Wheat Genetics Resource Center, Department of Plant Pathology and Department of Agronomy, Kansas State University, Manhattan, Kansas, 66506; email:
Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, 14853; email:
Annu Rev Phytopathol. 2016 Aug 4;54:79-98. doi: 10.1146/annurev-phyto-080615-100056.
Breeding for disease resistance is a central focus of plant breeding programs, as any successful variety must have the complete package of high yield, disease resistance, agronomic performance, and end-use quality. With the need to accelerate the development of improved varieties, genomics-assisted breeding is becoming an important tool in breeding programs. With marker-assisted selection, there has been success in breeding for disease resistance; however, much of this work and research has focused on identifying, mapping, and selecting for major resistance genes that tend to be highly effective but vulnerable to breakdown with rapid changes in pathogen races. In contrast, breeding for minor-gene quantitative resistance tends to produce more durable varieties but is a more challenging breeding objective. As the genetic architecture of resistance shifts from single major R genes to a diffused architecture of many minor genes, the best approach for molecular breeding will shift from marker-assisted selection to genomic selection. Genomics-assisted breeding for quantitative resistance will therefore necessitate whole-genome prediction models and selection methodology as implemented for classical complex traits such as yield. Here, we examine multiple case studies testing whole-genome prediction models and genomic selection for disease resistance. In general, whole-genome models for disease resistance can produce prediction accuracy suitable for application in breeding. These models also largely outperform multiple linear regression as would be applied in marker-assisted selection. With the implementation of genomic selection for yield and other agronomic traits, whole-genome marker profiles will be available for the entire set of breeding lines, enabling genomic selection for disease at no additional direct cost. In this context, the scope of implementing genomics selection for disease resistance, and specifically for quantitative resistance and quarantined pathogens, becomes a tractable and powerful approach in breeding programs.
培育抗病性是植物育种计划的核心重点,因为任何成功的品种都必须具备高产、抗病性、农艺性能和最终用途质量的完整组合。随着对改良品种的发展需求的加速,基于基因组学的育种正在成为育种计划中的重要工具。通过标记辅助选择,在培育抗病性方面已经取得了成功;然而,这项工作和研究的很大一部分都集中在识别、定位和选择主要抗性基因上,这些基因往往非常有效,但容易因病原体种群的快速变化而失效。相比之下,培育小基因数量抗性往往会产生更持久的品种,但这是一个更具挑战性的育种目标。随着抗性的遗传结构从单个主要 R 基因转变为许多小基因的分散结构,分子育种的最佳方法将从标记辅助选择转变为基因组选择。因此,针对数量抗性的基于基因组学的育种将需要全基因组预测模型和选择方法,就像对产量等经典复杂性状所实施的方法一样。在这里,我们研究了多个案例研究,测试了全基因组预测模型和对疾病抗性的基因组选择。一般来说,用于疾病抗性的全基因组模型可以产生适合应用于育种的预测准确性。这些模型也在很大程度上优于将应用于标记辅助选择的多元线性回归。随着对产量和其他农艺性状的基因组选择的实施,整个育种系的全基因组标记图谱将可用于基因组选择疾病,而无需额外的直接成本。在这种情况下,实施针对疾病抗性(特别是针对数量抗性和检疫病原体)的基因组选择的范围成为了一个可行且强大的育种计划方法。