Evaluation of variability of agronomic and morphological traits in doubled haploid wheat lines using multivariate statistical methods

To date, wheat counts as one of the most important cereal grains, feeding the increasing world population (Feldmann, 2001). Evaluation of the genetic diversity of wheat genotypes will provide a great opportunity to improve its yield quality and increase its grain output. The results of previous studies on wheat have shown correlations among grain yield and its components such as number of tillers, spike length, grain number per spike, thousand-kernel weight and harvest index. However, because of complex relations among different traits and their interactions with grain yield of wheat, simple correlations of these traits can not be directly and unequivocally used to clarify those relationships. Therefore, different statistical techniques can be employed in modeling the crop yield, including correlation, regression, path analysis, factor analysis, factor components, and cluster analysis (Leilah & Al-Khateeb 2005). The goal of this study was to identify variability in grain yield and some agronomic and morphological traits and their relationship among 150 doubled haploid wheat lines. Materials and Methods In this study, morphological and phenotypic traits of 150 doubled haploid wheat lines derived from three different crosses: 1) DH-26: Ghods*3/MV17, 2) DH-27: Flanders/3*Ghods, 3) DH-28: Hybrid Bersee/*3Ghods, which consisted of 75, 45 and 30 individuals, respectively, were evaluated. The experiment was conducted using an augmented design with six check cultivars including Parsi, Mihan, Bolani, Ghods, Hybrid Bersee and MV17, which were repeated over five blocks. Some agronomic traits such as days to %50 of flowering, days to maturity, spike length, number of spikelets per spike, number of grains per spike, grain weight per spike, thousand kernel weight, plant height, grain density per spike and grain yield of DH lines as well as check cultivars were evaluated. DH lines were classified with hierarchical cluster analysis using Ward method as well as using multivariate statistical methods such as factor analysis, stepwise regression and path analysis. Statistical procedures were carried out using SAS 9.0 and SPSS 16 software packages. Results and Conclusion The analysis of variance showed that there were significant differences for days to %50 of flowering, days to maturity, spike length, number of spikelets per spike, number of grains per spike, grain weight per spike, thousand kernel weight, plant height, grain density per spike and grain yield. The determination of coefficients of variations for phenotypic and physiological traits of all three DH populations indicated that most of the studied traits had a high genetic variability. Evaluation of grain yield among different population showed that DH-26 population with an average grain yield of 4.885 T/ha had higher grain yield compared to other two populations, although within each population several lines with higher yields and superior agronomic and morphological traits than their parents and control cultivars were identified. The coefficients of correlations among yield components showed that grain yield had a positive and significant correlation with grain weight per spike and thousand-kernel weight while its correlation with days to 50% of flowering, days to maturity and number of spikelets per spike was negative and significant. The results of factor analysis indicated that three major factors effective on earliness, spike and plant height explained %68.47 of the total variation. The results of stepwise regression analysis showed that thousands kernel weight had the most important effect on grain yield and explained 8.8% of the variation. The path analysis of data indicated that, number of spikelets per spike and thousand-kernel weight had the highest direct effects on grain yield. The current genetic variability of DH lines, which are derived from parents with good agronomic traits, especially yellow rust resistance (Bakhtiar et al., 2015), can be employed for the selection of the superior genotypes that possess all beneficial characteristics.