Direct Estimation of Mutation Rate for 10 Microsatellite Loci in Durum Wheat, Triticum turgidum (L.) Thell. ssp durum desf

Anne-Céline Thuillet, David Bru, Jacques David, Pierre Roumet, Sylvain Santoni, Pierre Sourdille and Thomas Bataillon

INRA-Station de Génétique et d'Amélioration des Plantes, Mauguio, Domaine de Melgueil, France;
INRA, Unité Génome, Station d'Amélioration des Plantes Domaine de Crouël, Clermont-Ferrand, France

Microsatellites are defined as tandemly repeated DNA sequences of 1–6 base pairs (bp). They are characterized by their repeated motif and their number of repeats. Because of their high level of variability in comparison with other neutral markers and because of their codominance, they have become widely used for mapping (Dib et al. 1996Citation ; Röder et al. 1998Citation ) and for studies of population genetics (Jarne and Lagoda 1996Citation ). Microsatellites represent a particular kind of mutation process by which the ancestral and the mutant alleles differ by a few repeats. Two mutation mechanisms are considered for repeated sequences: slippage of the DNA polymerase or unequal crossing-over (Sia et al. 1997Citation ). The first one generates additions or losses of one or a few repeat unit(s), whereas unequal crossing-over could lead to changes of several repeat units. Slippage of the DNA polymerase seems to play a major role in the generation of mutations in microsatellites (Eisen 1999Citation ).

A better knowledge of the mutation rate of microsatellites and of the factors that may influence the mutation rate would permit to improve their use in studies of population genetics (Di Rienzo et al. 1998Citation ; Donnelly 1999Citation ). Several attempts to estimate the mutation rate underlying the variability at microsatellite loci have been based on observed distributions of allelic frequencies in populations (Shriver et al. 1993Citation ; Valdes et al. 1993Citation ). However, many other evolutionary forces may influence the variability at microsatellite loci, and differences in diversity between loci may not directly reflect differences in mutation rates. Especially, indirect selective events and variations in the recombination rate may lead to local variations in the effective population size (Maynard Smith and Haigh 1974; Charlesworth et al. 1993). Thus, some cases have been reported where the authors could not conclude whether the variability at microsatellite loci was caused by differences in mutation rate or by variations in the effective population size (Schug et al. 1998Citation ; Payseur and Nachman 2000). So when possible, methods based on the direct observation of mutation events using experimental designs seem to be more relevant for the estimation of mutation rates. Studies document the pattern of mutation of microsatellites from direct observations of mutation events in humans (Weber and Wong 1993Citation ; Ellegren 2000Citation ; Xu, Peng, and Fang 2000Citation ). Relatively few data are available from direct observations in animals (Dallas 1992Citation ; Ellegren, Primmer, and Sheldon 1995Citation ; Schug, Mackay, and Aquadro 1997Citation ; Schlötterer et al. 1998Citation ), and only one very recent study deals with plant microsatellites (Udupa and Baum 2001Citation ).

In this study, we report a direct estimation of the mutation rate for 10 dinucleotide microsatellite loci in plants. Using ~500 durum wheat mutation accumulation lines, we were able to screen for new microsatellite mutations. Totally, an equivalent of ca. 5,000 meiotic cycles was considered. We provide an average mutation rate for microsatellites in wheat and allele-specific mutation rates, considering the different loci we analyzed. We observed a considerable variability in the estimated mutation rates and discuss the kind of mutation events observed.

The lines of durum wheat, Triticum turgidum (L.) Thell. ssp durum desf. (2n = 4x = 28), were derived from a single homozygous genotype through 8, 10, or 11 generations of controlled selfing. The lines were obtained as follows: the ancestral individual was a doubled haploid obtained by pollination of durum wheat (variety Sham I, ICARDA) with maize pollen, embryo rescue, and colchicine doubling (Dusautoir et al. 1995Citation ). After three generations of multiplication, ~500 accumulation lines were founded. At each generation, a single-seed descent procedure with controlled selfing was used (one spike per line was bagged individually).

