[R-sig-ME] Trivariate MCMCglmm

[Gemma Palomar García]

Hello,

We are using MCMCglmm package with a dataset of 1063 toads. We have three different characters: infection rate, fresh body weight and development rate and we would like to infer additive variance, dominance and maternal effect which it is possible with our breeding design. We want to study heritability and genetic correlations between the characters. The three were normalized with a logarithmic transformation and block was used as random factor to control the variation caused by the position of the containers.

Inferring dominance effect, we used nadiv package to obtain dominance matrix:

library(nadiv)
pedDom=read.table("Ped_Dom.txt",header=T)
Dom<-makeD(pedDom)
Dinv=Dom$Dinv
data$dom=data$animal

The construction of the trivariate model was the following:

model3.2=MCMCglmm(cbind(Infection_rate,Weight,Developm)~trait-1, 
                  random = ~us(trait):animal+us(trait):Mother
                  +us(trait):Block+us(trait):dom,
                  rcov=~us(trait):units,
                  ginverse=list(dom=Dinv), 
                  family = c("gaussian", "gaussian","gaussian"),
                  pedigree = pedDom,data = data, nitt = 5000000, 
                  thin = 100, burnin = 5000, prior = prior3.2)

And we used this prior:

prior3.2=list(G = list(G1 = list(V = diag(3), n = 2.002),
                       G2 = list(V = diag(3), n = 2.002),
                       G3 = list(V = diag(3), n = 2.002),
                       G4 = list(V = diag(3), n = 2.002)),
              R = list(V = diag(3), n = 2.002))

We obtained very bad mixing but DIC is lower than DIC of the model without dominance. Scaling the variables doesn’t seem to improve the model. Any advice of the model or the prior will be welcome to improve the mixing. 

On the other hand, characters with high dominance effects have a change in additive effects in the model without dominance. Additive effects are highly reduced when dominance is added to the model. Is this an overestimation of the additive variance in the model without dominance caused by its lack?

Thank you,

Gemma Palomar
PhD student of University of Oviedo, Spain






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