ArticlePlants (Basel, Switzerland)2020
Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils.
Article in Plants (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- Enhancing Across-Population Genomic Prediction for Maize Hybrids.Plants (Basel, Switzerland) · 2024Article
- Linkage mapping and genomic prediction of grain quality traits in tropical maize (Frontiers in genetics · 2024Article
- Review
- Identification of Drought Tolerant Rice (Plants (Basel, Switzerland) · 2023Article
- Host plant resistance for fall armyworm management in maize: relevance, status and prospects in Africa and Asia.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Review
- Advances in Cereal Crops Breeding.Plants (Basel, Switzerland) · 2021Article
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Authors and funding
18 authors.
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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 (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, 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 pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.
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