[R-sig-ME] MCMCglmm : Difference in additive genetic variance estimated in univariate vs bivariate models
chantepie at mnhn.fr
Thu Jul 19 11:56:52 CEST 2012
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
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