An outstanding question in human genetics has been the degree to which adaptation occurs from standing genetic variation or from de novo mutations. Here, we combine several common statistics used to detect selection in an Approximate Bayesian Computation (ABC) framework, with the goal of discriminating between models of selection and providing estimates of the age of selected alleles and the selection coefficients acting on them. We use simulations to assess the power and accuracy of our method and apply it to seven of the strongest sweeps currently known in humans. We identify two genes, ASPM and PSCA, that are most likely affected by selection on standing variation; and we find three genes, ADH1B, LCT, and EDAR, in which the adaptive alleles seem to have swept from a new mutation. We also confirm evidence of selection for one further gene, TRPV6. In one gene, G6PD, neither neutral models nor models of selective sweeps fit the data, presumably because this locus has been subject to balancing selection.
Detection of selected regions has been a major goal in population genetics in recent years [9]–[13]. Rather than working with the full data, all of these studies simplified their data by using various statistics designed to detect the signal of selection (see e. g. [12], [14]). These statistics may be classified in different categories, based on the information they exploit. First, functional differences between different codon positions, and the substitution rates of synonymous and non-synonymous sites were used by [15], [16]. Another approach relies on finding related populations, where selection acts on only one of them. This leads to locus-specific high population differentiation, which may be detected by statistics such as FST [17] or XP-EHH [18]. A third category of statistics is based on the length of haplotypes associated with a given allele. Haplotypes associated with the selected allele will on average be younger than haplotypes carrying the derived allele, and there will therefore be fewer recombination events that break up the haplotypes. Statistics such as EHH [9] and iHS [19] were developed to detect this pattern. Finally, the site frequency spectrum (SFS) can also be used to detect departures from neutrality and hence selection. SFS based statistics usually compare various estimators of the population mutation rate θ. The first and perhaps most well-known statistic in this category is Tajima' s D [20], but the statistic can be generalized [21], [22], and other statistics such as Fay and Wu' s H [23] belong to the same family. In this study, we are interested in distinguishing the SDN and SSV models of evolution for a single putatively adaptive mutation. Barrett & Schluter [24] identify three possible ways of identifying SSV: i) the selected allele may occur in an ancestral population, ii) an allele is shown to be older than the environment it is adaptive in and iii) the signature of selection at linked loci, the selected sweep, is different between SSV and SDN. Our approach is based on differences in the genetic signature of selection, but when possible, we will compare to inferences based on i) and ii). To understand the difference between the SSV model and the SDN model, it is important to realize that all the information regarding selection, and mode of selection, is captured by the allele frequency trajectory through time. In other words, the full allele frequency path through time would be a sufficient statistic for the selection coefficient, if it was known. As selection acts only to change the allele frequency in the selected site, and does not act directly on adjacent sites, the effects of the selection on linkage disequilibrium, haplotype patterns, allele frequencies in linked sites, etc. , are only through the effects caused by the change in allele frequency of the selected allele (hitch-hiking effects). This observation is the foundation for standard population genetic theory on selective sweeps (e. g. , [27]–[28]) and forms the basis for several simulation methods, in which the path of the selected mutation is first simulated and then neutral simulations are performed conditional on the allele frequency path [28]. Such simulation methods would be invalid if the allele frequency path did not contain all information regarding the selection coefficient acting on the selected mutation. Similarly, if the path of an allele is the same under the SSV and the SDN model, no additional genomic data could help us distinguish between the two models. Armed with this insight, we can further explore the differences between the two models. Figure 1a, 1b depict the trajectory, the number of copies of the selected allele through time for an SSV and SDN model. Looking backward in time, the adaptive alleles are selected at first in both models, and during this stage the two models do not differ at all. In the SSV model, however, the mutation stops being advantageous at some point in the past. Backwards from this time point, the mutation in the SSV model acts as a neutral allele, whereas the mutation in the SDN model is under selection. As selection is the same in the phase when both alleles are selected, the difference between the models is during the phase in which selection is acting on the mutation in the SDN model but not in the SSV model. How big is this difference? It depends on two parameters: the selective advantage of the mutation under the SDN model, and the frequency of the