Scholarlink

Key message <p>Four QTLs associated with sharp eyespot resistance in wheat were identified through genome-wide association study, and genomic prediction achieved high accuracy, providing valuable tools for practice in sharp eyespot resistance breeding.</p> Abstract <p>Sharp eyespot, caused by the soilborne fungus <i>Rhizoctonia cerealis</i>, is becoming a major disease affecting wheat production in China. To improve resistance to sharp eyespot in wheat, a genome-wide association study (GWAS) was conducted to uncover the genetic architecture underlying adult plant resistance, and genomic selection (GS) was evaluated for its potential to accelerate breeding. The disease index of sharp eyespot evaluated in 427 Chinese wheat accessions across five environments demonstrated a broad range of phenotypic variation and exhibited significant correlations among the environments. Disease index was negatively correlated with phenology but positively correlated with plant height. Thirty-five accessions (8.2%) showed stable moderate resistance across the environments. GWAS using 159,248 markers identified 35 significant SNPs associated with sharp eyespot resistance. These SNPs delineated four quantitative trait loci (QTLs): <i>QSe.jaas-1B</i> (583–586&#xa0;Mb), <i>QSe.jaas-5D</i> (464–470&#xa0;Mb), <i>QSe.jaas</i>-6B (191–207&#xa0;Mb), and <i>QSe.jaas-6D</i> (385&#xa0;Mb). QTL pyramiding demonstrated additive effects, and combining four favorable QTLs reduced disease index by 22%. GS using only 35 significant SNPs retained over 90% of the predictive ability of the full-genome marker set, with the Bayes B model (accuracy: 0.52–0.57) outperforming GBLUP (0.46–0.50). These findings provide key genomic resources, including stable resistant germplasm, QTLs with favorable haplotypes, and predictive models, which will be valuable for improving sharp eyespot resistance in wheat breeding programs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Characterization of the genetic architecture of adult plant resistance to sharp eyespot in Chinese wheat germplasm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月17日

查看全文
  • Caiyun Liu,
  • Wenling Zhai,
  • Ningning Kang,
  • Yanjiang He,
  • Bisheng Fu,
  • Wei Guo,
  • Qiaofeng Zhang,
  • Jin Cai,
  • Ying Liu,
  • Guang Qiu,
  • Hua Jiang,
  • Fujie Liu,
  • Feng Xu,
  • Huagang He,
  • Jizhong Wu

摘要

Key message

Four QTLs associated with sharp eyespot resistance in wheat were identified through genome-wide association study, and genomic prediction achieved high accuracy, providing valuable tools for practice in sharp eyespot resistance breeding.

Abstract

Sharp eyespot, caused by the soilborne fungus Rhizoctonia cerealis, is becoming a major disease affecting wheat production in China. To improve resistance to sharp eyespot in wheat, a genome-wide association study (GWAS) was conducted to uncover the genetic architecture underlying adult plant resistance, and genomic selection (GS) was evaluated for its potential to accelerate breeding. The disease index of sharp eyespot evaluated in 427 Chinese wheat accessions across five environments demonstrated a broad range of phenotypic variation and exhibited significant correlations among the environments. Disease index was negatively correlated with phenology but positively correlated with plant height. Thirty-five accessions (8.2%) showed stable moderate resistance across the environments. GWAS using 159,248 markers identified 35 significant SNPs associated with sharp eyespot resistance. These SNPs delineated four quantitative trait loci (QTLs): QSe.jaas-1B (583–586 Mb), QSe.jaas-5D (464–470 Mb), QSe.jaas-6B (191–207 Mb), and QSe.jaas-6D (385 Mb). QTL pyramiding demonstrated additive effects, and combining four favorable QTLs reduced disease index by 22%. GS using only 35 significant SNPs retained over 90% of the predictive ability of the full-genome marker set, with the Bayes B model (accuracy: 0.52–0.57) outperforming GBLUP (0.46–0.50). These findings provide key genomic resources, including stable resistant germplasm, QTLs with favorable haplotypes, and predictive models, which will be valuable for improving sharp eyespot resistance in wheat breeding programs.