ArticleThe plant genome2022
Development and validation of an optimized marker set for genomic selection in southern U.S. rice breeding programs.
Article in The plant genome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A multi-race blast resistance locus identified and validated in US elite rice via association and biparental mapping.The plant genome · 2026Article
- A yield-reducing introgression linked to sd1 in rice.The plant genome · 2026Article
- Review
- Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool.The plant genome · 2026Article
- From research to application: evaluation of literature-based and newly identified GWAS and GP-derived loci for anthracnose resistance in white lupin, across validation panels and environments.Molecular breeding : new strategies in plant improvement · 2026Article
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice.The plant genome · 2025Article
- Evaluating the effectiveness of selection indices and their genomic prediction using environmental and historical rice data.G3 (Bethesda, Md.) · 2025Article
- Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing.Biological research · 2024Review
- Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- A medium-density genotyping platform for cultivated strawberry using DArTag technology.The plant genome · 2023Article
- Use of genomic prediction to screen sorghum B-lines in hybrid testcrosses.The plant genome · 2023Article
- Genetic loci regulating the concentrations of anthocyanins and proanthocyanidins in the pericarps of purple and red rice.The plant genome · 2023Article
- Characterization of the USDAFrontiers in plant science · 2023Article
- Development and validation of an optimized marker set for genomic selection in southern U.S. rice breeding programs.The plant genome · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
The potential of genomic selection (GS) to increase the efficiency of breeding programs has been clearly demonstrated; however, the implementation of GS in rice (Oryza sativa L.) breeding programs has been limited. In recent years, efforts have begun to work toward implementing GS into the Louisiana State University (LSU) Agricultural Center rice breeding program. One of the first steps for successful GS implementation is to establish a suitable marker set for the target germplasm and a reliable, cost-effective genotyping platform capable of providing informative marker data with an adequate turnaround time. The objective of this study was to develop a marker set for routine GS and demonstrate its effectiveness in southern U.S. rice germplasm. The utility of the resulting marker set, the LSU500, for GS applications was demonstrated using four years of breeding data across 7,607 experimental lines and four elite biparental populations. The predictive ability of GS ranged from 0.13 to 0.78 for key traits across different market classes and yield trials. Comparisons between phenotypic selection and GS within biparental populations demonstrates similar performance of GS compared with phenotypic selection in predicting future performance. The prediction accuracies obtained with the LSU500 marker set demonstrates the utility of this marker set for cost-effective GS applications in southern U.S. rice breeding programs. The LSU500 marker set has been established through the genotyping service provider Agriplex Genomics, and in the future, it will undergo improvements to reduce the cost and increase the accuracy of GS.
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