| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
a Inst. of Plant Breeding, Seed Science, and Population Genetics, Univ. of Hohenheim, 70593 Stuttgart, Germany
b Dep. of Crop Science, Univ. of Illinois, Urbana, IL 61801
c CIMMYT, Mexico D.F., Mexico
* Corresponding author (melchinger{at}pz.uni-hohenheim.de).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
Abbreviations: AMOVA, analysis of molecular variance • CIMMYT, International Maize and Wheat Improvement Center • COP, coefficient of parentage • EST, expressed sequence tag • GS, genetic similarity • ME, megaenvironment • SSR, simple sequence repeat
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
More than one half of the wheat production environments are located in developing countries, which fall within the mandate of CIMMYT. In the 1980s, CIMMYT introduced the concept of breeding for different MEs. A ME is defined as a large, not necessarily contiguous area, which usually encompasses more than one country and is frequently transcontinental. It is characterized by similar biotic and abiotic stress conditions, cropping systems, and consumer demands (Rajaram et al., 1994). Twelve MEs have been classified, six of which are focused on efficient selection of better-adapted spring bread wheat, the dominant type of wheat in developing countries. The concept has permitted expanding breeding efforts relevant within each ME. In breeding for enhanced adaptation, adequate genetic diversity is a prerequisite for any crop improvement program. The genetic progress through selection is directly related to the variability present in the gene pool, and the quality of the genes contributed by the parents.
The COP is an indirect measure of genetic diversity among genotypes based on the probability that alleles at a certain locus are identical by descent. Calculation of COP values rests on simplifying assumptions regarding the relatedness of ancestors, parental contribution to the offspring, and absence of selection and genetic drift, which are not met under breeding conditions (Cox et al., 1985; Cowen and Frey, 1987). In contrast, molecular markers measure diversity directly at the DNA level. In studies of autogamous crops with low levels of apparent genetic variability such as wheat, soybean [Glycine max (L.) Merr.], and rice, SSRs proved to be a suitable marker system. They are generally genome specific, abundant, codominant in nature, and show a fairly uniform distribution over the genome. SSRs have been applied in many aspects of genetic diversity analyses such as genetic differentiation caused by selection (Stachel et al., 2000), fingerprinting of genotypes to analyze the structure of germplasm collections (Parker et al., 2002; Huang et al., 2002), and the analysis of temporal changes in diversity (Donini et al., 2000; Christiansen et al., 2002).
Traditional methods to develop SSRs are based on isolating and sequencing genomic libraries, which contain putative SSR tracts (Adams et al., 1992). A novel source for generating SSRs is provided by screening EST databases available online (Kota et al., 2001). This recent approach allows researchers to shift from the use of anonymous markers with unknown effect on the phenotype to markers physically associated with coding regions, which may more accurately reflect the effects of selection, both natural and artificial.
The objectives of the present study were to (i) evaluate the use of genomic and EST-derived SSRs for determining the genetic diversity among advanced spring bread wheat lines from the CIMMYT breeding program, (ii) compare genetic distances based on SSRs with the COP estimates of these wheat lines, and (iii) determine the diversity for SSRs within and among sets of lines targeted to different MEs.
|
MATERIALS AND METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
-lattice designs at Cd. Obregon (Sonora, Mexico) or Toluca (Mexico State, Mexico) in 2000 and 2001 under conditions simulating the different MEs for which they are being bred (e.g., full irrigation, reduced irrigation, drought, and heat stress, etc.). On the basis of their performance, 8 to 15 advanced lines were selected from yield trials representative of the first five spring bread wheat MEs (Table 2). The lines were chosen as candidates for further evaluation at international testing sites (Van Ginkel et al., 2002). Progenies from three crosses (Alucan/Duluca, PF869107/CEP8825//Milan and Babax/Amadina//Babax) were identified and selected for more than one ME.
|
|
DNA. SSR information was obtained from two different sources: 46 SSRs were collected from a conventional genomic library (genomic SSRs) developed at IPK Gatersleben by Röder et al. (1998)(and unpublished data) and 51 SSRs derived from ESTs (EST-SSR) with the prefix "DuPw" were kindly provided by DuPont, Wilmington, DE (Dupont, unpublished data; Eujayl et al., 2002). The SSRs from both sources were distributed equally over the genome. In addition, the 1BS/1R translocation EST-SSR marker Taglgap (Devos et al., 1995) and the SSR marker WMC56 developed by the Wheat Microsatellite Consortium (Agrogene, France) were used. Details for each of the 99 SSRs can be found online (http://www.cimmyt.org/english/webp/support/publications/support_materials/ssr_mw1.htm; verified 23 September 2003).