mutation at the time when selection first start acting in the SSV model. A good measure of the difference might be the allele age distribution at this point, which is plotted in Figure 1c and 1d for a mutation at a frequency of 1% and 5%, respectively. Unfortunately, it turns out that the difference it is rather small: While the allele age of a mutation at a low frequency does depend on the selection coefficient, the difference is very small if selection is weak. Clearly, it will be much easier to distinguish between the two models if selection is strong and if the frequency of the mutation is initially high in the SSV model. However, we cannot observe the trajectory directly, but only the diversity at linked site. It has been shown that the genetic signature of sweeps from standing variation differs in three important aspects from the signature of sweeps from new mutations [29]: at the same selection coefficient, the signal of selection from standing variation is 1) weaker and 2) affecting a narrower region. As a third difference, we expect an increased variance in both allele age and trajectory. Under the SSV model, the selected allele may be present on several haplotypes when selection starts, and these haplotypes will be affected equally strongly by selection. Thus, there will be more variation compared to SDN, and the, loss-of-diversity signal of selection will be weaker. The fact that the signal of selection affects a narrower region is due to the fact that the selected allele is older in the SSV model, and hence recombination had more time to break it up (Figure 1a, 1b). The increase in variance is evident from the large variance in the neutral phase of the allele trajectory in Figure 1b, and the wider distribution of the allele age of neutral alleles in Figure 1c and 1d. In Figure 1e and 1f we give the expected distribution of Fay and Wu' s H [23] and EHH [9], two statistics used to detect selection, and where we show that the signal is indeed expected to be weaker and affecting a narrower region under the SSV model. The objective of this paper is to develop and explore a statistical method for distinguishing between SSV and SDN models, and for providing associated estimates of relevant parameters. However, the method we develop is not intended as a new method for performing scans for selection in genome-wide data or for quantification of genome-wide levels of selection. For computational reasons, other methods might be more suitable for such genome-wide analyses. We focus on illustrating the method on a few loci previously hypothesized to be under selection in humans, but the method could as well be applied to other human loci or data from other species. To exploit the characteristics of selective sweeps discussed in the previous section, we combine different statistics and calculate them for different genomic regions. Using combinations of statistics to improve inference is not a new concept, and has been applied previously (e. g. [30]). Here, we choose an Approximate Bayesian Computation (ABC) framework for combining statistics [31], [32]. ABC has the advantage that it extends naturally to allow both model choice and parameter estimates under a given model. ABC was developed to estimate parameters of complex models in manageable computer time, and has been widely used in population genetics, most frequently to infer parameters for complex models of demographic history [32]–[37]. Several implementations of the ABC algorithm have recently been published [38]–[40], and in the past few years, various variations of the algorithm have been developed [41]–[43]. ABC is a rejection sampling algorithm used to calculate the posterior distribution of a parameter under a given model, used frequently when the likelihood cannot be calculated analytically. In ABC inference, a large number of data sets are simulated using parameters randomly drawn from a prior distribution. If a simulation does not match the observed data, it is rejected, otherwise it is retained. However, if the data is complex, the probability of a match is prohibitively low, and two important approximation steps are used: First, the data is transformed into a set of summary statistics. If these statistics are sufficient (i. e. retain all the information present in the data), this step is exact. However, in many cases, including this study, no sufficient statistics are known, and this step results in a first approximation step. In many cases, however, this transformation will still result in very low acceptance probabilities. Therefore, the condition of an exact match is relaxed. Specifically, the summary statistics based on the simulations (S) are compared to the summary statistics of observed data (S*). Using some distance measure δ, simulations are retained if |δ (S, S*) |<ε for an arbitrarily small distance ε. Frequently, some post-sampling adjustment is used in an attempt to correct for the error introduced in the second approximation step, and posterior distributions are estimated from the parameters of the retained simulations. In this study, we propose to use ABC to distinguish between a selective sweep from a new mutation and a selective sweep from standing variation. We use simulations to determine which parts of the parameter space the method has power to make this distinction, and aim to estimate parameters under both models. We then apply our method to seven genes that were previously reported to be under selection.
