Common bean is one of the widely consumed food security crop in Africa, Asia and South America. It is a rich source of protein, minerals and micronutrients. High genotype by environment interaction is one of the main challenge in breeding for high grain micronutrient concentration. The objective of this study was to estimate SNP markers associated with grain Fe and Zn concentration using 289 common bean genotypes and 11,480 SNP markers. The study revealed that 43 quantitative trait loci (QTLs) were associated with grain Fe and Zn concentration. Five quantitative trait nucleotides (QTNs), that is, QTN Fe_1.1, QTN Fe_6.3, QTN Fe_6.5, QTN Fe_10.3 and QTN Fe_11.6 were detected both at Haramaya and Melkassa locations. Two of the markers, that is, QTN Fe_6.3 and QTN Fe_6.5, were located on chromosome 06 while QTN Fe_1.1, QTN Fe_10.3 and QTN Fe_ 11.6 were residing on chromosomes 01, 10 and 11, respectively. Among these, QTN Fe_11.6 had a large and positive consistent effect across locations. The five stable QTNs along with the potential candidate genes could be used for Fe biofortification through marker assisted selection.

Introduction Common bean is one of the widely consumed food security crop in Africa, Asia, and South America. Understanding genetic diversity and population structure is crucial for designing breeding strategies. Materials Two hundred and eighty-nine germplasm were recently collected from different regions of Ethiopia and introduced from CIAT to estimate genetic diversity and population structure using 11,480 DArTSeq SNP markers. Results The overall mean genetic diversity and polymorphic information content (PIC) were 0.38 and 0.30, respectively, suggested the presence of adequate genetic diversity among the genotypes. Among the geographical regions, landraces collected from Oromia showed the highest diversity (0.39) and PIC (0.30). The highest genetic distance was observed between genotypes collected from SNNPR and CIAT (0.49). In addition, genotypes from CIAT were genetically more related to improved varieties than the landraces which could be due to sharing of parents in the improvement process. The analysis of molecular variance revealed that the largest proportion of variation was due to within the population both in geographical region (63.67%) and breeding status (61.3%) based classification. Model-based structure analysis delineated the 289 common bean genotypes into six hypothetical ancestoral populations. Conclusions The genotypes were not clustered based on geographical regions and they were not the main drivers for the differentiation. This indicated that selection of the parental lines should be based on systematic assessment of the diversity rather than geographical distance. This article provides new insights into the genetic diversity and population structure of common bean for association studies, designing effective collection and conservation for efficient utilization for the improvement of the crop.

In the present investigation, the genetic variability of 64 speckled type common bean genotypes were evaluated at Haramaya University during 2015 cropping season using 8 × 8 simple lattice design with three replications. The analysis of variance indicated highly significant (P<0.01) differences among genotypes for all the nine characters studied. High genotypic coefficient of variation (GCV) was observed for grain yield, while high phenotypic coefficients of variation (PCV) were recorded for number of seeds plant -1 , grain yield ha -1 and common bacterial blight resistance. For all the traits, estimates of PCV were higher than GCV indicating the presence of environmental influence. High heritability values and genetic advances were recorded in grain yield. Cluster analysis using Mahalanobis distance delineated the genotypes into six main groups. Cluster I contain the largest number of genotypes (43.75%) followed by clusters II (26.56%) and III (10.94%) while clusters IV, V and VI contain four genotypes each. The D 2 analysis indicated that there was a significant difference among the clusters. The maximum inter cluster distance was observed between cluster IV and V. Principal component analysis (PCA) was performed to assess the variation and correlation among genotypes for the traits and grouped them based on their performance. The combination of the first four principal components explained more than 85.74% of the genotypic variations. Therefore, exploiting the genetic diversity among clusters would broaden the genetic base of speckled bean breeding populations.