PCR reactions were performed in a model PTC225 thermocycler (MJ Research, Inc., Waltham, MA). Each 20-µL reaction mixture contained 25 ng template DNA, 150 nM of each primer, 250 µM dNTPs, 200 µM MgCl2, 1x PCR buffer and 2.5 U of Taq-polymerase. Forward primers were labeled at the 5' end with either one of three phosphoramidite fluorescent dyes 6-carboxyfluoresein, tetrachloro-6-carboxyfluoresein, or hexachloro-6- carboxyfluoresein. PCR was performed with the following standard temperature profile: 29 cycles with a 1 min denaturing step at 94°C, 2 min annealing temperatures between 50 and 64°C depending on the different primer combinations, and 2 min extension at 72°C. The 1-min time spread of the standard profile cycle was modified in some cases to fully optimize amplification conditions.
Amplification products were separated on an ABITM377 Sequencer (Perkin Elmer/Applied Biosystems, Foster City, CA) using 4.5% (w/v) polyacrylamide denaturing gels (acrylamide:bisacrylamide 29:1). Running conditions were 2400 V, 40 mA, 120 W electrophoresis power and 40 mW laser power. Products from up to five SSRs could be distinguished simultaneously because of the three different fluorescent dyes and migration distance differences. Fragment sizes were calculated semiautomatically by the computer software GeneScan 3.1 (Perkin Elmer/Applied Biosystems) by comparing fragments with an internal size standard (GeneScan 350 or 500) labeled with N,N,N,N,-tetramethyl-6-carboxyrhodamine. GeneScan fragments were assigned to alleles by the category function of the software Genotyper 2.1 (Perkin Elmer/Applied Biosystems). Sixty-four genotypes were run on each gel plus two wheat lines, Opata and Synthetic, as controls.
We could not optimize the amplification profile of nine SSRs for the scoring with Genotyper. These markers were optimized to run on small (16 by 20 cm) 6% (w/v), 19:1 acrylamide:bis-acrylamide denaturing gels (ATTO8 AE-6220). The gels were run for 2 h at about 350 V, with a 100-bp ladder as a standard. For fragment visualization, silver staining was applied according to Applied Biotechnology Center's Manual of Laboratory Protocols. The fragment length of each SSR was determined with the scientific image system Kodak ID 2.02 (Kodak, New Haven, CT).
Statistical Analyses
Reproducibility of SSR amplification and scoring was determined on the basis of the percentage of disagreements in the fragment size of the two standard lines, Opata and Synthetic, for gels loaded with the same markers. Allele frequencies at the 99 loci, total gene diversity (HT), gene diversity within MEs (HS), and the proportion of diversity resulting from gene differentiation between MEs (GST) were calculated according to Nei (1987). The measures were considered separately for the two SSR sources to examine the influence of the genome location of the markers (genomic and EST-SSRs).
The COP for all pairwise combinations of wheat lines was calculated on the basis of fully expanded genealogical information extracted from CIMMYT's International Wheat Information System (Payne et al., 2002). Calculations of COP were based on the assumptions described by St. Martin (1982), except for sister lines, which were assigned a COP of 0.56 instead of 1.0 following Cox et al. (1985). Genetic similarity (GS) assigned as 1 – Rogers' distance (Rogers, 1972) was estimated to compare SSR-based and COP estimates. Pearson's correlation coefficient (r) between GS and COP values was calculated for related pairs of lines (COP 
0.05). Standard errors of genetic similarity estimates were obtained by a bootstrap procedure with resampling over markers (Weir, 1996). Furthermore, modified Rogers' distance (Wright, 1978) was calculated among all possible pairs of lines as a basis for the application of multivariate methods, because it represents a Euclidean distance.
An analysis of molecular variance (AMOVA) on the basis of SSR data was computed to test the differentiation of the 68 genotypes according to the five MEs. The hierarchical analysis divides the total variance into variance components due to intra- or inter-ME differences and tests their significance. Principal coordinate analysis was performed on the basis of the modified Rogers' distances to visualize the dispersion of the genotypes (Gower, 1966). The K-means clustering algorithm was used to identify groups of similar lines, on the basis of a least-squares partitioning method, which divides a collection of objects into k clusters depending on minimum distances to the centers of the clusters (MacQueen, 1967). COP values were calculated to the six progenitors most frequently used in the crosses and averaged within each ME and K-means cluster.
All analyses were performed with the Plabsim software (Frisch et al., 2000), which is implemented as an extension of the statistical software R (Ihaka and Gentleman, 1996). The AMOVA was performed by the software package Arlequin 2.0 (Schneider et al., 2000).
