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a Laboratorio de Marcadores Moleculares, Instituto de Recursos Biológicos CIRN-INTA, 1712 Castelar, Buenos Aires, Argentina
b Instituto de Biotecnología CICVyA-CNIA, INTA 1712 Castelar, Buenos Aires, Argentina
c Dep. of Agronomy and Range Science, Univ. of California, Davis, CA 95616
* Corresponding author (jdubcovsky{at}ucdavis.edu)
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Abbreviations: AFLP, amplified fragment length polymorphism • bp, base pairs • GS, genetic similarity • f, kinship coefficient • PIC, Polymorphism Index Content • r, correlation coefficient • RFLP, restriction fragment length polymorphism • SSR, simple sequence repeat or microsatellite • UPGMA, unweighted pair-group method with arithmetic averages
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Restriction fragment length polymorphism (RFLP, Bostein et al., 1980) was one of the first DNA marker techniques used to characterize wheat cultivars (Vaccino et al., 1993) and assess genetic diversity (Kim and Ward, 1997; Paull et al., 1998). However, the relatively low level of polymorphism observed among elite wheat cultivars (Bryan et al., 1999) and the complexity and cost of the technique, limit the use of RFLP for routine cultivar identification. The polymerase chain reaction (PCR) technique facilitated the development of a second generation of simpler and lower-cost molecular markers, including SSR (also known as microsatellites, Tautz and Renz, 1984) and AFLP (Vos et al., 1995).
The SSR technique gained rapid acceptability because of its codominant nature, reproducibility, and high information content (De Loose and Gheysen, 1995). These loci are amplified by PCR using primers (18–25 bp long) specific for sequences flanking hypervariable regions of tandem repeats of 2 to 4 base pairs. The variation in the number of repeats present in these loci determines differences in length of the amplified fragments. This methodology is useful in identifying genotypes in self-pollinated species with low levels of genetic variability such as soybean [Glycine max (L.) Merr.] (Rongwen et al., 1995), rice (Oryza sativa L.) (Yang et al., 1994), and wheat (Domini et al., 2000).
In wheat, two independent studies showed that SSR provide a greater level of intraspecific polymorphism than RFLP (Röder et al., 1995, Plaschke et al., 1995) and prompted the development of more than 400 SSR loci in wheat (Röder et al., 1995; Devos et al., 1995; Plaschke et al., 1996; Bryan et al., 1997; Röder et al., 1998; Stephenson et al., 1998). The first SSR markers available were used to characterize eight European cultivars (Devos et al., 1995) and 11 Canadian cultivars (Lee et al., 1995) of wheat bread. In a more comprehensive study of 40 European bread wheat cultivars using 23 SSR, Plaschke et al. (1995) concluded that a relative small number of SSR was sufficient to discriminate this set of cultivars.
The AFLP technique combines the RFLP reliability with the power of PCR to amplify simultaneously many restriction fragments (Vos et al., 1995). This technique was used successfully to evaluate genetic diversity and genetic relationships in wheat (Salamini et al., 1997; Barrett and Kidwell, 1998; Domini et al., 2000), bean (Phaseolus vulgaris L.) (Tohme et al., 1996), rice (Mackill et al., 1996; Virk et al., 2000), tea (Camellia sinensis Kuntze) (Paul et al., 1997), barley (Hordeum vulgare L.) (Qi and Lindhout, 1997), and soybean (Maughan et al., 1996).
In this manuscript, we present the characterization of 105 bread wheat cultivars from Argentina using SSRs and AFLP markers. The first objective of this work was to develop an Identification Matrix to facilitate a rapid and accurate identification of the Argentine bread wheat germplasm. The second objective was to quantify the effect of a half century of wheat breeding on genetic diversity and to evaluate potential genetic erosion. Finally, we wanted to understand the contribution of different public and private breeding programs to the total genetic diversity of the Argentine gene pool.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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DNA was extracted from a bulk of leaves from five plants from each cultivar by the method described by Maroof et al. (1984). The cultivars used in this study are homozygous lines, but five plants per cultivar were pooled for DNA extraction to avoid the possibility of selecting a single contaminating seed. Gilbert et al. (1999) also recommended the use of pools from five plants to assess genetic variability with DNA markers in large plant germplasm collections.
