Nonneutral Admixture of Immigrant Genotypes in African Drosophila melanogaster Populations from Zimbabwe

Maximilian Kauer, Daniel Dieringer and Christian Schlötterer

Institut für Tierzucht und Genetik, Vienna, Austria

Correspondence: E-mail: christian.schloetterer{at}vu-wien.ac.at.


    Abstract
 TOP
 Abstract
 Introduction
 Material and Methods
 Results
 Discussion
 Acknowledgements
 Literature Cited
 
Drosophila melanogaster originated in Africa and colonized the rest of the world only recently (approximately 10,000 to 15,000 years ago). Using 151 microsatellite loci, we investigated patterns of gene flow between African D. melanogaster populations representing presumptive ancestral variation and recently colonized European populations. Although we detected almost no evidence for alleles of non-African ancestry in a rural D. melanogaster population from Zimbabwe, an urban population from Zimbabwe showed evidence for admixture. Interestingly, the degree of admixture differed among chromosomes. X chromosomes of both rural and urban populations showed almost no non-African ancestry, but the third chromosome in the urban population showed up to 70% of non-African alleles. When chromosomes were broken into contingent microsatellite blocks, even higher estimates of admixture and significant heterogeneity in admixture was observed among these blocks. The discrepancy between the X chromosome and the third chromosome is not consistent with a neutral admixture hypothesis. The higher number of European alleles on the third chromosome could be due to stronger selection against foreign alleles on the X chromosome or to more introgression of (beneficial) alleles on the third chromosome.

Key Words: Drosophila melanogaster • gene flow • selection • habitat adaptation • antagonistic pleiotropy


    Introduction
 TOP
 Abstract
 Introduction
 Material and Methods
 Results
 Discussion
 Acknowledgements
 Literature Cited
 
Drosophila melanogaster is assumed to have an African origin and colonized the rest of the world only recently (David and Capy 1988). Morphological, genetic and biogeographic data suggested that the European continent was colonized after the last glaciation 10,000 to 15,000 years ago. North America was mainly colonized from Europe only about 100 years ago (David and Capy 1988). Recent microsatellite data suggest that some North American D. melanogaster populations experienced admixture of African alleles (Caracristi and Schlötterer 2003), which seems to imply an additional colonization route from Africa (David and Capy 1988). Sub-Saharan African populations harbor the highest levels of genetic variability, whereas polymorphism outside of Africa is considerably lower (Andolfatto 2001; Kauer et al. 2002; Schlötterer and Harr 2002; Caracristi and Schlötterer 2003). Although African and non-African populations are substantially divergent in phenotype and genotype (Begun and Aquadro 1993), the molecular variation detected in non-African populations is mainly a subset of the variability present in Africa (Andolfatto 2001; Schlötterer and Harr 2002). Within (sub-Saharan) Africa and among non-African populations only, low levels of differentiation were detected (Begun and Aquadro 1995; Caracristi and Schlötterer 2003). Although the maintenance of the differentiation between African and non-African flies could be explained by geographic barriers, such as the Sahara Desert or the sea, with the increase in transportation of goods and human travel, a commensal species, such as D. melanogaster, could easily cross these barriers (Capy et al. 2000). D. melanogaster populations from Africa and other continents differ in various traits such as morphology, ethanol tolerance, or cuticular hydrocarbons (Vouidibio et al. 1989; Capy, Pla, and David 1993; Takahashi et al. 2001). Therefore, it can be assumed that African and non-African flies are sufficiently divergent that some biological reproductive barrier, such as mating preferences or hybrid disadvantage, has already emerged. Divergence could be due to neutral drift or adaptation to different habitats (Cooper and Lenski 2000; Hawthorne and Via 2001; Hendry 2001). Although a mating barrier has been described (Wu et al. 1995; Alipaz, Wu, and Karr 2001; see also Discussion) flies from Zimbabwe and non-African flies can be easily crossed in the laboratory, and it is not clear to what extent this behavior might impede crossings in nature. To get a more detailed picture of population differentiation between African and non-African flies, we inferred naturally occurring gene flow between Zimbabwean and European populations of D. melanogaster using presumably neutral microsatellite variation. Significant differences were observed between the third and the X chromosome. While evidence for admixture of European alleles was detected for the Zimbabwe third chromosomes in an urban population, the X chromosome in Zimbabwe showed almost no evidence for admixture.