We first wanted to assess how accurately we can estimate the selection coefficient and the age of the selected mutation from the SSV and SDN models. For this purpose, we performed ABC inference on simulated data sets with known parameter values. Results for a case of moderately strong selection (α = 400) are given in Figure 2, with α being the population scaled selection coefficient α = 4Ns. As can be seen from the figure, the mode is an accurate estimator of the true value for both models. However, in the SSV case the posterior distribution is much broader than under the SDN model, and the 95% confidence interval extends to the edges of the prior, indicating low accuracy in the estimate. For the initial frequency parameter, f1, the posterior differs only marginally from the prior, and therefore this parameter cannot be reliably estimated. We aim to identify parameter regions where we can distinguish between the SSV and the SDN model. As a control, we also consider a model of neutral evolution (NT), where an allele increases to high frequency solely due to genetic drift. In particular, we are interested in three parameters that are expected to have a strong influence on model choice accuracy: the selection parameter α, the frequency of the mutation when it became selective advantageous, f1, and the current frequency of the selected allele fcur. In Figure 3 and Figure S1, we explore the accuracy of our model choice procedure in three series as a function of α, f1, and fcur. . We find (Figure 3a) that in cases where α<100; the method cannot reliably distinguish between selection and a neutral model. This is not surprising, as for such values of α, standard neutrality tests have little or no power to detect selection [44], [45]. For selection coefficients of α = 100 and α = 200 the neutral model has a very low posterior probability and would be rejected, but we still do not have sufficient power to distinguish the signals from SSV from SDN. Only under strong selection (α = 1,000) do we have reasonable power to distinguish between SSV and SDN. Thus, we find that there is a parameter range of α between 100 and 500, in which selection can be reliably detected, but the two models of selection are statistically indistinguishable. In the second series (Figure 3b), we vary the initial allele frequency (f1). We find that simulations under the SSV model, with f1 = 1%, are identified as SDN models, but that the accuracy in model choice increases with f1. For larger values of f1, we can detect selection when selection is strong (α = 1,000). For high initial allele frequency (f1 = 20%) we correctly infer the true mode of selection even when α is 200. This suggests that the ability to distinguish the two models increases with f1. Furthermore, we also find a negative relationship between the estimated value of f1 for a data set and the posterior probability of the SDN model (Figures S2, S3 and S4): As we would expect, the larger the estimate of f1, the lower is the posterior probability of the SDN model, and we find a strong negative correlation (R2 = 0. 51) between these two quantities based on 1,000 simulations. In the third series (Figure 3c), we investigate the effect of the current allele frequency fcur on the model comparison. For simulations under the SSV model, we find that the accuracy strongly decreases with fcur. For fcur = 0. 2, we classify slightly less than half of the data sets correctly. This is in contrast to simulations under the SDN model, where the power to correctly classify simulated data sets gradually increases with fcur. Thus, in studies aimed at detecting selection on standing variation, the false positive rate should depend only slightly on fcur, but the false negative rate is expected to increases drastically when fcur is low. Figure 4 illustrates how the selection parameter (α) and the initial allele frequency (f1) affect the accuracy of model choice between the SSV, SDN and NT models for three values of fcur (fcur = 0. 95, fcur = 0. 8 and fcurr = 0. 5). As in Figure 3, the number of correctly assigned data sets increases with α, f1 and fcur. Under the SSV model, the gradient with which the power declines is strongest when fcur is large (95%, Figure 4a), and becomes less pronounced for smaller fcur (see Figure 4c and 4e). For fcur = 95% (Figure 4a), there is a region with f1>0. 05 and α>1,000 where there clearly is very high power to infer the correct model. On the other hand, for α<200 or f1<0. 03, we make incorrect inferences more than half of the time, indicating that in these regions of the parameter space, the signal of the sweep is too weak to discriminate between the SSV and SDN models. While that global pattern is the same for fcur = 0. 8 and fcur = 0. 5 (Figure 4c, 4e), the distinction between regions where we can and cannot assign simulated data sets correctly is less pronounced. Quite surprisingly, however, we find that for fcur = 0. 8, the number of correctly assigned data sets increases when selection is low. The same trend holds for fcur = 0. 5 (Figure 4e), however here the influence of selection is even weaker, and inference becomes quite ambiguous, with posterior probabilities ranging from 60% to 80% in the entire parameter space. In contrast, the pattern is much simpler for simulations under the SDN model (Figure 4b, 4d and 4f), where the probability to correctly identify the model increases with decreasing fcur. When fcur is set to 0. 95, we need a selection coefficient of α = 1,500 to make confident inferences. For fcur of 0. 8 and 0. 