A Two-Stage Method for Improving the Prediction Accuracy of Complex Traits by Incorporating Genotype by Environment Interactions in Brassica napus
A Two-Stage Method for Improving the Prediction Accuracy of Complex Traits by Incorporating Genotype by Environment Interactions in Brassica napus
复制标题
通过环境相互作用结合基因型来提高甘蓝型油菜复杂性状预测精度的两阶段方法
DOI：
10.1155/2020/7959508
复制
发表时间：
2020
影响因子：
1.4
通讯作者：
Yanyan Liu
中科院分区：
文献类型：
--
作者：
Sican Xiong;Meng Wang;Jun Zou;Jinling Meng;Yanyan Liu
提高目标复杂性状的预测精度是进行作物育种基因组选择的关键。针对在多环境下测量的复杂性状，提出了一种两阶段方法来求解一个线性模型，该模型将遗传效应和基因型×环境互作（G×E）效应联合建模。在第一阶段，利用最小绝对收缩和选择算子（LASSO）惩罚法确定数量性状基因座（QTL）。第二阶段采用普通最小二乘法（OLS）重新估计QTL效应。以甘蓝型油菜（B.）为例，应用该方法提高了开花期、含油量和单株籽粒产量的预测精度。napus）。结果表明，G × E效应显著降低了均方误差（MSE）。许多QTL是环境特异性的，并表现出较小的影响。平均而言，称为OLS post-LASSO的两阶段方法提供了最高的预测精度（FT，OC和SY的相关性分别为0.8789，0.9045和0.5507）。其次是标记×环境互作（M×E）基因组最佳线性无偏预测（GBLUP）模型（FT、OC和SY的相关性分别为0.8347、0.8205和0.4005），LASSO方法（FT、OC和SY的相关性分别为0.7583、0.7755和0.2718），以及分层GBLUP模型（FT、OC和SY的相关性分别为0.6789、0.6361和0.2860）。两阶段法在预测精度上有明显的提高，该研究为提高育种GS提供了方法和参考。
Improving the prediction accuracy of a complex trait of interest is key to performing genomic selection (GS) for crop breeding. For the complex trait measured in multiple environments, this paper proposes a two‐stage method to solve a linear model that jointly models the genetic effects and the genotype × environment interaction (G×E) effects. In the first stage, the least absolute shrinkage and selection operator (LASSO) penalized method was utilized to identify quantitative trait loci (QTL). Then, the ordinary least squares (OLS) approach was used in the second stage to reestimate the QTL effects. As a case study, this approach was used to improve the prediction accuracies of flowering time (FT), oil content (OC), and seed yield per plant (SY) inBrassica napus(B. napus). The results showed that theG×Eeffects reduced the mean squared error (MSE) significantly. Numerous QTL were environment‐specific and presented minor effects. On average, the two‐stage method, named OLS post‐LASSO, offers the highest prediction accuracies (correlations are 0.8789, 0.9045, and 0.5507 for FT, OC, and SY, respectively). It was followed by the marker × environment interaction (M×E) genomic best linear unbiased prediction (GBLUP) model (correlations are 0.8347, 0.8205, and 0.4005 for FT, OC, and SY, respectively), the LASSO method (correlations are 0.7583, 0.7755, and 0.2718 for FT, OC, and SY, respectively), and the stratified GBLUP model (correlations are 0.6789, 0.6361, and 0.2860 for FT, OC, and SY, respectively). The two‐stage method showed an obvious improvement in the prediction accuracy, and this study will provide methods and reference to improve GS of breeding.