Patterns of Microsatellite Variability Among X Chromosomes and Autosomes Indicate a High Frequency of Beneficial Mutations in Non-African D.
http://www.100md.com
分子生物学进展 2004年第7期
Institut für Tierzucht und Genetik, Veterin?rmedizinische Universit?t, Vienna, Austria
E-mail: christian.schloetterer@vu-wien.ac.at.
Abstract
We analyzed microsatellite variability at 42 X-linked and 39 autosomal loci from African and European populations of Drosophila simulans. The African D. simulans harbored significantly more microsatellite variability than the European flies. In the European population, X-linked polymorphism was more reduced than autosomal variation, whereas there was no significant difference between chromosomes in the African population. Previous studies also observed a similar pattern but failed to distinguish between a demographic event and a selection scenario. We performed extensive computer simulations using a wide range of demographic scenarios to distinguish between the two hypotheses. Approximate summary likelihood estimates differed dramatically among X chromosomes and autosomes. Furthermore, our experimental data showed a surplus of X-linked microsatellites with a significantly reduced variability in non-African D. simulans. We conclude that our data are not compatible with a neutral scenario. Thus, the reduced variability at X-linked loci is most likely caused by selective sweeps associated with the out-of-Africa habitat expansion of D. simulans.
Key Words: selective sweep ? microsatellites ? out-of-Africa ? Drosophila simulans
Introduction
The question of which genes mediate the genetic adaptation of organisms to a new environment is central to ecological genetics and could contribute to a better understanding of how new species emerge. Both D. melanogaster and D. simulans originated in sub-Saharan Africa and colonized the rest of the world only recently (David and Capy 1988; Lachaise et al. 1988). The availability of a completely sequenced genome in D. melanogaster in combination with a colonization history that required adaptation to dramatically different environmental conditions has made these species an almost perfect model to study the genetics of adaptation.
The comparison of African and non-African D. melanogaster populations has provided some interesting patterns. First, non-African D. melanogaster harbor less variability than the African flies, an effect that is particularly pronounced on the X chromosome. Second, in African populations, autosomal variability is reduced as compared with X-linked variability. Interestingly, these patterns hold irrespective of whether sequence variation or microsatellite variability is studied (Begun and Aquadro 1993, 1995; Schl?tterer, Vogl, and Tautz 1997; Andolfatto 2001; Kauer et al. 2002).
Three different explanations have been proposed for the more pronounced loss of variability on the X chromosome in non-African populations of D. melanogaster. First, the operational sex ratio might differ between African and non-African populations. If non-African D. melanogaster females have a higher variance in reproductive success than African females, this may result in a more pronounced loss in variability on X chromosomes (Charlesworth 2001). Second, a demographic event may differently affect X chromosomes and autosomes. Because of the smaller effective population size of X chromosomes, a bottleneck will more strongly reduce variability on X chromosomes than on autosomes (Andolfatto 2001; Kauer et al. 2002). Finally, if multiple beneficial mutations occurred, this would result in a more pronounced reduction in X-chromosomal variability because new recessive mutations are fixed more rapidly on the X chromosome, leading to a more pronounced hitchhiking effect (Aquadro, Begun, and Kindahl 1994; Andolfatto 2001; Kauer et al. 2002).
For the variability pattern observed in African populations, two possible explanations have been put forward. First, inversions are very common in D. melanogaster autosomes, especially in African populations (Lemeunier and Aulard 1992). They can inhibit recombination near their breakpoints when heterozygous. Inversions might, therefore, reduce autosomal variation, leading to an apparent excess of X-chromosomal variation in African populations (Andolfatto 2001). Second, the background selection model also predicts that more neutral variation will occur on the X chromosome than on the autosomes after correcting for different population sizes of the chromosomes (Charlesworth, Morgan, and Charlesworth 1993). Based on the available data, however, neither background selection (Kauer et al. 2002) nor a higher frequency of autosomal inversion polymorphisms in African flies (Andolfatto 2001) could be unequivocally rejected (Kauer, Dieringer, and Schl?tterer 2003).
For D. simulans, much less data are available, but for a North American population sample, less variability was also observed on X-linked genes (Begun and Whitley 2000). The authors concluded that this pattern is best explained by a high frequency of selective sweeps on the X chromosome. However, a reanalysis of the data showed that a bottleneck could have resulted in the same pattern (Wall, Andolfatto, and Przeworski 2002).
Here, we report the analysis of 81 polymorphic microsatellites in one African and one non-African population. The comparison of X-linked and autosomal microsatellite loci confirmed the more pronounced reduction in variability on the X chromosome in non-African flies. Ten X-linked microsatellites were identified to be deviating from neutral expectations. Using computer simulations, we show that this number cannot be explained by a bottleneck associated with the out of Africa habitat expansion.
Material and Methods
Population Samples and Microsatellite Loci
African fly samples (30 lines) were collected in Kibale forest, Uganda by M. Imhof in 2001. Thirty lines collected in Viareggio, Italy by B. Harr in 1999 represent the European sample. For both populations, first generation offspring of freshly collected females were used. Apart from three microsatellite loci isolated from D. simulans (Hutter, Schug, and Aquadro 1998), all loci were obtained from D. melanogaster. Monomorphic microsatellites were not included in the analysis. Primer sequences and amplification conditions are given in the Supplementary Material online.
For each line, genomic DNA was isolated from a single female fly by the high-salt extraction method (Miller, Dykes, and Polesky 1988). Ten-microliter polymerase chain reactions (PCR) were carried out with 100 ng of genomic DNA, 32P-labeled forward primer, 1.5 mM MgCl2, 200 μM dNTPs, 1 μM of each primer, and 0.5 U Taq polymerase. A typical cycling profile consisted of 30 cycles for 50 s at 94°C, 50 s at 48°C to 58°C (depending on the primers), and 50 s at 72°C. All PCRs were run with an initial denaturation step of 3 min at 94°C and a final elongation step of 45 min at 72°C for quantitative terminal transferase activity of the Taq polymerase. The PCR products were separated on 7% denaturing polyacrylamide gels (32% formamide, 5.6 M urea) and visualized by autoradiography. The size of the PCR product was assessed by loading a "slippage ladder" next to the amplified microsatellites (Schl?tterer and Zangerl 1999).
