Abstract
Hybrid performance during wheat breeding can be improved by analyzing genetic distance (GD) among wheat genotypes and determining its correlation with heterosis. This study evaluated the GD between 16 wheat genotypes by using 60 simple sequence repeat (SSR) markers to classify them according to their relationships and select those with greater genetic diversity, evaluate the correlation of the SSR marker distance with heterotic performance and specific combining ability (SCA) for heat stress tolerance, and identify traits that most influence grain yield (GY). Eight parental genotypes with greater genetic diversity and their 28 F1 hybrids generated using diallel crossing were evaluated for 12 measured traits in two seasons. The GD varied from 0.235 to 0.911 across the 16 genotypes. Cluster analysis based on the GD estimated using SSRs classified the genotypes into three major groups and six sub-groups, almost consistent with the results of principal coordinate analysis. The combined data indicated that five hybrids showed 20% greater yield than mid-parent or better-parent. Two hybrids (P2 × P4) and (P2 × P5), which showed the highest performance of days to heading (DH), grain filling duration (GFD), and GY, and had large genetic diversity among themselves (0.883 and 0.911, respectively), were deemed as promising heat-tolerant hybrids. They showed the best mid-parent heterosis and better-parent heterosis (BPH) for DH (-11.57 and -7.65%; -13.39 and -8.36%, respectively), GFD (12.74 and 12.17%; 12.09 and 10.59%, respectively), and GY (36.04 and 20.04%; 44.06 and 37.73%, respectively). Correlation between GD and each of BPH and SCA effects based on SSR markers was significantly positive for GFD, hundred kernel weight, number of kernels per spike, harvest index, GY, and grain filling rate and was significantly negative for DH. These correlations indicate that the performance of wheat hybrids with high GY and earliness could be predicted by determining the GD of the parents by using SSR markers. Multivariate analysis (stepwise regression and path coefficient) suggested that GFD, hundred kernel weight, days to maturity, and number of kernels per spike had the highest influence on GY.
Highlights
Wheat (Triticum aestivum L.), one of the most prominent food crops worldwide, is one of the most essential sources of protein in humans
The results of stepwise multiple linear regression (SMLR) showed that grain filling duration (GFD), Days to maturity (DM), hundred kernel weight (HKW), and number of kernels per spike (NKS) were significantly correlated to grain yield (GY), and their contribution rates were 0.294, 0.241, 0.111, and 0.076, respectively (Table 7); after the GFR parameter was removed, the R2 of this model was 0.722
Morphological analyses frequently require a large set of phenotypic data and more cropping seasons for screening and evaluation, which are often hindered by high environmental influences
Summary
Wheat (Triticum aestivum L.), one of the most prominent food crops worldwide, is one of the most essential sources of protein in humans. It represents 17% of the global crop area, feeding about 40% of world’s population and providing 20% of the total diet calories [1]. Agricultural productivity is remarkably affected by extreme weather events Multiple challenges such as high temperature stress and reduced water availability are the major concerns for all countries in the Arab region [2]. The incidence of diseases and pest infestation is increasing with global warming [3] All these negative influences threaten the sustainability of grain crop production. The steady rise in population, loss of agricultural lands to sustainable urbanization, and decrease in resource availability owing to climate change pose serious threats to the safe production of wheat [3]
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