[R-sig-ME] binary trait correlation across environments (experimental trials) using MCMCglmm?

Plough, Louis lplough @ending from umce@@edu
Wed Jan 2 23:18:49 CET 2019

Thanks Jarrod for the prompt response!

I think the residual variance was set at 1.   My prior  looks like this:

prior <- list(R = list(V = 1, nu = 3), G = list(G1 = list(V = diag(2), nu =

Not sure why the difference for *nu* in R vs G (3 vs 2)...might have been a
typo. Is there guidance on setting the gamma parameter for this kind of
binary trait, cross environment correlation model with the 'threshold'
family? In the low iteration (toy) run I did for the R-Sig-ME post, the
correlation was a bit lower than I would expect based on simply phenotypic
correlation of family means, so I think I might need to tweak *nu *for

*Can I also confirm that I am estimating the rG correctly with the
following code:*


Any reason to use 'TrialA:TrialB.animal' vs 'TrialB:TrialA.animal' in the
numerator? Or are they basically equivalent? I wouldn't want add them,
would I?

Thanks for your help!!!


On Wed, Jan 2, 2019 at 3:17 PM HADFIELD Jarrod <j.hadfield using ed.ac.uk> wrote:

> Hi,
> Your model bi_model_trial is the correct one. However you must fix the
> residual variance at one in the prior. You have estimated the genetic
> (co)variance matrix for the two trials, from which you can obtain the
> genetic correlation.  Alternatively, you could fit animal+animal:Trial
> which assumes the genetic variances in the two trials are the same and the
> correlation is positive. The correlation in this latter model is obtained
> as VAR(animal)/(VAR(animal)+VAR(animal:Trial)).  Also, there seems to be a
> problem with your Surv data as it has three levels rather than 2 and so a
> cutpoint is being estimated.
> Cheers,
> Jarrod
> On 2 Jan 2019, at 16:33, Plough, Louis <lplough using umces.edu> wrote:
> bi_model_trial
> The University of Edinburgh is a charitable body, registered in Scotland,
> with registration number SC005336.

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