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

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

doi:10.20944/preprints202007.0336.v3

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preprints.org > biology and life sciences > agricultural science and agronomy > doi: 10.20944/preprints202007.0336.v3

Preprint Article Version 3 Preserved in Portico This version is not peer-reviewed

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

^* ,

^* ,

Symphorien Agbahoungba

Angele Ibanda

Astere Bararyenya

Selma Ndapewa Nghituwamhata

Thomas Odong

Peter Wasswa

Mildred Ochwo-Ssemakula

Version 1 : Received: 13 July 2020 / Approved: 15 July 2020 / Online: 15 July 2020 (12:13:40 CEST)
Version 2 : Received: 3 September 2020 / Approved: 4 September 2020 / Online: 4 September 2020 (12:58:42 CEST)
Version 3 : Received: 11 September 2020 / Approved: 17 September 2020 / Online: 17 September 2020 (05:41:51 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Abstract

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 (PA) 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 BS was the remainder whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTS) 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 pioneers the use of GS for maize resistance to insect pests in sub-Saharan Africa.

Keywords

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

Subject

Biology and Life Sciences, Agricultural Science and Agronomy

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Comments (1)

Comment 1

Received: 17 September 2020
Commenter: Arfang BADJI
Commenter's Conflict of Interests: Author

Comment: This version is updated from the previous by addressing minor comments, especially on the abstract that was substantially shortened, from the second round of review with one reviewer.

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