ReviewMethods in molecular biology (Clifton, N.J.)2022
Genomic Prediction: Progress and Perspectives for Rice Improvement.
Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Genome-wide association and genomic prediction of anthracnose (Colletotrichum dematium) resistance in spinach.The plant genome · 2026Article
- A breeding strategy for high-oleic acid content and high-yield peanut using Tag SNPs and genomic selection.BMC plant biology · 2026Article
- The current state of the use of DNA markers for improving the efficiency of rice breeding in Japan.Breeding science · 2026Article
- Genome-Wide Identification and Characterization of the C2H2 Zinc Finger Gene Family in Pear (Biology · 2026Article
- Enhancing genomic prediction ability of blast resistance using genome-wide association study-derived marker weights in two rice (Oryza sativa L.) populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Article
- Accelerated breeding modernization: a global blueprint for driving genetic gains, climate resilience, and food security in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Optimizing genomic prediction with transfer learning under a ridge regression framework.The plant genome · 2025Article
- Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan.Rice (New York, N.Y.) · 2025Article
- A Feature Engineering Method for Whole-Genome DNA Sequence with Nucleotide Resolution.International journal of molecular sciences · 2025Article
- Gene Pyramiding Strategies for Sink Size and Source Capacity for High-Yield Japonica Rice Breeding.Rice (New York, N.Y.) · 2025Review
- Unraveling the genetic basis of general combining ability in CIMMYT elite bread wheat germplasm: implications for breeding strategies optimization.Frontiers in plant science · 2025Article
- Strategic risk analysis for the selection of stable and high-potential maize genotypes in multi-environment trials.PloS one · 2025Article
- A Penalized Regression Method for Genomic Prediction Reduces Mismatch between Training and Testing Sets.Genes · 2024Article
- Stochastic simulation to optimize rice breeding at IRRI.Frontiers in plant science · 2024Article
- Genomic selection for tolerance to aluminum toxicity in a synthetic population of upland rice.PloS one · 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
- Review
- Population improvement via recurrent selection drives genetic gain in upland rice breeding.Heredity · 2023Article
- Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice.G3 (Bethesda, Md.) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.