Inferring Parameters of the Distribution of Fitness Effects of New Mutations When Beneficial Mutations Are Strongly Advantageous and Rare

An efficient and robust ABC approach to infer the rate and strength of adaptation

Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in nonmodel species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to nonmodel genomes. We apply ABC-MK to the human proteome and a set of known virus interacting proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.

Between but Not Within-Species Variation in the Distribution of Fitness Effects

New mutations provide the raw material for evolution and adaptation. The distribution of fitness effects (DFE) describes the spectrum of effects of new mutations that can occur along a genome, and is, therefore, of vital interest in evolutionary biology. Recent work has uncovered striking similarities in the DFE between closely related species, prompting us to ask whether there is variation in the DFE among populations of the same species, or among species with different degrees of divergence, that is whether there is variation in the DFE at different levels of evolution. Using exome capture data from six tree species sampled across Europe we characterized the DFE for multiple species, and for each species, multiple populations, and investigated the factors potentially influencing the DFE, such as demography, population divergence, and genetic background. We find statistical support for the presence of variation in the DFE at the species level, even among relatively closely related species. However, we find very little difference at the population level, suggesting that differences in the DFE are primarily driven by deep features of species biology, and those evolutionarily recent events, such as demographic changes and local adaptation, have little impact.

CEGA: a method for inferring natural selection by comparative population genomic analysis across species

We developed maximum likelihood method for detecting positive selection or balancing selection using multilocus or genomic polymorphism and divergence data from two species. The method is especially useful for investigating natural selection in noncoding regions. Simulations demonstrate that the method outperforms existing methods in detecting both positive and balancing selection. We apply the method to population genomic data from human and chimpanzee. The list of genes identified under selection in the noncoding regions is prominently enriched in pathways related to the brain and nervous system. Therefore, our method will serve as a useful tool for comparative population genomic analysis.

Inferring the distributions of fitness effects and proportions of strongly deleterious mutations

The distribution of fitness effects is a key property in evolutionary genetics as it has implications for several evolutionary phenomena including the evolution of sex and mating systems, the rate of adaptive evolution, and the prevalence of deleterious mutations. Despite the distribution of fitness effects being extensively studied, the effects of strongly deleterious mutations are difficult to infer since such mutations are unlikely to be present in a sample of haplotypes, so genetic data may contain very little information about them. Recent work has attempted to correct for this issue by expanding the classic gamma-distributed model to explicitly account for strongly deleterious mutations. Here, we use simulations to investigate one such method, adding a parameter (plth) to capture the proportion of strongly deleterious mutations. We show that plth can improve the model fit when applied to individual species but underestimates the true proportion of strongly deleterious mutations. The parameter can also artificially maximize the likelihood when used to jointly infer a distribution of fitness effects from multiple species. As plth and related parameters are used in current inference algorithms, our results are relevant with respect to avoiding model artifacts and improving future tools for inferring the distribution of fitness effects.

impMKT: the imputed McDonald and Kreitman test, a straightforward correction that significantly increases the evidence of positive selection of the McDonald and Kreitman test at the gene level

The McDonald and Kreitman test is one of the most powerful and widely used methods to detect and quantify recurrent natural selection in DNA sequence data. One of its main limitations is the underestimation of positive selection due to the presence of slightly deleterious variants segregating at low frequencies. Although several approaches have been developed to overcome this limitation, most of them work on gene pooled analyses. Here, we present the imputed McDonald and Kreitman test (impMKT), a new straightforward approach for the detection of positive selection and other selection components of the distribution of fitness effects at the gene level. We compare imputed McDonald and Kreitman test with other widely used McDonald and Kreitman test approaches considering both simulated and empirical data. By applying imputed McDonald and Kreitman test to humans and Drosophila data at the gene level, we substantially increase the statistical evidence of positive selection with respect to previous approaches (e.g. by 50% and 157% compared with the McDonald and Kreitman test in Drosophila and humans, respectively). Finally, we review the minimum number of genes required to obtain a reliable estimation of the proportion of adaptive substitution (α) in gene pooled analyses by using the imputed McDonald and Kreitman test compared with other McDonald and Kreitman test implementations. Because of its simplicity and increased power to detect recurrent positive selection on genes, we propose the imputed McDonald and Kreitman test as the first straightforward approach for testing specific evolutionary hypotheses at the gene level. The software implementation and population genomics data are available at the web-server imkt.uab.cat.