Total genomic DNA was extracted for each line from 0.2 g of fresh leaves as described in Alix et al. (personal communication). The 10 loci used in this study (table 1 ) have been developed by Röder et al. (1998)Citation in bread wheat, Triticum aestivum (L.) Thell. (2n = 6x = 42). Their portability from bread wheat to durum wheat has been verified by PCR amplification in durum wheat, using primers defined for bread wheat under the same PCR conditions. Amplification reactions were performed in a final volume of 25 µl in the presence of 120 ng of template DNA, 250 nM of each primer, 0.2 mM of each deoxynucleotide, 1.5 mM MgCl2, and 1 unit Taq polymerase (Appligene-oncor). The PCR was carried out using a PTC 100 thermocycler (MJ Research). After 5 min at 94°C, 30 cycles were performed with 30 s at 94°C, 30 s at 50, 55, or 60°C (depending on the annealing temperature of the microsatellite), 30 s at 72°C, and a final extension step of 5 min at 72°C. Amplification products were loaded onto 6% denaturing polyacrylamide gels containing 7.5 M urea, 6% acrylamide, and 1x TBE buffer (0.09 M Tris–borate [pH 8] and 2 mM EDTA). Gels were run in 1x TBE at 60 W. Microsatellites were visualized by silver staining with a commercial kit (Promega). For 7 of the 10 loci used, the reference alleles and the mutant alleles were cloned and sequenced. The more intense band of each locus was cut from the gel. One hundred microliters of 1x TE (10 mM Tris [pH 8], 1 mM EDTA) was added. Pieces of gel were crushed with the tip of a pipette, frozen, then boiled for 30 s and centrifuged at 13,000 rpm for 5 min. Cold ethanol was added to the supernatant to precipitate the DNA. PCR products, resuspended in sterile water, were ligated into pGEM-T easy plasmid (Promega) according to the manufacturer's instructions and cloned in Escherichia coli XL1-Blue cells (Stratagene). One positive clone per allele cloned was sequenced using dye–primer chemistry, and analyzed on an ABI377 semiautomated sequencer (Qiagen). The sequences are available from GeneBank under the accession numbers AF275895, AF275896, AF275897, AF275898, AF275899, AF275900, and AF275901. For the last three loci, we could not obtain a clean sequence because of technical problems. However, for two of these three loci, we could deduce an approximated number of repeats. For each fragment with an available sequence, we compared the size on the gel and the size from the sequencing information. The sizes were approximately the same, presenting differences of zero, one or two base pairs, except for the locus Xgwm153, for which we observed a difference of 7 bp. We give in table 1 , the sizes deduced from both methods.


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Table 1 Characteristics of the Microsatellite Loci

 
For each locus, the mutation rate was calculated by dividing the number of observed mutations by the number (n) of possible mutation events. The total number of possible mutation events that can occur at a locus through our accumulation mutation design is 2gl, where g is the number of generations for which the lines are maintained, and l is the number of lines considered. However, as our lines are maintained by single-seed descent, about half of the mutations that occur are ultimately lost and can never be observed. Indeed, mutations occur at the heterozygous state and do not have a probability of 1 of staying in the lines that will be derived until the last generation. So, we have to divide the number of mutations not by 2gl, but by the number (n) of possible mutation events that we are able to observe.

To calculate n, let us consider a mutation that occurs for the first time in a line at generation 1 (at generation 0, all the lines are identical and homozygous for the initial allele A). This line presents the genotype Aa for the locus considered at generation 1. At generation g, this mutation has a probability (1/2)g - 1 of being observed in the heterozygous state, and a probability 1/2[1 - (1/2)g - 1] of being observed in the homozygous state because the line returns to the homozygous state half of the time fixing the mutated allele and half of the time recovering the initial allele. Thus, we have a total probability (1/2)g - 1 + 1/2[1 - (1/2)g - 1] of observing, at generation g, a mutation that occurred for the first time in a line at generation 1. This can be simplified as 1/2[1 + (1/2)i], where i = (g - the generation at which the mutation is observed for the first time). Mutations can occur for each line at each generation. So, i can vary from g - 1 to g - g, that is to say, from 0 to g - 1, and the number of possible mutation events that we are able to observe in our design is given by the formula:


The loci Xgwm148, Xgwm153, Xgwm257, Xgwm312, Xgwm372, Xgwm374, and Xgwm526, were screened at generation 8 for 469 lines—we obtained n8 = 4,686 for each locus. For the loci Xgwm99, Xgwm495, and Xgwm537, 435 lines were derived during 10 generations and 36 other lines during 11 generations. In this case, we obtain a total value n10 + n11 = 5,687 for each locus. An exact 95% confidence interval (CI) for the mutation rate can be calculated by assuming that the number of observed mutations at a locus is drawn from a Poisson distribution with mean {lambda} = µn, where µ is the mutation rate (Casella and Berger 1990Citation ). Given xi, the number of the observed mutation events at a locus i, the lower confidence limit is {lambda}Li/n, where {lambda}Li is the solution of


and the upper confidence limit is {lambda}Ui/n, where {lambda}Ui is the solution of


The same method can be used to obtain a CI for the average mutation rate across loci by using the total number of mutations observed and adjusting the value of n accordingly. We obtain a total value N = 7n8 + 3(n10 + n11), and the total number of observed mutations over the 10 loci follows a Poisson distribution of the parameter {lambda} = µN.

