ArticleFrontiers in plant science2023
Genome-wide association studies for a comprehensive understanding of the genetic architecture of culm strength and yield traits in rice.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Association-Based Analysis of Verticillium Wilt Resistance in a Bi-Parental Hop (Plants (Basel, Switzerland) · 2026Article
- Analysis of Genetic Variation of Rice Straw Characteristics and Its Influence on Biomass.Plant direct · 2026Article
- A multi-model genotype × environment interaction analysis discerning phenotypic plasticity of the strong culm trait in rice.Frontiers in plant science · 2026Article
- Genome-wide association study and genomic prediction of root system architecture traits in Sorghum (Sorghum bicolor (L.) Moench) at the seedling stage.BMC plant biology · 2025Article
- A Novel Gene,International journal of molecular sciences · 2024Article
- Needle and Branch Trait Variation Analysis and Associated SNP Loci Mining inInternational journal of molecular sciences · 2024Article
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Authors and funding
7 authors.
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Abstract
Lodging resistance in rice is a complex trait determined by culm morphological and culm physical strength traits, and these traits are a major determinant of yield. We made a detailed analysis of various component traits with the aim of deriving optimized parameters for measuring culm strength. Genotyping by sequencing (GBS)-based genome-wide association study (GWAS) was employed among 181 genotypes for dissecting the genetic control of culm strength traits. The VanRaden kinship algorithm using 6,822 filtered single-nucleotide polymorphisms (SNPs) revealed the presence of two sub-groups within the association panel with kinship values concentrated at<0.5 level, indicating greater diversity among the genotypes. A wide range of phenotypic variation and high heritability for culm strength and yield traits were observed over two seasons, as reflected in best linear unbiased prediction (BLUP) estimates. The multi-locus model for GWAS resulted in the identification of 15 highly significant associations (
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