General Commission for Scientific Agricultural-
Research, Aleppo Research Center
Tishreen University, Faculty of Agriculture
The understanding and study of wheat genetic composition is considered the basic mainstay for successful plant breeding programs which are relying more and more on new methodologies that are faster, more effective and reliable to produce improved cultivars with higher yield.
This research aimed to identify the most important wheat genotypes available in Syria and of importance to the General Commission for Scientific Agricultural Research (GCSAR), using morphological, biochemical and molecular characterization methods and compare the effectiveness and efficiency of these methods. In addition, the ability of some of these methods to detect the degree of homogeneity within the varieties was also performed. The research also aimed at studying marker-trait association using General Linear Model (GLM) to investigate reciprocal effects between studied loci and morphological characteristics.
Results of the morphological characterization showed that the studied durum and bread wheat genotypes reflected a high percentage of variability within both groups of genotypes. A higher percentage of heterogeneity was also realized within local varieties as compared with the improved ones for both durum and bread wheat varieties. When regression analysis was applied to the studied traits, significant differences were noted in three characteristics (for both types of wheat) namely: number of grains per spike, one thousand grain weight and the weight of grains per spike. A significant correlation was evident between grain weight and number of grains (in both types of wheat) whereas, all other correlations whether positive or negative were insignificant.
Regarding the biochemical characterization, both applied techniques i.e. A-PAGE and SDS-PAGE were able to show band variability among studied varieties with total polymorphic bands of 25. The percentage of genetic variation was 56% and 100% for durum and bread wheat varieties respectively, and the dendrogram showed two separate clusters according to the genome level i.e. the tetraploid level (durum wheat) and the hexaploid level (bread wheat). It was concluded that it is possible to use storage proteins as a useful indicator for characterizing wheat cultivars, studying genetic variability, registering new cultivars, and testing pedigree.
On the other hand, the aforementioned methodology was applied to detect the homogeneity within three durum and three bread wheat cultivars by testing 48 grains from each cultivar. Results revealed presence of heterogeneity in all gliadin and glutenin loci in all tested cultivars except for glutenin loci in the bread wheat cultivars Buhouth6 and Soued. Heterogeneity was higher in gliadin loci than that of glutenins. Within the gliadins, ‰- and – gliadins were the most heterogeneous followed by ²- gliadins, whereas, ±- gliadin was homogeneous with no single polymorphism being detected. As for glutenin loci, GLU-B1 was the most heterogeneous followed by GLU-D1 and GLU-A1 with the latter being distinguished with the presence of null allele in all durum wheat studied except for the local variety Nab-al-Jamal which carried the allelic subunit 2*. All improved durum and bread wheat varieties were characterized by a lower heterogeneity index (HetI) compared with the local varieties. It was concluded that in order to have complete picture of storage protein heterogeneity, it is imperative to use both electrophoresis techniques.
As for the molecular characterization, the same cultivars were assayed using the SSR- PCR based technique. The used 32 microsatellites covered almost the whole wheat genome. The polymorphic information content (PIC) across the tested loci ranged from 0 to 0.88 with average values of 0.57 and 0.65 for durum and bread wheat respectively. B-genome had the highest mean number of alleles (10.91) followed by A- genome (8.3) whereas D- genome had the lowest number (4.73). The correlation between PIC and allele number was significant in all genome groups accounting for 0.87, 0.74 and 0.84 for A, B and D genomes respectively, and over all genomes, the correlation was higher in tetraploid (0.8) than in hexaploid wheat varieties (0.5). The cluster analysis discriminated all varieties and clearly divided the two ploidy levels into two separate clusters that reflect the differences in genetic diversity within each cluster. This study demonstrates that microsatellites markers have unique advantages compared to other molecular and biochemical fingerprinting techniques in revealing the genetic diversity in Syrian wheat varieties that is crucial for wheat improvement.
The three methods were compared for several indices to know the relative efficiency of each method in the characterization of the studied genotypes. The morphological characterization method attained highest similarity index value (0.51) followed by the biochemical characterization methods (0.49), whereas, the molecular characterization method attained the lowest value (0.264). On the contrary, the molecular characterization method attained highest value for the genetic diversity index (1.16) followed by the biochemical characterization methods (0.55) and the morphological characterization method (0.421). The resulted genetic distances from all three methods were subject to regression analysis. Results showed that there was only one significant positive correlation (0.48) and this was between the biochemical and molecular methods, whereas no significant correlation was obtained between morphological and the molecular characterization methods (0.039) nor between the biochemical and morphological characterization methods (-0.022).
At last, a marker-trait association analysis was performed using General Linear Model (GLM) to investigate reciprocal effects between studied loci and morphological characters. The studied 32 SSR loci were tested for its association with the eight different morphological characters (32 x 8= 248). Out of that number, only 83 associations were significant, of which 25 associations were within spike length, 24 within awn length, 12 within one thousand grain weight, 8 within number of grains per spike, 7 within number of spikelets, 6 within grain weight/spike, and 3 within plant height. As for empty spikelets, a single association was revealed between it and one locus.