Comparison of Weighting in Two‐Stage Analysis of Plant Breeding Trials
Comparison of Weighting in Two‐Stage Analysis of Plant Breeding Trials
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植物育种试验两阶段分析中的权重比较
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
H. Piepho
中科院分区：
文献类型：
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作者：
J. Möhring;H. Piepho
一系列的植物育种试验常常是不平衡的并且具有复杂的遗传结构。为了降低计算成本，通常的做法是采用两阶段方法，其中估计每个位置的调整后均值，然后对这些调整后的均值执行混合模型分析。一个重要的问题是如何在第二步中对第一步的方法进行加权。因此，我们的目标是使用具有固定或随机遗传效应的混合模型，在分析四个典型系列植物育种试验时比较不同的加权方法。我们使用了四种已发布的加权方法并提出了三种新方法。使用一阶段分析作为基准，计算了四个评估标准来比较方法。我们发现两阶段分析给出了具有固定遗传效应的可接受的结果。当第二阶段的遗传效应被视为随机时，在四个数据集中的三个中，两阶段分析给出了可接受的结果。在这两种情况下，加权方法之间的差异很小，最佳加权方法取决于数据集，而不取决于评估标准。不加权的两阶段分析也产生了可接受的结果，但加权大多表现更好。在第四个数据集中，缺失的数据模式提供了丰富的信息，导致违反了一阶段和两阶段分析中的随机缺失 (MAR) 假设。在这种情况下，两种分析都不是严格有效的。
Series of plant breeding trials are often unbalanced and have a complex genetic structure. To reduce computing cost, it is common practice to employ a two-stage approach, where adjusted means per location are estimated and then a mixed model analysis of these adjusted means is performed. An important question is how means from the first step should be weighted in the second step. Our objective therefore was the comparison of different weighting methods in the analysis of four typical series of plant breeding trials using mixed models with fixed or random genetic effects. We used four published weighting methods and proposed three new methods. Four evaluation criteria were computed to compare methods, using one-stage analysis as benchmark. We found that the two-stage analysis gave acceptable results with fixed genetic effects. When genetic effects were taken as random in stage two, in three of four datasets the two-stage analysis gave acceptable results. In both cases differences between weighting methods were small and the best weighting method depended on the dataset but not on the evaluation criteria. A two-stage analysis without weighting also produced acceptable results, but weighting mostly performed better. In the fourth dataset the missing data pattern was informative, resulting in violation of the missing-at-random (MAR) assumption in one- and two-stage analysis. In this case both analyses were not strictly valid.