ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2021
Optimization of training sets for genomic prediction of early-stage single crosses in maize.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
- From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Rapid selection of early-maturing and low-ear position maize hybrids suitable for China using genomic prediction.Plant molecular biology · 2026Article
- Genomic selection of maize test-cross hybrids leveraged by marker sampling.The plant genome · 2025Article
- Optimization of sparse phenotyping strategy in multi-environmental trials in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Maximizing efficiency in sunflower breeding through historical data optimization.Plant methods · 2024Article
- Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Accelerating genetic gains for quantitative resistance to verticillium wilt through predictive breeding in strawberry.The plant genome · 2024Article
- Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program.Frontiers in plant science · 2024Article
- Stochastic simulation to optimize rice breeding at IRRI.Frontiers in plant science · 2024Article
- Genomic prediction in hybrid breeding: I. Optimizing the training set design.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- A comparison of methods for training population optimization in genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview.Frontiers in plant science · 2021Article
- TrainSel: An R Package for Selection of Training Populations.Frontiers in genetics · 2021Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
key messageTraining population optimization algorithms are useful for efficiently training genomic prediction models for single-cross performance, especially if the population is extended beyond only realized crosses to all possible single crosses. Genomic prediction of single-cross performance could allow effective evaluation of all possible single crosses between all inbreds developed in a hybrid breeding program. The objectives of the present study were to investigate the effect of different levels of relatedness on genomic predictive ability of single crosses, evaluate the usefulness of deterministic formula to forecast prediction accuracy in advance, and determine the potential for TRS optimization based on prediction error variance (PEVmean) and coefficient of determination (CDmean) criteria. We used 481 single crosses made by crossing 89 random recombinant inbred lines (RILs) belonging to the Iowa stiff stalk synthetic group with 103 random RILs belonging to the non-stiff stalk synthetic heterotic group. As expected, predictive ability was enhanced by ensuring close relationships between TRSs and target sets, even when TRS sizes were smaller. We found that designing a TRS based on PEVmean or CDmean criteria is useful for increasing the efficiency of genomic prediction of maize single crosses. We went further and extended the sampling space from that of all observed single crosses to all possible single crosses, providing a much larger genetic space within which to design a training population. Using all possible single crosses increased the advantage of the PEVmean and CDmean methods based on expected prediction accuracy. This finding suggests that it may be worthwhile using an optimization algorithm to select a training population from all possible single crosses to maximize efficiency in training accurate models for hybrid genomic prediction.
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