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<p><Emphasis Type="BoldItalic">Key message A data mining strategy capitalizing on high-resolution SNP information, sequence variant annotation and QTL information from three populations facilitated an efficient nomination of candidate genes for YR resistance, supported by Yr27 and functional annotations.</Emphasis></p><p><b>Abstract</b> Genome-wide association studies (GWAS) have become routine in many crops, but the prioritization of candidate genes remains challenging. Here, we developed a new approach to identify environment-specific quantitative trait loci (QTL) using GWAS and analyzed 5,840 wheat genotypes distributed over three experimental populations, including 5,243 single-cross hybrids and 597 elite lines. Resequencing the parental genotypes identified over 640,000 single-nucleotide polymorphisms (SNPs) after filtering. The hybrid panels were tested in 19 environments for susceptibility to <i>Puccinia striiformis</i> f. sp. <i>tritici</i> (<i>Pst</i>), a pathogen responsible for severe yellow rust (YR) epidemics especially after 2012. QTL patterns in diverse environments showed substantial differences, reflecting spatial and temporal dynamics. Combining high-resolution SNP information, sequence variant annotation and QTL information from different populations obtained via a novel GWAS approach enabled the efficient nomination of candidate genes for YR resistance loci of particular relevance for Central European wheat. The power of the developed strategy for mining data from different experimental populations was demonstrated as the validated resistance gene <i>Yr27</i> was identified as sole candidate gene for one QTL region.</p>

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From QTL to candidate genes: a data-driven approach to unravel the genetic architecture of yellow rust resistance in central European wheat

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • Jiaojiao Wang,
  • Renate H. Schmidt,
  • Guoliang Li,
  • Albrecht Serfling,
  • Jochen C. Reif,
  • Yong Jiang

摘要

Key message A data mining strategy capitalizing on high-resolution SNP information, sequence variant annotation and QTL information from three populations facilitated an efficient nomination of candidate genes for YR resistance, supported by Yr27 and functional annotations.

Abstract Genome-wide association studies (GWAS) have become routine in many crops, but the prioritization of candidate genes remains challenging. Here, we developed a new approach to identify environment-specific quantitative trait loci (QTL) using GWAS and analyzed 5,840 wheat genotypes distributed over three experimental populations, including 5,243 single-cross hybrids and 597 elite lines. Resequencing the parental genotypes identified over 640,000 single-nucleotide polymorphisms (SNPs) after filtering. The hybrid panels were tested in 19 environments for susceptibility to Puccinia striiformis f. sp. tritici (Pst), a pathogen responsible for severe yellow rust (YR) epidemics especially after 2012. QTL patterns in diverse environments showed substantial differences, reflecting spatial and temporal dynamics. Combining high-resolution SNP information, sequence variant annotation and QTL information from different populations obtained via a novel GWAS approach enabled the efficient nomination of candidate genes for YR resistance loci of particular relevance for Central European wheat. The power of the developed strategy for mining data from different experimental populations was demonstrated as the validated resistance gene Yr27 was identified as sole candidate gene for one QTL region.