[R-sig-ME] residual variances in glmer

[Hervé CHAPUIS]

Hello every one.
I am a real R-mix models-newbie. A colleague told me I should ask  the  
list.
Well, when dealing with  discrete traits  in animal genetics, we have 
many possibilities :
- use an home-made program based, for instance, on Gianola & Foulley 
(1993) algorithm.
- treat the data as a classical gaussian performance, use a linear mixed 
model (lmer works fine) and then compute the heritability coefficient on 
the observed scale  as h² = 4 x sire_variance (sire_variance + 
dam_variance + residual_variance).
After that, use the Dempster & Lerner formula to obtain the heritability 
on the  underlying scale.
- or use directly a general linear mixed model.

That's what I have done but I have been puzzled by the results.
On simulated data, (I have simulated a vector of gaussian performances 
accounting for Mendelian rules, before transforming them into binary 
data through a given threshold value)  the first two options give me  
"good" results and an estimated h² reasonably close  to the expected value.
If  I use glmer instead of lmer, I still obtain a result but I cannot 
safely obtain the h² assuming that the residual variance is 1, can I ?
If so, the estimated h² is very high, if not above 1.
Any hint ?

-- 
Cordialement, 


Hervé CHAPUIS
SYSAAF
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