The availability of expressed sequence data derived from gene discovery programs enables mining for simple sequence repeats (SSR), providing useful genetic markers for crop improvement. These markers are inexpensive, require minimal labour to produce and can frequently be associated with functionally annotated genes. This study presents the development and characterization of 24 expressed sequence tags (EST)‐SSR markers from Brassica napus and their cross‐amplification across Brassica species. The markers show reliable amplification, genome specificity and considerable polymorphism, demonstrating the utility of EST‐SSRs for genetic analysis of wild Brassica populations and commercial Brassica germplasm. 相似文献
GC-biased gene conversion (gBGC) is a recombination-associated process mimicking selection in favor of G and C alleles. It is increasingly recognized as a widespread force in shaping the genomic nucleotide landscape. In recombination hotspots, gBGC can lead to bursts of fixation of GC nucleotides and to accelerated nucleotide substitution rates. It was recently shown that these episodes of strong gBGC could give spurious signatures of adaptation and/or relaxed selection. There is also evidence that gBGC could drive the fixation of deleterious amino acid mutations in some primate genes. This raises the question of the potential fitness effects of gBGC. While gBGC has been metaphorically termed the “Achilles'' heel” of our genome, we do not know whether interference between gBGC and selection merely has practical consequences for the analysis of sequence data or whether it has broader fundamental implications for individuals and populations. I developed a population genetics model to predict the consequences of gBGC on the mutation load and inbreeding depression. I also used estimates available for humans to quantitatively evaluate the fitness impact of gBGC. Surprising features emerged from this model: (i) Contrary to classical mutation load models, gBGC generates a fixation load independent of population size and could contribute to a significant part of the load; (ii) gBGC can maintain recessive deleterious mutations for a long time at intermediate frequency, in a similar way to overdominance, and these mutations generate high inbreeding depression, even if they are slightly deleterious; (iii) since mating systems affect both the selection efficacy and gBGC intensity, gBGC challenges classical predictions concerning the interaction between mating systems and deleterious mutations, and gBGC could constitute an additional cost of outcrossing; and (iv) if mutations are biased toward A and T alleles, very low gBGC levels can reduce the load. A robust prediction is that the gBGC level minimizing the load depends only on the mutational bias and population size. These surprising results suggest that gBGC may have nonnegligible fitness consequences and could play a significant role in the evolution of genetic systems. They also shed light on the evolution of gBGC itself.GC-BIASED gene conversion (gBGC) is increasingly recognized as a widespread force in shaping genome evolution. In different species, gene conversion occurring during double-strand break recombination repair is thought to be biased toward G and C alleles. In heterozygotes, GC alleles undergo a kind of molecular meiotic drive that mimics selection (reviewed in Marais 2003). This process can rapidly increase the GC content, especially around recombination hotspots (Spenceret al. 2006), and, more broadly, can affect genome-wide nucleotide landscapes (Duret and Galtier 2009a). For instance, it is thought to play a role in shaping isochore structure evolution in mammals (Galtieret al. 2001; Meunier and Duret 2004; Duretet al. 2006) and birds (Websteret al. 2006). Direct experimental evidence of gBGC mainly comes from studies in yeast (Birdsell 2002; Manceraet al. 2008; but see Marsolier-Kergoat and Yeramian 2009) and humans (Brown and Jiricny 1987). However, associations between recombination and the nucleotide landscape and frequency spectra biased toward GC alleles provide indirect evidence in very diverse organisms (
Open in a separate windowThe impact of gBGC on noncoding sequences and synonymous sites has been studied in depth, especially because of confounding effects with selection on codon usage (Maraiset al. 2001). More recently, Galtier and Duret (2007) pointed out that gBGC may also interfere with selection when affecting functional sequences. They argued that gBGC could leave spurious signatures of adaptive selection and proposed to extend the null hypothesis of molecular evolution. Indeed, gBGC can lead to a ratio of nonsynonymous (dN) over synonymous (dS) substitutions above one (Berglundet al. 2009; Galtieret al. 2009), i.e., a typical signature of positive selection (Nielsen 2005). This hypothesis has been widely debated for human-accelerated regions (HARs). These regions are extremely conserved across mammals but show evidence of accelerated evolution along the human lineage, which has been interpreted as evidence of positive selection (Pollardet al. 2006a,b; Prabhakaret al. 2006, 2008). On the contrary, other authors argued that patterns observed in