Genome-wide association and genomic selection in animal breeding
Genome-wide association and genomic selection in animal breeding
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DOI：
10.1139/g10-076
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发表时间：
2010-11-01
期刊：
影响因子：
3.1
通讯作者：
Goddard, Mike
中科院分区：
文献类型：
--
作者：
Hayes, Ben;Goddard, Mike
家畜和人类全基因组关联研究的结果表明，单个数量性状基因座（QTL）对复杂性状（如产量）的影响可能很小;因此，需要大量QTL来解释这些性状的遗传变异。鉴于这种遗传结构，从标记辅助选择（MAS）程序中只使用少量的DNA标记来追踪有限数量的QTL的收益可能很小。这导致了替代技术的发展，用于使用可用的密集单核苷酸多态性（SNP）信息，称为基因组选择。基因组选择使用全基因组密集标记组，使得所有QTL可能与至少一个SNP连锁不平衡。基因组育种值被预测为这些SNP在整个基因组中的效应的总和。在奶牛育种中，可以实现的基因组估计育种值（GEBV）的准确性以及这些在生命早期可用的事实导致该技术的快速采用。在这里，我们讨论了必要的实验设计，以实现准确的预测GEBV在未来的几代人的数量方面的必要的标记和标记效应估计的参考人群的大小。我们还提出了一个简单的方法，用于实施基因组选择使用基因组关系矩阵。讨论的未来挑战包括使用全基因组序列数据来提高基因组选择的准确性和通过基因组关系管理近亲繁殖。
Results from genome-wide association studies in livestock, and humans, has lead to the conclusion that the effect of individual quantitative trait loci (QTL) on complex traits, such as yield, are likely to be small; therefore, a large number of QTL are necessary to explain genetic variation in these traits. Given this genetic architecture, gains from marker-assisted selection (MAS) programs using only a small number of DNA markers to trace a limited number of QTL is likely to be small. This has lead to the development of alternative technology for using the available dense single nucleotide polymorphism (SNP) information, called genomic selection. Genomic selection uses a genome-wide panel of dense markers so that all QTL are likely to be in linkage disequilibrium with at least one SNP. The genomic breeding values are predicted to be the sum of the effect of these SNPs across the entire genome. In dairy cattle breeding, the accuracy of genomic estimated breeding values (GEBV) that can be achieved and the fact that these are available early in life have lead to rapid adoption of the technology. Here, we discuss the design of experiments necessary to achieve accurate prediction of GEBV in future generations in terms of the number of markers necessary and the size of the reference population where marker effects are estimated. We also present a simple method for implementing genomic selection using a genomic relationship matrix. Future challenges discussed include using whole genome sequence data to improve the accuracy of genomic selection and management of inbreeding through genomic relationships.