring DFE and α from patterns of polymorphism, as given through the site frequency spectrum (SFS) and divergence data, has been a longstanding goal of evolutionary genetics. A widespread assumption shared by previous inference methods is that beneficial mutations only contribute negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and α is then predicted by contrasting the SFS with divergence data from an outgroup. We develop a hierarchical probabilistic framework that extends previous methods to infer DFE and α from polymorphism data alone. We use extensive simulations to examine the performance of our method. While an outgroup is still needed to obtain an unfolded SFS, we show that both a DFE, comprising both deleterious and beneficial mutations, and α can be inferred without using divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and α We compare our framework with one of the most widely used inference methods available and apply it on a recently published chimpanzee exome data set.