Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils
Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs)...
| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/110863 |
| _version_ | 1855523407847751680 |
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| author | Badji, Arfang Machida, Lewis Kwemoi, Daniel Bomet Kumi, Frank Okii, Dennis Mwila, Natasha Agbahoungba, Symphorien Ibanda, Angele Bararyenya, Astere Nghituwamhata, Selma Ndapewa Odong, Thomas L. Wasswa, Peter Otim, Michael Ochwo-Ssemakula, Mildred Talwana, Herbert Asea, Godfrey Kyamanywa, Samuel Rubaihayo, Patrick |
| author_browse | Agbahoungba, Symphorien Asea, Godfrey Badji, Arfang Bararyenya, Astere Ibanda, Angele Kumi, Frank Kwemoi, Daniel Bomet Kyamanywa, Samuel Machida, Lewis Mwila, Natasha Nghituwamhata, Selma Ndapewa Ochwo-Ssemakula, Mildred Odong, Thomas L. Okii, Dennis Otim, Michael Rubaihayo, Patrick Talwana, Herbert Wasswa, Peter |
| author_facet | Badji, Arfang Machida, Lewis Kwemoi, Daniel Bomet Kumi, Frank Okii, Dennis Mwila, Natasha Agbahoungba, Symphorien Ibanda, Angele Bararyenya, Astere Nghituwamhata, Selma Ndapewa Odong, Thomas L. Wasswa, Peter Otim, Michael Ochwo-Ssemakula, Mildred Talwana, Herbert Asea, Godfrey Kyamanywa, Samuel Rubaihayo, Patrick |
| author_sort | Badji, Arfang |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize
weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%,
and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa. |
| format | Journal Article |
| id | CGSpace110863 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1108632025-12-08T09:54:28Z Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils Badji, Arfang Machida, Lewis Kwemoi, Daniel Bomet Kumi, Frank Okii, Dennis Mwila, Natasha Agbahoungba, Symphorien Ibanda, Angele Bararyenya, Astere Nghituwamhata, Selma Ndapewa Odong, Thomas L. Wasswa, Peter Otim, Michael Ochwo-Ssemakula, Mildred Talwana, Herbert Asea, Godfrey Kyamanywa, Samuel Rubaihayo, Patrick marker-assisted selection maize defence mechanisms selección asistida por marcadores maíz mecanismos de defensa Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa. 2020-12 2021-01-14T13:29:07Z 2021-01-14T13:29:07Z Journal Article https://hdl.handle.net/10568/110863 en Open Access application/pdf MDPI Badji, A.; Machida, L.; Kwemoi, D.B.; Kumi, F.; Okii, D.; Mwila, N.; Agbahoungba, S.; Ibanda, A.; Bararyenya, A.; Nghituwamhata, S.N.; Odong, T.; Wasswa, P.; Otim, M.; Ochwo-Ssemakula, M.; Talwana, H.; Asea, G.; Kyamanywa, S.; Rubaihayo, P. (2020) Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils. Plants 10(29) 2021 22 p. ISSN: 2223-7747 |
| spellingShingle | marker-assisted selection maize defence mechanisms selección asistida por marcadores maíz mecanismos de defensa Badji, Arfang Machida, Lewis Kwemoi, Daniel Bomet Kumi, Frank Okii, Dennis Mwila, Natasha Agbahoungba, Symphorien Ibanda, Angele Bararyenya, Astere Nghituwamhata, Selma Ndapewa Odong, Thomas L. Wasswa, Peter Otim, Michael Ochwo-Ssemakula, Mildred Talwana, Herbert Asea, Godfrey Kyamanywa, Samuel Rubaihayo, Patrick Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils |
| title | Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils |
| title_full | Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils |
| title_fullStr | Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils |
| title_full_unstemmed | Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils |
| title_short | Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils |
| title_sort | factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils |
| topic | marker-assisted selection maize defence mechanisms selección asistida por marcadores maíz mecanismos de defensa |
| url | https://hdl.handle.net/10568/110863 |
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