[R-sig-ME] cross-sex genetic correlation

Simona Kralj Fiser simonakf at gmail.com
Wed Jul 26 14:33:14 CEST 2017


We are trying to estimate body mass (W) heritability and cross-sex genetic
correlation using MCMCglmm. Our data matrix consists of three columns: ID,
sex, and W. Body mass data is NOT normally distributed.

Following previous advice, we first separated weight data into two columns,
WF and WM. WF listed weight data for female specimens and “NA” for males,
and vice-versa in the WM column. We used the following prior and model

prior1 <- list(R=list(V=diag(2)/2, nu=2), G=list(G1=list(V=diag(2)/2,

modelmulti <- MCMCglmm(cbind(WF,WM)~trait-1, random=~us(trait):animal,
rcov=~us(trait):units, prior=prior1, pedigree=Ped, data=Data1, nitt=100000,
burnin=10000, thin=10)

The resulting posterior means of posterior distribution were suspiciously
low (e.g. 0.00002). We calculated heritability values anyway, using the

herit1 <- modelmulti$VCV[,'traitWF:traitWF.animal']/


herit2 <- modelmulti$VCV[,'traitWM:traitWM.animal']/


corr.gen <- modelmulti$VCV[,traitWF.traitWM.animal']/


We get heritability estimates of about 50%, which is reasonable, but
correlation estimates were extremely low, about 0.04%.

Suspecting the model was wrong, we used the original dataset with all
weight data in a single column and tried the following model:

prior2 <- list(R=list(V=1, nu=0.02), G=list(G1=list(V=1, nu=1, alpha.mu=0,

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

The model runs, but it refuses to calculate “herit” values, with the error
message “subscript out of bounds”. We’d also add that in this case, the
posterior density graph for sex2:sex.animal is not shaped like a bell.

What are we doing wrong? Are we even using the correct models?

Eva and Simona


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