HARs, such as the AT → GC substitution bias, the absence of a selective sweep signature, or the propensity to occur within or close to recombination hotspots, are more likely explained by gBGC rather than positive selection (Galtier and Duret 2007; Berglundet al. 2009; Duret and Galtier 2009b; but see also Pollardet al. 2006a who also suggested that gBGC might play a role in HARs evolution). It is thus crucial to take gBGC into account when interpreting genomic data.Moreover, Galtier and Duret (2007) initially suggested that gBGC hotspots could contribute to the fixation of slightly deleterious AT → GC mutations and could represent the Achilles'' heel of our genome. This hypothesis was reinforced later in primates, with evidence of gBGC-driven fixation of deleterious mutations in proteins (Galtieret al. 2009). A similar result was also found in some grass species, whose genomes are also supposed to be affected by gBGC (Gléminet al. 2006). Haudryet al. (2008) compared two outcrossing and two selfing grass species and showed that GC-biased genes exhibit higher dN/dS ratio in outcrossing than in selfing lineages. The reverse pattern would be expected under pure selective models because of the reduced selection efficacy in selfers (Charlesworth 1992; Glémin 2007). This pattern is in agreement with a genomic Achilles'' heel associated with outcrossing, while gBGC is inefficient in selfing species because they are mainly homozygous.Twenty years ago, Bengtsson (1990) already pointed out that biased conversion can generally affect the mutation load. The mutation load is the reduction in the mean fitness of a population due to mutation accumulation, which could lead to population extinction if it is too high (Lynchet al. 1995). At this time, Bengtsson concluded that “it is impossible to know if biased conversion plays a major role in determining the magnitude of the mutation load in organisms such as ourselves, but the possibility must be considered and further investigated (Bengtsson 1990, p. 186).” Now, one can propose gBGC could be such a widespread biased conversion process. It thus appears timely to thoroughly investigate the fitness consequences of gBGC through its potential effects on the dynamics of deleterious mutations. The fitness consequences of gBGC were also pointed out as a major future issue to be addressed by Duret and Galtier (2009a). In addition to the load, deleterious mutations have many other evolutionary consequences (for review see Charlesworth and Charlesworth 1998). They are thought to be the main determinant of inbreeding depression, i.e., the reduction in fitness of inbred individuals compared to outbred ones. They also play a key role in the evolution of genetic systems (sexual reproduction and recombination, inbreeding avoidance mechanisms, ploidy cycles), of senescence, or in the degeneration of nonrecombining regions, such as Y chromosomes. So far, we know little, if anything, about how gBGC might affect these processes.In his seminal work, Bengtsson (1990) did not address several important points. First, he did not include genetic drift in his model. Nearly neutral mutations, for which drift and selection are of similar intensities, are the most damaging ones because they can drift to fixation, unlike strongly deleterious mutations that are maintained at low frequency (Crow 1993; Lande 1994, 1998). While gBGC intensities are rather weak (Birdsell 2002; Spenceret al. 2006), they could markedly affect the fate of nearly neutral mutations (see also Galtieret al. 2009). Second, Bengtsson did not study the effect of gene conversion on inbreeding depression, while he showed that recessive mutations, mostly involved in inbreeding depression, are the most affected by gene conversion. Third, he did not envisage systematic GC bias with its opposite effects on A/T and G/C deleterious alleles. Fourth, while he noted that selfing affects both the efficacy of selection and that of conversion, he did not fully investigate the effect of mating systems. On one hand, selfing is efficient in purging strongly deleterious mutations causing inbreeding depression. However, since selfing is expected to increase drift, weakly deleterious mutations can fix in selfing species, contributing to the so-called “drift load” (Charlesworth 1992; Glémin 2007). Self-fertilizing populations are thus expected to exhibit low inbreeding depression and high drift load. On the other hand, gBGC, and thus its cost, vanishes as the selfing rate and homozygosity increase (Maraiset al. 2004). gBGC could thus challenge classical views on mating systems and it was even speculated that gBGC could affect their evolution (Haudryet al. 2008).Here I present a population genetics model that includes mutation, selection, drift, and gBGC, which extends previous studies (Gutz and Leslie 1976; Lamb and Helmi 1982; Nagylaki 1983a,b; Bengtsson 1990). I specifically examine how gBGC can affect inbreeding depression and the mutation load. I also focus on the effect of mating system, which is especially interesting with regard to the interaction between biased conversion and selection. Finally, I discuss how these results could give insight into how gBGC evolved.
