ArticleFrontiers in plant science2021
Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 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
- Randomization across breeding cohorts improves the accuracy of conventional and genomic selection.The plant genome · 2026Article
- Genomic prediction for grain yield and biotic stress resistance in field pea (Frontiers in plant science · 2026Article
- Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).The plant genome · 2025Article
- Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.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
- Constructing training sets for genomic selection to identify superior genotypes in candidate populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Genotype Performance Estimation in Targeted Production Environments by Using Sparse Genomic Prediction.Plants (Basel, Switzerland) · 2024Article
- Genomic prediction of regional-scale performance in switchgrass (Panicum virgatum) by accounting for genotype-by-environment variation and yield surrogate traits.G3 (Bethesda, Md.) · 2024Article
- Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop.The plant genome · 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
- The effect of cycles of genomic selection on the wheat (T. aestivum) genome.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Multi-environment Genomic Selection in Rice Elite Breeding Lines.Rice (New York, N.Y.) · 2023Article
- Enhancing genetic gain through the application of genomic selection in developing irrigated rice for the favorable ecosystem in Bangladesh.Frontiers in genetics · 2023Article
- Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.The plant genome · 2022Article
- Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding.The plant genome · 2022Article
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14 authors.
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Abstract
To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.
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