    Material and Methods
 TOP
 Abstract
 Introduction
 Material and Methods
 Results
 Discussion
 Acknowledgements
 Literature Cited
 
Microsatellites
We typed 77 and 74 microsatellites on the third chromosome and X chromosome, respectively. Microsatellites can be assumed to be very rarely affected by selection and can therefore be regarded as neutral markers. Genotyping of microsatellite loci followed standard protocols (Schlötterer and Zangerl 1999). Primer sequences, annealing temperatures, repeat motifs, and cytological position of all loci are available as online Supplementary Material at the journal's Web site.

Fly Strains
Zimbabwe flies were sampled from two locations, Sengwa Wildlife Reserve (ZS) or the capital city of Zimbabwe, Harare (ZH), and were kindly provided by C. F. Aquadro and C.-I. Wu. The lines established from both populations were propagated in the same laboratories, making them equally exposed to possible contamination by other D. melanogaster lines. To account for inbreeding effects in the Zimbabwean isofemale lines, we randomly discarded one allele from heterozygous individuals. European flies were from Italy (Naples and Rome, collected 1998 and 2001), Poland (Katovice, collected by Jacek Gorczyca, 2000) and Germany (Friedrichshafen, collected by B. Harr, 1998). Naples and Katovice flies were typed for the third chromosome; Rome and Friedrichshafen flies were typed for the X chromosome. For each European population, 30 F1 individuals (i.e., first generation progeny from a single, wild-caught, inseminated female) were used. Note that the fact that different populations are used for different chromosomes had purely technical reasons and does not introduce a bias as genetic differentiation between European populations is very low among the analyzed populations (table 1).


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Table 1 Genetic Differentiation Between Populations.

 
Measures of Genetic Differentiation
All calculations were done with Microsatellite-Analyzer (MSA) (Dieringer and Schlötterer 2003). Genetic distances were calculated as 1-proportion of shared alleles. {theta}-values were calculated as unbiased estimators of FST (Weir and Cockerham 1984). Significance levels for FST-values were calculated by permuting (1,000 times) genotypes among populations. We used the Bonferroni correction to account for multiple testing (Sokal and Rohlf 1995).

Interpretation of X Chromosomal and Third Chromosomal Differences
In this study, we performed a comparison of X chromosomes and autosomes. This may be complicated because these chromosomes differ for features like levels of variability (Andolfatto 2001; Kauer et al. 2002) and the presence of inversions (Lemeunier and Aulard 1992). Inversions can reduce the recombination rate on the autosomes, which in turn affects levels of natural variability (Begun and Aquadro 1992). In our study, however, we analyzed two African populations, which were similar in their X chromosomal (Harare: H = 0.77; Sengwa: H = 0.76) and autosomal (Harare: H = 0.66; Sengwa: H = 0.61) levels of variability and also in their allele distribution. Hence, all the above mentioned differences are expected to apply to both African populations to the same extent. Patterns differing between these populations are therefore unlikely to be caused by these features.

