ArticleMolecular breeding : new strategies in plant improvement2022
Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice.
Article in Molecular breeding : new strategies in plant improvement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Review
- Genome-wide association studies for identifying SNPs and candidate genes associated with grain quality traits in rice (Oryza sativa L.).The plant genome · 2026Article
- Association-Based Analysis of Verticillium Wilt Resistance in a Bi-Parental Hop (Plants (Basel, Switzerland) · 2026Article
- Pan-3D Genome Analysis Reveals the Roles of Structural Variation in Chicken Chromatin Architectures, Domestication and Production Traits.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Genome-Wide Association Studies for Grain Micronutrient Concentration in Spring Wheat (Plants (Basel, Switzerland) · 2025Article
- Genomic Prediction of Adaptation in Common Bean (International journal of molecular sciences · 2025Article
- Metabolic marker-assisted genomic prediction improves hybrid breeding.Plant communications · 2025Article
- A comprehensive review on rice responses and tolerance to salt stress.Frontiers in plant science · 2025Review
- Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.Scientific reports · 2024Article
- Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding.Heredity · 2023Article
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13 authors.
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Abstract
Genomic selection is an efficient tool for breeding selection, especially for quantitative traits controlled by multiples genes with low heritability. To validate the application of genomic selection in hybrid rice breeding, the yield and grain quality traits of 404 hybrid rice breeding lines were investigated, and the same accessions were genotyped by using a 56 K SNP chip. There were wide variances among the tested accessions for all the measured traits, and most of the traits were correlated. A total of 67 significant loci were identified for the yield-related traits, and 123 significant loci were identified for the grain quality traits by GWAS. Two of these loci associated with increasing grain yield but decreasing grain quality. The GEBVs of all the yield and grain quality traits were calculated by using 15 different prediction algorithms. The plant height, panicle length, thousand grain weight, grain length and width ratio, amylose content, and alkali value have higher predictability than other traits. However, the predictive accuracy of different GS models is different for different traits. This study provided useful information for genomic selection of specific trait using proper markers and prediction models. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01289-6.
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