Patterns of Microsatellite Variability Among X Chromosomes and
Autosomes Indicate a High Frequency of Beneficial Mutations in
Non-African D. simulans
Gerhard Schöfl and Christian Schlötterer
Institut für Tierzucht und Genetik, Veterinärmedizinische Universität, Vienna, Austria
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 Xlinked loci is most likely caused by selective sweeps associated with the out-of-Africa habitat expansion of D. 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;
Schlötterer, 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 nonAfrican 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
Key words: selective sweep, microsatellites, out-of-Africa, Drosophila simulans.
E-mail: [email protected].
Mol. Biol. Evol. 21(7):1384–1390. 2004
doi:10.1093/molbev/msh132
Advance Access publication March 24, 2004
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 Schlötterer 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
Molecular Biology and Evolution vol. 21 no. 7 Ó Society for Molecular Biology and Evolution 2004; all rights reserved.
Microsatellite Variability in D. simulans 1385
by a bottleneck associated with the out of Africa habitat
expansion.
X chromosomes). The adjusted estimates of variability
were calculated as
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 lM dNTPs, 1 lM of each primer, and 0.5 U
Taq polymerase. A typical cycling profile consisted of 30
cycles for 50 s at 948C, 50 s at 488C to 588C (depending on
the primers), and 50 s at 728C. All PCRs were run with an
initial denaturation step of 3 min at 948C and a final
elongation step of 45 min at 728C 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 (Schlötterer 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 Schlötterer 2003). Gene
diversity was corrected for small sample sizes by
multiplying by n/(n21), 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 Schlötterer 2003; Schlötterer 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 6 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
1
Hcorr ¼ 1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2
1 þ kð1=ð1 Hobs Þ 1Þ
ð1Þ
Vcorr ¼ kVobs
ð2Þ
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 Schlötterer [2002b, 2003]). To
detect significantly reduced loci, we calculated the lnRH
statistics (Kauer, Dieringer, and Schlötterer 2003; Schlötterer 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:
hPop1
ln½EðRHÞ ¼ ln E
hPop2
13
2 0 2
1
1
1
8lC7
6 B 1HPop1
B
7
C
¼ ln6
2
4E@
A5
1
1
1 8l
1HPop2
2 0
6 B
ffi ln4E@
1
1HPop1
1
1HPop2
2
2
1
1
13
C7
A5
ð3Þ
where H is related to h by the formula H ¼
1 2 (1/(1 1 2h)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 Schlötterer 2003;
Schlötterer 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 a ¼ 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
1386 Schöfl and Schlötterer
Table 1
Differences of Autosomal and X Chromosomal Microsatellite Variability in an African and European Population of
D. simulans
Va
H
Population
Africa
Europe
Autosome
X Chromosome
0.55 6 0.19
0.50 6 0.22
n.s. (P ¼ 0.07)
0.56 6 0.20
0.42 6 0.25
P , 0.001
n.s.
n.s.
Autosome
X Chromosome
1.37 (0.80, 3.26)
0.83 (0.47, 2.62)
n.s. (P ¼ 0.06)
1.88 (0.42, 5.57)
1.09 (0.16, 5.30)
P , 0.05
n.s.
n.s.
NOTE.—Variabilities are not corrected for effective population sizes of the chromosomes. In the case of heterozygosities, tests across chromosomes are Mann-Whitney
U tests; tests across populations are Wilcoxon signed rank tests. In the case of variances in repeat numbers, t-tests were performed. n.s. indicates not significant.
a
Mean variance in repeat numbers is given by median plus lower and upper quartiles in parentheses; for statistical tests, variance was log transformed.
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 h estimated from the gene diversity H in
the African flies, which was considered a prebottleneck
population (hauto ¼ 4Nautol ¼ 2.015, hx ¼ 4Nxl ¼ 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 f¼0.0001 to 0.1
and t ¼ 0.0001 to 0.1 3 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 (lnRH x ¼ 21.07,
lnRH auto ¼ 20.32). The criterion for matching was set
jlnRHobs – lnRHsimj , e, with n ¼ 2500 and e ¼ 0.2. Note
that allowing for different ranges of lnRH (i.e., e ¼ 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 (Heur ¼ 0.46 6
0.24, H afr ¼ 0.56 6 0.20, Wilcoxon signed rank test,
Z ¼ –4.11, P , 0.001) or variance in repeat numbers
(median Veur ¼ 0.88, q1 ¼ 0.24, q3 ¼ 3.79, median Vafr ¼
1.67, q1 ¼ 0.45, q3 ¼ 4.02, paired t-test on log transformed
data, t ¼ 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 Xlinked loci (table 1 and fig. 1). After accounting for
multiple testing, only the difference in gene diversity
Microsatellite Variability in D. simulans 1387
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).
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).
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 (lnRH x ¼–1.07 6 1.59, lnRH auto ¼ 20.32 6
1.11, t ¼ 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 ¼ 0.117, P ¼ 0.196) than were the
lnRH values on the X chromosomes (KolmogorovSmirnov test for goodness of fit, gmax ¼ 0.127, P ¼
0.084). Previous work showed that under neutrality, lnRH
follows a normal distribution (Kauer, Dieringer, and
Schlötterer 2003; Schlötterer 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.
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
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.
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.
So far, we conditioned the computer simulations on
the observed lnRH values only. As we observed eight Xlinked 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
1388 Schöfl and Schlötterer
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 ¼ 0.1 to 0.0001 and a timepoint
t ¼ 0.1 to 0.0001 4Ne generations ago.
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.
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 (Schlötterer 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.
Whereas in African D. melanogaster populations, X
chromosomes are more variable than autosomes, in D.
simulans, both chromosomes were about equally variable
(H x ¼ 0.56 6 0.20, Hauto ¼ 0.55 6 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.
Microsatellite Variability in D. simulans 1389
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.
Acknowledgments
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
Förderung der wissenschaftlichen Forschung grants to C. S.
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Lisa Matisoo-Smith, Associate Editor
Accepted March 8, 2004