No abstract

The genetic architectures of agronomic and yield-related traits are expected to involve multiple loci that are unlikely all to segregate for alternative alleles in a single bi-parental population. Therefore, the identification of quantitative trait loci (QTL) that are expressed in diverse genetic backgrounds of multiple bi-parental populations provides evidence about both background-specific and common genetic variants. The purpose of this study was to map QTLs for agronomic and yield related traits using three connected mapping populations of different genetic backgrounds, to gain insight into the genomic landscape of these important traits in elite Ethiopian sorghum germplasm. The three bi-parental populations, each with 207 F 2:3 lines were evaluated using an alpha lattice design with two replications under two moisture stress environments. Data analysis was done separately for each population using composite interval mapping, finding a total of 105 QTLs. All the QTLs identified from individual populations were projected on a combined consensus map, comprising a total of 25 meta QTLs for seven traits. The consensus map allowed us to deduce locations of a larger number of markers than possible in any individual map, providing a reference for genetic studies in different genetic backgrounds. The meta QTLs identified in this study could be used for marker-assisted breeding programs in sorghum after validation. Only one trait reduced leaf senescence, showed a striking bias of allele distribution, indicating substantial standing variation among the lines that might be employed in improving drought tolerance of sorghum.

Bread wheat is the world’s leading cereal grain, and more than one-third of the world’s population uses it as a staple food. The bread wheat production in Ethiopia is low compsssared to the national average yield, mainly due to the lack of high-yielding genotypes. This study was conducted during the 2019-2020 growing season to assess genetic variability and estimate the association of traits among bread wheat genotypes. The experiment consists of 49 bread wheat genotypes and is laid out in 7 × 7 simple lattice designs. The results showed significant differences ( p  < 0.01) among genotypes for most of the studied traits. Moderate genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) values were estimated for yield plant −1, thousand seed weight, and biomass yield. High heritability coupled with a high GAM was observed for thousand seed weight and yield plant –1. The grain yield showed a highly significant ( p  < 0.01) correlation with many yield-related traits at the phenotypic and genotypic levels. The biomass yield and the harvest index exerted the highest positive direct effect on grain yield at the genotypic level. The highest intercluster distance was observed between clusters I and IV (D2 = 31.86 ∗ ∗ ), followed by clusters II and IV (D2 = 29.21 ∗ ), and clusters II and III (D2 = 28.24 ∗ ), which indicated the chance of selecting a member of these clusters for hybridization. This experiment’s result indicates sufficient genetic variability among the tested genotypes, which provides ample scope for selecting superior and desired genotypes. Best-performed genotypes should be included in the future breeding program for further yield improvement. In conclusion, attention should be given to traits with moderate to high heritability and GAM, exerting a positive direct effect on the grain yield. However, the experiment should be repeated over locations and seasons to draw a definite conclusion.