Among the 10 loci screened in this study, 12 mutation events were detected. By sequencing the reference and mutant alleles, we could verify that the mutations were not the result of variations in the flanking regions but of variations in the repeated sequence. The estimated average mutation rate is 12/(7 x 4,686 + 3 x 5,687) = 2.4 x 10-4 mutation per allele per generation (95% CI = [1.4 x 10-4; 4.2 x 10-4]). Known mutation rates per allele and per generation range from 6.3 x 10-6 in Drosophila (Schug et al. 1997Citation ) to 10-2 in the human genome (Weber and Wong 1993Citation ). An indirect estimation that has been published for chloroplast mononucleotide microsatellites in Torrey pine gives values from 3.2 x 10-5 to 7.9 x 10-5 mutation per allele and per generation (Proven et al. 1999Citation ). Very recent values in chickpea show high mutation rates from direct estimation (10-2 and 3.9 x 10-3 averaged on 15 loci) for trinucleotides (TAA)n (Udupa and Baum 2001). Authors of this last study compared their findings with previous estimates and concluded that mutation rates for microsatellites in plants are higher than those in animals. They proposed that this difference could be because the number of cell divisions preceding the production of gametes is higher in vegetal organisms. However, they underlined that sequences with a high AT content are known to be particularly unstable (Schlötterer and Tautz 1992Citation ). Furthermore, loci presenting a lot of mutation events are particularly long in their study as they reach more than 40 repeat units. In our study, we analyzed GT or GA dinucleotides that present 10 to ~34 repeat units. We detected a lower average mutation rate than in chickpea; and when comparing dinucleotides of approximately similar number of repeats and composition, as in Schlötterer et al. (1998)Citation , the mutation rates we estimated did not differ from those previously observed in animals.

As mutation rates can be highly variable between and within microsatellite loci (Di Rienzo et al. 1998Citation ), we calculated allele-specific mutation rates, estimating them locus by locus. We found a 100-fold difference between the highest and the lowest estimates (table 1 ). This observation is consistent with the literature as numerous factors are known to influence the mutation rate of microsatellites. Effects of the type of motif (Ellegren, Primmer, and Sheldon 1995Citation ; Chakraborty et al. 1997Citation ) or effects of the structure of alleles (Brinkmann et al. 1998Citation ) can be cited, but the number of repeats seems to have the major effect on the mutation rate of microsatellite alleles (Wierdl, Dominska, and Petes 1997Citation ; Kruglyak et al. 1998Citation ; Schlötterer et al. 1998Citation ).

The sequencing of alleles of the wild type allowed us to obtain an approximate number of repeats of each microsatellite in the variety Sham I before mutation (table 1 ). We observed a difference in size between the sequence information and the bands on the gels. Mutations can occur during the cloning in E. coli; therefore, the cloning process could explain the differences in size. As differences were only of a few base pairs, we used the number of repeats that we could count on the sequence as an approximation. For the locus Xgwm153, we could not obtain the exact number of repeats because of a technical problem during the sequencing, but the sequencing information was sufficient to ensure that it contained at least 34 repeat units. From the sequence information, we can note that the loci for which we detected mutations are among the longest of our studies, in comparison with previous studies, despite the lack of precision for the locus Xgwm153.

Finally, we considered the kind of mutations that we observed. The mutation events that were detected all involved changes of only one repeat. Among the 12 mutation events detected, eight variants gained one repeat, and four lost one repeat (table 1 ). We did not apply a statistical test to our data as the number of mutation events was not large enough; however, earlier reports show the same trend (Primmer et al. [1996]Citation find 26 gains vs. 7 losses, and Ellegren [2000]Citation reports 18 gains vs. 4 losses for dinucleotides). It has also been shown that the mutation bias toward the gain of repeats depended on the length of the allele, longer alleles presenting a smaller bias (Xu, Peng, and Fang 2000Citation ). This assumption cannot be verified here as the set of loci analyzed is too small to contrast the loci on a broad range of numbers of repeat units.

Evaluating the relative importance of the different factors that can influence the mutation rate for microsatellites requires the study of a much larger sample of loci. We hope that the estimation of mutation rates for the set of microsatellites we describe here will be useful in further studies of population genetics to understand the selective historical and demographic processes that govern currently observable genetic diversity in wild and cultivated wheat populations.

Acknowledgements

This study was funded by a grant from Bureau des Ressources Génétiques. We thank Philippe Jarne for helpful discussions and two anonymous reviewers for their comments on an earlier version of this paper.

Footnotes

Pierre Capy, Reviewing Editor

Keywords: microsatellite mutation rate durum wheat, Triticum turgidum Back

Address for correspondence and reprints: Anne-Céline Thuillet, INRA-Station de Génétique et d'Amélioration des Plantes Mauguio, Domaine de Melgueil, 34130 Mauguio, France. thuillet{at}ensam.inra.fr . Back

Abbreviations: CI, confidence interval; IAM, infinite allele model; SMM, stepwise mutation model. Back

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Accepted for publication September 17, 2001.