Measures of Genetic Variation and Corrections for Effective Population Sizes of X Chromosomes and Autosomes
Variance in repeat number and expected heterozygosity (gene diversity) (Nei 1978) was calculated using the MSA software (Dieringer and Schl?tterer 2003). Gene diversity was corrected for small sample sizes by multiplying by n/(n–1), were n is the number of analyzed chromosomes. The variance in repeat number and, thus, the lnRV statistics are highly sensitive to insertion/deletions in the sequence flanking the microsatellite (Kauer, Dieringer, and Schl?tterer 2003; Schl?tterer and Dieringer 2004). Therefore, we focused on gene diversity and the lnRH statistics, which are relatively robust against indels in the flanking sequence, to detect genomic regions subjected to a selective sweep in non-African flies (see below).
Statistical analyses were performed using the SPSS version 11.0 software package. Descriptive statistics are given as arithmetic means ± SD unless stated otherwise. To account for the possible differences in the effective population sizes of X chromosomes and autosomes, we introduced a correction factor for the X-chromosomal variability measures. Assuming equal operational sex ratios and no selection. The correction factor k was 4/3 (based the ratio of the relative numbers of autosomes and X chromosomes). The adjusted estimates of variability were calculated as
and
Both equations assume the stepwise mutation model (Ohta and Kimura 1973).
Recent studies indicated no significant difference in male and female mutation rates in D. simulans (Bauer and Aquadro 1997; Betancourt, Presgraves, and Swanson 2002). Therefore, we assumed no systematic bias in microsatellite mutation rates among loci located on the X chromosomes and autosomes.
Detection of Positive Selection
Positive selection can be indicated by reduction in variability below neutral expectations at individual loci on a chromosome (reviewed in Schl?tterer [2002b, 2003]). To detect significantly reduced loci, we calculated the lnRH statistics (Kauer, Dieringer, and Schl?tterer 2003; Schl?tterer and Dieringer 2004). This test statistics considers the joint empirical distribution of all loci and identifies those that deviate significantly from the remainder of the genome. For each locus, the ratio of the gene diversity of two populations is calculated. Thus, all loci have the same expectation irrespective of locus-specific mutation rates. The gene-diversity based lnRH is then calculated as follows:
where H is related to by the formula H = 1 – (1/(1 + 2)1/2) (Ohta and Kimura 1973). Because lnRH values are approximately normally distributed, the probability that a given locus deviates from normality can be obtained from the density function of a standard normal distribution (Kauer, Dieringer, and Schl?tterer 2003; Schl?tterer and Dieringer 2004). When standardized by the mean and standard deviation of lnRH values of neutrally evolving loci from the same population, 95% of the loci are expected to have values between –1.96 and 1.96. Loci falling outside of this interval are considered as significantly deviating in variability at a significance level of = 0.05. It is important to note that when a substantial fraction of the analyzed loci have been subjected to directional selection, a standardization by these loci becomes problematical because the mean shifts toward more negative values and the standard deviation increases. Thus, the lnRH test statistic will become too conservative because only the most extreme lnRH values would fall in the lower tail of the distribution. Alternatively, a set of putatively neutrally evolving loci could be used for standardization. As we found a more pronounced reduction of variability on the X chromosome (see Results and Discussion), we standardized the X-chromosomal distributions with the mean and standard deviation of the third chromosome. This approach might not be appropriate if X chromosome and autosome are differentially affected by demographic events. Hence, we used coalescent simulations to explore the effect of this standardization strategy on the number of false positives.
Bottleneck Simulations
We performed coalescence simulations of simple bottleneck scenarios using the "Makesample" software (Hudson 2002). The simulation algorithm was modified to incorporate the stepwise mutation model of Ohta and Kimura (1973) to account for the mutation behavior of microsatellites. The numbers of mutations occurring on one branch were converted into microsatellite mutations by adding or subtracting with equal probability one repeat unit for each mutation. These simulations can be used to estimate the approximate summary likelihood (hereafter called "likelihood") of the observed lnRH values of our European D. simulans samples for given pairs of bottleneck parameters (f, t), (compare with Jensen, Charlesworth, and Kreitman [2002]). Simulations were conditioned on estimated from the gene diversity H in the African flies, which was considered a prebottleneck population (auto= 4Nautoμ = 2.015, x= 4Nxμ = 2.099). We used a model of constant population size to simulate data for an ancestral "African" population. "European" populations were simulated using a two-phase model. An instantaneous reduction in population size by a factor f was assumed to have occurred at timepoint t in the past. After the bottleneck, the population size increased exponentially until it reached the current population size. For autosomes, we chose the current population size to match the ancestral population size. The current population size of X chromosomes was set to 0.75 of the autosomal population size (i.e., we assumed a balanced sex ratio). The timepoint t was scaled in units of 4Ne generations, which are different between autosomal and X-chromosomal loci. Therefore, the X-chromosomal t was multiplied by the factor 1.33, which corresponds to the ratio of autosomal to X-chromosomal effective population sizes, assuming unbiased operational sex ratios.
