Cross-Validation in AMMI and GGE Models: A Comparison of Methods
Cross-Validation in AMMI and GGE Models: A Comparison of Methods
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DOI：
10.2135/cropsci2016.07.0613
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发表时间：
2017-01-01
期刊：
影响因子：
2.3
通讯作者：
Piepho, Hans-Peter
中科院分区：
文献类型：
--
作者：
Hadasch, Steffen;Forkman, Johannes;Piepho, Hans-Peter
在植物育种中，遗传型与环境的相互作用是育种者培育适应目标环境的遗传型的重要内容。为了研究这种相互作用，进行了多环境试验，这些试验通常在每个环境中被布置为随机完全区组设计(RCBD)或可分解不完全区组设计(RIBD)。多环境试验的分析可以通过AMMI(加性主效应和乘性互作)或GGE(基因和基因×环境互作)模型来完成。应用这些模型的目的是(I)确定数据背后的乘性项的真实数量，这是获得可靠的双曲线图所必需的；(Ii)尽可能精确地估计真实的基因-环境平均值。利用模拟的RCBD或rIBD数据，研究了9种不同的交叉验证(CV)方案的性能，其中一些方案代表了期望最大化算法。在一些CV方案中，每个基因-环境组合的一个重复被用于验证，而在另一些方案中，验证数据由一个估计的基因-环境平均值组成。本文还对RCBD数据进行了F-R检验。结果表明，在大多数情况下，对每个基因型-环境组合的一个重复进行抽样的CV方案在这两个目标方面优于其他CV方案。对于RCBD数据，F-R检验的表现类似于表现最好的CV方案。
In plant breeding, the interaction of genotypes and environments is of major interest for breeders to develop genotypes that are well adapted to target environments. To investigate this interaction, multi-environmental trials, which are typically laid out as randomized complete block designs (RCBD) or as resolvable incomplete block designs (rIBD) within each environment, are conducted. The analysis of multi-environmental trials may be done by the AMMI (additive main effects and multiplicative interaction) or by the GGE (genotype and genotype x environment interaction) model. The objectives in the application of these models are (i) to determine the true number of multiplicative terms underlying the data, which is needed to obtain reliable biplots and (ii) to estimate the true genotype-environment means as precisely as possible. Here, the performances of nine different cross-validation (CV) schemes, some of which represent expectation maximization algorithms, were investigated in terms of the two objectives using simulated RCBD or rIBD data. In some of the CV schemes, one replication of each genotype-environment combination was used for validation, whereas in the other schemes, the validation data consisted of one estimated genotype-environment mean. The performance of the F-R test was also investigated for the RCBD data. The results indicate that the CV schemes that sample one replication of each genotype-environment combination outperform the other CV schemes with regard to the two objectives in most scenarios considered. For the RCBD data, the F-R test performed similar to the best performing CV schemes.