Bayesian Admixture Analysis
The program Structure (Pritchard, Stephens, and Donnelly 2000) was used to assign individuals to homogenous clusters (populations) without consideration of the sampling localities. This program uses a Bayesian model–based clustering method for multilocus genotypes to simultaneously determine the most probable number of homogenous populations in a given data set and assign individuals to one or more of them. The number of clusters is inferred by calculating the probability P(X|K) of the data given a certain prior value of K (number of clusters) over a number of Monte Carlo Markov Chain (MCMC) iterations. The posterior probabilities P(K|X) can be calculated following Bayes' rule. The clusters are characterized by different allele frequencies, and, according to their allele distributions, individuals are probabilistically assigned to one or more clusters. The scores of individuals in the clusters correspond to the probability of ancestry in any one of them. In this study we assumed prior values of K from 1 to 5. All calculations shown in this report are based on 1,000,000 iterations of the MCMC, after a "burn-in" period of 50,000 iterations. (The burn-in period are the first iterations of the MCMC, which are dependent on the start configuration. These iterations are not incorporated in the final calculation of the posterior probability [Pritchard, Stephens, and Donnelly 2000].) We ran the program without incorporation of prior population information. Before extracting definite values, we ran the program for each prior of K for different numbers of iterations to check for homogeneity over runs. Long runs were made to get accurate estimations of P(X|K) (Pritchard, Stephens, and Donnelly 2000). All simulations were run under a model that allows for admixture between populations (Admixture model). We also used a new, unpublished version of Structure (version II, available from http://pritch.bsd.uchicago.edu/) to incorporate linkage information between the markers and to use a different model that accounts for correlation of allele frequencies among populations due to shared history (D. Falush et al., personal communication; see also description of the program available at the above internet site). Genetic map distances for D. melanogaster were downloaded from http://flybase.bio.indiana.edu/maps/lk/cytotable.txt. If not stated otherwise, we used the old version of Structure.

To check for heterogeneity of admixture along the chromosome, we divided our data for both chromosomes into five blocks of 14 to 18 adjacent microsatellites. On the third chromosome, we discarded two loci for this analysis that were not close to any other microsatellites. The absolute nucleotide positions (Mb) in the Drosophila sequence (release 2, available from: http://flybase.bio.indiana.edu/) and cytological positions (polythene bands) that were covered by the locus sets X-1 to X-5 and 3-1 to 3-5 on the respective chromosomes were X chromosome, X-1: 15 loci, Mb 1.31 to 3.52, band 1E-3F; X-2: 15 loci, Mb 3.64 to 5.67, band 3F-5C; X-3: 15 loci, Mb 5.71 to 9.32, band 5C-8E; X-4: 15 loci, Mb 9.32 to 14.55, band 8E-12F; X-5: 14 loci, Mb 15.14 to 19.94, band 13C-19C; chromosome 3, 3-1: 18 loci, Mb 3L2.29 to 10.13, band 62C-67D; 3-2: 14 loci, Mb 3R0.78 to 3.25, band 82C-84D; 3-3: 14 loci, Mb 3R3.64 to 5.87, band 84d-85f; 3-4: 14 loci, Mb 3R5.88 to 16.18, band 85f-92e; 3-5: 15 loci, Mb 3R17.29 to 26.33, band 93e-100a. For each of these blocks, we ran Structure with K = 2 (all other settings as above).


    Results
 TOP
 Abstract
 Introduction
 Material and Methods
 Results
 Discussion
 Acknowledgements
 Literature Cited
 
We analyzed 74 and 77 microsatellites mapping to the X chromosome and third chromosome, respectively. Consistent with previous studies, we found European and Zimbabwean flies to be well differentiated (Begun and Aquadro 1993; Begun and Aquadro 1995; Andolfatto 2001; Caracristi and Schlötterer 2003). The mean FST between European and Zimbabwean populations is 0.23 (P < 0.001), and the genetic distance based on the proportion of shared alleles is 0.57. Population differentiation among European populations is low but significant (table 1). Given that the level of differentiation between European populations is very low compared with the differentiation between African and non-African populations, this should not bias the analysis. Within Africa, population differentiation was lower than among European populations (table 1). A closer inspection of the genetic differentiation between the European populations and two populations from Zimbabwe indicated that the urban population collected in Harare was less differentiated from the European populations than the rural population collected at the Sengwa Wildlife Reserve (table 2).


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Table 2 FST Values and Genetic Distance Between European and Zimbabwean Populations.

 
Since recent studies indicated that the partitioning of variability differs dramatically among X chromosome and autosomes in D. melanogaster (Andolfatto 2001; Kauer et al. 2002), we analyzed both chromosomes separately. As the differentiation between the European populations is very low, the analysis of different European populations for the X chromosome and the third chromosome is not expected to influence our results. Surprisingly, the genetic differentiation was almost identical for X-linked microsatellites irrespective of whether Sengwa or Harare was compared with the European populations (tables 1 and 2). For the third chromosome, however, we found the Harare population to be more similar to Europeans than the Sengwa population, irrespective of whether we used FST or the proportion of shared alleles. Thus, the third chromosome harbors more non-African alleles than the X chromosome, and this effect is more pronounced in the Harare population.

