ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2022
Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Improving hybrid breeding efficiency in winter oilseed rape under sparse multi-environment testing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Phenomics-assisted sparse testing for potato breeding.Scientific reports · 2026Article
- Randomization across breeding cohorts improves the accuracy of conventional and genomic selection.The plant genome · 2026Article
- Predicting enviromically adapted varieties with big data.Genome biology · 2026Article
- Optimizing resource allocation inFrontiers in plant science · 2026Article
- Next-generation translational genomics for developing future crops.Functional & integrative genomics · 2025Review
- Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Optimization of sparse phenotyping strategy in multi-environmental trials in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic prediction of the performance of tropical doubled haploid maize lines under artificial Striga hermonthica (Del.) Benth. infestation.G3 (Bethesda, Md.) · 2024Article
- Genetic trends in the Kenya Highland Maize Breeding Program between 1999 and 2020.Frontiers in plant science · 2024Article
- Sparse testing designs for optimizing predictive ability in sugarcane populations.Frontiers in plant science · 2024Article
- Stochastic simulation to optimize rice breeding at IRRI.Frontiers in plant science · 2024Article
- Genomic selection for tolerance to aluminum toxicity in a synthetic population of upland rice.PloS one · 2024Article
- Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice.Rice (New York, N.Y.) · 2023Article
- Results from rapid-cycle recurrent genomic selection in spring bread wheat.G3 (Bethesda, Md.) · 2023Article
- Enhancing genetic gain through the application of genomic selection in developing irrigated rice for the favorable ecosystem in Bangladesh.Frontiers in genetics · 2023Article
- Training set designs for prediction of yield and moisture of maize test cross hybrids with unreplicated trials.Frontiers in plant science · 2023Article
- Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets.Frontiers in plant science · 2023Article
- Improving predictive ability in sparse testing designs in soybean populations.Frontiers in genetics · 2023Article
- Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.The plant genome · 2022Article
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6 authors.
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Abstract
key messageSparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1-9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder's advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.
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