We simulated a grid of 169 parameter combinations, ranging from a reduction in population size f = 0.0001 to 0.1 and t = 0.0001 to 0.1 x 4Ne generations ago. For each combination of parameters, we simulated 2,500 replicas for 42 X-chromosomal and 39 autosomal loci. For each bottleneck scenario, we calculated lnRH values using the bottleneck data set and simulated data assuming an equilibrium population. The likelihood of a particular combination of population size reduction and time since reduction was estimated as the proportion of n simulated data sets, for which lnRH values (obtained by computer simulations) matched the lnRH values for each chromosomes observed in the Italian sample (x = –1.07, auto = –0.32). The criterion for matching was set |lnRHobs – lnRHsim| < , with n = 2500 and = 0.2. Note that allowing for different ranges of lnRH (i.e., = 20% of the observed values for lnRHx and lnRHauto) gave qualitatively identical results (data not shown). Simulated data were parsed using a PERL script (available from the authors on request).
Results and Discussion
We surveyed a total of 81 microsatellite loci in isofemale lines from Kibale forest (Uganda, Africa) and Viareggio (Italy, Europe). Forty-two loci mapped to the X chromosome and 39 loci mapped to the third chromosome. In line with recent studies (Irvin et al. 1998; Hamblin and Veuille 1999; Andolfatto 2001), we found that the European population is significantly less variable than the African population, irrespective of whether variation was measured in terms of gene diversity (eur = 0.46 ± 0.24, afr = 0.56 ± 0.20, Wilcoxon signed rank test, Z = –4.11, P < 0.001) or variance in repeat numbers (median Veur = 0.88, q1 = 0.24, q3 = 3.79, median Vafr = 1.67, q1 = 0.45, q3 = 4.02, paired t-test on log transformed data, t = 2.87, P < 0.01). When analyzing autosomal and X-chromosomal loci separately, the difference in gene diversity and variance in repeat numbers between African and European populations was significant only for X-linked loci (table 1 and fig. 1). After accounting for multiple testing, only the difference in gene diversity remained significant. Note that an adjustment for the different effective population sizes of X chromosomes and autosomes (assuming an equal operational sex ratio) did not affect the above results (data not shown).
Table 1 Differences of Autosomal and X Chromosomal Microsatellite Variability in an African and European Population of D. simulans.
FIG. 1. Microsatellite variability on the X chromosome and third chromosome in an African and a European population. Error bars show the 95% confidence interval of mean expected heterozygosity (not corrected for the difference in effective population sizes of X chromosome and autosomes)
To test whether individual loci were significantly reduced in African or non-African populations, we used the lnRH test statistics. Nonstandardized lnRH values were significantly more negative on the X chromosome than on the autosome (x = –1.07 ± 1.59, auto = –0.32 ± 1.11, t = 2.43, P < 0.05) indicating a more pronounced loss of variability in European flies for the X chromosome than for the third chromosome. Interestingly, the distribution of lnRH values on the autosomes was closer to a normal distribution (Kolmogorov-Smirnov test for goodness of fit, gmax = 0.117, P = 0.196) than were the lnRH values on the X chromosomes (Kolmogorov-Smirnov test for goodness of fit, gmax = 0.127, P = 0.084). Previous work showed that under neutrality, lnRH follows a normal distribution (Kauer, Dieringer, and Schl?tterer 2003; Schl?tterer and Dieringer 2004). If the bad fit to the normal distribution for X-linked loci is caused by some outlier loci, the removal of loci located at the extreme of the distribution should generate an improved normal distribution. Figure 2 indicates that the removal of seven X-linked loci markedly enhanced the fit to a normal distribution. Interestingly, all of those loci were strongly reduced in non-African D. simulans.
FIG. 2. Fit to the normal distribution after removing outliers. We measured the fit of the data to the normal distribution by gmax, the largest difference between the observed and expected cumulative relative frequency distribution. For the X chromosome, gmax decreased markedly when the seven most extreme lnRH values were removed, indicating an improved fit to the normal distribution. Interestingly, all of the seven outliers removed on the X chromosome were strongly negative, indicating a reduction of variability in Europe. For autosomes, the fit to a normal distribution became worse when outlying data points were removed
The above analyses suggested that several X-linked loci deviate from neutral expectations, but no such pattern was recognized for the autosomal loci. Thus, we considered the autosomal data as a neutral data set and used them to standardize the lnRH values of X-linked microsatellites to identify loci deviating from neutral expectations (see Material and Methods). This standardization procedure identified a total of 10 X-chromosomal loci significantly deviating from neutral expectations, eight of which were located in the lower tail of the distribution (i.e., reduction in Europe), and the remaining two were located in the upper tail of the distribution (i.e., reduction in Africa). Interestingly, the lnRH test identified the same loci as significant outliers, which also were causing a distortion of the normal distribution of X-linked lnRH values (see above.) Table S2 in the Supplementary Material online provides an overview of the microsatellite loci with a significant reduction in variability.
Because of their different effective population size, X chromosomes and autosomes are differentially affected by demographic events. To test whether a population bottleneck could explain our microsatellite data, we performed coalescence simulations using a broad range of parameters. Figure 3 shows the likelihood surface of the observed European X-chromosomal and autosomal lnRH values for different timepoints (t) and magnitudes (f) of the bottleneck. For both chromosomes, no single, most likely demographic scenario was found. Rather, we observe a ridge of most likely demographic scenarios, ranging from an old but shallow bottleneck to a recent and pronounced reduction in population size. Despite these similarities, the likelihood surfaces for both chromosomes differ substantially. Although the likelihood surface of the X chromosome forms a very narrow ridge, we also observe a likelihood surface plateau for the autosome under a range of demographic scenarios (fig. 3). Furthermore, the ridges of the X-chromosomal and autosomal likelihood surfaces are well separated. These differences between X-linked and autosomal data suggest that both chromosomes are evolving differently. Nevertheless, the overlap between the likelihood surfaces indicates that, with a very low probability, our X-linked and autosomal microsatellite data could be fitted to the same demographic model.
