ArticleMolecular breeding : new strategies in plant improvement2023
Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (
Article in Molecular breeding : new strategies in plant improvement, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Genetic control of wheat flour end-use quality and rheology by genome-wide association studies.The plant genome · 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
- Genetic and Molecular Basis for Heat Tolerance in Rice: Strategies for Resilience Under Climate Change.Plants (Basel, Switzerland) · 2025Review
- Genome Selection for Fleece Traits in Inner Mongolia Cashmere Goats Based on GWAS Prior Marker Information.Animals : an open access journal from MDPI · 2025Article
- Genomic Selection for Cashmere Traits in Inner Mongolian Cashmere Goats Using Random Forest, Gradient Boosting Decision Tree, Extreme Gradient Boosting and Light Gradient Boosting Machine Methods.Animals : an open access journal from MDPI · 2025Article
- Optimization of Genomic Breeding Value Estimation Model for Abdominal Fat Traits Based on Machine Learning.Animals : an open access journal from MDPI · 2025Article
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- Metabolic marker-assisted genomic prediction improves hybrid breeding.Plant communications · 2025Article
- Improving genomic prediction of vitamin C content in spinach using GWAS-derived markers.BMC genomics · 2025Article
- Beyond the genome: the role of functional markers in contemporary plant breeding.Frontiers in plant science · 2025Review
- mmGEBLUP: an advanced genomic prediction scheme for genetic improvement of complex traits in crops through integrative analysis of major genes, polygenes, and genotype-environment interactions.Briefings in bioinformatics · 2024Article
- Review
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10 authors.
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Abstract
Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01423-y.
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