Impacts of gBGC on inbreeding depression:
Inbreeding depression is defined as the reduction in fitness of selfed (and more generally inbred) individuals compared to outcrossed individuals,(15)where and are the mean fitness of outcrosses and selfcrosses, respectively (Charlesworth and Charlesworth 1987; Charlesworth and Willis 2009). The approximation is very good in most conditions, because under weak (s ≪ 1) and strong selection (x ≪ 1) (see Gléminet al. 2003). Similar to the load, considering both sites for which either S or W alleles are deleterious, in proportion q and 1 – q, respectively, we get(16)
gBGC and the genetic basis of inbreeding depression in panmictic populations:
In infinite panmictic populations without gBGC, inbreeding depression depends only on mutation rates and dominance levels. Partially recessive mutations () contribute only to inbreeding depression, and the more recessive they are, the higher the inbreeding depression (Charlesworth and Charlesworth 1987). In finite populations, deterministic results hold for strongly deleterious mutations (s ≫ 1/Ne), which contribute mostly to inbreeding depression. Contrary to the load, weakly deleterious mutations (∼s ≤ 1/Ne) contribute little to inbreeding depression (Figure 4, a and c, and see Bataillon and Kirkpatrick 2000).Open in a separate windowFigure 4.—Inbreeding depression (×106) as a function of s without (a and c) or with (b and d) gBGC (b = 0.0002). (a and b) h = 0.2: thick lines, N = 5000; thin lines, N = 10,000; dashed lines, N = 50,000; dotted lines, N = 100,000. (c and d) N = 10,000: thick lines, h = 0.4; thin lines, h = 0.2; dashed lines, h = 0.1; dotted lines, h = 0.05. u = 10−6, λ = 2.Like the load, gBGC affects both the magnitude and the structure of inbreeding depression. In infinite populations, and more generally for strongly deleterious alleles (Nes ≫ 1), replacing x by xeq given by Equations 4 in Equations 15 and 16 leads to(17a)(17b)(17c)The effect of gBGC on inbreeding depression is not monotonic. Like the load, gBGC increases inbreeding depression if b > hs(1 − 2q/(q + λ − qλ)). However, contrary to the load, a strong gBGC decreases inbreeding depression, which tends to 0 as b increases, while the load tends to qs (Equation 10c). An analysis of Equation 17b shows that mutations that maximize inbreeding depression are those that also maximize the load, i.e., S deleterious mutations with s ≈ 2b.In finite populations, inbreeding depression must be integrated over the Φ distribution, which leads to(18)(see also Gléminet al. 2003). While it is not possible to get an analytical expression of (18), numerical computations (see appendixb) show that S deleterious mutations with s ≈ 2b also maximize inbreeding depression in finite populations (Figure 4). More broadly, inbreeding depression is maximal under the overdominant-like selection regime (gray area in Figure 2). Once again, even low to moderate gBGC markedly affects the genetic structure of inbreeding depression. First, mutations of intermediate effects contribute the most to inbreeding depression, i.e., up to one order of magnitude higher than strongly deleterious mutations (compare Figure 4a with 4b). Second, even nearly additive mutations can have a substantial effect (compare Figure 4c with 4d).Since little is known about the distribution of dominance coefficients, especially the dominance of mildly deleterious mutations (of the order of b), it is difficult to quantitatively predict the full impact of gBGC on inbreeding depression. We can conclude that, on average, gBGC should increase inbreeding depression. However, further insight into mutational parameters is crucial to assess the quantitative impact of gBGC.
