A Method for Inferring the Rate of Occurrence and Fitness Effects of Advantageous Mutations

Abstract

The distribution of fitness effects (DFE) of new mutations is of fundamental importance in evolutionary genetics. Recently, methods have been developed for inferring the DFE that use information from the allele frequency distributions of putatively neutral and selected nucleotide polymorphic variants in a population sample. Here, we extend an existing maximum-likelihood method that estimates the DFE under the assumption that mutational effects are unconditionally deleterious, by including a fraction of positively selected mutations. We allow one or more classes of positive selection coefficients in the model and estimate both the fraction of mutations that are advantageous and the strength of selection acting on them. We show by simulations that the method is capable of recovering the parameters of the DFE under a range of conditions. We apply the method to two data sets on multiple protein-coding genes from African populations of Drosophila melanogaster. We use a probabilistic reconstruction of the ancestral states of the polymorphic sites to distinguish between derived and ancestral states at polymorphic nucleotide sites. In both data sets, we see a significant improvement in the fit when a category of positively selected amino acid mutations is included, but no further improvement if additional categories are added. We estimate that between 1% and 2% of new nonsynonymous mutations in D. melanogaster are positively selected, with a scaled selection coefficient representing the product of the effective population size, N e , and the strength of selection on heterozygous carriers of $2.5.

Causes of natural variation in fitness: Evidence from studies of Drosophila populations

“…Because of its size, this dataset has much greater statistical precision than previous studies of Drosophila populations, but the overall pattern is broadly similar to those reported previously (23,28,31,32). Nonsynonymous variants are mostly weakly Box 1.…”

Inferences of Demography and Selection in an African Population of Drosophila melanogaster

“…However, there is reason to be hopeful that our insights into the adaptive landscape of this Uganda population of D. melanogaster are not entirely driven by background selection, as this process should not result in an excess of high-frequency derived alleles and linkage disequilibrium (and in particular the specific linkage disequilibrium pattern captured by the omega_ max statistic) (Kim and Nielsen 2004;Jensen et al 2007b;Pavlidis et al 2010), both of which are critical parameters for drawing inference about past beneficial fixations. Moreover, estimates of the selection coefficient of adaptive mutations from models incorporating deleterious mutations are comparable to our own (Schneider et al 2011), which gives us further confidence that our results have not been grossly compromised by failing to consider background selection.The discrepancies between our estimates and previous estimates may reflect our use of a demographic model including a bottleneck followed by growth, as our results indicate that estimation of the selection coefficient and rate of adaptation are overestimated if an incorrect demographic model is assumed. However, the general concordance of parameter estimates under recurrent hitchhiking models from a variety of data sets using a variety of approaches suggests that these estimates are likely to characterize the adaptive history of the X chromosome of D. melanogaster.…”

“…Models that estimate the distribution of fitness effects of new mutations Keightley and Eyre-Walker 2007;Boyko et al 2008;Schneider et al 2011;Wilson et al 2011) are consistent with both deleterious and advantageous mutations contributing significantly to segregating polymorphisms, with the vast majority of new mutations being deleterious. However, there is reason to be hopeful that our insights into the adaptive landscape of this Uganda population of D. melanogaster are not entirely driven by background selection, as this process should not result in an excess of high-frequency derived alleles and linkage disequilibrium (and in particular the specific linkage disequilibrium pattern captured by the omega_ max statistic) (Kim and Nielsen 2004;Jensen et al 2007b;Pavlidis et al 2010), both of which are critical parameters for drawing inference about past beneficial fixations.…”

Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data

“…The reasoning behind this is that strongly 120 selected beneficial mutations will fixate very quickly and that "at 121 most an advantageous mutation will contribute twice as much 122 heterozygosity during its lifetime as a neutral variant" (Smith 123 and Eyre-Walker 2002). This assumption is backed by one study 124 (Keightley and Eyre-Walker 2010) (Bustamante et al 2003;Piganeau and Eyre-Walker 128 2003; Boyko et al 2008;Schneider et al 2011;Gronau et al 2013;129 Galtier 2016), the majority of them do not estimate α. 130 Here, we develop a hierarchical probabilistic model that com-131 bines and extends previous methods, and that can infer both 132 the full DFE and α from polymorphism data alone.…”

“…We compare our method 146 and illustrate the resulting bias using the most widely used 147 inference method, dfe-alpha (Keightley and Eyre-Walker 2007;148 Eyre-Walker and Schneider et al 2011;Keight-149 ley and Eyre-Walker 2012). We also investigate the impact on 150 inference of misidentification of ancestral state.…”