We used a Bayesian model–based clustering method (Pritchard, Stephens, and Donnelly 2000) to investigate the hypothesis of a different amount of European ("cosmopolitan") alleles in the two populations from Zimbabwe and among X chromosomes and third chromosomes. For both chromosomes, we obtained the highest posterior probability for two clusters (K = 2, table 3), which corresponded to African and European populations. Using the admixture model of Structure, the clustering method also estimates the probability of ancestry for each individual in one of the two groups (African-European). Whereas all European individuals had very high probabilities of ancestry in only one group ("the European cluster"), African individuals were found to score in both groups (table 4). The probabilities of ancestry of African individuals in the European cluster were ranging from 0% up to 70% (data not shown, but see Appendix A). Consistent with FST analysis, we found the probability of ancestry in the European cluster to differ sharply between the two Zimbabwean populations as well as between chromosomes (table 4). The highest scores for Zimbabwean individuals in the European cluster were found on the third chromosome in the capital Harare, whereas the rural population, Sengwa, shows significantly less influence of the cosmopolitan type (table 4) (P < 0.01, Mann-Whitney U test). For the X chromosome, on the other hand, scores in the European cluster are significantly lower than for the autosome (table 4) (P < 0.001, Mann-Whitney U test). Despite not being statistically significant, a trend for more European alleles in the Harare population can also be seen on the X chromosome (table 4). As this analysis was based on a substantially larger number of non-African chromosomes than African ones (120 versus 35 on chromosome 3 and 120 versus 26 on the X chromosome), we repeated the analysis with a data set in which a number of European chromosomes were randomly discarded so that equal numbers of chromosomes in African and European flies were compared. This reduced data set resulted in the same difference between the X chromosomes and the autosome for the Sengwa and Harare population (Appendix A). Hence, the Bayesian model–based clustering method provided strong support for the presence of a larger number of European alleles on the third chromosome in the Harare population than expected by either X chromosomal data or the rural Sengwa population. The same qualitative differences between chromosomes and populations were observed when we used a newer version of Structure, which accounts for linkage among microsatellite loci or the shared history of African and non-African D. melanogaster populations (data not shown).


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Table 3 Inferring the Number of Populations Using Structure.

 

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Table 4 Assignment and Inferred Ancestry of Individuals by Structure.

 

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Appendix A Assignment of Individuals to the "European" Cluster Using Different Locus Sets on the X Chromosome.

 
Given the evidence for admixture in the Zimbabwean population, we were further interested in whether this pattern is seen across the entire chromosome or if it is limited to some chromosomal regions. First we analyzed locus-wise FST values between Harare and Sengwa populations and found a homogenous pattern of low differentiation across loci, and also no locus had a significant FST value after Bonferroni correction (results not shown). This result therefore provides no evidence for locus-specific differences between the populations (e.g., due to different inversion frequencies). We also plotted locus-wise FST values between the African and non-African populations according to their chromosomal position. This analysis showed stochastic fluctuations among loci on both chromosomes rather than adjacent parts of the chromosomes having more similar FST values than more distant ones (results not shown). Therefore, this analysis provided no evidence for spatial clustering of admixture along the chromosome. A different approach to detect spatial heterogeneity along the chromosomes is provided by the Bayesian model–based clustering method in which signals of individuals can be detected separately. Therefore we ordered the loci on both chromosomes according to their chromosomal position and divided the data into five sets, each consisting of 14 to18 adjacent loci (see Material and Methods). With these data sets we ran the program Structure assuming K = 2. In the Sengwa population, the probabilities of Zimbabwean ancestry were very similar among all sets of loci, irrespective whether they were located on the X chromosome or on the third chromosome (fig. 1 and Appendix A). For the Harare population, differences between the locus sets were more pronounced, showing the largest variance on the third chromosome (fig. 1b and Appendix B). The variance of the individual probabilities of ancestry between the locus sets is higher than the variance among individuals within sets. On the level of individuals, the difference in probability of non-African origin is very pronounced. Individual ZH1, for example, shows 35% and 99% probability of ancestry in the European cluster for locus sets 3-1 and 3-2, but only 4%, 5%, and 5% for locus sets 3-3, 3-4, and 3-5. Basically in all locus sets, at least one individual had admixture values higher than 0.5. Again this result was qualitatively the same when the linkage model or the correlated allele frequency model of Structure II was used (data not shown).



