BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype x Environment Interaction Models
BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype x Environment Interaction Models
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DOI：
10.1534/g3.118.200435
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发表时间：
2018-09-01
影响因子：
2.6
通讯作者：
Fritsche-Neto, Roberto
中科院分区：
文献类型：
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作者：
Granato, Italo;Cuevas, Jaime;Fritsche-Neto, Roberto
基因×环境互作是植物育种中的主要问题之一。为了理解和探索这一现象，已经建立了几个模型。在基因组时代，几种模型被用来通过使用标记来改进选择并同时解释GE互作。其中一些模型使用特殊的遗传协方差矩阵。此外，多环境试验的规模越来越大，这增加了计算挑战。在此背景下，我们提出了一个R包，它通常允许建立GE基因组协方差矩阵并拟合线性混合模型，特别是适用于少数基因组GE模型。在这里，我们提出了两个功能：一个是准备考虑基因组GE的基因组核，另一个是使用贝叶斯线性混合模型进行基因组预测。为了减少计算量，对稀疏协方差矩阵进行了具体处理，特别是对某些GE模型中存在的对角矩阵进行了分块处理。在与贝叶斯基因组线性回归(BGLR)的实证比较中，精度和均方误差相似；然而，计算时间比使用经典方法少五倍。贝叶斯基因组X环境相互作用(BGGE)是建立基因组GE核和进行基因组预测的一种快速、有效的选择。
One of the major issues in plant breeding is the occurrence of genotype x environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype x Environment Interaction (BGGE) is a fast, efficient option for creating genomic GE kernels and making genomic predictions.