5, this value decreases to 900 and 300, respectively. In summary, a high current allele frequency increases the power to distinguish between SSV from SDN (Figure 3c, Figure 4). The frequency with which the SDN model is correctly inferred increases slightly with decreasing fcur, presumably because the selected phase makes up a larger proportion of the trajectory. We illustrate our model choice procedure by analyzing seven genes that have previously been identified as candidates for being under selection. These genes are ADH1B, ASPM, EDAR, G6PD, LCT, PSCA and TRPV6. The genes were selected using the following set of criteria: i) there is evidence for selection from a previous study, ii) a putative causal mutation has been identified and iii) the putative causal site has reached a high frequency in at least one population, but has not yet reached fixation. In addition, we also analyzed four regions that were noncoding and presumably neutral. We retrieved polymorphism data from the 1000 Genomes Project low coverage data [46] using tabix [47]. Ancestral genotypes were inferred by comparison to the homologous chimpanzee allele. If a signal of selection was present in more than one population, we used data for the population where the selected site was most frequent, to facilitate inference. Model choice and parameter estimation were performed using the procedures described in the methods section. In contrast to the inference on simulated data sets, here we explicitly model varying recombination rates and the complex demographic history of the human population. Results for the sample genes are given in Figure 5 and Figure S4, as well as Table 1. For six of the seven genes analyzed, the neutral scenario was strongly rejected with a posterior probability of less than 1%, and we can confirm the prior evidence that these genes are under selection. Three of those genes, ADH1B, EDAR and LCT, were found to be under selection from a new mutation and one gene, TRPV6 could not be assigned with any significant probability to either model. Two genes, ASPM and PSCA, were found to be under selection from standing variation. Finally, none of the three models provided a good fit to observed data in the G6PD gene, suggesting that neither of the models is appropriate for this gene. In the following paragraphs, we will discuss each gene in some detail, and give estimates for selection coefficient and time when appropriate. All estimates are given with a point estimate for the mode, and the lower and upper bound of a 95% Highest Posterior Density interval in brackets. Estimates in years were made assuming a generation time of 25 years.
The distribution of summary statistics in Figure S5 illustrates the impact of choice of summary statistic for model inference [92]. Very high values of EHH are clearly indicative of the SDN model, at both a 10 kb and 20 kb distance. Both the SSV and SDN models are associated with low IHS values, whereas the neutral regions have IHS values closer to zero. Tajima' s D and Fay and Wu' s H are both very informative for model comparison, with SDN genes having very low D values, SSV genes having D values close to zero and neutral regions having positive D values. The main exception is the LCT gene, however, which we inferred to be selected from a de novo mutation, but which has a high D. The signal for SDN apparently comes more from the high EHH and low IHS values. In general, our results are highly concordant with previous studies of these genes. Our estimates tend to gene, G6PD, we could not make any inferences, because we could not reproduce the observed pattern of diversity using simulations of positive directional selection. G6PD shows an extremely narrow region of reduced diversity, surrounded by a region of high diversity. This may be due to balancing selection between malaria resistance and reduced efficiency oxygen transport introducing a signal that cannot be reproduced by our simple model of directional selection. Alternatively, the X-linked mode of inheritance of this locus is not concordant with the assumptions of our model. This also highlights one of the dangers of ABC: It is crucial that the models investigated are able to reproduce the data observed; otherwise false inferences may be drawn. This danger inherent to any ABC approach is also highlighted by the fact that misidentification of the selected site will bias model choice results towards SSV (Figure S6). This can be explained by the fact that even if the neutral site is closely linked to the selected site, it is likely to “escape” the sweep by recombining away from the selected haplotype, thus giving the signal of selection from standing variation. Similarly, analyzing data simulated under a population bottleneck under a constant size model will bias the results towards stronger selection and SDN (Figure S7), presumably due to the younger age of mutations being taken as evidence of strong selection. We have shown that it is much more difficult to estimate the model parameters α, and t1 from the SSV model than from the SDN model. This is unsurprising, as the SSV model has been shown to have a higher variance in allele age, which results in a higher expected variance for most summary statistics [8]. We further show that there is not enough information to estimate the initial frequency of the sweep f1. This is unsurprising, as the exact position of the switching point has likely only a minor effect on the data, especially as the effect of selection on the trajectory is weak when the allele frequency of the beneficial allele