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Article

Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

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Department of Agricultural Production, Makerere University, Kampala P.O. Box 7062, Uganda
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Alliance Bioversity-CIAT, Africa-Office, Kampala P.O. Box 24384, Uganda
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National Crops Resource Research Institute, Kampala P.O. Box 7084, Uganda
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Department of Crop Science, University of Cape Coast, Cape Coast PMB, Ghana
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Laboratory of Applied Ecology, University of Abomey-Calavi, Cotonou 01BP 526, Benin
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Authors to whom correspondence should be addressed.
Plants 2021, 10(1), 29; https://doi.org/10.3390/plants10010029
Received: 13 July 2020 / Revised: 11 September 2020 / Accepted: 14 September 2020 / Published: 24 December 2020
(This article belongs to the Special Issue Advances in Cereal Crops Breeding)
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. View Full-Text
Keywords: prediction accuracy; mixed linear and Bayesian models; machine learning algorithms; training set size and composition; parametric and nonparametric models prediction accuracy; mixed linear and Bayesian models; machine learning algorithms; training set size and composition; parametric and nonparametric models
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MDPI and ACS Style

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. Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils. Plants 2021, 10, 29. https://doi.org/10.3390/plants10010029

AMA Style

Badji A, Machida L, Kwemoi DB, Kumi F, Okii D, Mwila N, Agbahoungba S, Ibanda A, Bararyenya A, Nghituwamhata SN, Odong T, Wasswa P, Otim M, Ochwo-Ssemakula M, Talwana H, Asea G, Kyamanywa S, Rubaihayo P. Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils. Plants. 2021; 10(1):29. https://doi.org/10.3390/plants10010029

Chicago/Turabian Style

Badji, Arfang, Lewis Machida, Daniel B. Kwemoi, Frank Kumi, Dennis Okii, Natasha Mwila, Symphorien Agbahoungba, Angele Ibanda, Astere Bararyenya, Selma N. Nghituwamhata, Thomas Odong, Peter Wasswa, Michael Otim, Mildred Ochwo-Ssemakula, Herbert Talwana, Godfrey Asea, Samuel Kyamanywa, and Patrick Rubaihayo. 2021. "Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils" Plants 10, no. 1: 29. https://doi.org/10.3390/plants10010029

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