FIG. 3. The likelihood that lnRHx (blue contour) and lnRHa (red contour) from simulated data match the observed lnRH values given the bottleneck parameters t and f. For ease of comparison, the likelihood peaks of both chromosomes have been normalized to 0.6. Parameters range from a reduction in population size f = 0.1 to 0.0001 and a timepoint t = 0.1 to 0.0001 4Ne generations ago
So far, we conditioned the computer simulations on the observed lnRH values only. As we observed eight X-linked microsatellites with a significant reduction in variability in Europe, we were interested in whether such a surplus of loci with a significant reduction in variability could also result from a demographic event. Therefore, we conditioned our computer simulations to have at least as many loci with a significant reduction in variability as in our experimental data set. Additionally, we conditioned the simulations to fit the experimentally observed autosomal lnRH values. Interestingly, this conditioning indicated that none of the analyzed demographic scenarios could match our experimental observations. The highest probability of observing eight or more significantly reduced X-linked loci conditional on fitting the experimentally observed autosomal lnRH values did not exceed 0.0008. The difference between the two methods of conditioning is that the latter makes better use of the experimental data than the first method, thus having more power to reject the fit to neutral demographic models.
Although it is impossible to cover all conceivable demographic scenarios, we investigated a very broad range of models. As the lnRH test statistic is not very sensitive to admixture (Schl?tterer 2002a), it is not expected that more complex scenarios considering admixture would have changed the results. Thus, the lack of fit of our experimental data and the neutral coalescence simulations strongly suggests that the observed microsatellite variation has been influenced by selection.
By comparing the variance of lnRH values between X chromosomes and autosomes, we further validated the hypothesis that the contrasting pattern of X chromosomes and autosomes is largely shaped by selection. If selection were operating, then the lnRH values of loci linked to the target of selection should be more reduced than the remaining loci—thus, the variance of lnRH values should be increased. To obtain a measure of dispersion for this variance, we generated 100 pseudoreplicas by bootstrapping the lnRH values for each of the two groups of loci. Figure 4 shows that the mean variance of lnRH is substantially larger on the X chromosome than on the autosome. Thus, consistent with directional selection operating on the X chromosome, we observed a higher variance of lnRH on the X chromosome. Interestingly, computer simulations accounting for a broad range of demographic scenarios failed to show such a strong increase in variance of lnRH on the X chromosome compared with autosomes (data not shown). Thus, the higher variance of lnRH on the X chromosome is unlikely to be explained by a purely demographic scenario. More likely, some microsatellites on the X chromosome are linked to a site that experienced positive selection in the non-African D. simulans population. Interestingly, a recent survey of 21 autosomal and 23 X-linked D. simulans genes from an American population found a significant surplus of fixed nucleotide differences from D. melanogaster in six genes, five of which were located on the X chromosome (Begun 2002). This is also consistent with the notion of selection on X-linked loci in the non-African flies.
FIG. 4. Mean variance of 100 resamples of lnRH values for X chromosomes and autosomes. The error bars indicate the 95% confidence interval of the mean variance of lnRH.
Whereas in African D. melanogaster populations, X chromosomes are more variable than autosomes, in D. simulans, both chromosomes were about equally variable (x = 0.56 ± 0.20, auto = 0.55 ± 0.19). Neither of these patterns is expected under neutrality. Inversions segregating at different frequencies in African and non-African populations have been suggested as one possible explanation (Andolfatto 2001). Autosomal inversions are common in D. melanogaster and may suppress crossing-over in the inverted region when heterozygous. If there were enough inversions on the autosomes, this might increase the effect of selective sweeps and background selection and, hence, reduce autosomal variability. Because such inversions are virtually absent in D. simulans (Lemeunier and Aulard 1992), alternative selective forces, such as background selection (Charlesworth, Morgan, and Charlesworth 1993; Charlesworth 1996), may be shaping variation in African D. simulans (and possibly D. melanogaster).
Given the low coverage of the genome in our microsatellite screen, we have certainly missed a large fraction of the genomic regions affected by a selective sweep. Thus, our data do not necessarily imply that a larger number of beneficial mutations occurred on the X chromosome. It might be only because of the more pronounced hitchhiking effect on the X chromosome (Charlesworth, Coyne, and Barton 1987) that their presence is more conspicuous on the X chromosome than on the autosome. On the basis of allozyme data, it has been suggested that D. simulans spread worldwide more recently than D. melanogaster (Morton et al. 2004). In this case, adaptive selective sweeps associated with the colonization of non-African habitats might also be younger and have left clearer traces in D. simulans than in D. melanogaster.
As yet, we have always assumed equal reproductive success of males and females. Because males carry only one X chromosome and females carry two, the effective population size of the X chromosomes is strongly dependent on the variance of reproductive success of males and females (Charlesworth 2001). For instance, strong male competition or female choice might produce more variation in reproductive success among males than among females. Indeed, both D. melanogaster and D. simulans are drosophilids that show relative infrequent female remating as compared with other species of the family and have evolved certain male characters that enhance the likelihood of obtaining mates (Markow 2002). At least in North American populations, males of both species patrol emergence sites and commonly engage in teneral matings in the field (Markow 2000). These factors imply a strong competition among males for access to mating partners and suggest a larger variation of reproductive success among males. If this holds for all populations, the ratio of effective populations sizes of the X chromosome and autosomes may be closer to unity (Caballero 1994) and may, thus, help to explain the pattern observed in African populations. However, no data about unequal reproductive success is available for D. simulans.
Previous studies reported a strong population subdivision in African D. simulans populations (Hamblin and Veuille 1999). Because the lnRH test statistic focuses on gene diversity rather than relying on a comparison of relative allele frequencies (e.g., FST analyses), we do not expect that population differentiation in African D. simulans populations qualitatively affects our analyses, at least as long as the level of variability is similar among different African D. simulans populations. The further analysis of African populations will be an important contribution to understanding the demographic past of D. simulans.
