GWAS分析中表型值是使用BLUE值还是BLUP值？

https://www.researchgate.net/post/Does_any_one_have_an_idea_of_which_one_BLUE_or_BLUP_to_use_for_a_GWAS_analysis_of_a_trait_in_wheat_eg_resistance_to_rust

问题

Does any one have an idea of which one, BLUE or BLUP, to use for a GWAS analysis of a trait in wheat (e.g resistance to rust)

我做小麦的GWAS分析（比如说抗锈病），是是用BLUE值还是BLUP值？

问题进一步描述

I have 3 data sets of resistance evaluation from two locations generated in a field experiment of alpha-lattice design ( 2 replications of 300 materials and per each replication, 10 incomplete blocks containing 30 accessions). Two of the data sets are from the same location but different years; and the third one is a single year data from another location). So I am thinking of calculating the BLUE / BLUP of each location and a total one for the combined data to be used in the GWAS.

试验是alpha-lattice，2个重复，每个重复10个区组，共300分材料，3份数据，其中两份数据是同一地点不同年份，另一份是不同地点。我想知道怎么将这些数据合并，用于GWAS的分析？

解答：Jhonathan Pedroso Rigal dos Santos

Hi Sisay,

I would recommend to consider all design effects as random (properties BLUP, it is not a effect)  and the population structure and marker effects as fixed (properties BLUE, it is not a effect). There is no reason to perform separate analysis in each trial. It is always desirable one stage analysis (all model parameters are learned from the same likelihood). If you do this mentioned approach you would take the risk of have several false positive associations.
If you are not confident to perform one-stage analysis., I would suggest you to analyze  each trial individually, and considering all design effects as random, and genotypic effect as fixed. In the GWAS analysis modeling marker effects as fixed, and controlling for population structure either using the relationship matrix (including a genotypic random effect into the model), or modeling population structure as a fixed effect, or both. You can identify subpopulations using unsupervised or supervised clustering approach (PCA, structure, whatever).
I hope to be helpful,

推荐做法：一步到位法（One-stage）

将所有的试验因子作为随机因子，比如Rep，Block，Loc，Year等，将群体结构和标记作为固定因子，没有必要将每个数据分开考虑分析。放一起进行分析（One-stage），所有的参数都是一个likelihood。如果分开考虑，就要承担增加假阳性的风险。

如果你还想分开进行分析，那么可以将试验因子作为随机因子，将基因型作为固定因子，计算基因型的预测均值（predict means作为表型值）进行GWAS分析

模型推荐：GenStat mixed model

在option中选择predictmeans，会得到固定因子的预测均值，用它作为GWAS分析的表型值更准确！