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
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 (GEB...
| Autores principales: | , , , , , , , , , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2021
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/115724 |
| _version_ | 1855517166102642688 |
|---|---|
| author | Saradadevi, Renu Mukankusi, Clare Li, Li Amongi, Winnyfred Mbiu, Julius Peter Raatz, Bodo Ariza, Daniel Beebe, Stephen E. Varshney, Rajeev K. Huttner, Eric Kinghorn, Brian Banks, Robert Rubyogo, Jean-Claude Siddique, Kadambot H.M. Cowling, Wallace A. |
| author_browse | Amongi, Winnyfred Ariza, Daniel Banks, Robert Beebe, Stephen E. Cowling, Wallace A. Huttner, Eric Kinghorn, Brian Li, Li Mbiu, Julius Peter Mukankusi, Clare Raatz, Bodo Rubyogo, Jean-Claude Saradadevi, Renu Siddique, Kadambot H.M. Varshney, Rajeev K. |
| author_facet | Saradadevi, Renu Mukankusi, Clare Li, Li Amongi, Winnyfred Mbiu, Julius Peter Raatz, Bodo Ariza, Daniel Beebe, Stephen E. Varshney, Rajeev K. Huttner, Eric Kinghorn, Brian Banks, Robert Rubyogo, Jean-Claude Siddique, Kadambot H.M. Cowling, Wallace A. |
| author_sort | Saradadevi, Renu |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace115724 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1157242025-11-12T05:33:36Z 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 Saradadevi, Renu Mukankusi, Clare Li, Li Amongi, Winnyfred Mbiu, Julius Peter Raatz, Bodo Ariza, Daniel Beebe, Stephen E. Varshney, Rajeev K. Huttner, Eric Kinghorn, Brian Banks, Robert Rubyogo, Jean-Claude Siddique, Kadambot H.M. Cowling, Wallace A. grain legumes trace elements nutrients genomic features cooking leguminosas de grano oligoelementos nutrientes genetics 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. 2021-11 2021-10-28T09:39:47Z 2021-10-28T09:39:47Z Journal Article https://hdl.handle.net/10568/115724 en Open Access application/pdf Wiley Saradadevi, R.; Mukankusi, C.; Li, L.; Amongi, W.; Mbiu, J.P.; Raatz, B.; Ariza, D.; Beebe, S.; Varshney, R.K.; Huttner, E.; Kinghorn, B.; Banks, R.; Rubyogo, J.C.; Siddique, K.H.M.; Cowling, W.A. (2021) 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. Plant Genome, 2021:e20156. 16 p. ISSN: 1940-3372 |
| spellingShingle | grain legumes trace elements nutrients genomic features cooking leguminosas de grano oligoelementos nutrientes genetics Saradadevi, Renu Mukankusi, Clare Li, Li Amongi, Winnyfred Mbiu, Julius Peter Raatz, Bodo Ariza, Daniel Beebe, Stephen E. Varshney, Rajeev K. Huttner, Eric Kinghorn, Brian Banks, Robert Rubyogo, Jean-Claude Siddique, Kadambot H.M. Cowling, Wallace A. 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 |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | grain legumes trace elements nutrients genomic features cooking leguminosas de grano oligoelementos nutrientes genetics |
| url | https://hdl.handle.net/10568/115724 |
| work_keys_str_mv | AT saradadevirenu multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT mukankusiclare multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT lili multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT amongiwinnyfred multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT mbiujuliuspeter multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT raatzbodo multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT arizadaniel multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT beebestephene multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT varshneyrajeevk multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT huttnereric multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT kinghornbrian multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT banksrobert multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT rubyogojeanclaude multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT siddiquekadambothm multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica AT cowlingwallacea multivariategenomicanalysisandoptimalcontributionsselectionpredictshighgeneticgainsincookingtimeironzincandgrainyieldincommonbeansineastafrica |