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Recombination and base composition: the case of the highly self-fertilizing plant Arabidopsis thaliana



Rates of recombination can vary among genomic regions in eukaryotes, and this is believed to have major effects on their genome organization in terms of base composition, DNA repeat density, intron size, evolutionary rates and gene order. In highly self-fertilizing species such as Arabidopsis thaliana, however, heterozygosity is expected to be strongly reduced and recombination will be much less effective, so that its influence on genome organization should be greatly reduced.


Here we investigated theoretically the joint effects of recombination and self-fertilization on base composition, and tested the predictions with genomic data from the complete A. thaliana genome. We show that, in this species, both codon-usage bias and GC content do not correlate with the local rates of crossing over, in agreement with our theoretical results.


We conclude that levels of inbreeding modulate the effect of recombination on base composition, and possibly other genomic features (for example, transposable element dynamics). We argue that inbreeding should be considered when interpreting patterns of molecular evolution.


Recombination is probably a key factor in the evolution of genome organization in species such as yeast, mammals, Drosophila and C. elegans. In these species, genomic features such as nucleotide polymorphism [14], GC content [1, 58], codon bias [6, 9], intron size [10, 11], transposable element density [1214] substitution rates [1517] and gene order [18] vary widely within the genome, and are correlated with the local rate of crossing over. These observations are often explained as the result of various processes such as selective sweeps, background selection and weak Hill-Robertson interference (wHR), which all cause a reduction in the efficacy of natural selection in regions of reduced crossing over [1921].

Rates of crossing over have been shown to correlate not only with the GC content of synonymous sites, where weak natural selection is expected to act on codon-usage bias, but also with the GC content of noncoding sites [6, 22]. This is unlikely to be because GC bases are recombinogenic, as the correlation is far stronger with silent DNA than with total DNA [8]; see also [23]. This unexpected correlation may reflect the action of weak selection on noncoding GC, which would be less effective in regions of reduced recombination [24]. Alternatively, it could be an effect of biased gene conversion [8, 25, 26]. Biased gene conversion (BGC) is a process that preferentially converts A/T into G/C at sites heterozygous for AT and GC. The net effect of BGC is to increase the GC content of recombining DNA sequences. Assuming that the rate of this process is correlated with the rate of crossing over, BGC could therefore generate the observed increase in GC content in regions of high crossing over. An excess of AT→GC mutations in regions of high recombination could also lead to the observed correlation between GC content and recombination [27]. The relative importance of BGC, mutational biases, and wHR in driving these patterns remains unresolved [22, 28], although BGC may be the most likely explanation, especially in organisms such as yeast and mammals, where there is a strong correlation between recombination and GC content [7].

To date, most analyses of the role of recombination in determining genome structure have been done on outcrossing species, with the notable exception of the presumably partial selfer C. elegans [6], whose selfing rate is not precisely known. In contrast, less attention has been given to Arabidopsis thaliana, which is known to be an almost complete selfer with a selfing rate of approximately 99% in the natural populations that have been studied [29, 30]. High levels of inbreeding, as in A. thaliana, are expected to have important effects on the genomic structuring of base composition. Inbreeding leads to a strong increase in levels of homozygosity, which reduces the effective rate of recombination [31]. Therefore, processes sensitive to recombination and homozygosity, such as the effectiveness of selection on codon usage and the strength of BGC, will be affected by the high level of inbreeding apparently experienced by A. thaliana [7, 32].

Previous work has provided evidence for a correlation between gene expression and codon bias in Arabidopsis [33, 34], although the effect is weak. This suggests that translational selection is acting on codon bias in Arabidopsis. However, on the basis of the genes studied so far, no striking difference in codon bias between A. thaliana and its outcrossing congener A. lyrata has been observed, perhaps because of the population history of these species obscuring the expected patterns of molecular evolution [35]. Here we investigate the effect of inbreeding on the evolution of base composition (GC content and codon bias) within the A. thaliana genome, both theoretically and by DNA sequence analysis. Our goal was to test for an effect of recombination on GC content and codon bias in A. thaliana, and to use models to examine the joint effects of recombination and inbreeding on selection on codon usage and BGC, in order to help us to interpret the results from genome analysis. We show by computer simulation and modeling that selection on codon usage is not expected to vary with local recombination rate in a highly inbred species and that BGC is expected to be ineffective compared with an outcrossing species. We show that these predictions are consistent with the results of our analysis of the A. thaliana genome. We find no association between the local rate of crossing over and either codon bias or GC content (for both coding and noncoding regions).