Loss and fixation of strongly favoured new variants: Understanding and extending Haldane's result via the Wright-Fisher model

The implications of Haldane's analysis for the fixation of beneficial alleles lies at the heart of much of 'population genetic thinking' and underlies many approaches that have been tailored to the detection of positive selection. Within the framework of a branching process, Haldane gave an approximation for the probability that fixation ultimately occurs when the selective advantage of a beneficial allele is small (≪1). Here, we make no use of branching processes. Rather, we work solely within a finite-population Wright-Fisher framework. We use this framework to analyse where Haldane's result applies, and extend Haldane's analysis. In particular, we present results for: (i) the domain of applicability of Haldane's analysis; (ii) the probability that loss occurs up to a given time; (iii) the probabilities that loss and fixation ultimately occur; (iv) an analytic approximation associated with the probability of loss and fixation ultimately occurring; (v) quantification of the crossover from weak to strong selection; (vi) determination of the number of invasive alleles that have a significant probability (>0.95) of invading a novel population. We note that the results obtained for (ii), (iii) and (iv) hold for an arbitrary initial number of mutations, and for selection that can be arbitrarily strong. Our results have fundamental implications for population and conservation genetics, and open up new avenues to identify traces of historically beneficial alleles through comparative genomics.

How Good Are Predictions of the Effects of Selective Sweeps on Levels of Neutral Diversity?

Selective sweeps are thought to play a significant role in shaping patterns of variability across genomes; accurate predictions of their effects are, therefore, important for understanding these patterns. A commonly used model of selective sweeps assumes that alleles sampled at the end of a sweep, and that fail to recombine with wild-type haplotypes during the sweep, coalesce instantaneously, leading to a simple expression for sweep effects on diversity. It is shown here that there can be a significant probability that a pair of alleles sampled at the end of a sweep coalesce during the sweep before a recombination event can occur, reducing their expected coalescent time below that given by the simple approximation. Expressions are derived for the expected reductions in pairwise neutral diversities caused by both single and recurrent sweeps in the presence of such within-sweep coalescence, although the effects of multiple recombination events during a sweep are only treated heuristically. The accuracies of the resulting expressions were checked against the results of simulations. For even moderate ratios of the recombination rate to the selection coefficient, the simple approximation can be substantially inaccurate. The selection model used here can be applied to favorable mutations with arbitrary dominance coefficients, to sex-linked loci with sex-specific selection coefficients, and to inbreeding populations. Using the results from this model, the expected differences between the levels of variability on X chromosomes and autosomes with selection at linked sites are discussed, and compared with data on a population of Drosophila melanogaster.

Greater strength of selection and higher proportion of beneficial amino acid changing mutations in humans compared with mice and Drosophila melanogaster

Quantifying and comparing the amount of adaptive evolution among different species is key to understanding how evolution works. Previous studies have shown differences in adaptive evolution across species; however, their specific causes remain elusive. Here, we use improved modeling of weakly deleterious mutations and the demographic history of the outgroup species and ancestral population and estimate that at least 20% of nonsynonymous substitutions between humans and an outgroup species were fixed by positive selection. This estimate is much higher than previous estimates, which did not correct for the sizes of the outgroup species and ancestral population. Next, we jointly estimate the proportion and selection coefficient (p⁺ and s⁺, respectively) of newly arising beneficial nonsynonymous mutations in humans, mice, and Drosophila melanogaster by examining patterns of polymorphism and divergence. We develop a novel composite likelihood framework to test whether these parameters differ across species. Overall, we reject a model with the same p⁺ and s⁺ of beneficial mutations across species and estimate that humans have a higher p⁺s⁺ compared with that of D. melanogaster and mice. We show that this result cannot be caused by biased gene conversion or hypermutable CpG sites. We discuss possible biological explanations that could generate the observed differences in the amount of adaptive evolution across species.