《生命科学》 2018, 30(10): 1051-1059

绿色性状的基因定位和基因组选择的多变量方法

徐　扬，王　欣，徐辰武*

(扬州大学，江苏省作物遗传生理重点实验室/植物功能基因组学教育部重点实验室/江苏省作物
    基因组学和分子育种重点实验室，江苏省粮食作物现代产业技术协同创新中心，扬州 225009)

摘　要：摘　要：绿色性状的遗传改良为绿色超级稻的培育奠定了坚实的基础。绿色性状，如高产、抗病、抗逆、氮磷高效利用等，大多是受多基因控制的复杂性状。关联分析和基因组选择是对植物复杂数量性状进行遗传解析和改良的重要方法。在绿色超级稻的育种实践中，需要同时改良多个绿色性状。然而，目前关联分析和基因组选择方法大多仍专注于对单个性状的分析，忽略了性状间的相关性。现分别提出关联分析和基因组选择的多性状方法，两者充分利用了性状之间的遗传相关和环境相关信息；而模拟研究和实证研究均表明，多性状方法能有效提高基因定位和表型预测的准确性，为绿色性状的遗传改良提供重要技术支撑。

Multivariate approaches of gene mapping and genomic selection for green traits

XU Yang， WANG Xin， XU Chen-Wu*

(Jiangsu Key Laboratory of Crop Genetics and Physiology/ Key Laboratory of Plant Functional Genomics of the Ministry of Education/ Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding， Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops， Yangzhou University， Yangzhou 225009， China)

Abstract: Abstract: The genetic improvement of green traits has laid a solid foundation for the development of Green Super Rice. Green traits， such as high yield， disease resistance， stress resistance， nitrogen and phosphorus use efficiency，are mostly complex traits controlled by multiple genes. Association analysis and genomic selection are important methods for genetic analysis and improvement of complex quantitative traits in plants. In the breeding of Green Super Rice， it is necessary to improve many green traits simultaneously. However， most of the current association analysis and genomic selection methods are still focused on a single trait， ignoring the correlation between traits. Therefore， we proposed the multi-trait methods of association analysis and genomic selection. Both simulation and empirical studies show that the multi-trait methods effectively increase the accuracy of gene mapping and phenotype prediction. The proposed methods provide an important technical support for the genetic improvement of green traits.