ArticleG3 (Bethesda, Md.)2025
Evaluating the effectiveness of selection indices and their genomic prediction using environmental and historical rice data.
Article in G3 (Bethesda, Md.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Combining genomic prediction and multi-trait indices through stochastic simulations: do index type and deployment order affect genetic gain?TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- GWAS-informed genomic selection for cold tolerance in pepper (Capsicum annuum L.).BMC plant biology · 2026Article
- The next frontier for genomic prediction is computational.G3 (Bethesda, Md.) · 2026Article
- Predictive breeding and marker-assisted selection for grain quality and freezing tolerance in durum wheat.Frontiers in plant science · 2026Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
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Authors and funding
7 authors.
Funding
Abstract
Improving genetic gains in rice breeding programs requires accurate prediction methods for selection indices. Effective use of genomic prediction could significantly accelerate breeding cycles. The Smith index method (SIM), the eigenvalue selection index method (ESIM), and the desired gain index (DG) are linear combinations of trait phenotypic values y (I=b'y), and while the SIM and ESIM predict the net genetics merit (H=w'c), where w is the vector of economic weights and c is the unobserved genotypic values, the DG predicts the mean of genotypic values. To enhance genomic prediction accuracy, mixed linear and Bayesian models incorporate molecular markers to estimate genomic effects, resulting in genomic estimated breeding values. This study evaluated (1) the efficiency of the SIM, ESIM, and DG through their main parameters and (2) the predictive accuracy of 5 genomic prediction models utilizing historical rice (Oryza sativa) data from 2018 to 2021 to predict selection indices for 2022. The correlation between observed and predicted indices assessed the effectiveness of each genomic model. Models incorporating year-specific and environmental covariates significantly improved predictive performance. These findings underscore the importance of environmental covariates and indicate that the SIM is the most effective method for maximizing key index parameters, while the ESIM provides the best predictive accuracy for indices. Consequently, rice breeders are encouraged to use these indices to enhance genetic gains per selection cycle.
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