ArticleThe plant genome2021
Genome-enabled prediction for sparse testing in multi-environmental wheat trials.
Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- Fisher's Ideas and the Design of Field Experiments in Agronomy and Plant Breeding.Biometrical journal. Biometrische Zeitschrift · 2026Review
- Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Sparse phenotyping for wheat grain yield enabled by multiomics prediction.The plant genome · 2026Article
- Improving hybrid breeding efficiency in winter oilseed rape under sparse multi-environment testing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice.The plant genome · 2025Article
- Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).The plant genome · 2025Article
- Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials.The plant genome · 2025Article
- Optimizing fully-efficient two-stage models for genomic selection using open-source software.Plant methods · 2025Article
- Genotype Performance Estimation in Targeted Production Environments by Using Sparse Genomic Prediction.Plants (Basel, Switzerland) · 2024Article
- Article
- A marker weighting approach for enhancing within-family accuracy in genomic prediction.G3 (Bethesda, Md.) · 2024Article
- Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program.Frontiers in plant science · 2024Article
- Sparse testing designs for optimizing predictive ability in sugarcane populations.Frontiers in plant science · 2024Article
- Sparse multi-trait genomic prediction under balanced incomplete block design.The plant genome · 2023Article
- Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics.The plant genome · 2023Article
- Article
- The effect of cycles of genomic selection on the wheat (T. aestivum) genome.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy?Frontiers in genetics · 2023Article
- Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sparse testing in genome-enabled prediction in plant breeding can be emulated throughout different line allocations where some lines are observed in all environments (overlap) and others are observed in only one environment (nonoverlap). We studied three general cases of the composition of the sparse testing allocation design for genome-enabled prediction of wheat (Triticum aestivum L.) breeding: (a) completely nonoverlapping wheat lines in environments, (b) completely overlapping wheat lines in all environments, and (c) a proportion of nonoverlapping/overlapping wheat lines allocated in the environments. We also studied several cases in which the size of the testing population was systematically decreased. The study used three extensive wheat data sets (W1, W2, and W3). Three different genome-enabled prediction models (M1-M3) were used to study the effect of the sparse testing in terms of the genomic prediction accuracy. Model M1 included only main effects of environments and lines; M2 included main effects of environments, lines, and genomic effects; whereas the remaining model (M3) also incorporated the genomic × environment interaction (GE). The results show that the GE component of the genome-based model M3 captures a larger genetic variability than the main genomic effects term from models M1 and M2. In addition, model M3 provides higher prediction accuracy than models M1 and M2 for the same allocation designs (different combinations of nonoverlapping/overlapping lines in environments and training set sizes). Overlapped sets of 30-50 lines in all the environments provided stable genomic-enabled prediction accuracy. Reducing the size of the testing populations under all allocation designs decreases the prediction accuracy, which recovers when more lines are tested in all environments. Model M3 offers the possibility of maintaining the prediction accuracy throughout both extreme situations of all nonoverlapping lines and all overlapping lines.
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