Effective population size shapes the patterns of molecular
evolution in Drosophila
Background: A corollary of the nearly neutral theory of molecular evolution is
that effective population size determines the efficiency of natural selection. Population
size also seems to play a key role in the evolution of genome complexity [7].
Multispecies data on nucleotide diversity can be used to test the expected relationship
between population size and selection efficiency at the DNA level. Using nucleotide
data from Drosophila species, here we study the levels of synonymous polymorphism
among species and the possible causes of their differences.
Results: Differences in the levels of synonymous polymorphism among
Drosophila species were accurately assessed showing that around 22% of the variation
in synonymous polymorphism is explained by true differences among species. The
differences in synonymous polymorphism are not greatly affected by differences in
gene functions, phylogenetic relationships among species or demography. Selection
efficiency seems be positively correlated with the levels of synonymous polymorphism:
1) codon bias is positively correlated with the levels of synonymous polymorphism; 2)
synonymous polymorphism was found to be negatively correlated with the proportions
of repetitive sequences in the genomes; and 3) there are significant differences in the
proportion of adaptive substitutions in ortologue genes of pairs of species with
significant differences in the levels of synonymous polymorphism.
Conclusions: Differences in the levels of synonymous polymorphism are likely
due to differences in long-term effective population size. These differences seem to
have implications for the molecular evolution of the Drosophila species, as is suggested
by the positive relationships found among the levels of synonymous polymorphism and
(1) evidence of positive selection on codifying DNA and (2) the amount of bias for
preferred codons. Moreover, species with low synonymous polymorphism levels have a
higher proportion of repetitive sequences in their genomes, suggesting a lower
efficiency of natural selection in species with smaller population size. Effective
population size seems to affect the patterns of molecular evolution even within a group
of closely related species such as those belonging to the Drosophila genus.
1
Background
The neutral theory of molecular evolution claims that most evolutionary changes at the
molecular level are not caused by Darwinian natural selection acting on advantageous
mutants, but by random fixations of selectively neutral variants through the cumulative
effect of sampling drift on the continued input of new mutations. According to the
neutral theory, the rate of mutant substitutions in evolution is equal to the neutral
mutation rate, and this rate is independent of the population size and environmental
conditions [8]. The neutral theory assumes that mutations can be classified as neutral or
deleterious. However, Ohta [9] proposed that most “neutral mutations” are indeed
slightly deleterious, rather than strictly neutral. In this nearly neutral theory, the fate of a
mutation depends on the relative forces of selection and drift [10]. This observation
means that the evolutionary rate is higher in smaller populations than in larger
populations, because a slightly deleterious mutation behaves as selectively neutral when
the product of the effective population size by the selection coefficient (Nes) is smaller
than unity, while it may be efficiently selected if Nes is larger than unity [11]. The
effective population size (Ne) determines the degree to which gene frequencies are
faithfully transmitted across generations [7]. Estimates of the effective population size
can be obtained from the neutral nucleotide variation, since from neutral theory θ ≈
4Neµ (where θ is the average heterozygosity per site for neutral mutations; Ne is the
effective population size and µ is the neutral mutation rate per nucleotide site per
generation [11]).
In recent years, slightly advantageous as well as slightly deleterious amino acid
substitutions have been shown to occur in protein evolution [10, 12, 13]. Bustamante et
al. [12] showed that the mean selection coefficients are very weak for Arabidopsis (Nes
around -2 to 0.5) and Drosophila (Nes around 0.5 to 3), giving support to the nearly
neutral theory [10]. Moreover, Fay et al. [14] and Eyre-Walker et al. [15] showed that a
high fraction of amino acid mutations are slightly deleterious in mammals and
Drosophila. However, the proportion of substitutions that have been driven by positive
selection seems be high in humans and Drosophila (around 30–50 %), showing that the
fraction of positively selected mutations is not as small as would be expected by neutral
theory [13, 16-19]. Overall, both genetic drift and positive selection are the driving
2
forces of molecular evolution, but the general relative impact of both forces in shaping
the observed patterns of nucleotide variability is still under discussion.
Effective population is a key factor in the nearly neutral theory of molecular
evolution, because the fate of a mutation is determined by the product Nes. Akashi [3]
showed that for weakly selected silent mutations, the differences in codon bias between
D. melanogaster and D. simulans could be explained by differences in effective
population sizes. He estimated a coefficient of selection acting on preferred codons in
D. simulans of nearly Nes ≈ 2. Based on the observation that D. simulans seems to have
a larger population size than D. melanogaster [20], Akashi [3] concluded that selection
for preferred codons is inefficient in D. melanogaster. Furthermore, Vicario et al [21]
found differences in the levels of genomic codon bias in Drosophila species, suggesting
differences in effective population size as a possible explanation.
A recent study to detect positively selected genes in the genomes of humans and
chimpanzees showed that although the rate of nonsynonymous substitution in humans is
higher than in chimpanzees, the number of genes with evidence of positive selection is
substantially smaller in humans [22]. These results are explained by the reduced
efficacy of natural selection in humans because of their smaller long- term effective
population size.
Recently, a more far-reaching and fascinating new role has been given to
effective population size in relation with the evolution of genome complexity. Lynch
and Conery [7] have proposed that the changes in genome complexity from prokaryotes
to multicellular eukaryotes, including gene number, intron abundance and mobile
genetic elements, emerged passively in response to long-term population-size
reductions that accompanied increases in organism size. According to this hypothesis,
much of the restructuring of eukaryotic genomes was initiated by non-adaptive
processes, which in turn provided novel substrates for the secondary evolution of
phenotypic complexity by natural selection [7].
Multispecies data on nucleotide diversity is a very useful source of information
that could be used to test the expected relationship between population size and
selection at the DNA level. Drosophila species has been the focus of genetic and
evolutionary studies for decades, and now the availability of the sequences of 12
genomes of these species makes them the focus of evolutionary genomics [21]. The
availability of nucleotide polymorphism data in these species (DPDB “Drosophila
Polymorphism Data Base”[23, 24] allow us to test the hypothesis that population
3
effective size and selection efficiency are positively correlated. Since synonymous
polymorphism is thought to be mainly neutral (πs ≈ θ), the differences detected in the
levels of synonymous polymorphism among species could be due to differences in
effective population size. Therefore, in this paper we first have assessed the differences
in the levels of synonymous polymorphism among Drosophila species. Then, we
evaluate different possible causes that could explain the detected differences. The
results indicate that effective population size seems to be a major factor in explaining
the differences detected in the levels of synonymous polymorphism among species.
