# GWAS分析中表型值是使用BLUE值还是BLUP值？

### 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)

##### 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.

#### 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).

#### 模型推荐：GenStat mixed model

http://blog.sciencenet.cn/blog-2577109-1113041.html

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