ArticleThe plant genome2019
Genomic Prediction using Existing Historical Data Contributing to Selection in Biparental Populations: A Study of Kernel Oil in Maize.
Article in The plant genome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 28 citations in OpenAlex.
- Genomic prediction vs. gene-based crop models: a case study on rice trait prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).The plant genome · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.).BMC plant biology · 2024Article
- Low-density SNP markers with high prediction accuracy of genomic selection for bacterial wilt resistance in tomato.Frontiers in plant science · 2024Article
- Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).The plant genome · 2023Article
- Genomic versus phenotypic selection to improve corn borer resistance and grain yield in maize.Frontiers in plant science · 2023Article
- Discovery of genomic regions associated with grain yield and agronomic traits in Bi-parental populations of maize (Frontiers in genetics · 2023Article
- Genotyping-by-sequencing and genomic selection applications in hexaploid triticale.G3 (Bethesda, Md.) · 2022Article
- Genetic dissection of maize disease resistance and its applications in molecular breeding.Molecular breeding : new strategies in plant improvement · 2021Review
- Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction.Frontiers in plant science · 2021Article
- Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches.Plants (Basel, Switzerland) · 2020Review
- Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants.Plant communications · 2020Review
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Maize ( L.) kernel oil provides high-quality nutrition for animal feed and human health. A certain number of maize breeding programs seek to enhance oil concentration and composition. Genomic selection (GS), which entails selection based on genomic estimated breeding values (GEBVs), has proven to be efficient in breeding programs. Here, we estimate the robustness of predictions for the oil traits of maize kernels in biparental recombination inbred lines (RILs) using a GS model built based on an association population. Most statistical models, including ridge regression-best linear unbiased prediction (RR-BLUP), showed high prediction accuracy in the training population through a cross validation procedure. The training population size was more important than marker density and a statistical model for prediction performance. Using the optimized GS model, prediction of the biparental RIL population showed medium-high prediction accuracy (0.68) compared with prediction using only oil associated markers ( = 0.43). The potential to apply the GS model to another RIL population that is genetically less related to the training population was also examined, showing promising prediction accuracy in the top selected lines. Our results proved that genomic prediction using existing data is robust for the prediction of polygenic traits with moderate to high heritability.
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