Furthermore, we
tested the hypothesis that the selection efficiency is positively
correlated with effective population size by relating the levels of synonymous
polymorphism with i) the bias for preferred codons, ii) genome content, and iii) amount
of adaptive substitutions. The results show that both variables, the levels of codon bias
and the proportion of adaptive substitutions, seem to take higher values in species with
higher levels of synonymous polymorphism. Moreover, species with low synonymous
polymorphism have a higher proportion of repetitive sequences in their genomes,
suggesting a diminished efficiency of selection in species with smaller population size.
Our results provide strong evidence in favour of the importance of effective population
size as a factor determining the patterns of molecular evolution even within a group of
closely related species such as those belonging to the Drosophila genus.
Results
Differences in the levels of synonymous polymorphism among species
DPDB (Drosophila Polymorphism DataBase [23, 24]) provides polymorphism
data by gene and species in the Drosophila genus, giving several evaluations of the
quality of the estimates and additional information on the genes, such as the coordinates
to the D. melanogaster genome and Gene Ontology classification of the genes. A link to
the source information of the sequences in GenBank and EMBL, and the possibility of
reanalyzing any gene-specie dataset is also provided in the web page.
We performed a data filtering from DPDB (see methods) obtaining a dataset
with 751 estimates of synonymous polymorphism (πs) [25], belonging to 482 different
genes of 15 Drosophila species. The accession number of DPDB and the data of πs and
Tajima’s D (Tajima 1989) by species and genes are listed in Supplementary Table 1.
Figure 1 graphs the different mean πs values per species.
4
To assess the differences in synonymous polymorphism levels among
Drosophila species, we performed an unbalanced variance analysis. Since different
types of genes are included in the 15 analyzed species, the datasets by species could
contain a biased sample of genes with high codon bias or with high rate of selective
sweeps which would decrease the level of synonymous polymorphism in a particular
species. Therefore, a two-factor analysis (gene and species) was performed. The result
shows that significant differences were found for the species factor, but neither the gene
factor nor the interaction between species and genes were significant (Table 1). Since
the data of D. melanogaster alone account for 40% of the dataset (Figure 1), the same
analysis was performed excluding the data of this species, showing a similar result
(Table 1).
To evaluate which species differed in their πs values, we performed a post hoc
pairwise Tukey test estimating the differences among pairs of the species. Because the
effect of a single gene among the 482 different genes analyzed can be undetected by the
variance analysis, we also performed a paired T test among ortologue genes of pairs of
species (Table 2). The results of the Tukey test showed that the pairs of species dmeldsim, dsim-dmir, dpse-dmir, dyak-dsim, dmir-dmel, dsim-dsan, dmir-dari, dmir-dame,
dmir-dkik and dkik-dsan are significantly different in their levels of πs. The results of
the paired T test were coincident, showing that the pairs of species dmel-dsim (N=114),
dsim-dyak (N=10) and dsim-dmir (N=10) are significantly different in theirs πs values
(Table 2). Significant differences detected by Tukey test were borderline significant for
the pair of species dpse-dmir (N=9) and dmir-dmel (N=10) and not significant for dsimdsan (N=5) by paired T Test (Table 2). Moreover, paired T test detected significant
differences which were undetected by Tukey test between the pairs of species dsimdsec, dsec-dmau, dmel-dsec and dmel-dpse (Table 2). Finally, there are significant
differences between pairs of species detected by Tukey test, dmir-dari; dmir-dame,
dmir-dkik, dmir-dmoj and dkik-dsan, that could not be tested by paired T tests because
no data for ortologue genes are available for these pairs of species. These results are
consistent with the previous ANOVA, indicating that the differences in πs among
species are not due to the different sampled genes within each species, because the
paired T test compares the same set of genes for each pair of species.
The differences in levels of πs between dmel and dsim were highly significant
both by Tukey test and paired T test. African and derived populations of D.
melanogaster and D simulans seem to have different levels of synonymous
5
polymorphism [26-29]. We therefore grouped the gene sequences by their origin and
recalculated the πs values by gene. We obtained a dataset of 91 ortologue genes from
non-African populations and 23 ortologue genes from African populations of D.
melanogaster and D. simulans. We detected significant differences among πs values in
Non-African populations of these two species, but the differences were not significant
in African populations (Table 2).
All comparisons in Table 2, including dsim and dmel with other species, are
based on non-African populations of these species.
Various different causal factors could lead to the differences detected in the
levels of πs among species in the above analysis: 1) Different selective constraints in
diverse functional categories of genes of Drosophila species could affect the πs levels
by selectively acting on linked non-synonymous sites [19, 21, 30]; 2) species belonging
to different groups of species have different average levels of recombination rates [24,
33] which could affect the levels of synonymous polymorphism [31]; and 3) deviations
of the neutral equilibrium by demographic events, such as population bottlenecks,
population structure or population expansion could influence the level of synonymous
polymorphism [34, 35].
Differences in selective constraint among genes. We performed ten different
ANOVAs testing the model πs = species + GO. Where GO is a component that sorts
genes as belonging or not to one of ten different categories of biological process at level
3 of the Gene Ontology (see Methods). The results are summarized in Table 4. While,
the species component has a significant effect on the levels of πs; the differences in the
levels of πs due to differences in functional categories only were detected for genes
belonging to functional categories related to metabolic processes (cellular metabolism;
macromolecule metabolism and primary metabolism) and sexual reproduction. These
genes have significantly lower πs values than genes from other functional categories
(Supplementary Results, Table 2).
The functional category effect suggests different selective constraints for these
genes. To test this assumption, the levels of constraints (πn / πs) were investigated for
the four categories, showing that the genes involved in metabolic processes are
significantly more constrained than the rest of genes (average πn / πs = 0.13 vs. 0.28);
while the genes involved in sexual reproduction are less constrained than the other
6
genes (average πn / πs = 0.30 vs. 0.22), although in this last case the differences were not
significant. Nevertheless, the percentage of the variance explained by the differences
between species was 14.38 % and the mean percentage of the variance explained by the
functional differences was 2.6 %, demonstrating once more that the variation in the
levels of πs is mainly due to proper differences in species (Table 3).
