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Genetics, Vol. 176, 2371-2379, August 2007, Copyright © 2007
doi:10.1534/genetics.106.069450
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* Department of Molecular Biology and Genetics and Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14853
1 Corresponding author: Section of Ecology, Behavior and Evolution, AP&M 4th Floor Annex, University of California, La Jolla, California 92037.
E-mail: jjensen{at}ucsd.edu
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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max) that appears to have power to identify regions recently shaped by positive selection. Most notably, for demographic parameters relevant to non-African populations of Drosophila melanogaster, we demonstrate that selected loci are distinguishable from neutral loci using this statistic.
Since SNP data contain information about linkage disequilibrium (LD) in addition to site frequencies, it has been hypothesized that this additional information could be utilized for hypothesis testing and allow for greater discriminatory power. Specifically, a number of theoretical and simulation results have demonstrated that LD is an important signature of a selective sweep (e.g., PARSCH et al. 2001; PRZEWORSKI 2002; SABETI et al. 2002; WOOTTON et al. 2002; KIM and NIELSEN 2004; EBERLE et al. 2006; STEPHAN et al. 2006). Therefore, it is reasonable to think that vital information is being ignored by not considering associations between markers. The extent to which incorporating LD may improve our ability to distinguish selection from demography has been largely unexplored.
KIM and NIELSEN (2004) examined the effects of including LD into the KIM and STEPHAN (2002) likelihood framework. They describe three patterns of LD predicted from a genealogical model that are proposed to be potentially unique to a selective sweep. First, a high level of LD is expected in regions near, but not immediately adjacent, to the target of selection. Second, a high level of LD is expected on both sides of the target, but should not span the site of selection. Finally, there is a strong correlation between high-frequency-derived alleles (as measured by Fay and Wu's H-statistic) and LD, such that the probability of observing these alleles is greater in regions of strong LD. They thus proposed a new composite-likelihood method designed to incorporate this information. They note, however, that the improvement made by including LD is small, suggesting that most relevant information is efficiently captured by considering only the site-frequency spectrum, owing to the correlation between LD and high-frequency-derived alleles. Importantly, their result pertains specifically to the case of distinguishing between a selective and a neutral equilibrium model.
STEPHAN et al. (2006) analytically studied a three-locus model of genetic hitchhiking in which one locus is under positive selection while the other two are neutral and partially linked. While they further support a number of the conclusions described in KIM and NIELSEN (2004) and further generalize their results, they also note that when the direction of LD is polarized with respect to the more common allele at each neutral site, more positive than negative LD is created after a selective sweep. They propose that this pattern may indeed be unique to a selection model, and thus hitchhiking may have a distinctively patterned LD-reducing effect near the target of selection. Encouraged by this result, we undertook a simulation study to explore if there were patterns of linkage disequilibrium that are indeed unique to models of positive selection relative to nonequilibrium models, which may aid in the discovery of adaptively important loci.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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= 75 and 4Nr = 100, where r is the probability per generation of crossing over for the entire simulated region, values roughly corresponding to a typical Drosophila melanogaster data set. The bottleneck model has five parameters: the population mutation rate ( = 4N0µ, where N0 is the effective size of the ancestral population), the population recombination rate (
= 4N0r), the time at which the derived population recovered from the bottleneck (tr), the duration of the bottleneck (d), and the severity of the bottleneck (
). In the figures, the time of the bottleneck (tb) is often referred to—where tb = tr + d. Simulations of population subdivision under an island model are performed with two subpopulations and scaled migration rate, M = 4Nm, where m is the fraction of migrants in each subpopulation in each generation. The sampling scheme is denoted by n = {n1, n2}, where n1 and n2 refer to the numbers of chromosomes sampled from the first and second subpopulations, respectively. In this study, we examine equal and unequal sampling from the subpopulations, for M = 0.1, 1, 4, and 10. To distinguish from bottlenecks and subdivisions, we refer to the model of neutral evolution under random mating and constant size as the equilibrium neutral model.
