ArticleThe plant genome2023
Genomic selection strategies to increase genetic gain in tea breeding programs.
Article in The plant genome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Association mapping and genome prediction reveal a complex genetic architecture for resistance to black and frosty pod rot in cacao (Theobroma cacao L.) populations.The plant genome · 2026Article
- Genomic insights and SSR marker development for trait improvement in Pleurotus giganteus for tropical cultivation.BMC plant biology · 2025Article
- Enhancing the prediction accuracy of groundnut yield by integrating significant markers and modeling genotype × environment interaction.The plant genome · 2025Article
- Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials.The plant genome · 2025Article
- Optimal implementation of genomic selection in clone breeding programs exemplified in potato: II. Effect of selection strategy and cross-selection method on long-term genetic gain.The plant genome · 2025Article
- Optimizing the selection of quantitative traits in plant breeding using simulation.Frontiers in plant science · 2025Review
- Hybrid Prediction in Horticulture Crop Breeding: Progress and Challenges.Plants (Basel, Switzerland) · 2024Review
- PNNGS, a multi-convolutional parallel neural network for genomic selection.Frontiers in plant science · 2024Article
- Stochastic simulation to optimize rice breeding at IRRI.Frontiers in plant science · 2024Article
- Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population.Rice (New York, N.Y.) · 2023Article
- Genomic selection strategies to increase genetic gain in tea breeding programs.The plant genome · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Tea [Camellia sinensis (L.) O. Kuntze] is mainly grown in low- to middle-income countries (LMIC) and is a global commodity. Breeding programs in these countries face the challenge of increasing genetic gain because the accuracy of selecting superior genotypes is low and resources are limited. Phenotypic selection (PS) is traditionally the primary method of developing improved tea varieties and can take over 16 yr. Genomic selection (GS) can be used to improve the efficiency of tea breeding by increasing selection accuracy and shortening the generation interval and breeding cycle. Our main objective was to investigate the potential of implementing GS in tea-breeding programs to speed up genetic progress despite the low cost of PS in LMIC. We used stochastic simulations to compare three GS-breeding programs with a Pedigree and PS program. The PS program mimicked a practical commercial tea-breeding program over a 40-yr breeding period. All the GS programs achieved at least 1.65 times higher genetic gains than the PS program and 1.4 times compared with Seed-Ped program. Seed-GSc was the most cost-effective strategy of implementing GS in tea-breeding programs. It introduces GS at the seedlings stage to increase selection accuracy early in the program and reduced the generation interval to 2 yr. The Seed-Ped program outperformed PS by 1.2 times and could be implemented where it is not possible to use GS. Our results indicate that GS could be used to improve genetic gain per unit time and cost even in cost-constrained tea-breeding programs.
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