Taxonomic relationships among species. We performed a fully nested
ANOVA to test whether the differences in the levels of synonymous polymorphism
could be explained by differences in the subgenus or taxonomic groups of species. The
differences in the levels of synonymous polymorphism explained by differences in
subgenus were null (Table 5), while differences among species groups explained a very
low percentage of the total variance (1.42%) and the differences among species within
groups explained a significant percentage of the total variation in the levels of
synonymous polymorphism (14.2%; Table 5); indicating that the differences in the
levels of synonymous polymorphism could be attributed mainly to differences among
species within the groups of species.
The results in the previous section show that some percentage of the variance of
πs is explained by differences in the selective constraints of the genes. Therefore, to get
a more accurate percentage of the variance explained by the differences among species,
data were grouped by the functional category of the genes, and the fully nested ANOVA
was performed for each functional categories. In all analysis the species component
explained a higher percentage of the variance (Supplementary Results, Table 3). The
average percentage of the variance in the levels of πs, explained by the differences
among species for 10 datasets when genes were grouped by their function was 22%
(Supplementary Results, Table 3).
Deviations from the neutral equilibrium. To evaluate whether the differences
in the levels of synonymous polymorphism could be due to deviation of the neutral
equilibrium of the populations, we tested differences in the D statistics of the Tajima
test [34]. Tajima’s D statistics measure the deviation from the expected selection-drift
equilibrium in a population with constant size. Deviation from the neutral equilibrium
could be produced by selective events, such as selective sweeps or demographic events,
such as bottlenecks or population expansion [34]. However, demographic events will
generate an overall deviation across all genomes [13]. Therefore, if the levels of
7
synonymous polymorphism are affected by demographic events in a species, the mean
value of Tajima’s D would be different for this species. The mean values of D are
shown in Table 1. As with πs, we performed an unbalanced ANOVA to detect
differences among mean values of D by species and a post hoc Tukey and paired T tests
for ortologue genes to detect differences among pairs of species. The ANOVA showed
significant differences in the mean values of D among species (F
(14,736)
= 3.01,
p<0.001). Tukey test showed that the differences could be mainly attributed to D.
miranda, which have a mean value of D significantly different to the rest of the species.
Moreover, differences were found between the pair of species dyak-dsim. Paired T test
confirmed this last difference. However, the differences in Tajima’s D between D.
miranda and the rest of the species were not detected by paired T test. An examination
of the data of this species showed that the differences in the values of D in D. miranda
are due to 16 genes from the NeoX chromosome of this species. Bachtrog and
Andolfatto [2] found that genes from NeoX chromosome seem to deviate from
equilibrium, maybe due to a higher rate of adaptive substitutions in this chromosome
combined with demographic events [2]. The Tukey analysis without these genes do not
show significant differences in Tajima’s D between D. miranda and the rest of species.
Paired T test for Tajima’s D statistic was performed for all pairs of species with
ortologue genes and evidence of significant differences in the levels of synonymous
polymorphism (Table 2). Significant differences in Tajima’s D values by paired T test
for ortologue genes were only found between NA_dmel and NA_dsim (p = 0.010, N =
91) and between dyak and NA_dsim (p = 0,005, N=10). Results of coalescent
simulations indicate that the mean values of Tajima’s D by species are within the
expected values under a neutral model (Supplementary Results).
Testing the hypothesis that selection efficiency is positively correlated with
effective population size
The whole analysis of the previous section indicates that the differences in πs
observed in the dataset are mainly explained by differences among species, which could
be differences in effective population size. In the next sections we will test the
hypothesis that selection efficiency and effective population size are positively
correlated, assuming that differences in synonymous polymorphism are representative
of differences in effective population size (Ne). Differences in selection efficiency were
8
approached by differences in: 1) selection for preferred codons, 2) genome contents, and
3) proportion of adaptive substitutions.
1. Selection of preferred codons in Drosophila species
Codon bias has been widely detected in Drosophila species [3, 36-38]. Recently,
an analysis of the whole genome data of 12 species of Drosophila showed differences in
the levels of codon bias among species, which could be explained by differences in
effective population size [1]. In our dataset, there are data for 8 of out the 12 species
studied by Vicario et al. [1]. Therefore, to test the hypothesis that the differences in the
levels of codon bias are due to differences in the effective population sizes, we
performed analyses of correlation and regression between πs and average genomic ENC.
The results show that πs is negatively correlated with ENC (rSpearman = -0.149, p=
0.0003, N=594; Figure 2a). The regression was significant (r2 = 0.037, F1,538=20.7;
p<0.0001; N=540) as measured by the F statistics of the ANOVA of the regression,
following Sokal and Rholf [39]. This result could be biased by the different number of
πs values from different species (Figure 2a). We investigated this bias by analyzing a
dataset (Dataset 2) with 30 genes of D. melanogaster and D. simulans selected at
random, and obtaining confidence intervals by bootstrapping of the correlation. The
results of the correlation in this new dataset (N = 175) was rSpearman = -0.151, p= 0.046
with confidence intervals at 95% of -0.273 to -0.024. This negative association found
between ENC and πs is indicative of a positive association between codon bias and πs,
which could be expected if the πs levels are indicators of effective population size,
which agrees with Vicario et al.’s [1] hypothesis.
2. Differences in genome contents among Drosophila species.
Lynch and Conery [7] have pointed out the importance of genetic drift in
genome evolution. Basically, they have shown that effective population size is
negatively correlated with genome size, the number of different types of transposable
elements and the number and size of introns in the genomes. The implications of these
results is that much of the restructuring of eukaryotic genomes was initiated by
nonadaptive processes, and this in turn provided novel substrates for the secondary
evolution of phenotypic complexity by natural selection [7].
With recently published genome data from the twelve species of Drosophila, we
can test Lynch and Conery’s [7] hypothesis among related species belonging to a single
genus. The relationship between πs for 8 Drosophila species and different genome
9
variables (percentage of coding, intron and repeat sequences in the genomes) was
studied by correlation and regression analysis. As in the section above these correlations
could be biased by the different number of values of πs from different species. We
investigated this bias, performing the correlations with Dataset 2 and bootstrapping the
correlation. The results are shown in Table 6. Correlations betweens percentage of
repetitive sequences in the genome and πs were negative and significant (Table 6, Figure
3 a-d).