Modeling selective sweeps:
We model positive selection using coalescent simulations for a region of M nucleotides. At time 
in the past (measured in units of 4N generations), a beneficial allele has fixed in the population at position X. For all single-sweep simulations, X lies in the interval [1, M]. The simulation consists of a neutral phase, which is the standard coalescent with recombination (HUDSON 1983), and a selective phase (BRAVERMAN et al. 1995). At time in the past, the simulation enters the selective phase, which is modeled as a structured coalescent process (e.g., KAPLAN et al. 1988; BRAVERMAN et al. 1995), and time is incremented in small units, 
, until the frequency of the beneficial allele first reaches x(t) < 
, at which point the simulation continues in a neutral phase until the most recent common ancestor of the sample is reached. Full details of the single-sweep simulations are found in THORNTON and JENSEN (2007).
We also considered a model of selective sweeps occurring in the genome at a rate determined by 
, the expected number of sweeps per recombination unit in the last 4N generations (KAPLAN et al. 1989; BRAVERMAN et al. 1995). Here we allow for selective sweeps both within the region of M nucleotides as well as at linked sites. We do this because we simulate a relatively large neutral region (M = 104), and the probability of a sweep within that region may not be negligible for large , assuming a constant across the genome. In this model, the time until the next selective phase is entered is exponentially distributed with rate 
, where 
is the scaled recombination rate between adjacent base pairs. The first half of this rate accounts for sweeps flanking the sequenced region, and the M accounts for sweeps within the region. Given that a selective phase is entered, the selected site is located within the M nucleotides with probability 
; otherwise it is located at a linked site up to a maximum genetic distance of 2
(where = 2Ns) on either side of the sampled region (see KAPLAN et al. 1989 and DURRETT and SCHWEINSBERG 2004 for details).
Briefly, the expected time between successive hitchhiking events is E[tL], the expected length of a hitchhiking event, plus E[tS], the expected time until the next fixation of a selected allele. For the model considered here, this equals 
in units of 4N generations. For example, for the case of = 5000, = 10–5, = 10, sweeps are occurring on average every 
time units for the 
30-kb region (2 + 104 + 2 = 30 kb). This extrapolates to approximately one sweep per 80 generations somewhere in the 120-Mb euchromatic portion of the D. melanogaster genome.
We estimated LD for two sample sizes (n = 12 and 50) and 90 parameter combinations generated by considering all combinations of 
, and 
. These parameters cover cases where we expect hitchhiking effects to be minimal ( = 10–7, = 100) to those where the effect should be substantial ( = 10–5, = 5000). For these simulations, we used N = 106.
Statistics:
We evaluate the likelihood-ratio test (comparing a neutral equilibrium model and a single-sweep equilibrium model) proposed by KIM and NIELSEN (2004) under all simulated scenarios. We also examine the LD statistic that they proposed to more specifically quantify the extent to which "sweep-like" patterns of LD are being generated under alternative models. This statistic, termed , defined as
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increases with increasing LD within each group and decreasing LD between groups (i.e., the larger the value of the statistic the more sweep-like the underlying pattern). For a data set, the value of l that maximizes (max) is found. Singletons were excluded prior to calculation. Because the statistic is two tailed, rejections may be the result of values of max that are either too large or too small relative to the null. |
RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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(= 2Ns) increases (Figure 1A). An island model with two subdivided populations was also evaluated, and we considered a sampling scheme in which all alleles are sampled from one subpopulation, as well as one in which the subpopulations are sampled equally. We find that the test has a large false positive rate (FPR) (or type I error) under both scenarios when migration is rare, and the FPR gradually decreases as 4Nm increases owing to the deterioration of population structure (Figure 1B).
