Hi,

I use the MCMCglmm function in the genomic selection context in the 
univariate case. In fact I have for each trait one marker matrix 
constituted of 0,1 and 2. The rows are the individuals and the columns 
the SNPs. In a first time, we consider that each SNP follow a normal 
distribution with the *same *variance. So I use the following model:

prior.1.1 <- list(G=list(G1=list(V=diag(x = as.numeric(scale), nrow=1, 
ncol=1),nu=ddl),
                           R=list(V=matrix(scale),nu=ddl))

mcmc.fit.1.1 <- MCMCglmm(P~ 1,random=~idv(SNP),prior=prior.1.1,
                   data=data1.1,
                   nitt=5000, burnin=1000,verbose=FALSE,
                   thin=10,pr=TRUE)

So, I obtained a common variance associated to my SNPs.


The second step is a bivariate analysis. I would like to obtain a 
(co)variance matrix 2*2  associated to the trait1 and trait 2 and the 
correlation between both. (one variance for the SNPs for the trait 1 and 
one for the trait 2). But I don't know how i can do. The following model 
give me only one variance for all SNPs and both traits.

     prior.3<- list(G=list(G1=list(V=diag(x = as.numeric(scale), nrow=1,
                           ncol=1),n=1)),
               R=list(V=diag(x=as.numeric(0.1),nrow=2,ncol=2),n=2))

     mcmc.fit.3 <- MCMCglmm(cbind(P1,P2)~ trait-1,random=~idv(trait:SNP),
                   rcov=~idh(trait):units,
                   prior=prior.3,
                   data=data1.3,family=c("gaussian","gaussian"),
                   nitt=2000, burnin=500,verbose=FALSE,
                   thin=10,pr=TRUE)



thanks for your help,




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