The correlation between the percentage of coding sequences in the genome and
πs was positive and significant, when the dataset with all data was analyzed, but this
result seems to be affected by the higher number of data of D. melanogaster and D.
simulans in that dataset (Table 6). The results of correlation analyses between the
percentage of intronic sequences and πs were contradictory, depending on the measure
of genome size (see Methods) and the dataset (Table 6). The regression analyses
considering πs as an independent variable showed similar results, suggesting that
between 2 and 6% of the variation in the percentage of repetitive sequences in the
genomes could be explained by the effective population size (Table 6).
3. Differences in the proportion of adaptive substitution among
Drosophila species.
In a complete genome analysis, Bakewell et al (2007) found that the chimpanzee
lineage has more positively selected genes than the human lineage. Their interpretation
of this result is that selection efficiency was weaker in the human lineage due to a lower
effective population size.
We performed an exploratory analysis to find differences in the proportion of
adaptive substitutions for ortologue genes between pairs of species with evidence of
differences in the levels of πs detected by Tukey and/or paired T test (Table 2); except
for the comparison dsim- dsan, since there are no nonsynonymous polymorphisms in
the dataset. We calculated an average alfa ( ) and an average number of adaptive
substitutions (ā) following Smith and Eyre-Walker [16] (see methods). The calculated
 are presented in the Table 7. All estimated  are within the confidence intervals
estimated by bootstrapping for the values of α by gene (equation 2 of Smith and EyreWalker [16]; see Methods). The confidence intervals were very wide in many of the
cases where the number of genes analyzed is low (Supplementary Results, Table 4 and
10
Table 5). Thus, the estimated are representative of the genes analyzed, but not of the
species. The effect of each gene in the estimates of  based in 10 or fewer genes were
evaluated by resampling without replacement calculating the in N datasets (where N
is the number of genes) without a different gene each time. The results show that the
differences in  between pairs of species are not greatly affected by single genes
(Supplementary Results, Table 6) except in the comparison of dmel-dmir. In this dataset
the  of dmel varied widely (from 0.03 to 0.98) depending on the genes included
(Supplementary Results, Table 6). The differences in the values of average number of
adaptive substitutions (ā =  * Dn) are significant in all the cases, except in the
comparisons dmel-dpse and dmel-dmir (Table 7). In all comparisons the results show
that species with significantly higher levels of synonymous polymorphism have a higher
proportion of adaptive substitutions for ortologue genes, except in the comparison dmirdmel (Table 2 and Table 7). Furthermore, the relation Dn/Ds showed the same trend in
most of the comparisons (Table 7); suggesting a higher number of non synonymous
substitutions than expected by the number of synonymous substitutions in the species
with higher levels of synonymous polymorphism.
Discussion
Differences at synonymous polymorphism levels among Drosophila species
We found significant differences in the levels of synonymous polymorphism among
Drosophila species (Table 2). For those species whose polymorphism had been already
studied, our estimates of synonymous polymorphism were similar to those previously
reported [2, 6, 17, 19, 40]. These differences among species could not be explained
either by differences in different sampled genes for the different species, nor by
differences in selective constraints among genes (Tables 3 and 4). The small effect
detected in selective constraints among genes does not explain the differences found
among species. Genes related with metabolic processes were shown to be more
constrained than other genes, whereas genes related with sexual reproduction were less
constrained. These results are in agreement with the evolutionary rates calculated for the
genes belonging to these functional categories in genome wide analyses [21].
The phylogenetic relationships among species do not seem affect the differences
in the levels of synonymous polymorphism found among Drosophila species (Table 5,
and Supplementary Results, Table 3).
11
Population bottlenecks, structure or expansion could affect the levels of
polymorphism in the species [34, 35]. Our results of the analysis of Tajma’s D to detect
deviations in neutrality due to the demographics of the species showed differences for
data of Neo X chromosome of D. miranda, and for the comparison between D. simulans
with D. melanogaster and D. yakuba. Deviations in neutral equilibrium of the genes of
the Neo X chromosome in D. miranda had already been detected by Bactrogh and
Andolfatto [2] and are possibly due to a combination of adaptive selection and
demographic events [2]. We think that the differences found in the values of Tajima’s D
in the comparisons of dsim-dyak and dsim-dmel (Table 2) are due to D. simulans. Since
the mean values of Tajima’s D are not significantly different in other comparisons
including D. melanogaster or D. yakuba. Moreover, Bierne and Eyre-Walker [41]
suggest that, although the effective population size of D. simulans has remained fairly
stable, populations of D. simulans have not reached a stochastic steady state. This is
because D. simulans seems be a large long term effective population size and the neutral
alleles are not fixed yet [41], and it is congruent with the higher mean value of Tajima’s
D found for this species when compared with D. melanogaster or D. yakuba. We did
not perform an accurate evaluation of the deviation of neutral equilibrium due to
demographic events for each species. However, our result of the analysis of Tajima’s D
suggests that the bulk of the differences found in the levels of synonymous
polymorphism are not caused by demographic events. A more in-depth analysis using
genome wide polymorphism data would be necessary to fully rule out the demographic
effect in the differences on synonymous polymorphism among Drosophila species.
Differences in the effective population size among Drosophila species has been
proposed many times in various different studies. Aquadro [20] and Akashi [4] had
proposed differences in effective population size between D. melanogaster and D.
simulans. However, whether these differences are true or a side-effect of the
demographic histories of these two species has been widely discussed [27, 42, 43].
Drosophila sechellia is a species endemic to a island and a small effective population
size had been proposed for this species [6]. Moreover, differences in effective
population size have been proposed for the sister species D. miranda and D.
pseudoobscura [5]. Finally a recent study of the genome wide levels of codon bias
among Drosophila species suggests that the differences found among the levels of
codon bias could be attributed to differences in effective population size [1]. Given all
this evidence indicating differences in effective population size among Drosophila
12
species in addition to the results presented in the first section of this study, we consider
that the differences in the levels of synonymous polymorphism found here could be
indicative of differences in effective population size.