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FIG. 1. Boxplots of assignment of individuals to the "European" cluster (inferred European ancestry) using different locus sets on the X chromosome and autosome (actual values can be found in Appendix A). Boxes include the two interquartiles containing 50% of the data. Horizontal lines within the boxes represent the median. Vertical lines lead to highest and lowest values but not including extreme values. Extreme values were defined as lying outside of 1.5 box lengths and were represented by black dots. In each figure the five locus sets (L1 to L5) for the Harare and Sengwa sample are shown. (A) X chromosome. (B) Third chromosome

 

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Appendix B Assignment of Individuals to the "European" Cluster Using Different Locus Sets of the Third Chromosome.

 

    Discussion
 TOP
 Abstract
 Introduction
 Material and Methods
 Results
 Discussion
 Acknowledgements
 Literature Cited
 
Our microsatellite survey provided evidence for the presence of non-African alleles on the third chromosome in an African D. melanogaster population collected in Harare, the capital of Zimbabwe. In contrast, the X chromosome harbored almost no non-African alleles. Interestingly, this discrepancy between X chromosome and third chromosome was not found when a rural population (Sengwa) was analyzed. Given that the differences between the X chromosome and the autosome were not detected in both populations from Zimbabwe, locus-specific and also chromosome-specific effects causing an erroneous signal of admixture in Zimbabwe seem unlikely, as these would result in a consistent pattern in both populations. For example, a higher power to detect admixture on the third chromosome than on the X chromosome (e.g., due to more variability on the third chromosome in non-African populations) should apply to both sample locations from Zimbabwe.

An issue of special interest are chromosomal inversions known to segregate on the autosomes of D. melanogaster at high frequencies (Lemeunier and Aulard 1992). The heterogeneity in admixture among individuals and chromosomal regions (fig. 1b and Appendix B) could, in principle, be also attributed to chromosomal inversions. For example, old inversions causing tight linkage among loci on the third chromosome that are segregating in Zimbabwe populations but are fixed in European populations (e.g., due to selection) could be misinterpreted as admixture of non-African alleles. Such a scenario, however, is unlikely to account for the inferred non-African ancestry in Zimbabwe, as Sengwa and Harare showed very low differentiation over all loci (table 1) and also locus-wise FST values did not provide evidence for differentiated genomic regions. Thus, if the inversions have been segregating in Zimbabwe for a long time they should be shared between both populations and occur at similar frequencies.

As we observed a high fraction of non-African alleles on the third chromosome only in Harare, this scenario seems to be not compatible with our data. Nevertheless, direct proof that Sengwa and Harare populations are not differentiated with respect to inversions could be only gained if these populations were karyotyped.

Alternatively, our observation could be explained by a combination of selection and demography. Whereas in the rural Sengwa population, Zimbabwean ancestry was observed for both chromosomes, non-African ancestry up to 70% was detected on the third chromosome in the Harare population. In several individuals collected in Harare, we detected chromosomal segments of almost pure non-African descent (>80% [Appendix A]). This result suggests that the rural population experiences less admixture of non-African genotypes than the population collected at the capital Harare. This is consistent with more transportation from Europe to the capital than to the wildlife reserve and also with other studies from D. melanogaster in Africa that found more "cosmopolitan-like" flies in towns than in the countryside (Vouidibio et al. 1989; Capy, Pla, and David 1993). Importantly, we also found a striking difference between the third chromosome and X chromosome in Harare. As neutral admixture affects both chromosomes to the same extent, admixture alone is not sufficient to explain our data.

