Martini Johannes W R, Gao Ning, Crossa José
International Maize and Wheat Improvement Center (CIMMYT), Veracruz, CP, Mexico.
School of Life Sciences, Sun Yat-Sen University, Guangzhou, China.
Methods Mol Biol. 2022;2467:341-357. doi: 10.1007/978-1-0716-2205-6_12.
In this chapter, we discuss the motivation for integrating other types of omics data into genomic prediction methods. We give an overview of literature investigating the performance of omics-enhanced predictions, and highlight potential pitfalls when applying these methods in breeding. We emphasize that the statistical methods available for genomic data can be transferred to the general omics case. However, when using a framework of omic relationship matrices, the standardization of the variables may be more relevant than it is for a genomic relationship matrix based on single-nucleotide polymorphisms.
在本章中,我们讨论了将其他类型的组学数据整合到基因组预测方法中的动机。我们概述了研究组学增强预测性能的文献,并强调了在育种中应用这些方法时可能存在的潜在陷阱。我们强调,可用于基因组数据的统计方法可以推广到一般的组学情况。然而,在使用组学关系矩阵框架时,变量的标准化可能比基于单核苷酸多态性的基因组关系矩阵更为重要。