Recombination and codon usage

Previous simulation results have shown that, in outcrossing species, weak selection on codon usage is expected to be significantly reduced in genomic regions with low rates of crossing over because of wHR effects [21, 36, 37]. We modified the model of [21] by adding one additional parameter, the selfing rate S (see Materials and methods). The results for S = 0%, 50% and 99% for several values of the population recombination rate 4N e r (where N e is effective population size and r is the per base rate of recombination) and two values of the strength of selection 4N e s (where s is the selection coefficient) are presented in Figure 1. The effect of crossing over on the efficacy of selection on codon usage decreases strongly with S. For S = 99%, which is probably close to the true value for A. thaliana [29, 30], virtually no effect of crossing over is observed. This result reflects the strong reduction in the range of effective rates of recombination present in a selfing genome; high levels of homozygosity dramatically reduce the effective rate of recombination [31], and therefore a given difference in r between two genomic regions will produce much greater differences in effective recombination rates in an outcrosser than in a selfer. Therefore, the theory predicts very weak or no associations between selection on codon usage and the rate of crossing over in A. thaliana. Results with intermediate selfing rates of 50% show a similar effect of recombination to that with complete outcrossing, suggesting the presence of a threshold level of inbreeding that leads to uncoupling of recombination from codon bias evolution.

Figure 1

Simulation results showing the effect of recombination on the effectiveness of selection for codon usage under inbreeding. The figure shows the relationship between the recombination parameter 4N e r and the frequency of the optimal codon (F op ) for selfing rates (S) of 0 (black bars), 0.5 (gray bars) and 0.99 (white bars). (a) Value of the strength of selection 4N e s = 1; (b) 4N e s = 4. All simulations involved 1,000 sites where 4N e u = 0.04.

In A. thaliana, selection on codon usage seems to be relatively weak [34]. Thus, it is quite hard to identify the so-called 'optimal' codons, which are preferentially retained by translational selection. In other species such as Drosophila and C. elegans, optimal codons have been shown to correspond to the most abundant tRNAs in cells [38, 39]. Using tRNA gene number as a proxy for tRNA expression, we redefined the list of optimal codons in A. thaliana as those corresponding to major tRNAs (S.I.W, C.B.K.Yau, M. Looseley, and B. Meyers, unpublished data). The frequency of these newly defined optimal codons (hereafter denoted F op ) is more strongly correlated with the level of gene expression than was the case in previous work (Spearman rank coefficient R s = 0.26 with p < 10-4). This is consistent with the idea that this new index better captures translational selection on codon bias than do previous ones.

We then compared selection on codon usage measured by F op with the rate of crossing over estimated from the comparison of genetic and physical maps for each chromosome arm (see Materials and methods). Figure 2 shows that there is no relationship between these parameters, with F op being equal to approximately 0.49 throughout the genome. R s = -0.02 with p < 10-4. Although the p value is highly significant, the correlation coefficient implies that only 0.04% of the variability in F op is explained by the rate of crossing over, and the p value reflects the large number of genes used in the analysis. There is thus virtually no genome-wide correlation between F op and the rate of crossing over. It has been noted previously for other species that the genome-wide correlation between codon bias and recombination is weak, although large differences can be observed among chromosomes or chromosomal regions with very different rates of crossing over [7]. This effect may result from poor map-based point estimates of recombination for any given locus, while global averages are much more reliable, as well as other causes [7, 22]. In A. thaliana, as in many species, crossovers are suppressed near centromeres [40]. If we compare the centromeric regions with the remainder of the genome, we find at most a 1% difference: in centromeric regions, F op = 0.477 (n = 2005), and in the other regions, F op = 0.486 (n = 13,243). In contrast, comparisons of centromeric regions with other genomic regions in Drosophila show striking differences, which are larger than 20% [6]. Taken together, these observations suggest that selection on codon usage does not vary with the rate of crossing over in A. thaliana, in agreement with the theory.

Figure 2

Genome-wide relationship between the frequency of optimal codons (F op ) and the rate of crossing over in A. thaliana. Each crossing-over bin contains approximately 10% of the genes of the dataset. See Materials and methods section for details of the measurement of F op and the rate of crossing over, in centimorgans per megabase (cM/MB).