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
|
The mean COP value over the 68 wheat genotypes was 0.14 and ranged from 0.01 to 0.87 for closely related pairs (Table 4). The mean COP values within MEs did not substantially differ between the five MEs. Thirty-nine percent of the COP values were smaller than 0.10, indicating that theoretically less than 10% of the genetic material segregating in ancestral populations was identical by descent in any two cultivars.
|
The mean COP and GS values between MEs were of similar size as the means within MEs (Table 5). ME1HT was most distant to ME2 and ME3 on the basis of COP, and ME1IR most distant to ME3 on the basis of GS values. The AMOVA confirmed these results in that 92% of the total variation was found within MEs and just 8% between MEs (data not shown). The correlation between GS and COP values was r = 0.43.
|
|
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
Our results agree with other studies in rice (Cho et al., 2000), grape (Vitis ssp., Scott et al., 2000), and wheat (Eujayl et al., 2002) in that the overall level of polymorphism for genomic SSRs was higher than for EST-SSRs. However, compared with the latter study with 64 durum wheat lines, where on average 5.5 alleles per locus for genomic SSRs and 4.1 for EST-SSRs were found, we detected slightly lower average numbers of alleles for both genomic (5.2) and EST-SSRs (3.2). The somewhat higher level of diversity reported by Eujayl et al. (2002) may be attributable to the more variable material in their study, which comprised a sample of various lines and landraces with different genetic backgrounds. Here, we studied advanced breeding lines ready for international dissemination to developing country breeding programs.
Higher-order repeat motifs were applied in our sample of EST-SSRs. In comparison to dinucleotide motifs, they are generally less polymorphic and insensitive to single-nucleotide polymorphisms in the flanking regions of the SSRs, which facilitate the designation of allele sizes (Chakraborty et al., 1997; Song et al., 2002; Mogg et al., 2002). This explains the higher reproducibility and the lower degree of heterozygosity or heterogeneity for EST-SSRs compared with genomic SSRs in our study.
EST-SSRs are assumed to reflect more accurately the effects of selection under which the germplasm has been developed. However, only a slightly better differentiation of the allele frequencies between MEs was observed for EST-SSRs than for genomic SSRs. Wheat is highly autogamous and, therefore, differential selection of fitness-related target loci will also affect genomic SSRs linked to them. Further evidence shows a functional importance of genomic SSR structures, which may cause some form of balancing selection (Innan et al., 1997; Li et al., 2000; Li et al., 2002).
Future opportunities to combine markers and phenotypic data in association studies may improve the application of EST-SSRs in the evaluation of germplasm, as exemplified in maize by Thornsberry et al. (2001). A specific marker could then be used to examine the functional diversity at a certain locus. We speculate that EST-SSRs from genes contributing to specific ME adaptation might have revealed more clearly the effects of selection in our germplasm.
Correlation between Pedigree and SSR-Based Distance Estimates
In agreement with previous studies in wheat, the correlation between GS and COP estimates reported here was low but significant (r = 0.43). This low correlation can be explained by the unrealistic assumptions in calculating COP values and the substantial variation in GS estimates of unrelated lines (Graner et al., 1994). With increasing relatedness of lines, the association between COP and their corresponding GS values should become tighter. In fact, a higher correlation (r = 0.55) between COP (COP 0.01) and GS estimates was found in the study of Plaschke et al. (1995), where the average COP among a set of European wheats was 0.29. However, selection and drift are presumably also important factors reducing the correlation between GS and COP due to a shift in the genomic contribution of parental lines, particularly during the early selfing generations.
Variation of Genetic Diversity in Megaenvironments
Provided molecular markers represent an accurate picture for the genetic diversity at functional genes, the true genetic differences between the germplasm targeted for different MEs are small. Several reasons might explain this observed absence of genetic differentiation: (i) selection based on ME adaptation has not been practiced long enough to differentiate the germplasm, (ii) genes conferring fitness to one ME are not unique to that ME and may confer fitness to several MEs, and (iii) adaptation to MEs is not based on an accretion of random genes but rather a limited set of specific genes.
CIMMYT's concept of breeding for different MEs was implemented in the 1980s. Thus, selection history for ME adaptation has presumably been too short to result in a detectable genetic differentiation. In addition, shuttle breeding between two environmentally contrasting sites in Mexico (Cd. Obregon and Tuluca) during the selfing generations and intermating of germplasm adapted to different ME may have leveled the genetic differentiation between MEs (Rajaram and Van Ginkel, 2001).
The uniform level of relatedness between genotypes targeted to the five MEs (Table 4) and the principal coordinate analysis (Fig. 1) suggest the presence of a single core germplasm providing genes or gene combinations conferring fitness to several MEs. This is not surprising, because some MEs differ only in few of the classification criteria. For example, the high rainfall environment ME3 can be characterized as a specific subenvironment of ME2 and, consequently, selection to both environments depends on the same major abiotic and biotic stresses (Table 2).
