Multivariate genomic analysis and optimal contributions selection predicts high genetic gains in cooking time, iron, zinc, and grain yield in common beans in East Africa

Abstract

Common bean (Phaseolus vulgaris L.) is important in African diets for protein, iron (Fe), and zinc (Zn), but traditional cultivars have long cooking time (CKT), which increases the time, energy, and health costs of cooking. Genomic selection was used to predict genomic estimated breeding values (GEBV) for grain yield (GY), CKT, Fe, and Zn in an African bean panel of 358 genotypes in a two‐stage analysis. In Stage 1, best linear unbiased estimates (BLUE) for each trait were obtained from 898 genotypes across 33 field trials in East Africa. In Stage 2, BLUE in a training population of 141 genotypes were used in a multivariate genomic analysis with genome‐wide single nucleotide polymorphism data from the African bean panel. Moderate to high genomic heritability was found for GY (0.45 ± 0.10), CKT (0.50 ± 0.15), Fe (0.57 ± 0.12), and Zn (0.61 ± 0.13). There were significant favorable genetic correlations between Fe and Zn (0.91 ± 0.06), GY and Fe (0.66 ± 0.17), GY and Zn (0.44 ± 0.19), CKT and Fe (−0.57 ± 0.21), and CKT and Zn (−0.67 ± 0.20). Optimal contributions selection (OCS), based on economic index of weighted GEBV for each trait, was used to design crossing within four market groups relevant to East Africa. Progeny were predicted by OCS to increase in mean GY by 12.4%, decrease in mean CKT by 9.3%, and increase in mean Fe and Zn content by 6.9 and 4.6%, respectively, with low achieved coancestry of 0.032. Genomic selection with OCS will accelerate breeding of high‐yielding, biofortified, and rapid cooking African common bean cultivars.

Optimal Contribution Selection Improves the Rate of Genetic Gain in Grain Yield and Yield Stability in Spring Canola in Australia and Canada

“…BRIO principles are flexible to include new technology as it arises. For example, we applied these principles to the breeding of a common bean with genomic relationship information [ 32 ]. Genomic relationship information (G-BLUP) may be combined with additive relationship information (A-BLUP) in single-step H-BLUP analysis [ 31 ] which should further improve the accuracy of the PBV and help to accelerate genetic gain.…”

“…New germplasm can be added to the program in any cycle. In some crops, it may be necessary to bulk seeds from single S 0 plants over 2 selfing generations (S 0,2 bulks) to generate sufficient seed for phenotyping [ 20 ], such as in common bean [ 32 ].…”

Validation of a QTL on Chromosome 1DS Showing a Major Effect on Salt Tolerance in Winter Wheat

“…The biology of cellular plants strengthens our understanding of the diverse network of traits related to salinity tolerance and increases the structural genomics and functional methods suitable for use in the detection of the quantitative trait loci (QTLs) genes of interest linked with specific traits [ 57 ]. Therefore, understanding salt tolerance mechanisms and analyzing salt stress-related genes and their functions will provide a theoretical basis for understanding the stress signal network and pathways for the improvement of the target crop [ 58 ].…”

Potential Breeding Strategies for Improving Salt Tolerance in Crop Plants

“…Current crop physiology, genetics, and genomics techniques have given rise to new intuitions regarding tolerance in salinity, thereby providing a more profound knowledge of networks related to genes and new methods help to achieve the zero hunger goal (Lee et al 2011;Rahman et al 2019Rahman et al , 2020a. The biology of cellular plants strengthens our understanding of the diverse network of traits related to salinity tolerance and increases the structural genomics and functional methods suitable for use in the detection of the quantitative trait loci (QTLs) genes of interest linked with specific traits (Saradadevi et al 2021). The genes are the key targets for transgenic generation and through molecular breeding, the QTLs can be used to improve crops.…”