Selection for preferred codons
We found a slight but significant correlation between synonymous
polymorphism and the whole-genome ENC estimates [1] in eight of the twelve species
with sequenced genomes (rSpearman = -0.14956, p = 0.0003, N = 594; Figure 2). The
regression between the codon bias (dependent variable) and the πs (independent
variable) was significant (r2 = 0.0371, F(1,538) = 20.7; p<0.0001; N = 540), showing that
nearly 3.7% of the variation in the levels of codon bias is due to differences in πs. This
result can be interpreted in terms of nearly neutral theory. In addition to the differences
detected in the patterns of mutations and selection on synonymous sites in Drosophila
species [38], the coefficients of selection for preferred codons are very weak (Nes ≈ 2
for D. simulans and D. sechellia [3, 38]). Therefore, if the differences observed in the πs
levels among species are due to differences in effective population size, the expected
correlation between mean πs and ENC will be negative, since ENC is an inverse
measure of codon bias. Thus, weak positive selection seems be more effective in species
with larger effective population size. Vicario et al. [1] have showed that the levels of
codon bias between the twelve genome-sequenced species of Drosophila are
significantly different. They interpreted their results in terms of differences in effective
population size among the Drosophila species. Therefore, the results presented here
provide quantitative evidence in favour of Vicario et al.’s [1] hypothesis that the
differences in observed levels of codon bias are due to differences in effective
population size.
Genome content
Lynch and Conery [7] showed that many of the features of complex genomes
could be initiated as a non adaptive process due to reductions in effective population
size. Following their analysis strategy, we have tested their hypothesis using the data
from the sequenced genomes of Drosophila and our estimates of 4Nes (πs). We found
that πs is negatively correlated with the percentage of repetitive sequences in the
genomes, which is an approximate measure of the content of transposable elements in
the genomes (Table 6). This correlation does not seem to be biased either by the dataset
or by the estimation of the percentage of repetitive sequences in the genomes.
According to Lynch and Conery’s [7] interpretation, in species with low effective
13
populations size, selection is ineffective against mildly deleterious insertions into
introns or insertions of transposable elements, which increase genome size. Bosco et al.
[44] showed that the differences in genome size among species are mainly due to the
differences in repetitive DNA satellite contained in the heterochromatin. The two
estimates of percentage of repetitive sequences analysed in this study are based on the
euchromatic portion of the genomes and may be underestimations. Moreover there are
differences in the two measures of percentage of repeats in the genomes considered
here. This variation is noticeable mainly in dmel, dsec and dvir (Figures 3 a-d).
According to the citometric measures of the genome size and the results of Bosco et al.
[44], the percentage of repeats calculated based on the number of the base pairs that are
repeat as determined by RepeatMasker (%repeat 1; see methods) seems be
underestimated for dvir and overestimated for dmel and dsec. In addition, although there
is evidence of a small effective population size for D. sechellia (Figure 3) [6]; the very
low levels of synonymous polymorphism estimated by this specie could be biased by
the genes analysed in this study. Thus, our results agree with Lynch and Conery [7]
results showing that genetic drift could play a crucial role in genome evolution. Genome
size seems to increase by insertions of sequences that are nearly deleterious, which are
effectively eliminated by natural selection or fixed by genetic drift, depending of the
population effective size.
The results of the correlations between synonymous polymorphism and the
percentage of coding and intron sequences in the genomes were more difficult to
reconcile between the two analysed datasets. Although there seems to be a trend
towards a positive association between the proportion of coding sequences in the
genomes and πs, it seems to be weak.
Estimates of genome content are currently rough and unfinished for the bulk of
the species analysed, an improvement in these estimates and data from synonymous
polymorphism based a higher number of genes and of the 4 species lacking in this study
would help to sort out these relationships in more detail.
Adaptive selection
Selection efficiency, defined as the effectiveness of natural selection in
determining the fate of a mutation, depends on the product of effective population size
and the selection coefficient (Nes, [8]). Therefore, for two species differing in
population size and with similar selection coefficients, we expected to find more
14
evidence of positive selection in the species with larger Ne. Bakewell et al (2007) found
evidence of this assumption when analyzing the genome of chimpanzees and humans.
The results of the comparisons of the proportion of adaptive substitutions presented in
Table 7 suggest a similar interpretation for Drosophila species; that is, species with
higher levels of effective population size have a higher proportion of adaptive
substitutions.
However, the results presented in Table 7 are based on comparisons of a small
number of genes and thus, these are only suggestive but not conclusive. The results in
Table 7 show the same trend as those in the other two analyses of selection efficiency,
showing that in species with higher levels of synonymous polymorphism (Table2)
adaptive selection seems be more frequent. An exception is the comparison between D.
melanogaster and D. miranda (Table 7), but this seems to be affected by the genes
analyzed (Supplementary Results, Table 6).
As the comparisons including D. melanogaster and D. simulans and other
species were made with genes sampled in non-African populations the differences
found in  could not be due to differences in long term effective population size.
Changes in population size might affect the proportion of mutations that are effectively
neutral [45]. Therefore, the demographic process may have a major influence in
selective constraint, violating the assumption of McDonald and Kreitman’s test [45, 46]
of the constant constraint across the lineages [41, 45, 47]. The  could be
overestimated when the current population size is larger than in the past [41, 45]. This is
because slightly deleterious mutations that have been fixed in the past are not longer
segregating as polymorphism [41]. However, the population size of D. melanogaster
and D. simulans seem to have declined since their expansion out of Africa [4, 19, 40].
In fact, the levels of synonymous polymorphism found in this study for African
populations are higher than non-African population for this two species (Table2).
Therefore, the  for these two species could be underestimated rather than
overestimated, when compared with other species [41]. However, the differences found
in  between D. melanogaster and D. simulans might be affected by the demographic
histories of these two species.
Estimations of the proportion of adaptive substitutions from D. melanogaster
and D. simulans have been made in several studies [13, 16, 17, 19, 47]. Moreover, a
comparison of the proportion of adaptive substitutions in D. melanogaster and D.
simulans were widely evaluated, compared and discussed by Bierne and Eyre -Walker
15
[41], taking into account differences in linkage disequilibria in the genes analyzed and
demographic histories. Our estimations of  from these two species in the comparison
from non-African populations are similar to the obtained by them and other authors [17,
41, 47]. Fay et al. [13] found that the estimates of adaptive substitutions in D.
melanogaster could be underestimated by the slightly deleterious mutations segregating
as polymorphism. We do not excluded singletons of our analysis; however, the 
calculated for D. melanogaster in the lineage D. melanogaster-D. yakuba was similar to
the calculated by Bierne and Eyre-Walker excluding singletons [41] and Shapiro et
al.[17] in a larger dataset. It is possible that many of the singletons were lost in our D.
melanogaster dataset by calculating the number of polymorphic sites equalling the
number of samples with D. simulans (see Methods). Although differences in
synonymous polymorphism for 23 genes of African populations of D. melanogaster and
D. simulans are not significant (Table 2), the level of synonymous polymorphism in D.
simulans is still higher than in D. melanogaster. The  calculated for 21 of these genes
was similar to that obtained for non-African populations ( A_dmel = 0.33; A_dsim
= 0.53). This result seem be contradictory under our hypothesis having into account a
recent result indicating no differences in effective population size between African
populations of D. melanogaster and D. simulans [27]. However, these estimates of 
are based on a small number of samples. More accurate estimates of synonymous
polymorphism and  from African population of these two species would be necessary
to sort out what are the exact differences between the long term effective population
sizes of these two species.
