ArticleThe plant genome2021
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.
Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Balancing gain and diversity: Optimal contribution selection for sustainable genetic improvement in sugarcane.The plant genome · 2026Review
- Optimising realised genetic gain with low forward predictive ability across cycles for cooking time and correlated traits in common bean based on multivariate genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Performance of multi-trait and single-trait genomic selection for grain Fe and Zn concentrations in sorghum under different breeding constraints.Scientific reports · 2025Article
- Advances in breeding for enhanced iron and zinc concentrations in common bean in eastern Africa.Journal of experimental botany · 2025Review
- Evolutionary computing to assemble standing genetic diversity and achieve long-term genetic gain.The plant genome · 2024Article
- Plant Iron Research in African Countries: Current "Hot Spots", Approaches, and Potentialities.Plants (Basel, Switzerland) · 2023Review
- Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Accuracy of Selection in Early Generations of Field Pea Breeding Increases by Exploiting the Information Contained in Correlated Traits.Plants (Basel, Switzerland) · 2023Article
- Optimal Contribution Selection Improves the Rate of Genetic Gain in Grain Yield and Yield Stability in Spring Canola in Australia and Canada.Plants (Basel, Switzerland) · 2023Article
- Validation of a QTL on Chromosome 1DS Showing a Major Effect on Salt Tolerance in Winter Wheat.International journal of molecular sciences · 2022Article
- The Plant Genome special issue: Advances in genomic selection and application of machine learning in genomic prediction for crop improvement.The plant genome · 2021Article
- 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.The plant genome · 2021Article
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15 authors.
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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.
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