Acknowledgements
We are grateful to D. Dieringer, M. Kauer, and the other members of C.S.'s lab for helpful advice during various phases of the project and to C. Vogl for helpful discussions. This work has been supported by Fonds zur F?rderung der wissenschaftlichen Forschung grants to C. S.
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E-mail: christian.schloetterer@vu-wien.ac.at.
Abstract
We analyzed microsatellite variability at 42 X-linked and 39 autosomal loci from African and European populations of Drosophila simulans. The African D. simulans harbored significantly more microsatellite variability than the European flies. In the European population, X-linked polymorphism was more reduced than autosomal variation, whereas there was no significant difference between chromosomes in the African population. Previous studies also observed a similar pattern but failed to distinguish between a demographic event and a selection scenario. We performed extensive computer simulations using a wide range of demographic scenarios to distinguish between the two hypotheses. Approximate summary likelihood estimates differed dramatically among X chromosomes and autosomes. Furthermore, our experimental data showed a surplus of X-linked microsatellites with a significantly reduced variability in non-African D. simulans. We conclude that our data are not compatible with a neutral scenario. Thus, the reduced variability at X-linked loci is most likely caused by selective sweeps associated with the out-of-Africa habitat expansion of D. simulans.
Key Words: selective sweep ? microsatellites ? out-of-Africa ? Drosophila simulans
Introduction
The question of which genes mediate the genetic adaptation of organisms to a new environment is central to ecological genetics and could contribute to a better understanding of how new species emerge. Both D. melanogaster and D. simulans originated in sub-Saharan Africa and colonized the rest of the world only recently (David and Capy 1988; Lachaise et al. 1988). The availability of a completely sequenced genome in D. melanogaster in combination with a colonization history that required adaptation to dramatically different environmental conditions has made these species an almost perfect model to study the genetics of adaptation.
The comparison of African and non-African D. melanogaster populations has provided some interesting patterns. First, non-African D. melanogaster harbor less variability than the African flies, an effect that is particularly pronounced on the X chromosome. Second, in African populations, autosomal variability is reduced as compared with X-linked variability. Interestingly, these patterns hold irrespective of whether sequence variation or microsatellite variability is studied (Begun and Aquadro 1993, 1995; Schl?tterer, Vogl, and Tautz 1997; Andolfatto 2001; Kauer et al. 2002).
Three different explanations have been proposed for the more pronounced loss of variability on the X chromosome in non-African populations of D. melanogaster. First, the operational sex ratio might differ between African and non-African populations. If non-African D. melanogaster females have a higher variance in reproductive success than African females, this may result in a more pronounced loss in variability on X chromosomes (Charlesworth 2001). Second, a demographic event may differently affect X chromosomes and autosomes. Because of the smaller effective population size of X chromosomes, a bottleneck will more strongly reduce variability on X chromosomes than on autosomes (Andolfatto 2001; Kauer et al. 2002). Finally, if multiple beneficial mutations occurred, this would result in a more pronounced reduction in X-chromosomal variability because new recessive mutations are fixed more rapidly on the X chromosome, leading to a more pronounced hitchhiking effect (Aquadro, Begun, and Kindahl 1994; Andolfatto 2001; Kauer et al. 2002).
For the variability pattern observed in African populations, two possible explanations have been put forward. First, inversions are very common in D. melanogaster autosomes, especially in African populations (Lemeunier and Aulard 1992). They can inhibit recombination near their breakpoints when heterozygous. Inversions might, therefore, reduce autosomal variation, leading to an apparent excess of X-chromosomal variation in African populations (Andolfatto 2001). Second, the background selection model also predicts that more neutral variation will occur on the X chromosome than on the autosomes after correcting for different population sizes of the chromosomes (Charlesworth, Morgan, and Charlesworth 1993). Based on the available data, however, neither background selection (Kauer et al. 2002) nor a higher frequency of autosomal inversion polymorphisms in African flies (Andolfatto 2001) could be unequivocally rejected (Kauer, Dieringer, and Schl?tterer 2003).
For D. simulans, much less data are available, but for a North American population sample, less variability was also observed on X-linked genes (Begun and Whitley 2000). The authors concluded that this pattern is best explained by a high frequency of selective sweeps on the X chromosome. However, a reanalysis of the data showed that a bottleneck could have resulted in the same pattern (Wall, Andolfatto, and Przeworski 2002).
Here, we report the analysis of 81 polymorphic microsatellites in one African and one non-African population. The comparison of X-linked and autosomal microsatellite loci confirmed the more pronounced reduction in variability on the X chromosome in non-African flies. Ten X-linked microsatellites were identified to be deviating from neutral expectations. Using computer simulations, we show that this number cannot be explained by a bottleneck associated with the out of Africa habitat expansion.
Material and Methods
Population Samples and Microsatellite Loci
African fly samples (30 lines) were collected in Kibale forest, Uganda by M. Imhof in 2001. Thirty lines collected in Viareggio, Italy by B. Harr in 1999 represent the European sample. For both populations, first generation offspring of freshly collected females were used. Apart from three microsatellite loci isolated from D. simulans (Hutter, Schug, and Aquadro 1998), all loci were obtained from D. melanogaster. Monomorphic microsatellites were not included in the analysis. Primer sequences and amplification conditions are given in the Supplementary Material online.
For each line, genomic DNA was isolated from a single female fly by the high-salt extraction method (Miller, Dykes, and Polesky 1988). Ten-microliter polymerase chain reactions (PCR) were carried out with 100 ng of genomic DNA, 32P-labeled forward primer, 1.5 mM MgCl2, 200 μM dNTPs, 1 μM of each primer, and 0.5 U Taq polymerase. A typical cycling profile consisted of 30 cycles for 50 s at 94°C, 50 s at 48°C to 58°C (depending on the primers), and 50 s at 72°C. All PCRs were run with an initial denaturation step of 3 min at 94°C and a final elongation step of 45 min at 72°C for quantitative terminal transferase activity of the Taq polymerase. The PCR products were separated on 7% denaturing polyacrylamide gels (32% formamide, 5.6 M urea) and visualized by autoradiography. The size of the PCR product was assessed by loading a "slippage ladder" next to the amplified microsatellites (Schl?tterer and Zangerl 1999).