Conclusions
This study presents a multispecies analysis of the levels of synonymous
polymorphism among Drosophila species. Differences detected in the levels of
synonymous polymorphism among species are not greatly affected by differences in
gene functions, phylogenetic relationships among species or demography. Although we
cannot demonstrate that the differences in the levels of synonymous polymorphism are
true differences in long-term effective population size, it seems a likely possibility.
These differences in effective population size could have implications on the molecular
evolution of Drosophila species, as suggested by the analysis testing differences in
selection efficiency. 1) The differences in whole-genome estimates of codon bias among
16
Drosophila species are correlated with the differences in the levels of synonymous
polymorphism, indicating that weak selection is possibly more effective for larger
estimated effective population size. 2) We found evidence that the possible nonadaptive
fixation of sequences in the genomes is negatively correlated with levels of synonymous
polymorphism of the species, indicating lower efficiency of the natural selection in
species with lower effective population size. And 3) Species with lower synonymous
polymorphism seem to have smaller proportions of adaptive substitutions in ortologue
genes. Overall analyses show that Drososphila species may have several different
effective population sizes, which seem to affect the patterns of molecular evolution of
these species.
Methods
Data
Data on synonymous polymorphism (πs[25]) and Tajima’s D [34] by gene, were
obtained from DPDB (Drosophila Polymorphism Data Base http//dpdb.uab.es, [23]). To
get reliable estimates of πs, the available data of the database was filtered according to
the following criteria:
1. Only species with more than five analyzed genes and πs estimates based
on 4 or more sequences were considered
2. Alignments with total gap length or length differences larger than 30%
were discarded.
3. Genes from transposable elements (gag, pol, etc.) and pseudogenes were
discarded (N=9).
The filtered dataset consisted of 754 estimates of πs, belonging to 482 different
genes of 15 Drosophila species (D. americana, D. arizonae, D. kikkawai, D.
mauritiana, D. melanogaster, D. miranda, D. mojavensis, D. persimilis, D.
pseudoobscura, D. santomea, D. sechellia, D. simulans, D. subobscura, D. virilis and
D. yakuba) and of four different groups of species (melonagaster, obscura, virilis and
repleta). The estimates of πs in the dataset were based on alignments with an average of
16.22 sequences and 1,181.62 analysed sites, with an average and standard deviation
values of 0.0165 and 0.0063, respectively. Although, we do not have information on the
chromosomic location of all the genes analysed from the different species, the
17
distribution of the ortologue genes in the D. melanogaster genome seems be random
(almost the same number of genes from different chromosome arms).
The genes in the dataset were classified according to their category in the Gene
Ontology, (Biological Process at level 3); using the web site FatiGO
(http://fatigo.bioinfo.cipf.es, [48]). The ten most well-represented categories in the
dataset were used to group the genes by their functions: anatomical structural
development (15.5%); cell communication (16.5%); cellular metabolic process (53.1%);
cellular organization and biogenesis (14.1%); cellular developmental process (15.9%);
macromolecule metabolic process (46.8%); multicellular organismal development
(26.3%); primary metabolic process (50.4%); regulation of biological process (27.3%)
and sexual reproduction (10.9%)
Genomic codifying sequences of D. yakuba, D. melanogaster , D mojavensis
and D. pseudoobscura were retrieved from precomputed alignment of genome
sequences from Vista Genome Browser (http://pipeline.lbl.gov/cgi-bin/gateway2 , [49]).
Genome data: genome size, total coding and intron sequences and repeat
coverage were obtained from [21]. The percentages of coding and intron sequences in
the genomes were calculated as the total coding or intron sequences on the total genome
size. Two measures of total genome size were considered, one calculated as the sum of
the base pairs in genomic scaffold>200,000bp [21] and the other based on flow
citometry [44].
The total percentage of repeats in the genomes was obtained from the UCSC
web page (http://genome.ucsc.edu/, [50]) as calculated with RepeatMasker.
The total genomic codon bias was obtained from Vicario et al.[1] as measured
by ENC (Effective Number of Codons; [51]).
Analysis
The variable πs is not normally distributed; therefore, the values of πs were
transformed to natural logarithm, thereby obtaining a normally distributed dataset which
allowed us to perform parametric analyses (ANOVAs and Regression analysis).
Since different genes are included in the 15 analysed species, any difference
found among species could be artifactual due to this gene heterogeneity. To take into
account this potential source of error, several different approaches were used:
1.
A two-factor analysis of the variance (unbalanced) was carried
out to test if the observed differences in πs values (dependent variable) were due
18
to species or genes. Since the data of D. melanogaster alone account for 40% of
the dataset (Figure 1), the same analysis was performed excluding the data of
this species.
2.
Both, post hoc Tukey test for log transformed data and two tailed
paired T tests (for ortologue genes in pairs of species), were carried out to test all
pairwise differences among species.
Differences in selective constraint. The various different selective constraints
of the genes belonging to different functional categories were tested by ten unbalanced
analyses of variance with the underlying model: πs = species + GO. Where GO is a
component that classifies the genes as belonging or not to one of the ten categories of
biological process at level 3.
Phylogenetic relationships among species. The potential effect of the
phylogenetic relationship among the species was tested by a fully nested ANOVA
grouping the species in Subgenus and groups of species ( Subgenus: Sophophora and
Drosophila and groups: melanogaster, virilis, repleta or obscura). Nested ANOVAS
were also performed for ten datasets grouping the data by their function (categories of
biological process at level 3).
