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.
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,
Hello Jhonathan,
Many thanks for your swift answer!!! I will go through it hopping for it helps as you hopped so.
Dear Jhonathan,
I have been trying to get a way of generating the BLUEs first and consequently, use them as a single trial data set for the GWAS. This way I will minimize the risk of having several false positive associations. I want to use Restricted Maximum Likelihood (RELM) analysis in Genstat for generating the BLUEs. I would greatly appreciate if you can suggest me how to go about just in case you have some experience?
I strongly recommend to use BLUE, as you are doing a two-stage analysis. BLUE will allow you to have an 'adjusted mean' for each genotype according to design effects, and maybe other covariates in your model. And this is what you want, a more precise mean.This translates into using your model effects (for example replicate fixed and incomplete blocks and plots random), but your genotype (or clone) effect FIXED.
If you use BLUP then you are doing shrinkage of your genetic effects. This will mean that your genetic effects are moved towards the mean according to theri information, yes, they will be your best predictions of those random effects, but they will be adjusted by theri sample size and the vairance associated with theri data. This is what a random effects does, but the issue is that you eliminate part of the genetic signal if is random, and then often you will end up with more noise than what you want for your GWAS. Hence, you fit is as fixed, and then once you do your GWAS it will be a random effect, but it will not be double shrinkaged.
Good luck
The use of BLUP to predict random effects was first described by C. R. Henderson. In 1948, Henderson’s professional career in animal breeding and genetics began with his appointment as an Associate Professor at Cornell University, where he remained for nearly 30 years. During this time, he developed Henderson’s mixed model equations (MME, see below) for linear mixed models in order to obtain BLUPs of breeding values (or any random effect) and BLUEs of fixed effects. for GWAS it should be BLUEs as genotypes will be fitted as fixed in stat analysis.
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