Although many genetic diversity studies confirmed the use of SSRs for germplasm identification, it is an open question whether they are able to reveal functional diversity. Adaptation to different ME might be attributable to only a small number of genes regulating the underlying physiological processes, which were not reflected by the applied SSRs (Nevo, 2001). An exception was the cluster of acid soil resistant lines in Fig. 1, but this could also be explained by the fact that 8 of the 14 lines had the highly acid soil resistant line Tinamou as a parent in their pedigree.
High levels of genetic diversity were found within the germplasm targeted to each ME. To warrant diversity in every breeding cycle, on average about 25% of the crossing block entries are replaced with outstanding new introductions (Van Ginkel et al., 2002). In our study too, at least 20 wheat lines were included with ancestors from nonconventional sources such as Chinese, durum, or synthetic hexaploid wheats.
The high genetic diversity observed within each ME is desired in the CIMMYT wheat program for two reasons. First, the wheat producing areas combined in each ME are fairly diverse and often transcontinental. Diseases and especially races of fungal pests may vary considerably between regions as well as quality demands due to different wheat processing techniques and utilization of the end product. Furthermore, environmental conditions in the target areas are fluctuating based on highly variable seasons (Trethowan et al., 2002). Newly developed CIMMYT lines should reach the cooperating wheat research programs in the respective MEs with still sufficient inherent genetic variation remaining among them for many traits. This enables cooperators to reselect and release cultivars adapted to their own local needs. Therefore, a sufficient number of diverse CIMMYT breeding lines for each ME reduces the risk of genetic vulnerability.
Second, breeding conditions at the test sites in Mexico and the targeted ME are not always closely associated. Trethowan et al. (2001) examined the relationship among various locations using yield data from CIMMYT's Semi-Arid Wheat Yield Trials corresponding to ME4. They found that the yield performance under residual moisture stress at the test location in Cd. Obregon (Mexico) was a good predictor of yield performance at other locations experiencing equivalent late drought stress, but this was not true when drought patterns were different, e.g., when early drought stress occurred.
The common genetic base of our germplasm targeted to different ME did not reflect CIMMYT's efforts to breed for a large number of environments. We speculate that assembling more diversified germplasm pools for different MEs and yield evaluation at ME-specific key sites could achieve higher genetic differentiation. However, increased genetic diversity does not necessarily lead to higher productivity or adaptation. While SSRs are a powerful tool for genotype identification, their usefulness for revealing genotype differentiation regarding specific traits such as adaptation to certain MEs could not be proven in the present study and warrants further research. In particular, the augmented use of EST-SSRs from genes with known functions should be very powerful for this purpose and, thus, could give further directions in the management of wheat breeding programs aiming to optimally serve their clients.
|
ACKNOWLEDGMENTS |
|---|
Received for publication March 17, 2003.
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
This article has been cited by other articles:
|
|
 |
|
  S. Chao, W. Zhang, J. Dubcovsky, and M. Sorrells Evaluation of Genetic Diversity and Genome-wide Linkage Disequilibrium among U.S. Wheat (Triticum aestivum L.) Germplasm Representing Different Market Classes Crop Sci., May 31, 2007; 47(3): 1018 - 1030. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Reynolds, F. Dreccer, and R. Trethowan Drought-adaptive traits derived from wheat wild relatives and landraces J. Exp. Bot., January 1, 2007; 58(2): 177 - 186. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. M. Bonman, H. E. Bockelman, B. J. Goates, D. E. Obert, P. E. McGuire, C. O. Qualset, and R. J. Hijmans Geographic Distribution of Common and Dwarf Bunt Resistance in Landraces of Triticum aestivum subsp. aestivum Crop Sci., May 18, 2006; 46(4): 1622 - 1629. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Ram, N. Jain, V. Dawar, R. P. Singh, and J. Shoran Analyses of Acid-PAGE Gliadin Pattern of Indian Wheats (Triticum aestivum L.) Representing Different Environments and Periods Crop Sci., May 27, 2005; 45(4): 1256 - 1263. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Dreisigacker, P. Zhang, M. L. Warburton, B. Skovmand, D. Hoisington, and A. E. Melchinger Genetic Diversity among and within CIMMYT Wheat Landrace Accessions Investigated with SSRs and Implications for Plant Genetic Resources Management Crop Sci., February 23, 2005; 45(2): 653 - 661. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| The SCI Journals | Agronomy Journal | Vadose Zone Journal | |||
| Journal of Natural Resources and Life Sciences Education |
Soil Science Society of America Journal | ||||
| Journal of Plant Registrations | Journal of Environmental Quality |
The Plant Genome | |||