Deviations from neutral equilibrium. Differences in the values of Tajima’s D
statistic were used to test possible differences in deviations of neutral equilibrium. The
differences in Tajima’s D statistic were tested by an unbalanced ANOVA, Tukey test
and paired T test for ortologue genes of pairs of species. Coalescent simulations were
performed with intermediate values of recombination using DNAsp [52], giving the
mean values of segregating sites, samples and number of sites.
Testing the hypothesis that selection efficiency is positively correlated with
effective population size.
Correlations and regression analyses were performed between each genome
variable (genome size, percentage of genic sequences, percentage of intron sequences,
percentage of repeat sequences and total ENC) and πs. Bootstrapping of the correlations
was performed using Resampling software from D. C. Howell.
The proportion of average proportion ( ) and average number of adaptive
substitution (ā) was estimated following the equations 1 to 3 of Smith and EyreWalker[16]
19
The numbers of synonymous polymorphic sites (Ps), no synonymous
polymorphic sites (Pn), synonymous fixed changes (Ds) and nonsynonymous fixed
changes (Dn) were calculated, using DNAsp [52] and the website http://mkt.uab.es. To
enable comparisons between the ( ) between a pairs of species the gene alignments of
the species were aligned with a genomic codifying sequences of a specie external to the
tested lineage, using Muscle [53]. Thus, the comparisons among dsec-dmau, dsec-dmel,
dsec-dsim and dmel-dsim were made by calculating the number of divergent sites (Dn
and Ds) from D. yakuba. In the comparison between the pair of species dsim-dyak the
divergence was calculated from D. pseudoobscura ; in the comparison of dmir-dpse the
divergence was calculated from D. melanogaster; and in the comparison between
obscura species group versus melanogaster species group the divergence was calculated
from D. mojavensis. A possible bias can arise in the estimation of the number of
polymorphic sites (Pn and Ps) by a different number of sequences in the genes being
compared between pairs of species. Therefore, to avoid this bias the number of
sequences was equalled in each gene-to-gene comparison. All the alignments were
revised by eye and are available from the authors on request. The list of genes compared
for each species pair is shown in Table 5 of the Supplementary Results. Differences in
the number of genes compared in Table 2 and Table 7 are due to bad quality alignments
or lack of ortologue sequences to calculate divergence. Confidence intervals of  were
obtained by bootstrapping the values of α by gene.
All statistical analysis were made using SAS 9.1 statistical program (SAS
Institute Inc.).
Authors' contributions
All authors conceived this project and drafted the manuscript. NP carried out the data
searching, filtering and analysis. All authors have interpreted and discussed the results,
read and approved the final manuscript.
Acknowledgements
The authors would like to thank Josefa Gonzalez and three anonymous reviewers for
their useful comments and Raquel Egea for corrections on the first version of the
manuscript. This work was funded by the Ministerio de Educaciòn y Ciencia (Grant
20
BFU-2006-08640). N.P. was supported by a grant from the Departament de Genètica i
Microbiologia of the Universitat Autònoma de Barcelona.
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Table 1: Mean and standard deviation of the synonymous polymorphism and
Tajima’s D for the 15 species of Drosophila analyzed.
Mean ± Std dev. Mean ± Std dev of
πs
Tajima's D
Species N genes
8
0.030 ± 0.021
0.087 ± 0.633
dkik
17
0.019 ± 0.013
0.150 ± 0.882
dmau
305
0.016 ± 0.132
-0.331 ± 0.829
dmel
16
0.008 ± 0.015
-0.497 ± 0.611
dsan
11
0.005
±
0.009
-0.314 ± 0.879
dsec
174
0.022 ± 0.018
-0.134 ± 0.814
dsim
26
0.014 ± 0.015
-0.870 ± 0.645
dyak
42
0.008 ± 0.007
-0.378*± 0.962
dmir
6
0.017 ± 0.011
-0.631 ± 0.438
dper
20
0.022 ± 0.016
-0.620 ± 0.830
dpse
10
0.021 ± 0.016
-0.771 ± 0.712
dsub
42
0.017 ± 0.016
-0.330 ± 0.823
dari
45
0.016 ± 0.012
-0.244 ± 0.994
dmoj
25
0.019 ± 0.010
-0.571 ± 0.593
dame
7
0.012 ± 0.009
-0.482 ± 1.021
dvir
* without data from genes of the Neo-X chromosome (see text)
Table 2: Results of the analysis of variance of two factors testing the interaction
between genes and species factors, with and without data from D. melanogaster. The
dependent variable was the natural logarithm of πs.
Source
species
gene
species*gene
DF
14
452
205
all data
F
6.13
1.18
0.69
Pr >F
0.0049
0.4265
0.8343
without D. melanogaster
DF
F
Pr >F
13
16.84
0.0073
283
3.47
0.1147
94
1.69
0.3299
24
Table 3. Differences of the values of πs between pairs of species evaluated by
paired T test for ortologues genes. Significant differences are bold faced.
Mean
sp1
dame
dame
dari
NA_dmel
A_dmel
dmel
dmel
dmel
dmel
dmel
dmel
dmel
dmir
dmir
dper
dpse
dsec
dsim
dsim
dsim
dsim
a
b
sp2
dsim
dvir
dmoj
NA_dsim
A_dsim
dsec
dpse
dmir
dkik
dame
dmau
dyak
dper
dpse
dpse
dsim
dmau
dsec
dmir
dsan
dyak
N genes
6
6
42
91a
23b
11
9
10
10
6
16
10
5
9
5
10
11
11
10
5
9
Mean
πs sp1 πs sp2 p T test
0.018
0.015
0.017
0.013
0.015
0.017
0.012
0.011
0.015
0.014
0.019
0.012
0.008
0.009
0.013
0.021
0.006
0.026
0.023
0.010
0.021
0.025
0.474
0.009
0.123
0.016
0.796
0.020 0.00003
0.032
0.063
0.006
0.002
0.020
0.021
0.006 0.083 *
0.025
0.105
0.019
0.061
0.019
0.790
0.010
0.399
0.017
0.128
0.015 0.066*
0.018
0.190
0.027
0.522
0.021
0.032
0.006
0.007
0.006
0.006
0.025 0.109*
0.008
0.045
Data from Non African populatios of these two species
Data from African populations of these two species
* Species with means levels of πs significantly different by Tukey test.