PCR Markers
SSR loci used in this study were developed by Devos et al. (1995) (Xpsp, Table 1), Röder et al. (1995) (Xgwm, Table 1), and Ma et al. (1996) (Xcnl, Table 1). Primer sequences and annealing temperatures are included in Table 1.
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AFLP assays were performed as described in Kahn et al. (2000). Briefly, 500 ng of wheat genomic DNA were subject to restriction-ligation in a single step during 6 h in a 30-µL reaction mix (10 mM Tris-acetate pH 7.5, 10 mM MgAc, 50 mM KAc, 5 mM DTT, 50 ng/mL BSA, PstI (5U), MseI (5U), and T4 DNA ligase 1U, 5 pmol PstI adaptors, 50 pmol MseI adaptors, and 12 pmol ATP). Five microilters of each adaptor-ligated template DNA were preamplified in a 25-µL PCR reaction containing 75 ng of both P01 and M01 AFLP primers (5'-GAC TGC GTA CAT GCA GA-3' and 5'-GAT GAG TCC TGA GTA AA-3', respectively), 0.2 mM dNTPs, 1x PCR buffer (1.5 mM MgCl2) and 1U of Amplitaq LD (Perkin Elmer). Selective amplifications were performed with 1 µL of nondiluted preamplification product and 30 ng of each selective nonlabeled +3 primer using the cycling conditions described by Vos et al. (1995). Ten microliters of formamide dye were added to the 20-µL PCR reactions and amplification products were separated in 6% (w/v) denaturing polyacrylamide gels and stained by the same procedure as for the SSR gels. The four selective primer combinations assayed were P36 = ACC and M44 = ATC, P40 = AGC and M39 = AGA, P31 = AAA and M41 = AGG, and finally P41 = AGG and M40 = AGC.
Data Analysis. Variability Estimation
Variability for each locus was measured using the Polymorphism Index Content (PIC) (Anderson et al., 1993)
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For the AFLP analysis, each polymorphic fragment was scored as a locus with two allelic classes. Absolute PIC values of SSR and AFLP markers are not comparable because the maximum PIC value of an AFLP locus is 0.5. However, comparisons between genetic diversity values for different groups of cultivars (breeding programs, decades, etc.) within each marker class are valid. The AFLP analysis was included here to validate the patterns of genetic diversity observed by means of the SSR markers using an independent set of molecular markers.
Genetic diversity was estimated as a measure of genetic variation by the formula of Weir (1996),
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Differences in genetic diversity between decades and breeding programs were evaluated by two-way analysis of variance. PIC values were calculated for each locus for the cultivars grouped in each breeding program or released during each decade. Loci were used as blocks to separate the variation among loci from the error term and increase the sensitivity of the statistical analysis. Normality, additivity, and homogeneity of variances were tested by Shapiro-Wilk, Tukey, and Levene's tests, respectively (SAS Institute, 1994). Variance heterogeneity in the ANOVA among breeding programs was corrected by a LOG10 (X + 1) transformation (average genetic diversity values presented on Table 3 are on the original scale).
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Kinship coefficient (f) was calculated by means of a linear algorithm and following the assumptions of Cox et al. (1986). Accurate pedigree records were available only for 82 cultivars. Calculations were made on the basis of parentage information extracted from CIMMYT database by the IWIS program, version 1 (Fox et al., 1996).
Correlations between similarity matrices derived from SSR, AFLP, and kinship coefficients were calculated by Pearson product-moment and the significance of the correlation was tested by Mantel's test (Mantel, 1967) with the NTSYS program (MXCOMP module). A second correlation was calculated between similarity matrices derived from kinship coefficients and molecular markers with pairs of cultivars that had kinship coefficients >0.10, following the recommendation of Plaschke et al. (1995).
