Hi,

If I understand well, if you want to take into account the kinship between
individuals in genetics models, you have to specify a random effect that is
the individuals levels and specify that this effect follows a distribution
with a variance/covariance matrix equal to 2*K*Vg (basically). 2*Vg is just
a number and K is the kinship matrix.
I am currently using R to do such genetics models to do association
mapping. I ask to other people that have done that before me and if I
understand well, no packages allows to specify such a variance/covariance
matrix for a random effect except ASREML. But you have to pay a license to
use it. I am using this package for my study.

Concerning the question of putting an observation-level effect, I begin to
understand it but you have to check with others perhaps. I will take the
association mapping case as an example (I hope you know it a bit). You have
a sample of individuals that are evaluated within a repeated block design
for example. So each individuals is repeated end therefore, the
observations level is not **yet** the individual level. The classical first
step is to estimate (predict is the good word I guess) BLUP for the
individual level with a model that takes into account the experimental
design parameters. After this step, you obtain a dataset in which the
observation level is the individual level. The second step consists in
testing the association between the BLUP and some markers. In this model,
you specify a random effect which is the individual level for with you
specify the variance/covariance matrix 2KVg. So, at this step, you use a
random effect at the observation level. I talked about that with
biostatiticians (I hope this traduction is good) because I was surprised
that an effect could be at the same level as the observations level because
there won't be enough degree of freedom in the model. They explained me
that, the correlation between individuals, specified in the kinship matrix,
acts like repeating each individuals in their common part in other
individuals. Well, I am sorry that I'm not able to put this idea in words
in a better way, I hope it will help.

I hope my e-mail will help.
Yves Rousselle

2012/3/14 Jonas Klasen <klasen@mpipz.mpg.de>

> Hi,
> the kinship is a known variance-covariance matrix which allows a
> separation of a observation-level random effect at the "G-side" from the
> residuals at the  "R-side".
>
> ----- Original Message -----
> From: Juliet Hannah <juliet.hannah@gmail.com>
> Date: Tuesday, March 13, 2012 19:40
> Subject: [R-sig-ME] observation level random effects/kinship model
> To: r-sig-mixed-models@r-project.org
>
> > All,
> >
> > I was reading the following post:
> >
> > https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/005184.html
> >
> > which discusses why an observation-level random effect does not make
> > sense in the linear mixed model
> > case.
> >
> > Does anyone know of any references that discuss this?
> >
> > And for the genetics folks out there, isn't this what the kinship
> > model is: an observation-level random effect
> > that is correlated by degree of relatedness?
> >
> > Thanks,
> >
> > Juliet Hannah
> >
> > _______________________________________________
> > R-sig-mixed-models@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
> ______________________________________________________
>
> Jonas Klasen
> PhD student
> Genome Plasticity and Computational Genetics
> Max Planck Institute for Plant Breeding Research
> ______________________________________________________
>
>
>
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>
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
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>
>


-- 
Yves Rousselle
Docteur en Génétique Végétale
http://yvesdaroussette.free.fr

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