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Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils

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Title Factors influencing genomic prediction accuracies of tropical maize resistance to fall armyworm and weevils
 
Creator Badji, Arfang
Machida, Lewis
Kwemoi, Daniel Bomet
Kumi, Frank
Okii, Dennis
Mwila, Natasha
Agbahoungba, Symphorien
Ibanda, Angele
Bararyenya, Astere
Nghituwamhata, Selma Ndapewa
Odong, Thomas
Wasswa, Peter
Otim, Michael
Ochwo-Ssemakula, Mildred
Talwana, Herbert
Asea, Godfrey
Kyamanywa, Samuel
Rubaihayo, Patrick
 
Subject marker-assisted selection
maize
defence mechanisms
selección asistida por marcadores
maíz
mecanismos de defensa
 
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.
 
Date 2020
2021-01-14T13:29:07Z
2021-01-14T13:29:07Z
 
Type Journal Article
 
Identifier 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
2223-7747
https://hdl.handle.net/10568/110863
https://doi.org/10.3390/plants10010029
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format 22 p.
application/pdf
 
Publisher MDPI AG
 
Source Plants