: Received: 13 July 2020 / Approved: 15 July 2020 / Online: 15 July 2020 (12:13:40 CEST)
: Received: 3 September 2020 / Approved: 4 September 2020 / Online: 4 September 2020 (12:58:42 CEST)
: Received: 11 September 2020 / Approved: 17 September 2020 / Online: 17 September 2020 (05:41:51 CEST)
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. Plants2021, 10, 29.
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.
Genomic selection (GS) can accelerate variety release by shortening the variety development phase when factors that influence prediction accuracies (PA) of genomic prediction (GP) models such as training set (TS) size and relationship with the breeding set (BS) are optimized beforehand. In this study, PAs for the resistance to fall armyworm (FAW) and maize weevil (MW) in a diverse tropical maize panel composed of 341 double haploid and inbred lines were estimated using 16 parametric, semi-parametric, and nonparametric algorithms with a 10-fold and 5 repetitions cross-validation strategy. For MW resistance, 126 lines that had both genotypic and phenotypic data were used as a TS (37% of the panel) and the remaining lines, with only genotypic data, as a BS. Regarding FAW damage resistance, two TS determination strategies, namely: random-based TS (RBTS) with increasing sizes (37, 63, 75, and 85%) and pedigree-based TS (PBTS) were used. For both MW and FAW resistance datasets with an RBTS of 37%, PAs achieved with phenotypic best linear unbiased predictors were at least as twice as higher than those realized with best linear unbiased estimators. The PAs achieved with BLUPs for MW resistance traits varied from 0.66 to 0.82. The PAs with BLUPs for FAW resistance datasets ranged from 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%. The PAs with BLUPs for FAW resistance with PBTS were generally high varying from 0.83 to 0.86, except for the third dataset which had the largest TS (86.22% of the panel) with PAs ranging from 0.11 to 0.75. GP models showed generally similar predictive abilities for each trait while the TS designation was determinant. There was a highly positive correlation (R=0.92***) between TS size and PAs for the RBTS approach while, for the PBTS, these parameters were highly negatively correlated (R=-0.44***), indicating the importance of the relationship between the TS and the BS with the smallest TS (31%) achieving the highest PAs (0.86). This study paves the way towards the use of GS for maize resistance to insect pests in sub-Saharan Africa.
Prediction accuracy; Mixed linear and Bayesian models; Machine Learning algorithms; Training set size and composition; Parametric and nonparametric models
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