is low [27]. We further notice that the accuracy of our model choice procedure decreases when the signal of selection is weak. Consistent with previous findings, selection is very hard to detect if α is below about 100 [5], [11], [45]. This is also the point where our method gains power to distinguish between SVN from SDN. The initial frequency required to detect standing variation is moderate at around 3% for weak selection and 2% for stronger selection. However, selection has to be rather strong, at around α = 1,000 and initial frequencies have to be above 5% to allow accurate inference. Presumably, this is because below this threshold, the stochasticity of the trajectory is very large even under selection, and the difference between the two scenarios is small (see also Figure 1c). These findings are not particularly surprising, as selection scans based on summary statistics have been shown in general to have low power under these conditions [45]. These findings certainly limit the scope of our approach. Could we do better with a different strategy? As discussed in the introduction, the ABC approach simplifies data in two ways. First, instead of using the full data, we use an array of summary statistics. Second, we substitute an exact match between observation and simulations with an approximate match, depending on “close” simulations. Regarding the use of summary statistics, we note that summary statistics have been widely used to detect selection from genetic data [9]–[11], [14], [19], and currently provide the only way to detect selection from DNA sequence data. No full likelihood based method is available to detect selection from DNA sequence data that could be adapted to distinguish between the two sweep models entertained here. The second simplification step is based on the number of simulations performed and the tolerance interval and is imposed by computational constraints. We examine the effect of different numbers of simulations and tolerance cutoffs on our results by calculating relative error rates of the posterior mean and the false negative rate of the model choice. We show in Table S1 that increasing the number of simulation by a large amount or changing the rejection parameter does not significantly improve our results, indicating that we do not lose a lot of information at this stage. This shows that the ABC approach reliably estimates the posterior based on the summary statistics, and as such use all the information available in these statistics. Statistics such as EHH, iHS, Tajima' s D, etc, do not contain information that will allow us to provide more reliable estimates. It the light of this, it may appear disappointing that our method does not provide more accurate parameter estimates and more power to distinguish between models. However, it is important to realize that as previously argued, all information regarding selection is in the frequency path of the selected allele [26], [27]. For relatively small selection coefficients and/or small initial frequencies of the selected allele, the paths are very similar for the SSV and SDN models. Even if a full likelihood method could be developed, it is unlikely that it had much more power to distinguish between models. A further simplification in our method is the restriction to a single population. Population differentiation measures, such as FST, are one of the most successful ways to detect sweeps from standing variation [24], [86], and the inclusion of more realistic models of demography may improve our accuracy. Such models, however, require an additional estimation of multi-population demographic history, which greatly increases the complexity of the model. While we applied our method only to human candidate loci, it should be possible to easily translate it to other species. In particular, as our simulation results suggest that we have more power to distinguish SDN and SSV if selection is strong, species with large population sizes, such as e. g. Drosophila or many microorganisms may be very promising targets for a similar study. Another possible target might be species with very strong artificial selection, such as domesticated animals or plants, where we may gain valuable insights on the domestication history of these species. Of course, our approach could also be combined with ancient DNA (e. g. [83]), which could provide much narrower confidence intervals on time estimates and also help improve estimates of selection coefficients. The two selection models we consider here, the SSV and the SDN models, are nested models, Setting f1 = 1/2N in the SSV model recovers the SDN model. To facilitate Bayesian model choice we assign positive probability to f1 = 1/2N, and base our inferences on a choice between f1 = 1/2N and f1 ∼ U (0,0. 2) (See Methods). ABC based model choice has recently been criticized and been shown to be biased in some cases where the statistics used are not sufficient [93], [94]. While some of the specific issues raised by [91] are not applicable in our setting because we consider nested models, we do not base our inference on sufficient statistics and the statistical properties of our model choice procedure are, therefore, largely unknown. To address this issue, and in general to validate our approach, we use a method introduced in [95]. We show in Figure S8 that our estimated probabilities only show bias for very small values of the Bayes factor, where there appears to be a bias towards inference of the SDN model for simulations generated under the SSV model with very low values of f1.
