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

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Date
2021Author
Badji, Arfang
Machida, Lewis
Kwemoi, Daniel Bomet
Kumi, Frank
Okii, Dennis
Mwila, Natasha
Agbahoungba, Symphorien
Ibanda, Angele
Bararyenya, Astere
Ndapewa Nghituwamhata, Selma
Odong, Thomas
Wasswa, Peter
Otim, Michael
Ochwo-Ssemakula, Mildred
Talwana, Herbert
Asea, Godfrey
Kyamanywa, Samuel
Rubaihayo, Patrick
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Show full item recordAbstract
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, 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.