Recombination and GC content

BGC can be seen as a sort of meiotic drive, in which GC gametes are favored over AT gametes [41]. As high levels of inbreeding are associated with a strong decrease in heterozygosity, the strength of BGC should be dramatically reduced in inbreeders, because BGC can occur only in heterozygotes. The expected change in GC content due to BGC in the case of inbreeding can be derived straightforwardly from population genetics theory (see Materials and methods for details). By using standard diffusion equations modifed for inbreeding one can obtain the GC content at equilibrium under BGC, mutation, drift and inbreeding (see Materials and methods for details). The GC content at equilibrium (p*) depends on the effective population size (N e ), the mutational bias (α = u/v, where u is the mutation rate from GC→AT and v the reverse mutation rate), the coefficient of BGC (ω), and the selfing rate (S) (see Materials and methods).

In Figure 3, we plot the expected values of p* according to the scaled measure of the strength of BGC (4N e ω with different mutational biases (α) and selfing rates (S)). In Figure 3a, we show that BGC has little effect on expected GC content in highly selfing populations (S = 0.99) compared to outcrossing populations (S = 0), regardless of the strength of BGC. This means that, in a highly selfing population, genomic regions with high recombination and thus high BGC (high ω) are expected to have a very similar GC content to genomic regions with low recombination and thus little or no BGC (low or null ω). Figure 3b shows that a slight difference between such genomic regions can be observed in a partial selfer, with S = 50%, for example. In A. thaliana, where S has been estimated to be approximately 0.99 [29, 30], the average GC contents for introns, 5' flanking regions and 3' flanking regions are 32.1%, 32.7% and 32.5%, respectively, with an overall mean of approximately 32%. Thus, a mutational bias of 2 (that is, u/v = 2) describes well the average GC content of noncoding DNA (see Materials and methods for details). This suggests that the results obtained with S ~1 and α = 2, are probably the closest to reality in A. thaliana. With these parameters, Figure 3 shows that no effect of BGC on GC content is expected.

Figure 3

The effect of inbreeding on biased gene conversion (BGC). (a) The consequences of BGC for GC content for outcrossing populations (S = 0) and highly self-crossing populations (S = 0.99). The results are shown with no mutational bias (u/v = 1) or with a mutational bias towards AT (u/v = 2). (b) The GC content expected for various selfing rates with no (4N e ω = 0), moderate (4N e ω = 1) or strong (4N e ω = 5) BGC. The mutational bias was set to 2. See Materials and methods for details of the model.

In Figure 4, we plot the GC content for third codon positions of coding DNA (GC 3 ) and intron DNA (GC 3 ) against the rate of crossing over in A. thaliana. No significant correlations between GC 3 and GC i with recombination is observed. The correlation coefficients are very weak for both GC 3 (R s = -0.03 with p = 0.0002) and GC i (R s = -0.04 with p = 0.01). In both cases, less than 0.2% of the variability in GC content is explained by recombination. Again, we checked for a difference between centromeric regions and the remainder of the genome. In centromeric regions, GC 3 = 41.4% and GC i = 32.3%, and in the other regions GC 3 = 43.3% and GC i = 32.8%. Thus, we observe a 2% difference for GC 3 and a 0.5% difference for GC i . Again these differences have a p value lower than 0.05 (with a nonparametric Kolmogorov test) but these minor differences may have no biological meaning. In contrast, the corresponding difference in GC content in Drosophila is as large as 20% for coding and 5% for noncoding DNA [6]. Our results from the genome analysis thus seem to be in agreement with theory.

Figure 4

Genome-wide relationship between GC content and the rate of crossing over in A. thaliana. (a) Silent DNA (GC 3 ); (b) intron DNA (GC i ). Each crossing-over bin contains approximately 10% of the genes of the dataset. See Materials and methods for details of the measurement of GC content and the rate of crossing over, in centimorgans per megabase (cM/MB). The results are the same if only genes with total intron size greater than 100 nucleotides (or with larger cutoff values) are included.


Base composition in inbreeders versus outcrossers

Our genome analysis suggests that recombination has little effect on base composition in A. thaliana. Neither codon usage bias nor GC content are correlated with the local rate of crossing over. Our theoretical work suggests that, first, selection on codon usage is not expected to vary with crossing over in highly inbred species and, second, that BGC is inefficient in highly inbred species. We also expect the global efficacy of selection on codon usage to be lower in inbred than in outbred species (see Figure 1). Interestingly, the level of codon usage is low in A. thaliana and high in Drosophila [34], in agreement with the respective levels of outcrossing in these species. Subsequent comparisons of selfing versus outcrossing Arabidopsis species have, however, shown a less clear pattern [35].