In order to keep our problem simple, we condition on two important parameters: We assume that the exact site under selection is known from extraneous information, and we furthermore assume that the allele frequency fcur of that site at the time of sampling, tcur = 0 is known. The interpretation of the parameters is depicted graphically in Figure 1a. Unless noted otherwise, we assume a panmictic diploid population of size N with an additive selection model where the ancestral homozygous, heterozygous and derived homozygous genotypes have fitness 1,1+ s/2 and 1 + s, respectively. However, the methodology applied here can easily be adapted to more complex scenarios, e. g. models involving multiple populations, more sophisticated demographic models, and other models of selection. For most simulated data sets, we will report the population scaled mutation rate α = 4Ns, as the shape of the allele frequency trajectory depends only on that compound parameter [25]. However, for most of the genes we analyze previous estimates were made directly on s rather than the compound parameter. To facilitate comparisons, we report s for the genes we analyzed. We use a standard ABC approach [31], [32], using a post-sampling adjustment in the form of a GLM [96]. We used the ABCToolbox package [40], for specifying priors, rejection sampling and post-sampling adjustment. Unless specified otherwise, we perform 105 simulations per model, and retained the 100 (0. 1%) simulations with associated Euclidean distance between observed and simulated summary statistics closest to zero. To assess how the number of simulations and acceptance rates influence our results, we analyze 10,000 random data sets with up to 107 simulations and varying acceptance rates. We show that these parameters have very little impact on the relative error for both the model choice and parameter estimates in Table S1. All our data sets used for both the ABC inference and the assessment of our procedure are simulated using a modified version of the coalescent simulator mbs [102]. Mbs allows simulation of genetic data sets with a single selected site using the structured coalescent [26]. mbs first simulates the allele frequency trajectory of the site under selection, and then generates a data set conditional on that trajectory. We simulate allele frequency trajectories using Euler' s method on the unscaled backwards diffusion equation with selection (eq 7. 1 in [103]). This equation makes it very easy to incorporate population size changes by just changing the variance term. To simulate sweeps from standing variation, we set the selection coefficient (s) to zero the first time the trajectory reaches f1. To analyze simulated data sets, we generally simulate a 100 kb region with a recombination rate of 1. 5 cM/Mb. For the human genes, we simulate the gene and a 50 kb flanking region on both sides, resulting in regions that are usually between 100 kb and 150 kb wide. Recombination rates and hotspots are modeled by using the HapMap recombination map [104] in the application to selected genes. For all simulated data sets we assume a constant-sized population. For the analysis of human genes, we use the population history estimated by [105]. Specific regions and details of the used regions are given in Table S2. To ensure that our method does not suffer from a high false positive rate, we also analyze regions 5 Mb downstream from the candidate genes, as they are presumably neutral. For three of genes (ASPM, G6PD, and PSCA), no data was available for these downstream regions, so we analyzed the remaining loci. Candidate loci for selection were chosen using the following criteria: i) they were required to have a derived allele frequency between 0. 7 and 0. 9 and ii) to be as closely to 5 Mb away from the actual candidate locus in the upstream gene as possible. We estimate parameters from our models using the standard ABC procedure described above. The parameters we estimate are the mutation rate μ, the age of the sweep t1 the selection coefficient s and, only under the SSV model, the initial frequency f1 for the SSV model. In particular we want to determine if our posteriors are unbiased, and if we were able to get reasonable confidence in our estimates. To do this, we simulate data sets with fixed parameters and plotted the average posterior distribution for all parameters in Figure 2. For model choice, our main goal is to calculate the relative probabilities of the models given the data, i. e. Pr (SSV | data), Pr (SDN | data) and Pr (NT | data), which we calculate using the marginal densities as proposed by [96]. To identify parameter regions where there is power to distinguish between the models, we simulate 1,000 data sets each under 30 different scenarios in three series, corresponding to three parameters of interest: The strength of selection α, the frequency when the mutation became selective advantageous f1 and fcur, the frequency at which the mutation is observed. To test the algorithm for approximating Bayes factors, we also use a simulation approach. The estimator of the posterior probability from k simulations, , should have the property where m is a model indicator functions for a specific model. Also, for a particular draw from the posterior, m (0), we expect, if the simulation algorithm works properly. In other words, , should asymptotically equal, i. e. if = c, we expect a proportion c of simulations to have been obtained from model m. Equivalently, for an estimated log Bayes factor, log10 = c, we expect a proportion 10c/ (1+10c) of draws to be from model m. This prediction is tested in Figure S8, based on 10,000 random data sets from both the SDN and SSV model.
Considerable effort has been devoted to detecting genes that are under natural selection, and hundreds of such genes have been identified in previous studies. Here, we present a method for extending these studies by inferring parameters, such as selection coefficients and the time when a selected variant arose. Of particular interest is the question whether the selective pressure was already present when the selected variant was first introduced into a population. In this case, the variant would be selected right after it originated in the population, a process we call selection from a de novo mutation. We contrast this with selection from standing variation, where the selected variant predates the selective pressure. We present a method to distinguish these two scenarios, test its accuracy, and apply it to seven human genes. We find three genes, ADH1B, EDAR, and LCT, that were presumably selected from a de novo mutation and two other genes, ASPM and PSCA, which we infer to be under selection from standing variation.
natural selection
human genetics
genetics
population genetics
biology
evolutionary biology
evolutionary processes
genetics and genomics
evolutionary genetics
evolutionary theory
Distinguishing between Selective Sweeps from Standing Variation and from a De Novo Mutation