[R-sig-ME] Questions about MCMCglmm and marker data

[Jarrod Hadfield]

Hi,

It is the effects that are to be considered normal, not the way the  
genotypes are coded.

It is not possible to fit this model in the current version of  
MCMCglmm, but it will be available in the next. I hope to submit this  
version to CRAN in the next couple of weeks.

Cheers,

Jarrod






Quoting Emmanuel Curis <curis at pharmacie.univ-paris5.fr> on Thu, 23 Aug  
2012 12:10:46 +0200:

> Hello,
>
> I'm not familiar with this kind of genomic problems, but if the
> columns are the number of a given SNP for a given individual and can
> be only 0, 1 and 2, is this really possible to consider they follow a
> normal distribution?
>
> On Thu, Aug 23, 2012 at 11:54:49AM +0200, Marie Denis wrote:
> « 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,
> «
> «
> «
> «
> « 	[[alternative HTML version deleted]]
> «
> « _______________________________________________
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>
> --
>                                 Emmanuel CURIS
>                                 emmanuel.curis at univ-paris5.fr
>
> Page WWW: http://emmanuel.curis.online.fr/index.html
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>



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