The budding yeast Saccharomyces cerevisiae is thought to have a high level of inbreeding in natural populations [42], and recent high estimates of the inbreeding coefficient from a population of the close relative S. paradoxus [43] suggest that long-term rates of selfing may be high. One might therefore expect a similar pattern in the genome of S. cerevisiae to that observed in A. thaliana. Although there is no evidence for a strong effect of recombination on the rate of protein evolution once gene expression is controlled for [44], recombination rates are strongly correlated with GC content in yeast [8]. However, in contrast to A. thaliana, yeast has exceptionally high rates of recombination, approximately 100-fold higher than the multicellular model systems Arabidopsis, Drosophila and C. elegans [7]. This may counteract the reduction in effective recombination rates caused by inbreeding, to such a degree that the strength of BGC may be significant. Indeed, a study of nucleotide variation at a prion-like gene in S. cerevisiae estimated a similar effective rate of recombination to that in Drosophila [45], despite possibly high rates of inbreeding in budding yeast.

In C. elegans, the level of codon bias is intermediate between that of A. thaliana and Drosophila [34], and there is also a significant correlation between crossing over and GC content in this species [6]. This is puzzling, in view of the low levels of genetic diversity (suggesting a low effective population size) [46, 47] and the high levels of linkage disequilibrium (suggesting very restricted recombination due to inbreeding) [46]. There are three possible explanations for this pattern: first, C. elegans is in fact a fairly outcrossing species, and has recently suffered a population bottleneck that reduced its levels of genetic variability; second, it has only become a self-fertilizing hemaphrodite relatively recently (this possibility cannot be excluded, since we lack knowledge of its close relatives) [48]; and third, our models of the evolution of base composition are in error. Further information on the evolutionary biology of C. elegans and its relatives is needed to solve this problem.

How to explain variation in base composition

Base composition is fairly variable across the A. thaliana genome, both for codon bias and GC content (see Figure 5). Recombination does not seem to be a determinant of this variation in A. thaliana. What could be the other possible determinants? It is well known that codon bias has multiple determinants: gene expression and protein length, for instance [34]. Here, we find that a total of approximately 20% of the variability in F op is explained by gene expression (measured by expressed sequence tag (EST) or massively parallel signature sequencing (MPSS) data) and protein length (S.I.W., C.B.K. Yau, M. Looseley, and B. Meyers, unpublished data), leaving 80% to be explained. The rate of nonsynonymous substitutions per site (d N ) seems to be another strong determinant of codon bias in Drosophila [16]. However, no large-scale dataset of orthologous pairs between A. thaliana and its close relatives (required to estimate d N ) is currently available, so we cannot assess the contribution of d N to variability in F op . Both the GC content at synonymous sites and at introns are likely to be influenced by genetic drift [49]. The cumulative effects of mutation, selection (in the case of synonymous sites) and drift should generate random variation in GC content across the genome. This can be explored by looking whether the distribution of GC content over genes (see Figure 5) follows a binomial distribution. However, the differences between the expected values (estimated using the mean GC content) and the observed values were statistically significant for both GC 3 and GC i (data not shown). Variation in GC content does not seem to be fully explained by the effects of genetic drift.

Figure 5

Distribution of base composition among genes in A. thaliana.

This suggests that other factors, such as local differences in level of mutational bias, may contribute to the patterns of base composition in A. thaliana. If there are strong local effects driving the variation in GC, we would expect a strong positive correlation between GC 3 and GC i across genes, as observed in humans [26, 50] and Drosophila [51]. In contrast, we find a weak but significant negative correlation between GC 3 and GC i (R s = -0.115; p < 10-4); this is the case even when the correlation between GC 3 and codon bias is factored out (R s = -0.115; p < 10-4). This suggests that local heterogeneity in mutational bias does not explain the variation in GC, and provides further evidence against a major effect of BGC in local GC variation in this species. The uncoupling of GC 3 and GC i , and the residual variation in GC content, may result from the action of selective constraint on some intron sequences, and differences in the mutational context of introns and synonymous sites.

