[R-sig-ME] Meta-analysis for heritability using MCMCglmm?

[Jackie Wood]

Hi Jarrod and Ken,

Hope you had a great New Year! Thanks so much for your responses to my
inquiry. Given that we've been using MCMCglmm all along, we'll probably
stick with it unless there's a compelling reason to change programs. We'll
be running the h2 models in the coming days and will specify a Gaussian
distribution as Jarrod suggested; we have quite a bit of data so hopefully
the residuals will behave!

The advice is much appreciated as always!
Jackie

On Fri, Dec 26, 2014 at 1:58 AM, Jarrod Hadfield <j.hadfield at ed.ac.uk>
wrote:

> Hi Jackie,
>
> The data are not binomial they are continuous: a beta distribution is
> probably most appropriate for continuos observations bounded by 0 and 1.
> However, although heritabilities are bounded by 0 and 1, heritability
> estimates are not necessarily so, depending on the method of inference (for
> example it would be possible to get a negative parent-offspring regression,
> either by chance or through certain types of maternal effect).
>
> We have just finished a meta-analysis of h2 estimates and just treated
> them as Gaussian. The distribution of the residuals wasn't far off and I
> think the conclusions are robust to the distributional assumptions. Have
> you checked your residuals - do they look badly non-normal?
>
>
> Cheers,
>
> Jarrod
>
>
>
>
>
> Quoting Ken Beath <ken.beath at mq.edu.au> on Wed, 24 Dec 2014 12:30:03
> +1100:
>
>  If you have the original data giving the numerator and denominator for the
>> proportion then it is binomial data, and can be modelled in a
>> met-analysis.
>> I don't know if this can be done with MCMCglmm but should be possible with
>> STAN, JAGS or BUGS. All will require a bit of effort in setting up the
>> model.
>>
>> On 24 December 2014 at 07:17, Jackie Wood <jackiewood7 at gmail.com> wrote:
>>
>>  Dear R-users,
>>>
>>> I am attempting to conduct a meta-analysis to investigate the
>>> relationship
>>> of narrow-sense heritability with population size. In previous work, I
>>> have
>>> used MCMCglmm to conduct a formal meta-analysis which allowed me to
>>> account
>>> for the effect of sampling error through the argument "mev". This was
>>> relatively easy to do for a continuous response variable, however,
>>> heritability is presented as a proportion and is therefore bounded by 0
>>> and
>>> 1 which clearly changes the situation.
>>>
>>> In fact, I am not actually certain if it possible to conduct a formal
>>> weighted meta-analysis on the heritability data using MCMCglmm. I have
>>> seen
>>> elsewhere where data presented as a proportion (survival, yolk-conversion
>>> efficiency for example) has been logit transformed and fitted using a
>>> Gaussian error distribution (though this was done using REML rather than
>>> Bayesian modelling) but I don't know if this is a legitimate strategy
>>> for a
>>> formal meta-analysis using heritability as a response variable since any
>>> transformation applied to the heritability data would also need to be
>>> applied to the standard errors?
>>>
>>> I would greatly appreciate any advice on this matter!
>>>
>>> Cheers,
>>> Jackie
>>>
>>> --
>>> Jacquelyn L.A. Wood, PhD.
>>> Biology Department
>>> Concordia University
>>> 7141 Sherbrooke St. West
>>> Montreal, QC
>>> H4B 1R6
>>> Phone: (514) 293-7255
>>>
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>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>>
>>
>>
>> --
>>
>> *Ken Beath*
>> Lecturer
>> Statistics Department
>> MACQUARIE UNIVERSITY NSW 2109, Australia
>>
>> Phone: +61 (0)2 9850 8516
>>
>> Building E4A, room 526
>> http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/
>>
>> CRICOS Provider No 00002J
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>>
>>
>>
>
>
> --
> The University of Edinburgh is a charitable body, registered in
> Scotland, with registration number SC005336.
>
>
>


-- 
Jacquelyn L.A. Wood, PhD.
Biology Department
Concordia University
7141 Sherbrooke St. West
Montreal, QC
H4B 1R6
Phone: (514) 293-7255

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