Joint effect of gBGC and mating system on the load and inbreeding depression:
Selfing, or more generally inbreeding, slightly reduces the segregating load through the purging of recessive mutations (Ohta and Cockerham 1974), but can substantially increase the fixation load because of the effective population size reduction under inbreeding: (see above and Pollak 1987; Nordborg 1997; Glémin 2007). In numerical examples, I assumed that α decreases with F according to the background selection model (Charlesworthet al. 1993; Nordborget al. 1996), as in Glémin (2007). With gBGC, selfing thus has two opposite effects on the fixation load. Selfing increases the drift load sensu stricto but decreases the fixation load due to gBGC. A surprising consequence is that the load can be higher in outcrossing than in selfing populations (Figure 5). Quantitatively this is also expected, even with a gBGC hotspot affecting just 3% of the genome (Figure 5 and Open in a separate windowFigure 5.—Effective population size (a and b) and the load (×106) (c–f) as a function of F for different gBGC intensities (thick lines, b = 0; thin lines, b = 0.0001; dashed lines, b = 0.0002; dotted lines, b = 0.0005). The effective population size depends on F under the background selection (BS) model (Charlesworthet al. 1993), using Equations 16 and 17 in Glémin (2007): , where U is the genomic deleterious mutation rate, R is the genomic recombination rate, sd is the mean selection coefficient against strongly deleterious mutations, and hd is their dominance coefficient. N = 10,000, U = 0.2, hd = 0.1, and sd = 0.05. (a, c, and e) R = 5, “weak” BS; (b, d, and f) R = 0.5, “strong” BS. (c and d) Load averaged over half GC and half AT deleterious alleles, with a bias in favor of AT alleles. (e and f) Load averaged over 10% of GC deleterious alleles and 90% of AT deleterious alleles with a bias in favor of AT alleles; see Figure 3. h = 0.5, u = 10−6, and λ = 2.Generally, the effect of selfing is simpler for inbreeding depression. Purging, Ne reduction, and suppression of gBGC contribute to decreasing inbreeding depression in selfing populations (Figure 6a). However, there are special cases in which maximum inbreeding depression is reached for intermediate selfing rates (Figure 6b). In such cases, in outcrossing populations, gBGC is strong enough to sweep polymorphism out and reduce inbreeding depression (b > s, regime 1 in Figure 2). As the selfing rate increases, gBGC declines, and the selection dynamics become overdominant-like (regime 2, Figure 2), thus maximizing inbreeding depression. For high selfing rates, gBGC vanishes (regime 3 in Figure 2) and deleterious alleles are either purged or fixed if there is substantial drift. This is similar to the effect of selfing on inbreeding depression caused by asymmetrical overdominance, where inbreeding depression also peaks for intermediate selfing rates (Ziehe and Roberds 1989; Charlesworth and Charlesworth 1990). In the present case, the range of parameters leading to this peculiar behavior is narrow because the overdominant-like region depends on the selfing rates and can vanish either for low or for high selfing rates (Figure 2).Open in a separate windowFigure 6.—Inbreeding depression (×106) as a function of F for different gBGC intensities (thick lines, b = 0; thin lines, b = 0.0001; dashed lines, b = 0.0002; dotted lines, b = 0.0005). Inbreeding depression is averaged over half GC and half AT deleterious alleles. The effective population size depends on F as in Figure 5 (same parameters). (a) s = 0.002; (b) s = 0.0005; (c) s = 0.0002. h = 0.2, u = 10−6, and λ = 2.
Minimum load and the evolution of gBGC and recombination landscapes:
Although gBGC may have deleterious fitness consequences, it is surprising that it evolved in many taxa (Duret and Galtier 2009a). Birdsell (2002) initially suggested that gBGC may have evolved as a response to mutational bias toward AT (λ > 1, here). Indeed, I show that a minimum load is reached for weak gBGC (b ≈ ln(λ)/4N, Equation 14). This result is very general whatever the distribution of fitness effects of mutations (appendixd). However, the range of optimal gBGC is narrow, and gBGC increases the load as far as b > ln(λ)/2N (appendixc). In humans, using N = 10,000 and λ = 2, gBGC levels that minimize the load are ∼1.17 × 10−5, i.e., one order of magnitude lower than the average bias observed in recombination hotspots (Myerset al. 2005). However, selection on conversion modifiers will not necessarily minimize the load because of gametic disequilibrium generated between modifiers and fitness loci (Bengtsson and Uyenoyama 1990). Selection for limitation of somatic AT-biased mutations could also have selected for GC-biased mismatch repair machinery (Brown and Jiricny 1987). If the bias level that would be selected for somatic reasons is >ln(λ)/2N, a side effect would be the generation of a substantial load at the population level. Finally, it is interesting to note that when synonymous codon positions are under selection for translation accuracy, optimal gBGC levels can be higher than gBGC levels that minimize the protein load, especially when most optimal codons end in G or C ().Conversely, gBGC could also affect the evolution of recombination landscapes, which could evolve to reduce the gBGC load. Surprisingly, for a given recombination/conversion level, the hotspot distribution does not appear to be optimal (Nishant and Rao 2005), one can speculate that the hotspot localization outside genes could be a response to avoid the deleterious effects of gBGC.Up to now, these verbal arguments have not been assessed theoretically (but see Bengtsson and Uyenoyama 1990 for a different kind of conversion bias). Population genetics models are necessary to test these hypotheses concerning the evolution of gBGC and recombination landscapes and to pinpoint the key parameters that might govern their evolution.
