最佳线性无偏预测
二元分析
单变量
选择(遗传算法)
生物
可预测性
粮食产量
特质
多元统计
统计
生物技术
数学
计算机科学
农学
机器学习
程序设计语言
作者
Shibo Wang,Yongsheng Xu,Han Qu,Yanru Cui,Ruidong Li,John M. Chater,Lei Yu,Rui Zhou,Renyuan Ma,Yuhan Huang,Yiru Qiao,Xuehai Hu,Weibo Xie,Zhenyu Jia
摘要
Abstract The multivariate genomic selection (GS) models have not been adequately studied and their potential remains unclear. In this study, we developed a highly efficient bivariate (2D) GS method and demonstrated its significant advantages over the univariate (1D) rival methods using a rice dataset, where four traditional traits (i.e. yield, 1000-grain weight, grain number and tiller number) as well as 1000 metabolomic traits were analyzed. The novelty of the method is the incorporation of the HAT methodology in the 2D BLUP GS model such that the computational efficiency has been dramatically increased by avoiding the conventional cross-validation. The results indicated that (1) the 2D BLUP-HAT GS analysis generally produces higher predictabilities for two traits than those achieved by the analysis of individual traits using 1D GS model, and (2) selected metabolites may be utilized as ancillary traits in the new 2D BLUP-HAT GS method to further boost the predictability of traditional traits, especially for agronomically important traits with low 1D predictabilities.
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