All statistical analyses were performed by SAS programs (SAS Institute, 1994). Throughout the text, variation measures indicated after the means are the standard deviations of the distributions.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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AFLP
The four pairs of primers described in Materials and Methods were used to generate AFLP fingerprintings. The number of polymorphic bands range from 9 to 27 with an average of 17.8 ± 7.6 per primer combination. PIC values ranged from 0.26 to 0.38 with an average value of 0.30 ± 0.15.
The average similarity coefficient among cultivars based on 71 polymorphic AFLP alleles showed a normal distribution with and average of 0.55 ± 0.10. Similarity coefficients ranged from 0.91 (‘Victoria INTA’ and ‘Saira INTA’) to 0.24 (‘Prointa Pincén’ and ‘Prointa Bonaerense Redomón’).
Genetic Relationships
Individual dendrograms based on SSR (105 cultivars) and AFLP (96 cultivars) are available at http://agronomy.ucdavis.edu/Dubcovsky/Argentina/SSR_Argentina.htm. Nine DNA samples were degraded and could not be used in the AFLP study. A dendrogram based on combined SSR and AFLP data for the 67 bread wheat cultivars, for which both types of molecular markers and pedigree data were available, is presented in Fig. 2 (38 cultivars with unknown pedigree information were excluded from this analysis).
No clear clustering of cultivars by breeding program or year of release was observed in any of the dendrograms. However, cultivars that belong to the same cluster group generally share common ancestors. Available pedigree information was validated in most cases by the SSR data. For example, the 252-bp allele present in ‘Klein 32’ at Xpsp3000, and associated with good breadmaking quality (Manifesto et al., 1998), was inherited from ‘Americano 25e’ through ‘Buck Quequén’, ‘General Urquiza’, and ‘San Martín’. The 252-bp allele also is present in other cultivars that share some of these ancestors (‘Klein Granador’, ‘Buck Manantial’, ‘Buck Cencerro’, ‘Buck Atlántico’, ‘Buck Cimarrón’, ‘Buck Napostá’, ‘Buck Pangaré’, and ‘Oncativo INTA’) but is absent in the rest of the germplasm.
Correlation between Similarity Matrices
Kinship coefficients were calculated for the 67 cultivars with available pedigree information. Kinship coefficients had a maximum value of f = 0.64 and a minimum of f = 0.00, with an average value of f = 0.08 ± 0.13. Sixty-three percent of the pairs of cultivars analyzed in this study showed kinship coefficient lower than 0.10.
Two correlations coefficients based on different subsets of cultivar pairs were calculated between f and SSR similarity matrices, and between f and AFLP similarity matrices. The first correlation was based on all 2211 pairs of cultivars and the second one on the 690 pairs of cultivars with f > 0.10 (Table 2). All correlations were statistically significant (Mantel test, P < 0.01). The correlation between the SSR and AFLP similarity matrices also was low (r = 0.27) but highly significant (Mantel test, P < 0.001).
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A highly significant correlation was detected between genetic diversity within breeding programs determined by SSR and the corresponding values calculated by AFLP (r = 0.92, P = 0.004). Moreover, a negative correlation was observed between kinship coefficients within breeding programs and genetic diversity values estimated by SSR (r = -0.75) and AFLP (r = -0.79, Table 3). These significant correlations indicate that these three independent sets of data likely reflect the same pattern of genetic diversity and validate the use of these data to analyze the partitioning of genetic diversity among Argentine wheat breeding programs.
Variation of Genetic Diversity in Time
Genetic diversity values estimated with SSR showed no significant differences among groups of cultivars released during the four different periods considered here (Table 4). Genetic diversity values for the different periods were very similar to the total variation on the basis of all the 105 cultivars (SSR, Table 4).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The identification matrix based on these SSR provides a rapid and reliable method for cultivar identification that might be used for quality control in certified seed production programs, to identify sources of seed contamination, and to maintain pure and clean germplasm collections.
The average PIC value of these SSR is higher than the PIC value of 0.46 obtained with RFLP markers for the high molecular weight glutenins for the same set of cultivars. The high PIC of this set of SSR is partially related to the selection of highly polymorphic SSRs. However, the average PIC value of unselected SSRs also was 50% higher than the previous RFLP PIC estimate. This result is similar to that reported by Röder et al. (1995) and confirms the advantage of SSR compared with RFLP markers for genotype identification in wheat. As expected for dominant markers, AFLP markers showed lower PIC values than SSR. However, for species with no available SSRs, the AFLP technique provides a useful alternative for genotype identification compared to RFLP.