25
Table 4. Results of ten analyses of variance of the model πs = species + GO. GO is a component sorting genes as belonging or not to one
of ten different categories of biological process at level 3 of the Gene Ontology. The dependent variable is the natural logarithm of πs.
anatomical structural development
cell communication
cellular metabolic process
cellular organization and biogenesis
cellular developmental process
macromolecule metabolic process
multicellular organismal development
primary metabolic process
regulation of biological process
sexual reproduction
Species
component
F (14, 619)
p
<0,0001
6.09
<0,0001
6.09
<0.0001
6.20
<0.0001
6.07
<0.0001
6.08
<0.0001
6.21
<0.0001
6.11
<0.0001
6.24
<0.0001
6.10
<0.0001
6.11
GO
component
F(1, 619)
p
2.19
0.1398
2.16
0.1421
0.0004
12.90
0.08
0.7750
1.28
0.2582
0.0002
14.14
3.83
0.0509
<0,0001
17.10
2.85
0.0921
0.0366
4.36
Table 5. Results of the fully nested analysis of variance, grouping the species by their phylogenetic relationship (subgenus and groups).
The dependent variable was the natural logarithm of πs.
Source
DF
F
p
Var Comp. % of Total
Subgenus
1
0,64
0,507042
-0.040*
0
Group
2
1,05
0,383304
0,02
1,48
6,70E-10
Species
11
6,30
0,14
14,2
Error
604
0,83
84,31
Total
618
0,98
* Value of the component of variance is negative, and the% of the total is estimated by zero.
26
Table 6. Correlation and regression analyses between πs and genome variables. In the regression analysis, the independent variable
was the natural logarithm of πs, and significance was calculated based on the F statistic of the ANOVA of the regression. Significant
values are bold faced. The confidence interval at 95% were calculated by bootstrapping of the correlations.
Dataset 2
All data
R Spearman
(p) N=594
R2
F(1,538)
(p)
R Spearman (CI 95%)
(p ) N=175
R2
F(1,172)
(p)
perc_repeat1‡
-0.175
(<.001)
0.059
33.85
(<0.001)
-0.239 (-0.338 - -0.061)
(0.001)
0.048
8.83
0.003
perc_repeat2†
-0.154
(<0.001)
0.025
13.91
(0.001)
-0.148(-0.29 - -0.031)
(0.049)
0.016
2.49
0.061*
perc_coding1a
0.113
(0.0059)
0.043
24.35
(<0.001)
0.020 (-0.121 - 0.215)
(0.585)
0.006
0.99
0.322
perc_intron1a
-0.123
(0.0031)
0.005
2.53
(0.112)
-0.091 (-0.103 - 0.159)
(0.243)
0.001
0.51
0.477
perc_coding2b
0.1273
(0.002)
0.021
11.36
(<0.001)
0.069 (-0.127 - 0.131)
(0.267)
0.007
1.78
0.183
0.050 (-0.113- 0.126)
0.009
1.39
0.106
0.010
5.27
(0.533)
0.237
(0.001)
(0.022)
‡ percentage of the base pairs in the total genome that are repeats, as detected by repeatmasker (UCSC, [50] )
† repeat coverage in percentages, calculated as the fraction of scaffolds >200kb covered by repeats [21]
a percentage of coding and intron sequences, calculated as total coding and total intron sequence from [21]divided by genome size estimated as the sum of the base pairs in
genomic scaffold>200,000bp [21]
b percentage of coding and intron sequences, calculated as total coding and total intron sequence from [21] divided by genome size estimates based on flow citometry [44]
* the regression is significant without data of dsec (F (1,147)= 4.58; p= 0.033; see Figure 3 d)
perc_intron2b
27
Table 7. Differences in the proportion of adaptive substitutions among species of Drosophila with evidences of differences in the levels
of πs from Table 2.
Species
group
Species Species N genes Avg(Pn/Ps+1)a Avg Dn/ Avg Dsa
1
2
sp1 - sp2
sp1 - sp2
a
sp1 - sp2
Dnb
sp1 - sp2
χ2
p
Species
Divb
dmel
dsec
10
0.66 - 0.82
1.50 - 1.09
0.56-0.25
277 - 345
42.27 <0.00001
dyak
dsim
dsec
10
0.61 - 0.82
1.36 - 1.09
0.55-0.25
431 - 345
76.87. <0.00001
dyak
dmau
dsec
10
0.75 - 0.82
1.42 - 1.09
0.47-0.25
342 - 345
25.98 <0.00001
dyak
dmel
dsim
88
0.58 - 0.59
0.82 – 1.02
0.30-0.43
2858 - 3202
98.44 <0.00001
dyak
dyak
dsim
dpse
dmir
0.30 - 0.23
0.16 - 0.38
(0.21*) - (0.22*)
0.38 - 0.35
0.20-0.35
346 - 283
27.98 <0.00001
0.35 - 0.54
0.54 -0.30
95 - 204
(0.35*) - (0.23*) (0.40*)-(0.04*) (84*) - (44*) 16.02 0.00006
dpse
obscura
8
8
(6*)
obscura
dmel
dpse
5
0.05 - 0.05
0.22 - 0.33
0.76-0.85
99 - 103
vs
dsim
dmir
8
0.04 - 0.19
0.30 - 0.30
0.87-0.37
284 - 237
144.11 <0.00001
dmoj
dmel
dmir
6
0.17 - 0.11
0.42 - 0.27
0.58-0.62
85 - 100
0.351
dmoj
melanogaster
melanogaster
a
Average value of Pn divided Ps+1; average value of Dn divided average value of Ds and
2.92
0.087
0.553
dmel
dmoj
 calculated following Smith and Eyre-Walker[16] as 1- (Avg(Pn/Ps+1)
/(Avg Dn/ Avg Ds)) (see methods)
b
The divergence values were calculated from a species external to the tested lineage.
*
without data of the genes from the Neo X chromosome (see text)
28
Figure legends
Figure 1. Mean and standard error of πs for the 15 studied species of Drosophila.
Figure 2. Scatterplot of the relationship between codon bias (whole-genome ENC) and
πs.
Figure 3. Scatterplot of the relationships between πs (a and c), mean πs (b and c) and the
percentage of repetitive sequences estimated from genomes of Drosophila species. a)
and b) percentage of the base pairs in the total genome that are repeats, as detected by
repeatmasker in the web siteUCSC, [50]. c) and d) repeat coverage in percentages,
calculated as the fraction of scaffolds >200kb covered by repeats [21].
29