In the following we discuss different nonneutral scenarios, which could potentially explain the observed pattern of admixture. First we discuss the possibility that European alleles could have a selective advantage in the Zimbabwean populations (i.e., positive selection for introgressed alleles in Zimbabwe). In contrast, the next two sections assume that European alleles have a selective disadvantage in Zimbabwe (i.e., purifying selection against these alleles in Zimbabwe).

Beneficial Effects of European Alleles
One nonneutral explanation for the different degree of admixture could be that some of the European alleles could have a selective advantage in Zimbabwe. A potential example could be insecticide resistance, which has recently been suggested to have spread throughout worldwide populations (Daborn et al. 2001). With the fixation of such beneficial alleles during a selective sweep, a larger genomic region around such alleles could be fixed in the African population via hitchhiking (Maynard Smith and Haigh 1974). Hence, if several beneficial alleles are located on the third chromosome, this would result in more admixture on the third chromosome than on the X chromosome. However, it is not obvious that multiple beneficial alleles should be located on the third chromosome, but none on the X chromosome. Whether the different environments from which the African populations were sampled (urban versus rural) affect the degree of admixture cannot be inferred from our data, as our sample sizes are rather restricted. The absence of the European alleles in the Sengwa sample might either be due to restricted gene flow between Sengwa and Harare or to selection against European alleles in the rural populations. Studying larger sample sizes and sample gradients from the town to the countryside could shed more light on this.

Sexual Selection
Sexual selection assumes that fast evolving traits for mating preferences could have evolved in different directions in the Zimbabwean and non-African populations. A secondary contact of populations divergent for mating preferences could therefore result in assortative mating and in postmating isolation. Interestingly, such a pattern of mating preference has been described for the Zimbabwean populations, the Z-M mating behavior (Wu et al. 1995; Hollocher et al. 1997a). In brief D. melanogaster from Zimbabwe (Z) and also from some other African populations prefer to mate with flies from their own populations when given the choice between these and so called "cosmopolitan flies" (M) (i.e., flies from outside of Africa). This mating difference was found to be asymmetrical in two ways. First only African flies discriminate but not non-African flies. Second only female African flies discriminate; males readily mate with cosmopolitan females. Whereas a wide range of Z-values (degree of mate discrimination of females) was observed in the Harare population, the rural Sengwa population had on average a stronger Z-like (i.e., discriminating) mating behavior (Hollocher et al. 1997a). Mapping studies indicated that the assortative mating behavior mapped primarily to the autosomes, but only small effects were found on the X chromosome (Hollocher et al. 1997b; Ting, Takahashi, and Wu 2001). More recently evidence for a postmating barrier was also found as crosses of Z-females with cosmopolitan males yielded fewer offspring than the reciprocal cross (Alipaz, Wu, and Karr 2001).

The asymmetry of the M-Z trait makes it difficult to predict to what extent the mixing of Zimbabwean and cosmopolitan populations is impaired by this behavior. For example, the preference of mating in one direction (M females with Z males) could impose a form of differential selection (e.g., stronger selection on the introgressed X chromosome in males [see below]) on the introgression of X chromosomes versus autosomes. On the other hand, less admixture could be expected on the third chromosome because most variance for the mating trait has been mapped to the autosomes (Hollocher et al. 1997b; Ting, Takahashi, and Wu 2001). To confirm that the higher admixture is a general phenomenon of autosomes, rather than of the third chromosome, we reanalyzed a new data set consisting of microsatellite variation on the X chromosome and the second chromosome (Caracristi and Schlötterer 2003) in Zimbabwe/Harare and non-African populations. Despite that a smaller number of loci was characterized, we also found less admixture in Harare on the X chromosome than on the second chromosome in the data set of Caracristi and Schlötterer (2003) (data not shown). The low degree of admixture on the X chromosome seems to contrast the observation that most of the genes involved in Z-ness are located on the autosomes. Nevertheless, it has to be noted that QTL maps, such as the one constructed by Hollocher et al. (1997b), are not expected to reflect the different selection intensities on X chromosomes and autosomes under natural conditions.

