ArticleFrontiers in plant science2021
Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview.
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 29 papers.
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29 citing papers in PubMed.
- Photosynthesis-informed breeding to enhance Soybean (Glycine max) yield potential: a framework integrating high-throughput phenomics, genomic prediction and G×E×M.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
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- A selection index with minimal genetic relatedness for multi-trait data via binary quadratic programming.Plant methods · 2025Article
- Genomic prediction-aided incorporation of genetic resources into elite breeding: lessons from a collaborative multiparental design in flint maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Breaking down data silos across companies to train genome-wide predictions: A feasibility study in wheat.Plant biotechnology journal · 2025Article
- Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).The plant genome · 2025Article
- Genomic prediction in Persian walnut: Optimization levers according to genetic architecture of complex traits.The plant genome · 2025Article
- Multi-trait/environment sparse genomic prediction using the SFSI R-package.The plant genome · 2025Article
- Maximizing the accuracy of genetic variance estimation and using a novel generalized effective sample size to improve simulations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials.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
- Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.Plants (Basel, Switzerland) · 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
- Genomic dissection of additive and non-additive genetic effects and genomic prediction in an open-pollinated family test of Japanese larch.BMC genomics · 2024Article
- Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans.Frontiers in genetics · 2024Article
- Training set optimization is a feasible alternative for perennial orphan crop domestication and germplasm management: anFrontiers in plant science · 2024Article
- Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program.Frontiers in plant science · 2024Article
- Leveraging trait and QTL covariates to improve genomic prediction of resistance to Fusarium head blight in Central European winter wheat.Frontiers in plant science · 2024Article
- A comparison of design algorithms for choosing the training population in genomic models.Frontiers in genetics · 2024Article
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Abstract
Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework.
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