Scholarlink

<p>Genomic prediction (GP) has become an essential tool for accelerating modern plant breeding, particularly for complex traits. We evaluated different GP approaches for pre-breeding in thirteen biparental strawberry families derived from crosses between <i>Fragaria virginiana</i> and <i>Fragaria chiloensis</i>, focusing on resistance to powdery mildew (PM) in both leaves and fruits. Five-fold cross-validation using Genomic Best Linear Unbiased Prediction (GBLUP) for combined-year data yielded mean predictive abilities (PAs) of 0.56 and 0.36 for leaf and fruit resistance, respectively. Family-based GBLUP analyses showed higher PAs for closely related families, ranging from 0.10 to 0.89. Simulations identified key parameters: training sets comprising 40% of the population provided stable predictions for both traits, while ≈8300 SNPs were sufficient for predicting leaf resistance. However, PA for fruit resistance remained consistently low regardless of marker density. We then compared GBLUP with a marker-assisted model that iteratively incorporated the major resistance loci as fixed effects. This strategy increased PA by ≈10–30% for leaf resistance and ≈25–47% for fruit resistance across models. We further applied cross-environment forward prediction in independent greenhouse and separate field validation trials. The GBLUP model maintained substantial PA when trained on either the full population or a 40% subset, demonstrating robustness in predicting new genotypes across distinct environments. Our findings highlight the importance of taking trait genetic architecture into account to enhance PAs for PM resistance in strawberry. Together, these results provide evidence-based thresholds for training population size and marker density, offering a framework for efficient implementation of genomic selection in strawberry pre-breeding populations.</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

Genomic prediction of wild-derived powdery mildew resistance for strawberry (Fragaria × ananassa) pre-breeding

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月17日

查看全文
  • Attiq ur Rehman,
  • Hanni Kärkkäinen,
  • Alan H. Schulman,
  • Napoleon Vargas Jurado,
  • Ismo Strandén,
  • Tuuli Haikonen

摘要

Genomic prediction (GP) has become an essential tool for accelerating modern plant breeding, particularly for complex traits. We evaluated different GP approaches for pre-breeding in thirteen biparental strawberry families derived from crosses between Fragaria virginiana and Fragaria chiloensis, focusing on resistance to powdery mildew (PM) in both leaves and fruits. Five-fold cross-validation using Genomic Best Linear Unbiased Prediction (GBLUP) for combined-year data yielded mean predictive abilities (PAs) of 0.56 and 0.36 for leaf and fruit resistance, respectively. Family-based GBLUP analyses showed higher PAs for closely related families, ranging from 0.10 to 0.89. Simulations identified key parameters: training sets comprising 40% of the population provided stable predictions for both traits, while ≈8300 SNPs were sufficient for predicting leaf resistance. However, PA for fruit resistance remained consistently low regardless of marker density. We then compared GBLUP with a marker-assisted model that iteratively incorporated the major resistance loci as fixed effects. This strategy increased PA by ≈10–30% for leaf resistance and ≈25–47% for fruit resistance across models. We further applied cross-environment forward prediction in independent greenhouse and separate field validation trials. The GBLUP model maintained substantial PA when trained on either the full population or a 40% subset, demonstrating robustness in predicting new genotypes across distinct environments. Our findings highlight the importance of taking trait genetic architecture into account to enhance PAs for PM resistance in strawberry. Together, these results provide evidence-based thresholds for training population size and marker density, offering a framework for efficient implementation of genomic selection in strawberry pre-breeding populations.