Efficiency of augmented p-rep designs in multi-environmental trials
Efficiency of augmented p-rep designs in multi-environmental trials
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DOI：
10.1007/s00122-014-2278-y
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发表时间：
2014-05-01
影响因子：
5.4
通讯作者：
Piepho, Hans-Peter
中科院分区：
文献类型：
--
作者：
Moehring, Jens;Williams, Emlyn R.;Piepho, Hans-Peter
本文表明，在多环境试验中的无重复设计是最有效的。如果每个环境复制是必要的，那么增强的p-rep设计优于增强和复制的设计在小黑麦和maize.Abstract在植物育种中，增强设计与unreplicated条目经常用于早代测试。由于种子数量有限，这种设计允许在多环境试验（MET）中使用最大数量的环境。检查图可以估计区组效应、误差方差和MET中其他不相关试验的连接。Cullis等人（J Agri Biol Environ Stat 11：381-393，2006）提出用导致部分重复（p-rep）设计的重复条目的图来替换来自网格图设计的检查图。威廉姆斯等人（Biom J 53：19-27，2011）将该想法应用于增强设计（增强的p-代表设计）。虽然p-rep设计越来越多地用于MET中，但在MET中的复制和非复制设计之间的范围内，缺乏对增强p-rep设计和增强设计的效率的比较。我们模拟了遗传效应，并根据这四个设计分配它们到小黑麦和玉米均匀性试验的小区产量。这些设计在环境数量上有所不同，但具有固定的条目数量和总地块。在模拟中，误差模型和固定或随机进入效应的假设是不同的。我们扩展了我们的模拟小黑麦的数据，包括相关的条目效应，这是常见的基因组选择。结果表明，无重复和增强的p-代表设计的优势和使用随机进入效应的偏好，特别是在相关效应反映条目之间的关系。与纯粹基于随机化的模型相比，空间误差模型具有较小的优势。
Key message The paper shows that unreplicated designs in multi-environmental trials are most efficient. If replication per environment is needed then augmented p-rep designs outperform augmented and replicated designs in triticale and maize.Abstract In plant breeding, augmented designs with unreplicated entries are frequently used for early generation testing. With limited amount of seed, this design allows to use a maximum number of environments in multi-environmental trials (METs). Check plots enable the estimation of block effects, error variances and a connection of otherwise unconnected trials in METs. Cullis et al. (J Agri Biol Environ Stat 11:381-393, 2006) propose to replace check plots from a grid-plot design by plots of replicated entries leading to partially replicated (p-rep) designs. Williams et al. (Biom J 53:19-27, 2011) apply this idea to augmented designs (augmented p-rep designs). While p-rep designs are increasingly used in METs, a comparison of the efficiency of augmented p-rep designs and augmented designs in the range between replicated and unreplicated designs in METs is lacking. We simulated genetic effects and allocated them according to these four designs to plot yields of a triticale and a maize uniformity trial. The designs varied in the number of environments, but have a fixed number of entries and total plots. The error model and the assumption of fixed or random entry effects were varied in simulations. We extended our simulation for the triticale data by including correlated entry effects which are common in genomic selection. Results show an advantage of unreplicated and augmented p-rep designs and a preference for using random entry effects, especially in case of correlated effects reflecting relationships among entries. Spatial error models had minor advantages compared to purely randomization-based models.