Genomic selection for genotype performance and stability using information on multiple traits and multiple environments.
Genomic selection for genotype performance and stability using information on multiple traits and multiple environments.
复制标题
利用关于多个性状和多个环境的信息，为基因型表现和稳定性进行基因组选择。
DOI：
10.1007/s00122-023-04305-1
复制
发表时间：
2023-04-07
影响因子：
5.4
通讯作者：
Tolhurst, D. J.
中科院分区：
文献类型：
--
作者：
Bancic, J.;Ovenden, B.;Gorjanc, G.;Tolhurst, D. J.
在基因组选择的部分可分离因子分析方法中包含多个性状和多个环境，为育种者提供了一个信息框架，以利用基因型与环境与性状的相互作用进行有效的选择。本文开发了一种单阶段基因组选择（GS）方法，该方法在部分可分离的因素分析框架内整合了多个性状和多个环境的信息。因子分析线性混合模型是分析多环境试验（MET）数据的有效方法，但尚未扩展到多性状多环境GS。使用所有信息的优点是育种者可以利用基因型与环境与性状的相互作用（GETI）来获得相关性状和环境之间更准确的预测。部分可分因子分析线性混合模型（SFA-LMM）是基于一个三向可分结构，包括性状间的因子分析矩阵、环境间的因子分析矩阵和基因型间的基因组关系矩阵。然后添加对角矩阵以使每个性状的不同基因型与环境相互作用（GEI）模式和每个环境的不同基因型与性状相互作用（GTI）模式成为可能。结果表明，SFA-LMM提供了一个更好的适合比可分离的方法和一个类似的适合不可分离和部分可分离的方法。SFA-LMM的显着特点是，随着基因型、性状和环境数量的增加，它将包括比所有其他方法更少的参数。最后，一个选择指数是用来证明同时选择的整体性能和稳定性。这项研究代表了植物育种分析进步的重要延续，特别是随着涉及大量基因型，性状和环境的高通量数据集的出现。在线版本包含补充材料，可通过10.1007/s 00122 -023-04305-1获得。
The inclusion of multiple traits and multiple environments within a partially separable factor analytic approach for genomic selection provides breeders with an informative framework to utilise genotype by environment by trait interaction for efficient selection. This paper develops a single-stage genomic selection (GS) approach which incorporates information on multiple traits and multiple environments within a partially separable factor analytic framework. The factor analytic linear mixed model is an effective method for analysing multi-environment trial (MET) datasets, but has not been extended to GS for multiple traits and multiple environments. The advantage of using all information is that breeders can utilise genotype by environment by trait interaction (GETI) to obtain more accurate predictions across correlated traits and environments. The partially separable factor analytic linear mixed model (SFA-LMM) developed in this paper is based on a three-way separable structure, which includes a factor analytic matrix between traits, a factor analytic matrix between environments and a genomic relationship matrix between genotypes. A diagonal matrix is then added to enable a different genotype by environment interaction (GEI) pattern for each trait and a different genotype by trait interaction (GTI) pattern for each environment. The results show that the SFA-LMM provides a better fit than separable approaches and a comparable fit to non-separable and partially separable approaches. The distinguishing feature of the SFA-LMM is that it will include fewer parameters than all other approaches as the number of genotypes, traits and environments increases. Lastly, a selection index is used to demonstrate simultaneous selection for overall performance and stability. This research represents an important continuation in the advancement of plant breeding analyses, particularly with the advent of high-throughput datasets involving a very large number of genotypes, traits and environments. The online version contains supplementary material available at 10.1007/s00122-023-04305-1.
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DOI：
10.1186/1297-9686-41-21
发表时间：
2009-01-30
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Meyer K
通讯作者：
Meyer K
影响因子：
--
作者：
Sansaloni C;Petroli C;Jaccoud D;Carling J;Detering F;Grattapaglia D;Kilian A
通讯作者：
Kilian A
影响因子：
1.9
作者：
Smith, A;Cullis, B;Thompson, R
通讯作者：
Thompson, R
影响因子：
3.1
作者：
Cullis, B. R.;Smith, A. B.;Cowling, W. A.
通讯作者：
Cowling, W. A.
影响因子：
2.6
作者：
Meyer, K.
通讯作者：
Meyer, K.