One important assumption of our analysis is that A. thaliana has been self-fertilizing for a sufficiently long time to remove any historical effects of recombination on base composition. This is questionable, given the fact that its closest known relatives are all obligate outcrosssers, including A. lyrata [52]. The most extreme case is complete cessation of outcrossing and complete relaxation of selection or BGC since the divergence of A. thaliana from A. lyrata. Under this assumption, Equation (4) given in Materials and methods implies that the present-day deviation of GC content of a genomic region of A. thaliana from the completely neutral value is equal to the initial deviation multiplied by exp(-(u + v)t), where t is the time since divergence. This can be related to the expected DNA sequence divergence at completely unconstrained sites (after a Poisson correction for multiple hits), which is equal to 4α vt/(1 + α) [49]. From the base composition of A. thaliana centromeric DNA (see Results), we can estimate α as 2.12, assuming that this reflects mutational equilibrium. Given that the maximum silent-site divergence between the two species is about 0.2 [35], this implies that (u + v)t = 0.23, so that the current deviation of A. thaliana GC content from the mutational equilibrium value is expected to be at least 80% of the value in A. lyrata. Conversely, this means that the maximal departure of GC content in a genomic region of A. lyrata from mutational equilibrium is at most a factor of exp((u + v)t) times that for the corresponding region of A. thaliana, that is, about 25% greater. If A. thaliana has only been highly selfing for a proportion of the time since divergence, the value will be proportionately lower. This suggests that regional variation in GC content in A. lyrata should be relatively modest, a prediction which can be tested when more genomic information on A. lyrata is available.

The influence of population subdivision

Our theoretical model of drift, selection and mutation considered only a single population, but it is known that A. thaliana has strong population structure [29, 53]. In addition, within-population silent nucleotide site diversity is very low compared with A. lyrata, but the diversity when pooled over samples from different localities is similar for the two species [53]. This indicates that the effective population sizes of local populations are very low in A. thaliana, that migration among populations is limited, and that the effective population size determining the diversity among alleles randomly chosen from the species as a whole is not greatly reduced. The relatively high total diversity also suggests that local extinction and recolonization of populations does not play a major part in controlling genetic diversity [54].

This raises the question of what measure of effective population size is appropriate for determining the base composition under BGC or weak selection. In addition to mutational bias, theory shows that base composition is controlled by the relative fixation probabilities of new mutations from GC to AT and vice versa [21, 55]. If migration is conservative (that is, the number of migrants entering each population equals the number leaving), with selection of the form of Equation (1) in Materials and methods, these fixation probabilities are controlled by the same N e as is appropriate for the mean level of neutral diversity within demes, which is the same as for a population lacking any subdivision [5658]. With nonconservative migration, rigorous theoretical results are not available, but heuristic models and computer simulations suggest that fixation probabilities will be usually be lower than with conservative migration [5961]. These considerations imply that our conclusion, that fixation probabilities in A. thaliana will be closer to the neutral values than in A. lyrata for sites affected by BGC or weak selection, is either unaffected by population structure, or is a conservative one.


We have shown that inbreeding affects base composition by modulating the effectiveness of recombination. Inbreeding has also been shown to affect the dynamics of transposable elements [32, 6264]. Taken together, these studies suggest that mating system can have a major effect on genome organization, particularly when the levels of inbreeding are high, and should be taken into account when interpreting patterns of molecular evolution. Other population parameters such as demographic history [35, 65, 66] and population subdivision [64], should also be considered when analyzing patterns of genome evolution.

Materials and methods

Genomic approach

Sequence data

We wanted to build an ACNUC database for the A. thaliana complete genome, because this allows the user to make complex queries [67]. We required the A. thaliana complete genome to be in GenBank format to do this. As far as we know, the only release available in this format is release 1 (see [68]). However, the gene predictions in this release may contain some errors. To circumvent this problem, we used 15,248 genes for which we had evidence for gene expression (EST or MPSS, see below) and whose intron-exon structure has not changed from release 1 to release 3, increasing the chance that they correspond to true genes with accurate annotations. Coding sequences and intron sequences of these genes were used for further analysis.

Recombination data

We used the rates of crossing over from a previous study [64]. These were obtained by comparisons of genetic and physical maps for each chromosome arm. A polynomial was fitted to the data and the derivative of this polynomial curve was used to estimate the local rate of crossing over as a function of the position in the chromosome arm (for details and data see [64] and [69]). We could not use a sliding window approach to estimate the local rate of crossing over because of the scarcity of genetic markers.

