品种选育在农业生产中占有十分重要的地位, 育种值估计是品种选育的核心。随着遗传标记的发展, 尤其是高通量的基因分型技术, 使得从基因组水平估计育种值成为可能, 即基因组选择。文章将基因组选择的方法分为两类：一是基于估计等位基因效应来预测基因组估计育种值(GEBV), 如最小二乘法, 随机回归-最佳线性无偏预测(RR-BLUP)、Bayes、主成分分析等方法; 二是基于遗传关系矩阵来预测GEBV, 通过采用高通量标记构建个体间的遗传关系矩阵, 然后用线性混合模型来预测育种值, 即GBLUP法, 并以这两种分类简要介绍了基因组选择各种方法的大致原理。影响基因组选择准确性的因素主要有标记类型和密度、单倍型长度、参考群体大小和标记-数量性状基因座(QTL)连锁不平衡(LD)大小等; 在基因组选择的各种方法中, 一般说来Bayes方法和GBLUP方法具有较高的准确性, 最小二乘法最差; GBLUP计算速度快, 能够将标记和系谱结合起来, 因而比其他方法更具优势。尽管基因组选择取得了很大进展, 但在理论方面还面临着一些挑战, 如联合育种、长期选择的遗传进展及如何解析与性状有关和无关的标记等。基因组选择在一些动植物育种上已经开始应用, 在人类遗传倾向预测和进化动力学研究中也有潜在的应用前景。基因组选择在个体间亲缘关系的量化上有了突破, 比传统方法更加精确, 因此, 基因组选择将会是动植物育种史上革命性的事件。
Selective breeding is very important in agricultural production and breeding value estimation is the core of selective breeding. With the development of genetic markers, especially high throughput genotyping technology, it becomes available to estimate breeding value at genome level, i.e. genomic selection (GS). In this review, the methods of GS was categorized into two groups: one is to predict genomic estimated breeding value (GEBV) based on the allele effect, such as least squares, random regression-best linear unbiased prediction (RR-BLUP), Bayes and principle component analysis, etc; the other is to predict GEBV with genetic relationship matrix, which constructs genetic relationship matrix via high throughput genetic markers and then predicts GEBV through linear mixed model, i.e. GBLUP. The basic principles of these methods were also introduced according to the above two classifications. Factors affecting GS accuracy include markers of type and density, length of haplotype, the size of reference population, the extent between marker-QTL and so on. Among the methods of GS, Bayes and GBLUP are usually more accurate than the others and least squares is the worst. GBLUP is time-efficient and can combine pedigree with genotypic information, hence it is superior to other methods. Although progress was made in GS, there are still some challenges, for examples, united breeding, long-term genetic gain with GS, and disentangling markers with and without contribution to the traits. GS has been applied in animal and plant breeding practice and also has the potential to predict genetic predisposition in humans and study evolutionary dynamics. GS, which is more precise than the traditional method, is a breakthrough at measuring genetic relationship. Therefore, GS will be a revolutionary event in the history of animal and plant breeding.
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