Preprint Article Version 1 This version is not peer-reviewed

Genomic Prediction of Tropical Maize Resistance to Fall Armyworm and Weevils: Genomic Selection Should Focus on Effective Training Set Determination

Version 1 : Received: 13 July 2020 / Approved: 15 July 2020 / Online: 15 July 2020 (12:13:40 CEST)

How to cite: 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.; Ochwo-Ssemakula, M.; Talwana, H.; Asea, G.; Otim, M.; Kyamanywa, S.; Rubaihayo, P. Genomic Prediction of Tropical Maize Resistance to Fall Armyworm and Weevils: Genomic Selection Should Focus on Effective Training Set Determination. Preprints 2020, 2020070336 (doi: 10.20944/preprints202007.0336.v1). 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.; Ochwo-Ssemakula, M.; Talwana, H.; Asea, G.; Otim, M.; Kyamanywa, S.; Rubaihayo, P. Genomic Prediction of Tropical Maize Resistance to Fall Armyworm and Weevils: Genomic Selection Should Focus on Effective Training Set Determination. Preprints 2020, 2020070336 (doi: 10.20944/preprints202007.0336.v1).

Abstract

Genomic selection (GS) can accelerate variety release by shortening 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. Both phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) were predicted using 17 parametric, semi-parametric, and nonparametric algorithms with a 10-fold and 5 repetitions cross-validation strategy. n. For both MW and FAW resistance datasets with an RBTS of 37%, PAs achieved with BLUPs were at least as twice as higher than those realized with BLUEs. The PAs achieved with BLUPs for MW resistance traits: grain weight loss (GWL), adult progeny emergence (AP), and number of affected kernels (AK) varied from 0.66 to 0.82. The PAs were also high for FAW resistance RBTS datasets, varying from 0.694 to 0.714 (for RBTS of 37%) to 0.843 to 0.844 (for RBTS of 85%). The PAs for FAW resistance with PBTS were generally high varying from 0.83 to 0.86, except for one dataset that had 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 degree of kinship 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.

Subject Areas

Prediction accuracy; Mixed linear and Bayesian models; Machine learning algorithms; Training set size and composition; Parametric and nonparametric models

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