Codon bias

This was estimated using the frequency of optimal codons (F op ). The list of optimal codons for A. thaliana was revised (S.I.W, C.B.K.Yau, M. Looseley, and B. Meyers, unpublished data) by identifying the optimal codons as those corresponding to the major tRNAs, whose cellular concentration was estimated from tRNA gene number following [39, 70, 71]. To check for a correlation between F op and gene expression, we used EST data (as in [34]) and MPSS data (see [72]) as estimates of the level of gene expression. F op was computed with a modified version of a previously described program [34].

Theoretical approach

Hill-Robertson interference and inbreeding

Computer simulations were run following the reversible mutation, selection and drift multilocus model of [20]. The model assumes equal rates of forward and back mutation, with a population mutation rate (4N e u) of 0.04. Simulations were run assuming a scaled selection intensity 4N e s of 1 and 4, with 1,000 mutable sites, and a population size of N e = 100. Although this effective size is likely to be an underestimate of the true value for A. thaliana, the most important determinants of the level of interference are the product of the scaled mutation parameter 4N e u and the number of mutable sites, and the scaled selection coefficient 4N e s [20]. We modified the program to include the selfing rate S, where gametes are formed by random mating with probability (1 - S), and by self-fertilization with probability S. As in [20], selection was additive, with codominant heterozygous effects at individual sites (that is, the relative fitness at an individual locus is 1 + s for the heterozygote, and 1 and 1 + 2s for the alternative homozygotes).

Biased gene conversion and inbreeding

BGC favours GC over AT in the context of recombination between polymorphic DNA sequences [7, 8, 26]. It is formally equivalent to meiotic drive, which acts only in heterozygtes to cause a departure from 1:1 Mendelian segregation of alleles [73]. Assume that alternative GC and AT alleles at a site are neutral and that the ratio of GC:AT gametes from GC/AT heterozygotes is k:k - 1. In a random-mating population, the change in frequency p after one generation of an allele with GC at a given site is [74]:

Δp = 2p(1 - p)(2k - 1)    (1)

If the population is inbred, the frequency of heterozygotes is reduced to 2p(1 - p)(1 - F), where F is the inbreeding coefficient [73]. At equilibrium under a mixture of selfing with probability S and random mating with probability 1 - S, F = S/(2 - S) [31]. After taking selfing into account, Equation (1) becomes:

Δp = ω p (1 - p)(1 - F)    (2)

where k = 0.5(1 + ω).

We must also consider the effects of genetic drift on finite inbred populations. The effect of genetic drift in a single isolated population is inversely proportional to the effective population size (N e ). Under a wide range of conditions, N e for an inbred population is approximately equivalent to that for a random mating population with otherwise similar demography, divided by (1 + F) [31, 75, 76]. When BGC, mutation and genetic drift are weak, their effects are additive and we can work directly with the Li-Bulmer formula for equilibrium, which is derived from diffusion theory [21, 49, 55]. The GC content at equilibrium is given by the approximate equation:

where u is the rate of mutation from GC→AT and v is the reverse mutation rate.

If selection or BGC is completely relaxed after reaching an equilibrium (as the one given by Equation (3) for BGC), the process of change in GC content is described by the standard linear expression for change under mutation pressure [77]. The new equilibrium GC content, p**, is equal to v/(u + v), and the GC content at time t, p t is given by:

p t - p** = (p* - p**) exp(-(u + v)t).     (4)

Additional data files

Additional data available with this article online, show the codon bias and base composition in Arabidopsis thaliana (Additional data file 1). It lists all genes analyzed in our analysis of base composition, combined with point estimates of recombination rate and base composition for each gene.


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We are grateful to Blake Meyers for sharing unpublished MPSS data and to Deborah Charlesworth for helpful comments on the manuscript. G.M. is a European Union Marie Curie postdoctoral fellow. B.C. is supported by a Royal Society Research Professorship, and S.W. was supported by a Commonwealth Fellowship and an NSERC postdoctoral fellowship.

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Marais, G., Charlesworth, B. & Wright, S.I. Recombination and base composition: the case of the highly self-fertilizing plant Arabidopsis thaliana. Genome Biol 5, R45 (2004).

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  • Codon Usage
  • Effective Population Size
  • Additional Data File
  • Base Composition
  • Codon Bias