植物小基因数量抗病育种基因组选择研究进展

Dagnachew Bekele, K. Tesfaye, A. Fikre
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引用次数: 4

摘要

为了加快作物改良品种的开发,基因组学辅助植物育种正在成为一种重要的工具。在传统育种和标记辅助选择的基础上,抗病育种取得了一些成果。大多数抗病研究都集中在主要抗病基因上,这些基因虽然很容易因致病种的快速变化而被破坏,但却非常有效。相比之下,培育小基因的数量抗性可以产生更持久的植物品种,尽管它是非常缓慢和具有挑战性的育种。随着植物抗病遗传结构从单一的主R基因向多个次要的定量基因转变,基因组选择(GS)是植物分子育种最合适的方法,而不是标记辅助选择或传统育种。随着新的基因组工具的出现,GS已成为预测基因型性能以改善遗传复杂数量性状的最重要方法之一。因此,GS通过使用全基因组序列数据来预测后代的育种价值,有助于加快育种中遗传增益的速度。因此,GS的数量抗性育种将需要全基因组预测模型和选择方法,就像对经典复杂性状实施的那样。随着对产量和其他经济上重要性状的GS的实施,全基因组标记谱可用于整套育种品系,使抗病基因组选择成为可能,而不需要额外的直接成本。因此,最近的研究进展,包括GS+ de novo GWAS模型(GS+)和最高水平定量抗性与R基因组合的GS (QR +R基因)个体,有望进一步推进植物的抗病育种,并简要讨论。
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Recent Developments in Genomic Selection for Minor Gene Quantitative Disease Resistance Plant Breeding
To speed up the development of improved crop varieties, genomics assisted plant breeding is becoming an important tool. With traditional breeding and marker assisted selection, there have been several achievements in breeding for diseases resistance. Most research for disease resistance has been focused on major disease resistance genes which are highly effective although very vulnerable to breakdown with rapid changes in pathogenic races. In contrast, breeding for minor gene quantitative resistance can produce more durable plant varieties although it is very slow and challenging breeding. As the genetic architecture of the plant disease resistance shifts from single major R genes to many minor quantitative genes, the most appropriate approach for molecular plant breeding is genomic selection (GS) than marker assisted selection or conventional breeding. With the advent of new genomic tools, GS has emerged as one of the most important approaches for predicting genotype performance to improve genetically complex quantitative traits. Consequently, GS helps to accelerate the rate of genetic gain in breeding by using whole genome sequence data to predict the breeding value of offspring. GS breeding for quantitative resistance will therefore necessitate whole genome prediction models and selection methodology as implemented for classical complex traits. With the implementation of GS for yield and other economically important traits, whole genome marker profiles are available for the entire set of breeding lines, enabling genomic selection for disease resistance with no additional direct cost. Therefore, recent developments in GS including a two stream GS + de novo GWAS models (GS+) and GS for combined highest level of quantitative resistance with R genes (QR +R gene) individuals are expected to further advance disease resistance plant breeding and briefly discussed.
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