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饲用植物复杂性状的基因组预测:多年生禾本科植物案例

Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.

作者信息

Barre Philippe, Asp Torben, Byrne Stephen, Casler Michael, Faville Marty, Rognli Odd Arne, Roldan-Ruiz Isabel, Skøt Leif, Ghesquière Marc

机构信息

INRAE, UR P3F, Lusignan, France.

Center for Quantitative Genetics and Genomics, Aarhus University, Slagelse, Denmark.

出版信息

Methods Mol Biol. 2022;2467:521-541. doi: 10.1007/978-1-0716-2205-6_19.

Abstract

The majority of forage grass species are obligate outbreeders. Their breeding classically consists of an initial selection on spaced plants for highly heritable traits such as disease resistances and heading date, followed by familial selection on swards for forage yield and quality traits. The high level of diversity and heterozygosity, and associated decay of linkage disequilibrium (LD) over very short genomic distances, has hampered the implementation of genomic selection (GS) in these species. However, next generation sequencing technologies in combination with the development of genomic resources have recently facilitated implementation of GS in forage grass species such as perennial ryegrass (Lolium perenne L.), switchgrass (Panicum virgatum L.), and timothy (Phleum pratense L.). Experimental work and simulations have shown that GS can increase significantly the genetic gain per unit of time for traits with different levels of heritability. The main reasons are (1) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (2) a reduction in the duration of selection cycles, and less importantly (3) an increase in the selection intensity associated with an increase in the genetic variance used for selection. Nevertheless, several factors should be taken into account for the successful implementation of GS in forage grasses. For example, it has been shown that the level of relatedness between the training and the selection population is particularly critical when working with highly structured meta-populations consisting of several genetic groups. A sufficient number of markers should be used to estimate properly the kinship between individuals and to reflect the variability of major QTLs. It is also important that the prediction models are trained for relevant environments when dealing with traits with high genotype × environment interaction (G × E). Finally, in these outbreeding species, measures to reduce inbreeding should be used to counterbalance the high selection intensity that can be achieved in GS.

摘要

大多数饲草品种都是专性异交植物。其传统育种方式包括,首先在隔离种植的植株上针对抗病性和抽穗期等高遗传力性状进行初选,然后在草皮上针对饲草产量和品质性状进行家系选择。由于这些物种具有高度的多样性和杂合性,以及在极短的基因组距离上连锁不平衡(LD)的相关衰减,阻碍了基因组选择(GS)在这些物种中的应用。然而,新一代测序技术与基因组资源的开发相结合,最近推动了GS在多年生黑麦草(Lolium perenne L.)、柳枝稷(Panicum virgatum L.)和梯牧草(Phleum pratense L.)等饲草品种中的应用。实验工作和模拟表明,GS可以显著提高不同遗传力性状单位时间内的遗传增益。主要原因是:(1)能够根据单株的基因组估计育种值(GEBV)对草皮水平上测量的性状进行选择;(2)缩短选择周期的持续时间,不太重要的是(3)与用于选择的遗传方差增加相关的选择强度增加。尽管如此,要在饲草中成功实施GS,还应考虑几个因素。例如,研究表明,当处理由几个遗传群体组成的高度结构化的元群体时,训练群体和选择群体之间的亲缘关系水平尤为关键。应使用足够数量的标记来正确估计个体之间的亲缘关系,并反映主要QTL的变异性。当处理具有高基因型×环境互作(G×E)的性状时,预测模型针对相关环境进行训练也很重要。最后,在这些异交物种中,应采取措施减少近亲繁殖,以抵消GS中可能实现的高选择强度。

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