cross-sex genetic correlation

Hi!

I have some further questions on the cross-sex genetic correlations.

It seems that many studies fail to obtain sample sizes that is big 
enough to get the realistic 95% CI. One of the solutions I noticed is 
comparing the models (ASReml) where rmf is set to 1 or 0. How could 
that be done when using MCMCglmm models? I was thinking of comparing

1. model 1, rmf=1

prior1<-list(G=list(G1=list(V=matrix(p.var*0.5),n=1)),R=list(V=matrix(p.var*0.5),n=1))

model1 <- MCMCglmm(trait ~ sex, random = ~animal, pedigree = 
pedigree,data = data, nitt = 100000, thin = 100, burnin = 15000, prior 
= prior1,verbose = FALSE)

2. model 2 allowing sexes to have different variance and covariance

prior2 <- list(R=list(V=diag(2), nu=0.02), G=list(G1=list(V=diag(2), 
nu=2, alpha.mu <http://alpha.mu/>=c(0,0),alpha.V=diag(2)*1000)))

model1 <- MCMCglmm(trait~sex, random=~us(sex):animal, 
rcov=~idh(sex):units, prior=prior2, pedigree=Ped, data=Data1, 
nitt=100000, burnin=10000, thin=10)


3. model 3 allowing sexes to have different variance, but covariance = 
0 --> rmf = 0

prior2 <- list(R=list(V=diag(2), nu=0.02), G=list(G1=list(V=diag(2), 
nu=2, alpha.mu <http://alpha.mu/>=c(0,0),alpha.V=diag(2)*1000)))

model3 <- MCMCglmm(trait~sex, random=~idh(sex):animal, 
rcov=~idh(sex):units, prior=prior2, pedigree=Ped, data=Data1, 
nitt=100000, burnin=10000, thin=10)


My reasoning may be stupid, I hope that's not forbidden :-)

Is there any other way? Fixing variances with prior?

Thank you. Best wishes

Simona


On 26 July 2017 at 14:42, Jarrod Hadfield <j.hadfield at ed.ac.uk 
<mailto:j.hadfield at ed.ac.uk>> wrote:

    Hi,

    The second way is a *much* better way of doing it but should give
    the same answer. However, in both cases the residual covariance is
    not identifiable (no individual is both male and female) and so
    you should use idh rather than us.

    The "subscript out of bounds" error is to do with your code that
    post-processes the model output not an issue with MCMCglmm.
    Probably you have used the wrong names for the (co)variance
    components.

    Also, you haven't passed the prior to MCMCglmm, nor is the prior a
    valid one for the problem as it specifies scalar variances rather
    than 2x2 covariance matrices. You could try

    prior2 <- list(R=list(V=diag(2), nu=0.02),
    G=list(G1=list(V=diag(2), nu=2, alpha.mu
    <http://alpha.mu>=c(0,0),alpha.V=diag(2)*1000)))

    Cheers,

    Jarrod




    On 26/07/2017 13:33, Simona Kralj Fiser wrote:

        model <- MCMCglmm(W~sex, random=~us(sex):animal,
        rcov=~us(sex):units,
        prior=prior2, pedigree=Ped, data=Data1, nitt=100000,
        burnin=10000, thin=10)



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