Ecological Selection
As a consequence of adaptation to one habitat fitness can be reduced in another habitat, leading to a reduction in gene flow between populations adapted to two different habitats (Cooper and Lenski 2000; Hawthorne and Via 2001; Hendry 2001). Both theoretical and experimental studies demonstrated that such an ecological adaptation mediated reduction in gene flow could even result in speciation (Rice and Hostert 1993; Gavrilets 1999; Schluter 2001; Ogden and Thorpe 2002). Examples could be thermal adaptation or changes in life cycle to adapt to temperate climates. Based on the population history of D. melanogaster it has previously been suggested that the habitat expansion out of Africa was facilitated by selective sweeps (Begun and Aquadro 1995; Kirby and Stephan 1996; Kauer et al. 2002). Recently, various studies have provided independent evidence that the fixation of beneficial mutations is more common than assumed under the neutral theory of molecular evolution (Kimura 1983; Bustamante et al. 2002; Fay, Wyckoff, and Wu 2002; Smith and Eyre-Walker 2002). Furthermore, adaptive explanations were also suggested for phenotypic traits differing between African and non-African populations (Vouidibio et al. 1989; Takahashi et al. 2001). If non-African flies have accumulated many mutations enhancing fitness outside of Africa, they can be expected to have lost some of their adaptations to the African environment. Such ecological trade-offs ("antagonistic pleiotropy") were demonstrated in experimental E. coli populations (Cooper and Lenski 2000; Cooper, Bennett and Lenski 2001). European alleles could, therefore, be deleterious in an African context and should be purged from the population by natural selection. Our results also have similarity to other studies, which described strong selection against hybrids and thus a maintenance of the integrity of divergent populations (Via, Bouck, and Skillman 2000; Hawthorne and Via 2001). However we have no direct evidence for antagonistic pleiotropy of differential adapted alleles in and outside of Africa. Therefore, it remains speculative whether our results could be explained by environmental selection.

Stronger Selection on the X Chromosome
The observed difference between X chromosomes and autosomes could be caused by hemizygosity of the X chromosomes in males. As males carry only a single copy of the X chromosomes, selection is more effective for recessive mutations located on the X chromosome (Charlesworth, Coyne, and Barton 1987; Aquadro, Begun, and Kindahl 1994). Assuming that African populations (and urban populations in particular [see above]) are challenged by a continuous influx of non-African alleles, and these alleles are recessive and deleterious in Zimbabwean flies, selection will remove non-African alleles more efficiently on the X chromosome than on the autosomes. Thus, European alleles are maintained for longer time spans on the autosomes, leading to a higher proportion of European alleles on the autosomes. Irrespective of the selective force acting, hemizygosity of X chromosomes in males could therefore explain the observed difference between X chromosomes and autosomes.

Alternatively, if the number of European alleles deleterious in Zimbabwe is higher on the X chromosome, this could also explain the sheer absence of European alleles on the Zimbabwean X chromosomes. This hypothesis seems, however, unlikely given that X chromosomal gene density is not higher than the autosomal gene density (Hey and Kliman 2002). Finally, a higher impact of selection on the X chromosome could be due to stronger epistatic interactions on the X chromosome.


    Acknowledgements
 TOP
 Abstract
 Introduction
 Material and Methods
 Results
 Discussion
 Acknowledgements
 Literature Cited
 
We are thankful to R. Butlin, D. Falush, B. Harr, J. Pool, M. Richie, C. Vogl, members of the CS lab, and two anonymous reviewers for helpful comments on earlier versions of the manuscript. Many thanks to C. Aquadro, B. Harr, J. Gorczyca, and C. I. Wu for collecting and sharing fly stocks. This work was supported by FWF grants to C.S.


    Footnotes
 
David Rand, Associate Editor Back


    Literature Cited
 TOP
 Abstract
 Introduction
 Material and Methods
 Results
 Discussion
 Acknowledgements
 Literature Cited
 

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Accepted for publication April 4, 2003.