Measures of Genetic Variation and Corrections for Effective Population Sizes of X Chromosomes and Autosomes
Variance in repeat number and expected heterozygosity (gene diversity) (Nei 1978) was calculated using the MSA software (Dieringer and Schl?tterer 2003). Gene diversity was corrected for small sample sizes by multiplying by n/(n–1), were n is the number of analyzed chromosomes. The variance in repeat number and, thus, the lnRV statistics are highly sensitive to insertion/deletions in the sequence flanking the microsatellite (Kauer, Dieringer, and Schl?tterer 2003; Schl?tterer and Dieringer 2004). Therefore, we focused on gene diversity and the lnRH statistics, which are relatively robust against indels in the flanking sequence, to detect genomic regions subjected to a selective sweep in non-African flies (see below).
Statistical analyses were performed using the SPSS version 11.0 software package. Descriptive statistics are given as arithmetic means ± SD unless stated otherwise. To account for the possible differences in the effective population sizes of X chromosomes and autosomes, we introduced a correction factor for the X-chromosomal variability measures. Assuming equal operational sex ratios and no selection. The correction factor k was 4/3 (based the ratio of the relative numbers of autosomes and X chromosomes). The adjusted estimates of variability were calculated as
and
Both equations assume the stepwise mutation model (Ohta and Kimura 1973).
Recent studies indicated no significant difference in male and female mutation rates in D. simulans (Bauer and Aquadro 1997; Betancourt, Presgraves, and Swanson 2002). Therefore, we assumed no systematic bias in microsatellite mutation rates among loci located on the X chromosomes and autosomes.
Detection of Positive Selection
Positive selection can be indicated by reduction in variability below neutral expectations at individual loci on a chromosome (reviewed in Schl?tterer [2002b, 2003]). To detect significantly reduced loci, we calculated the lnRH statistics (Kauer, Dieringer, and Schl?tterer 2003; Schl?tterer and Dieringer 2004). This test statistics considers the joint empirical distribution of all loci and identifies those that deviate significantly from the remainder of the genome. For each locus, the ratio of the gene diversity of two populations is calculated. Thus, all loci have the same expectation irrespective of locus-specific mutation rates. The gene-diversity based lnRH is then calculated as follows:
where H is related to by the formula H = 1 – (1/(1 + 2)1/2) (Ohta and Kimura 1973). Because lnRH values are approximately normally distributed, the probability that a given locus deviates from normality can be obtained from the density function of a standard normal distribution (Kauer, Dieringer, and Schl?tterer 2003; Schl?tterer and Dieringer 2004). When standardized by the mean and standard deviation of lnRH values of neutrally evolving loci from the same population, 95% of the loci are expected to have values between –1.96 and 1.96. Loci falling outside of this interval are considered as significantly deviating in variability at a significance level of = 0.05. It is important to note that when a substantial fraction of the analyzed loci have been subjected to directional selection, a standardization by these loci becomes problematical because the mean shifts toward more negative values and the standard deviation increases. Thus, the lnRH test statistic will become too conservative because only the most extreme lnRH values would fall in the lower tail of the distribution. Alternatively, a set of putatively neutrally evolving loci could be used for standardization. As we found a more pronounced reduction of variability on the X chromosome (see Results and Discussion), we standardized the X-chromosomal distributions with the mean and standard deviation of the third chromosome. This approach might not be appropriate if X chromosome and autosome are differentially affected by demographic events. Hence, we used coalescent simulations to explore the effect of this standardization strategy on the number of false positives.
Bottleneck Simulations
We performed coalescence simulations of simple bottleneck scenarios using the "Makesample" software (Hudson 2002). The simulation algorithm was modified to incorporate the stepwise mutation model of Ohta and Kimura (1973) to account for the mutation behavior of microsatellites. The numbers of mutations occurring on one branch were converted into microsatellite mutations by adding or subtracting with equal probability one repeat unit for each mutation. These simulations can be used to estimate the approximate summary likelihood (hereafter called "likelihood") of the observed lnRH values of our European D. simulans samples for given pairs of bottleneck parameters (f, t), (compare with Jensen, Charlesworth, and Kreitman [2002]). Simulations were conditioned on estimated from the gene diversity H in the African flies, which was considered a prebottleneck population (auto= 4Nautoμ = 2.015, x= 4Nxμ = 2.099). We used a model of constant population size to simulate data for an ancestral "African" population. "European" populations were simulated using a two-phase model. An instantaneous reduction in population size by a factor f was assumed to have occurred at timepoint t in the past. After the bottleneck, the population size increased exponentially until it reached the current population size. For autosomes, we chose the current population size to match the ancestral population size. The current population size of X chromosomes was set to 0.75 of the autosomal population size (i.e., we assumed a balanced sex ratio). The timepoint t was scaled in units of 4Ne generations, which are different between autosomal and X-chromosomal loci. Therefore, the X-chromosomal t was multiplied by the factor 1.33, which corresponds to the ratio of autosomal to X-chromosomal effective population sizes, assuming unbiased operational sex ratios.
We simulated a grid of 169 parameter combinations, ranging from a reduction in population size f = 0.0001 to 0.1 and t = 0.0001 to 0.1 x 4Ne generations ago. For each combination of parameters, we simulated 2,500 replicas for 42 X-chromosomal and 39 autosomal loci. For each bottleneck scenario, we calculated lnRH values using the bottleneck data set and simulated data assuming an equilibrium population. The likelihood of a particular combination of population size reduction and time since reduction was estimated as the proportion of n simulated data sets, for which lnRH values (obtained by computer simulations) matched the lnRH values for each chromosomes observed in the Italian sample (x = –1.07, auto = –0.32). The criterion for matching was set |lnRHobs – lnRHsim| < , with n = 2500 and = 0.2. Note that allowing for different ranges of lnRH (i.e., = 20% of the observed values for lnRHx and lnRHauto) gave qualitatively identical results (data not shown). Simulated data were parsed using a PERL script (available from the authors on request).
