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

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dc.contributor.authorBadji, Arfang
dc.contributor.authorMachida, Lewis
dc.contributor.authorKwemoi, Daniel Bomet
dc.contributor.authorKumi, Frank
dc.contributor.authorOkii, Dennis
dc.contributor.authorMwila, Natasha
dc.contributor.authorAgbahoungba, Symphorien
dc.contributor.authorIbanda, Angele
dc.contributor.authorBararyenya, Astere
dc.contributor.authorNdapewa Nghituwamhata, Selma
dc.contributor.authorOdong, Thomas
dc.contributor.authorWasswa, Peter
dc.contributor.authorOtim, Michael
dc.contributor.authorOchwo-Ssemakula, Mildred
dc.contributor.authorTalwana, Herbert
dc.contributor.authorAsea, Godfrey
dc.contributor.authorKyamanywa, Samuel
dc.contributor.authorRubaihayo, Patrick
dc.date.accessioned2022-08-22T21:32:43Z
dc.date.available2022-08-22T21:32:43Z
dc.date.issued2021
dc.description.abstractGenomic 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, FAWresistance 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.en_US
dc.identifier.citationBadji,A.;Machida, L.;Kwemoi, D.B.; Kumi, F.; Okii, D.; Mwila, N.; Agbahoungba, S.; Ibanda, A.; Bararyenya, A.; Nghituwamhata, S.N.; Odong, T.; et al. Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm andWeevils. Plants 2021, 10, 29. https://dx.doi.org/10.3390/ plants10010029en_US
dc.identifier.urihttps://dx.doi.org/10.3390/ plants10010029
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4382
dc.language.isoenen_US
dc.publisherPlantsen_US
dc.subjectPrediction accuracyen_US
dc.subjectMixed linear and Bayesian modelsen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectTraining set size and compositionen_US
dc.subjectParametric and nonparametric modelsen_US
dc.titleFactors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevilsen_US
dc.typeBooken_US
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