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Figure 2 summarizes the averages and standard deviations of max under the equilibrium neutral, nonequilibrium neutral, and equilibrium selection models examined for n = 50 (n = 12 not shown). There are a number of notable features. First, under the equilibrium neutral model ( = 0), max-values were observed between 2 and 3 for common (n = 12) and large (n = 50) sample sizes, with small standard deviations (n = 50 shown in Figure 2A). A number of equilibrium selection models produced distinctive distributions of max. For large n, max is greatest when the selective event was recent and strong. In contrast, for small sample sizes, individual observed values of max may be reduced relative to the null for large selection coefficients—owing to the fact that there is very little variation within the 10-kb region following such a severe sweep, an effect that is exacerbated in small sample sizes.
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max near that observed under neutral equilibrium conditions even for large sample sizes (Figure 2C), while severe bottlenecks (99% reduction) result in a distribution with large values in the tail. For example, a 99% reduction at time tb = 0.01 4N generations in the past, with a recovery tr = 0.0011 4N generations ago, has an average max near 5 (Figure 2D).
Distinguishing nonequilibrium selection models from nonequilibrium neutral models:
The THORNTON and ANDOLFATTO (2006) bottleneck model estimated for D. melanogaster was singled out for specific analysis. Results are also presented for sweeps in an equilibrium population for comparison (Figure 3, a and b). The distribution of the max-statistic is largely overlapping between the neutral and nonneutral scenarios for the bottlenecked population, particularly for n = 12 (Figure 3c). Thus, these results, taken with those from Figure 2, strongly suggest that the patterns of linkage disequilibrium proposed to be unique to positive selection are being replicated under realistic demographic models. Moreover, these results highlight the relative difficulty of inferring selection in nonequilibrium vs. equilibrium populations. The selection distributions of max observed under nonequilibrium models are considerably less distinctive than those under equilibrium models (e.g., Figure 3c vs. Figure 3a and Figure 3d vs. Figure 3b).
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max are partially distinguishable between neutral and selected loci in bottlenecked populations. For = 500, the great majority of replicates are distinguishable from neutrality, with very large values (max > 5) being produced with high probability. While it is seemingly counterintuitive, larger values of result in smaller values of max, particularly in the bottlenecked relative to equilibrium populations. For very large values ( = 2500), the distribution is still partially distinct from neutrality, particularly in the direction of values of max that are too small. The twofold diversity-reducing effect of a bottleneck plus a strong sweep largely eliminates variation within the 10-kb region. Accounting for departures in both directions, these results indicate that loci that have experienced recent and strong selection may often be identifiable in nonequilibrium populations (at least for the parameter space estimated by THORNTON and ANDOLFATTO 2006), with both small and large values of max being consistent with selection (max < 2 and max > 4, respectively). This suggests that the max-statistic is of value when evaluating both African and non-African sequence data alike.
To better evaluate the utility of the max-statistic, we present receiver operating characteristic (ROC) curves. In brief, ROC curves plot power as a function of the false positive rate, where an ideal performance would be a curve near the left and the top of the graph (i.e., high power is achieved with a very low FPR). The diagonal represents the situation in which there is a linear relationship between power and FPR (e.g., 50% power corresponds to a 50% FPR). ROC curves are ideal for these comparisons, as they do not summarize performance merely at a single arbitrarily selected value, but across all possible values. The ROC curve can be used to evaluate the gain in power achieved by using a type I error rate other than the standard 0.05. In particular, one may prefer to choose a value that balances the probability of misclassification of either class [i.e., the probability of false positives (i.e., type I error) and false negatives (i.e., power)].
Examining ROC curves for our bottleneck with selection data sets, we observe a number of interesting features (Figure 4). Once again, results are also presented for sweeps in an equilibrium population for comparison (n = 12, Figure 4A; n = 50, Figure 4B). For small sample sizes in a bottlenecked population (n = 12, Figure 4C), the max-statistic has 50% power to detect strong selection, if an 15% FPR is accepted. Beyond that point, to increase power, a nearly linear increase in type I error must be accepted. Notably, for a 5% cutoff, the test statistic has almost no power. Reiterating a previous point, because this is a two-tailed test, a number of these rejections are in the direction of too little LD relative to the null, particularly for large . For larger sample sizes (n = 50, Figure 4D), a different pattern is observed. A 5% FPR corresponds to 60% power to detect strong selection. To achieve 80% power the accepted type I error would approach 30%. For weaker selection ( = 500), a 5% FPR corresponds to 20% power. While greater power is achieved with a lower FPR in equilibrium populations, these results indicate that max is a useful statistic in bottlenecked population as well, as long as sample sizes are large (n = 50 vs. n = 12).
