[R-sig-ME] MCMCglmm : Difference in additive genetic variance estimated in univariate vs bivariate models

[Stephane Chantepie]

Dear all,

I have been running animal models to estimate whether Va of a trait was 
changing with age, and the correlation between this trait expressed in 
different age classes. The problem is I have a difference in additive genetic 
variance (Va) estimated in univariate vs bivariate models.

To be clearer: I have run an animal model on “Spz_9” (spz trait for age 9) and 
obtained a Va = 0.15 (lower = 0.04; upper = 0.88). The intervals are pretty 
large because I do not have a lot animals in the pedigree (171 animals) but it 
seems that the model succeeds in estimating Va. When I run a bivariate model 
c(Spz_9,Spz_5), the Va estimations of spz_9 dramatically increases with Va = 
1.02 (lower = 0.35; upper = 1.71). The Va posterior distributions of the trait 
is well shaped, so I do not know whether there is a problem or not. Va 
increases when I run a bivariate model between spz_9 and some other traits 
(spz_2 or spz_3) and the Va values for spz_9 are also closed to 1.

I have used Gaussian distributions and informative prior (V =(Phenotypic 
variance/2) , nu =1 ), no fixed effect and (birth age +animal) random effects.

I am wondering what these differences mean? Could the problem come from a lack 
of information? Can the covariance between spz_9 and spz_5 be well estimated?

Many thanks in advance for your help,

All the best