Results and Discussion
We surveyed a total of 81 microsatellite loci in isofemale lines from Kibale forest (Uganda, Africa) and Viareggio (Italy, Europe). Forty-two loci mapped to the X chromosome and 39 loci mapped to the third chromosome. In line with recent studies (Irvin et al. 1998; Hamblin and Veuille 1999; Andolfatto 2001), we found that the European population is significantly less variable than the African population, irrespective of whether variation was measured in terms of gene diversity (eur = 0.46 ± 0.24, afr = 0.56 ± 0.20, Wilcoxon signed rank test, Z = –4.11, P < 0.001) or variance in repeat numbers (median Veur = 0.88, q1 = 0.24, q3 = 3.79, median Vafr = 1.67, q1 = 0.45, q3 = 4.02, paired t-test on log transformed data, t = 2.87, P < 0.01). When analyzing autosomal and X-chromosomal loci separately, the difference in gene diversity and variance in repeat numbers between African and European populations was significant only for X-linked loci (table 1 and fig. 1). After accounting for multiple testing, only the difference in gene diversity remained significant. Note that an adjustment for the different effective population sizes of X chromosomes and autosomes (assuming an equal operational sex ratio) did not affect the above results (data not shown).
Table 1 Differences of Autosomal and X Chromosomal Microsatellite Variability in an African and European Population of D. simulans.
FIG. 1. Microsatellite variability on the X chromosome and third chromosome in an African and a European population. Error bars show the 95% confidence interval of mean expected heterozygosity (not corrected for the difference in effective population sizes of X chromosome and autosomes)
To test whether individual loci were significantly reduced in African or non-African populations, we used the lnRH test statistics. Nonstandardized lnRH values were significantly more negative on the X chromosome than on the autosome (x = –1.07 ± 1.59, auto = –0.32 ± 1.11, t = 2.43, P < 0.05) indicating a more pronounced loss of variability in European flies for the X chromosome than for the third chromosome. Interestingly, the distribution of lnRH values on the autosomes was closer to a normal distribution (Kolmogorov-Smirnov test for goodness of fit, gmax = 0.117, P = 0.196) than were the lnRH values on the X chromosomes (Kolmogorov-Smirnov test for goodness of fit, gmax = 0.127, P = 0.084). Previous work showed that under neutrality, lnRH follows a normal distribution (Kauer, Dieringer, and Schl?tterer 2003; Schl?tterer and Dieringer 2004). If the bad fit to the normal distribution for X-linked loci is caused by some outlier loci, the removal of loci located at the extreme of the distribution should generate an improved normal distribution. Figure 2 indicates that the removal of seven X-linked loci markedly enhanced the fit to a normal distribution. Interestingly, all of those loci were strongly reduced in non-African D. simulans.
FIG. 2. Fit to the normal distribution after removing outliers. We measured the fit of the data to the normal distribution by gmax, the largest difference between the observed and expected cumulative relative frequency distribution. For the X chromosome, gmax decreased markedly when the seven most extreme lnRH values were removed, indicating an improved fit to the normal distribution. Interestingly, all of the seven outliers removed on the X chromosome were strongly negative, indicating a reduction of variability in Europe. For autosomes, the fit to a normal distribution became worse when outlying data points were removed
The above analyses suggested that several X-linked loci deviate from neutral expectations, but no such pattern was recognized for the autosomal loci. Thus, we considered the autosomal data as a neutral data set and used them to standardize the lnRH values of X-linked microsatellites to identify loci deviating from neutral expectations (see Material and Methods). This standardization procedure identified a total of 10 X-chromosomal loci significantly deviating from neutral expectations, eight of which were located in the lower tail of the distribution (i.e., reduction in Europe), and the remaining two were located in the upper tail of the distribution (i.e., reduction in Africa). Interestingly, the lnRH test identified the same loci as significant outliers, which also were causing a distortion of the normal distribution of X-linked lnRH values (see above.) Table S2 in the Supplementary Material online provides an overview of the microsatellite loci with a significant reduction in variability.
Because of their different effective population size, X chromosomes and autosomes are differentially affected by demographic events. To test whether a population bottleneck could explain our microsatellite data, we performed coalescence simulations using a broad range of parameters. Figure 3 shows the likelihood surface of the observed European X-chromosomal and autosomal lnRH values for different timepoints (t) and magnitudes (f) of the bottleneck. For both chromosomes, no single, most likely demographic scenario was found. Rather, we observe a ridge of most likely demographic scenarios, ranging from an old but shallow bottleneck to a recent and pronounced reduction in population size. Despite these similarities, the likelihood surfaces for both chromosomes differ substantially. Although the likelihood surface of the X chromosome forms a very narrow ridge, we also observe a likelihood surface plateau for the autosome under a range of demographic scenarios (fig. 3). Furthermore, the ridges of the X-chromosomal and autosomal likelihood surfaces are well separated. These differences between X-linked and autosomal data suggest that both chromosomes are evolving differently. Nevertheless, the overlap between the likelihood surfaces indicates that, with a very low probability, our X-linked and autosomal microsatellite data could be fitted to the same demographic model.