gBGC and the evolution of mating systems:
Deleterious mutations also play a crucial role in the evolution of mating systems. They are the main source of inbreeding depression, which balances the automatic advantage of selfing. The drift load is also thought to contribute to the extinction of selfing species. Since they are mainly homozygous, selfing species are mostly free from gBGC and its deleterious impacts. I discuss below how this might affect the evolution of mating systems.
Inbreeding depression and the shift in mating systems:
Inbreeding depression plays a key role in the evolution of mating systems (Charlesworth and Charlesworth 1987; Charlesworth 2006b). Since it balances the automatic advantage of selfing, high inbreeding depression favors outcrossing, while selfing can evolve when it is low. Moreover, selfing helps to purge strongly deleterious mutations, thus decreasing inbreeding depression. This positive feedback reinforces the disruptive selection on the selfing rate and prevents the transition from selfing to outcrossing (Lande and Schemske 1985).Theoretical results suggest that, in most conditions, gBGC would reinforce inbreeding depression in outcrossing populations (Figure 6), which would prevent the evolution of selfing. In reverse, if selfing is initially selected for, recurrent selfing would reduce the load through both purging and avoidance of gBGC. Under this scenario, gBGC would reinforce disruptive selection on mating systems. However, under some conditions (see Figure 6), inbreeding depression peaks at intermediate selfing rates, as observed for asymmetrical overdominance (Ziehe and Roberds 1989; Charlesworth and Charlesworth 1990). In theory, this could prevent the shift toward complete selfing and maintain stable mixed mating systems (Charlesworth and Charlesworth 1990; Uyenoyama and Waller 1991). However, this pattern is observed under restrictive conditions and it is very unlikely on the whole-genome scale. Dominance patterns are crucial for predicting inbreeding depression, especially with gBGC. Contrary to the load, it is thus difficult to evaluate the quantitative impact of gBGC on inbreeding depression. However, increased inbreeding depression in outcrossing species subject to gBGC seems to be the most likely scenario.
gBGC and the long-term evolution of mating systems:
In the long term, the gBGC-induced load also challenges the “dead-end hypothesis,” which posits that, because of the reduction of selection efficacy, self-fertilizing species would accumulate weakly deleterious mutations in the long term, eventually leading to extinction (Takebayashi and Morrell 2001). Because of gBGC, not drift, outcrossing species could also accumulate a load of weakly deleterious mutations (Figure 7), and they could suffer from a higher load than highly self-fertilizing species (Haudryet al. (2008) found that in two outcrossing grass species, but not in two self-fertilizing ones, the dN/dS ratio is significantly higher for genes exhibiting GC enrichment. They speculated that substitutions in these genes might contribute to increasing the load in these two outcrossing grass species. Such results are still very sparse. In plants, evidence of strong gBGC is mainly restricted to grasses (but see Wrightet al. 2007). It will be necessary to conduct more in-depth studies to assess the phylogenetic distribution of gBGC in plants and other hermaphrodite organisms and to further test the genomic Achilles'' heel hypothesis in relation to mating systems. While theoretically possible, the quantitative effect of gBGC on the evolution of mating systems remains a new, open, and challenging question.
Conclusion:
I showed that the interaction between gBGC and selection might have surprising qualitative consequences on load and inbreeding depression patterns. Given the few quantitative data available on gBGC levels and selection intensities (mainly in humans), it turns out that even weak genome-wide gBGC can have significant fitness impacts. gBGC should be taken into account not only for sequence analyses (Berglundet al. 2009; Galtieret al. 2009), but also for its potential fitness consequences, for instance concerning genetic diseases. Interferences between gBGC and selection also give rise to new questions on the evolution of mating systems. However, most of the challenging conclusions given here have yet to be quantitatively evaluated. Quantification of gBGC and its interaction with selection in various organisms will be crucial in the future. 相似文献