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= 75, = 100) and humans ( = = 10). Examining both n = 12 and n = 50 across all values, the resulting max-values do not differ significantly from those expected under neutrality (results not shown).
Examining ROC curves better reveals the difficulty of detecting recurrent selection. Figure 5 plots n = 50, = 75, and = 10 or 100 (Figure 5, A and B, respectively) for three rates of sweeps ( = 10–7, 10–6, and 10–5). For the low-recombination case (Figure 5A), the lowest rate of sweeps is essentially imperceptible, with a 50% FPR corresponding to 50% power (solid line in Figure 5A). For higher rates of sweeps the situation is scarcely better, with a 5% FPR approaching only 30% power. For high-recombination regions the situation is slightly worse. Occasionally the performance is poorer than that of the null model (i.e., the ROC curve is below the diagonal), owing to the fact that the distribution is contained completely within that of the null. Basically, for rare sweeps (solid line in Figure 5B), there is roughly the same mean, but less variance, in max. Thus, these results indicate that recurrent selection is extremely difficult to detect using this statistic, as it is for other site-frequency spectrum-based approaches—particularly for Drosophila-like recombination parameters. For human-like recombination parameters, the situation is slightly better, primarily owing to the fact that lower recombination rates result in stronger patterns of LD across larger portions of the genome.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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max-calculation is being maximized to regions of reduced or absent variation, and haplotype blocks that coalesce during the bottleneck are found in flanking regions with high probability.
Apart from considering a variety of nonequilibrium neutral, equilibrium-sweep, and non-equilibrium-sweep models, we also examine recurrent-sweep models. Although the fixed- case is of value for quantifying the performance of these approaches, the recurrent model is arguably nearer biological reality. While the true rate of sweeps in natural populations is unknown, there are distinct challenges for both the high and low values of that we have considered. If the rate of sweeps is high, then there may be many recent sweeps across the genome that existing methods will have power to detect. However, if the rate is this great, then there is an appreciable probability that sweeps are occurring on already swept backgrounds. This multiple-sweep effect will result in very different patterns in the site-frequency spectrum, particularly with regard to high-frequency-derived alleles (KIM 2006), and thus linkage disequilibrium, owing to the correlation between the two statistics.
If the rate of sweeps is low, then sweeps will be old on average, and patterns of variability will have recovered (PRZEWORSKI 2002). In other words, a low rate implies that there will not be many regions of the genome that have experienced a recent enough sweep to be readily detectable by existing methods. Thus, the fixed- single-sweep simulations represent potentially infrequent evolutionary events. This argument of course relies on a uniform rate of sweeps over an organism's recent evolutionary history. Although this assumption may be approximately accurate, it is likely violated in a number of organisms. For example, many domesticated crop species have experienced very recent and extreme artificial selection—although the effect of overlapping sweep patterns will likely be of importance here as well. Additionally, under this "domestication" scenario, models that consider selection on standing variation will likely be more relevant than selection on new mutations, a process that results in very different patterns of variation (PRZEWORSKI et al. 2005).
To better evaluate the empirical relevance of the max-statistic, ROC plots were examined for a number of the most germane scenarios. Most significantly, for the bottleneck parameters inferred by THORNTON and ANDOLFATTO (2006), the max-statistic appears to have good power to differentiate adaptive loci from neutral loci in bottlenecked populations. For small sample sizes, accepting a 15% type I error corresponds to >50% power to detect selected loci (Figure 4C); and for larger sample sizes, a 5% type I error corresponds to >60% power, when selection is strong (Figure 4D). This result is extremely encouraging, given the difficulty that this bottleneck parameter space presents for existing and commonly used test statistics (e.g., JENSEN et al. 2005).