FIG. 3. The likelihood that lnRHx (blue contour) and lnRHa (red contour) from simulated data match the observed lnRH values given the bottleneck parameters t and f. For ease of comparison, the likelihood peaks of both chromosomes have been normalized to 0.6. Parameters range from a reduction in population size f = 0.1 to 0.0001 and a timepoint t = 0.1 to 0.0001 4Ne generations ago
So far, we conditioned the computer simulations on the observed lnRH values only. As we observed eight X-linked microsatellites with a significant reduction in variability in Europe, we were interested in whether such a surplus of loci with a significant reduction in variability could also result from a demographic event. Therefore, we conditioned our computer simulations to have at least as many loci with a significant reduction in variability as in our experimental data set. Additionally, we conditioned the simulations to fit the experimentally observed autosomal lnRH values. Interestingly, this conditioning indicated that none of the analyzed demographic scenarios could match our experimental observations. The highest probability of observing eight or more significantly reduced X-linked loci conditional on fitting the experimentally observed autosomal lnRH values did not exceed 0.0008. The difference between the two methods of conditioning is that the latter makes better use of the experimental data than the first method, thus having more power to reject the fit to neutral demographic models.
Although it is impossible to cover all conceivable demographic scenarios, we investigated a very broad range of models. As the lnRH test statistic is not very sensitive to admixture (Schl?tterer 2002a), it is not expected that more complex scenarios considering admixture would have changed the results. Thus, the lack of fit of our experimental data and the neutral coalescence simulations strongly suggests that the observed microsatellite variation has been influenced by selection.
By comparing the variance of lnRH values between X chromosomes and autosomes, we further validated the hypothesis that the contrasting pattern of X chromosomes and autosomes is largely shaped by selection. If selection were operating, then the lnRH values of loci linked to the target of selection should be more reduced than the remaining loci—thus, the variance of lnRH values should be increased. To obtain a measure of dispersion for this variance, we generated 100 pseudoreplicas by bootstrapping the lnRH values for each of the two groups of loci. Figure 4 shows that the mean variance of lnRH is substantially larger on the X chromosome than on the autosome. Thus, consistent with directional selection operating on the X chromosome, we observed a higher variance of lnRH on the X chromosome. Interestingly, computer simulations accounting for a broad range of demographic scenarios failed to show such a strong increase in variance of lnRH on the X chromosome compared with autosomes (data not shown). Thus, the higher variance of lnRH on the X chromosome is unlikely to be explained by a purely demographic scenario. More likely, some microsatellites on the X chromosome are linked to a site that experienced positive selection in the non-African D. simulans population. Interestingly, a recent survey of 21 autosomal and 23 X-linked D. simulans genes from an American population found a significant surplus of fixed nucleotide differences from D. melanogaster in six genes, five of which were located on the X chromosome (Begun 2002). This is also consistent with the notion of selection on X-linked loci in the non-African flies.
FIG. 4. Mean variance of 100 resamples of lnRH values for X chromosomes and autosomes. The error bars indicate the 95% confidence interval of the mean variance of lnRH.
Whereas in African D. melanogaster populations, X chromosomes are more variable than autosomes, in D. simulans, both chromosomes were about equally variable (x = 0.56 ± 0.20, auto = 0.55 ± 0.19). Neither of these patterns is expected under neutrality. Inversions segregating at different frequencies in African and non-African populations have been suggested as one possible explanation (Andolfatto 2001). Autosomal inversions are common in D. melanogaster and may suppress crossing-over in the inverted region when heterozygous. If there were enough inversions on the autosomes, this might increase the effect of selective sweeps and background selection and, hence, reduce autosomal variability. Because such inversions are virtually absent in D. simulans (Lemeunier and Aulard 1992), alternative selective forces, such as background selection (Charlesworth, Morgan, and Charlesworth 1993; Charlesworth 1996), may be shaping variation in African D. simulans (and possibly D. melanogaster).
Given the low coverage of the genome in our microsatellite screen, we have certainly missed a large fraction of the genomic regions affected by a selective sweep. Thus, our data do not necessarily imply that a larger number of beneficial mutations occurred on the X chromosome. It might be only because of the more pronounced hitchhiking effect on the X chromosome (Charlesworth, Coyne, and Barton 1987) that their presence is more conspicuous on the X chromosome than on the autosome. On the basis of allozyme data, it has been suggested that D. simulans spread worldwide more recently than D. melanogaster (Morton et al. 2004). In this case, adaptive selective sweeps associated with the colonization of non-African habitats might also be younger and have left clearer traces in D. simulans than in D. melanogaster.
As yet, we have always assumed equal reproductive success of males and females. Because males carry only one X chromosome and females carry two, the effective population size of the X chromosomes is strongly dependent on the variance of reproductive success of males and females (Charlesworth 2001). For instance, strong male competition or female choice might produce more variation in reproductive success among males than among females. Indeed, both D. melanogaster and D. simulans are drosophilids that show relative infrequent female remating as compared with other species of the family and have evolved certain male characters that enhance the likelihood of obtaining mates (Markow 2002). At least in North American populations, males of both species patrol emergence sites and commonly engage in teneral matings in the field (Markow 2000). These factors imply a strong competition among males for access to mating partners and suggest a larger variation of reproductive success among males. If this holds for all populations, the ratio of effective populations sizes of the X chromosome and autosomes may be closer to unity (Caballero 1994) and may, thus, help to explain the pattern observed in African populations. However, no data about unequal reproductive success is available for D. simulans.
Previous studies reported a strong population subdivision in African D. simulans populations (Hamblin and Veuille 1999). Because the lnRH test statistic focuses on gene diversity rather than relying on a comparison of relative allele frequencies (e.g., FST analyses), we do not expect that population differentiation in African D. simulans populations qualitatively affects our analyses, at least as long as the level of variability is similar among different African D. simulans populations. The further analysis of African populations will be an important contribution to understanding the demographic past of D. simulans.
Acknowledgements
We are grateful to D. Dieringer, M. Kauer, and the other members of C.S.'s lab for helpful advice during various phases of the project and to C. Vogl for helpful discussions. This work has been supported by Fonds zur F?rderung der wissenschaftlichen Forschung grants to C. S.
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