Genetic Relationships
Although these data are not extensive enough for a thorough characterization of the genetic relationships among this large set of cultivars, they can be used as a first draft of these relationships. The lack of clustering of cultivars by breeding program in Fig. 2 was expected on the basis of the available pedigree information. This information showed that breeding programs frequently use cultivars from other Argentine breeding programs as parental lines in their own crosses and that they use similar CIMMYT materials.
Correlations between the SSR and AFLP similarity matrices and the kinship coefficient matrix were low as expected from the low number of loci included in this study. However, the fact that this correlation was significant indicates that the information present in this small subset of molecular markers partially reflects the genealogical history of these cultivars. Consensus between dendrograms might be used to identify the conserved groups present in the different dendrograms. Low correlations between kinship coefficient and similarity matrices based on molecular markers also were reported in other studies including wheat, barley and oat (Avena sativa L.) cultivars (Graner et al., 1994; Plaschke et al., 1995; Schut et al., 1997; Bohn et al., 1999).
Variation of Genetic Diversity among Breeding Programs
Differences in genetic diversity detected between breeding programs that differ greatly in the number of released cultivars is expected. However, two important conclusions may be draw from this analysis. First, there are no significant differences in genetic diversity between the large public (INTA) and private breeding programs (Buck and Klein) in Argentina. Second, the average diversity within each of these three large breeding programs is very similar to the total genetic diversity present in the complete Argentine germplasm (Table 3). This suggests that each of the large breeding programs contains a representative sample of the complete diversity of the Argentine germplasm. This similar distribution of genetic variation among breeding programs is consistent with the limited clustering of cultivars by breeding program observed in Fig. 2.
Variation of Genetic Diversity during the Last Half Century
There is a general belief that modern breeding practices led to significant decrease of genetic diversity in modern cultivars (Vellvé 1993). There is concern that this erosion of the genetic variability might result in the reduction of the plasticity of the crops to respond to changes in climate, pathogen populations, agricultural practices, or quality requirements. However, the homogeneous genetic diversity values found in the Argentine bread-wheat cultivars released during the last half-century contradict this general belief. The variability available to the growers today is actually higher than the new variability released in the 1990s (Table 4). If cultivars released in the 1980s that are still grown in Argentina are included in the calculations, the diversity values for the 1990s increase from 0.681 to 0.722 for SSR and from 0.307 to 0.378 for AFLP.
Similar results to those reported here for the Argentine spring-wheat germplasm were recently reported for the dominant winter-wheat UK cultivars released between 1934 and 1994 (Domini et al., 2000). Analysis of SSR and AFLP markers for 55 UK wheat cultivars indicated that plant breeding in the UK has resulted in a qualitative, rather than a quantitative shift in the diversity over time. Souza et al. (1994) found similar results examining genetic diversity in spring wheat cultivars grown in the Yaqui Valley of Mexico and the Punjab of Pakistan. These regions also were beneficiaries of the semidwarf cultivars released during the Green Revolution in the early 1960s and no significant decrease in genetic diversity was observed.
A detailed analysis of the pedigree of modern Argentine germplasm showed that variability was maintained by the use of derivatives of old Argentine cultivars and the permanent introgression of new materials from programs from other countries, particularly the material from CIMMYT. Alleles from old cultivars such as 38MA, Buck Atlantico, Buck Quequén, Ciano, Frontana, General Roca MAG, Lin Calel MA, Mentana, Nianari 59, Sinvalocho, and Sonora 64 are still present in modern Argentine breeding programs. The high genetic diversity values found in the public wheat-breeding program INTA and the private program Buck suggests that these two programs have made a major contribution to maintain the genetic diversity of Argentine wheat germplasm during the last half-century.
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ACKNOWLEDGMENTS |
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Received for publication June 9, 2000.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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