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=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=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

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=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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