For recurrent selective sweeps, the situation appears less encouraging. Even for strong selection, large sample sizes, and low rates of recombination, a 5% FPR corresponds to only 20% power to detect selected loci (Figure 5A). For other parameter combinations, the results are essentially near the null (Figure 5B). It is important to note that the max-statistic is designed for situations in which sequence data span the site of a fixed beneficial mutation. Thus, under a recurrent selection model in which sweeps are occurring across a genomic region that is very large relative to the sampled region, it may not be surprising that this statistic has low power. As such, performance will be maximized when the swept region has been previously localized, as is assumed in the fixed- simulations—although it is crucial to account for the ascertainment bias introduced by preselecting regions (THORNTON and JENSEN 2007). Either way, the challenges presented for both high and low rates of recurrent sweeps discussed above remain.
Given the difficulties observed under recurrent selection models when the target of selection has not been sequenced, we equally anticipate that the max-statistic will have limitations for detecting other types of selection. Specifically, while selection from standing variation generates patterns that differ strongly from neutrality (PRZEWORSKI et al. 2005), the allele will appear swept only if the selective pressure began while it was segregating at very low frequency (see Figure 7 of STEPHAN et al. 2006). Otherwise the expectation of strong LD flanking the target, and reduced LD across the target, described by KIM and NIELSEN (2004) and STEPHAN et al. (2006), which the max-statistic is designed to detect, will not be created. Additionally, this statistic will likely be inappropriate to detect partial sweeps. Although this model may produce strong linkage disequilibrium, owing to the fact that the beneficial allele has undergone at least part of the rapid increase in frequency, the LD pattern discussed here is expected to be created only at the time of fixation. Other LD-based methods have been proposed that would be more appropriate for the detection of partial sweeps, such as the extended haplotype heterozygosity (EHH) approach (e.g., VOIGHT et al. 2005).
A number of important points need to be mentioned. First, these results are of particular relevance to derived populations of species that have experienced a population size reduction associated with colonization. Ancestral populations of these species with stable demographic histories are less likely to be producing spatial patterns of variation that replicate sweep predictions, particularly as population structure was not observed to replicate sweep-like patterns of LD. This suggests that searching for adaptively important loci in these more stable ancestral populations will likely be fruitful. Apart from nonequilibrium considerations, however, and given the recurrent-sweep results, the impact of different rates of recurrent sweeps () needs to be considered when analyzing empirical data—regardless of whether the population is ancestral or derived. However, whether this rate is so great as to obscure individual sweep patterns, in humans or in flies or in any other natural population, remains an open question.
Nevertheless, simulations suggest that identifying adaptively important regions is possible even in bottlenecked populations, despite the fact that we observe neutral bottleneck models to be capable of producing patterns of LD previously proposed to be unique to hitchhiking models. Specifically, loci under strong selection ( > 500) produce a distribution of max that is only partially overlapping with the neutral case—and we demonstrate that by accepting a modest type I error it is possible to achieve significant power. The direction of the rejection, however, differs along with the intensity of selection, with very strong selection rejecting because of too little LD relative to the null. These combined results suggest that the max-statistic should be used alongside SFS-based methods when analyzing polymorphism data and in particular that it appears to allow for the identification of adaptive loci even in non-equilibrium populations—a challenge that has historically been very difficult to address. If Ne is taken to be on the order of 1 x 106, selection coefficients on the order of s = 0.0025 may be identifiable using existing statistics in ancestral and derived populations alike. Previous analyses suggest that selection coefficients of this magnitude are not unrealistic for natural populations (e.g., ENDLER 1986). However, the true distribution is unknown, and uncoupling the average strength of sweeps from the average rate of sweeps remains a formidable and an important challenge (WIEHE and STEPHAN 1993; KIM 2006).
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ACKNOWLEDGEMENTS |
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FOOTNOTES |
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LITERATURE CITED |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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