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Heritability of ordinal data in MCMCglmm and estimatingfixed effects
2 messages · Samantha Patrick, David Duffy
2 days later
On Fri, 2 Nov 2012, Samantha Patrick wrote:
The pearson's correlations between observations ranges from 0.18 - 0.75, depending on the year of testing (average 0.48; one year has a very low repeatability). One of the reasons why I question whether using a Gaussian distribution (fitting a LMM) is correct is that the Mother- Offspring (M-O), father- offspring (F-O) and sib-sib (S-S)regressions all have a Pearson's R2 <0.05. Using polychoric correlations (I ran these in the polycor package but as I understand it will run the same test?)
Yes.
[polychoric r's compared to Pearson r's] are very different: M-O = 0.34 F-O = 0.40 S-S = 0.13 The conclusions seems to be that the best model to estimate heritability would be to fit the first observation per individual, such that: Trait1~ Colony, random =~animal + BYEAR and examine the models with and without BYEAR, fitted in MCMCglmm. I can then use repeated measures to estimate the repeatabilities and extract the blups or single scores per individual using an IRT model.
The alternative is the full multivariate genetic (or even genetic time series model[1]), where you could see if the between-occasion correlations are genetic or environmental (the latter may include measurement error). This should run in MCMCglmm as an ordinal model. You can test if the covariances have a simple structure as under a straight measurement error model. If the between-occasion correlations average ~0.5 you should have OK power I think. [1] eg http://www.tweelingenregister.org/nederlands/verslaggeving/NTR_publicaties/Boomsma_BG_1987.pdf
| David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph: INT+61+7+3362-0217 fax: -0101 / * | Epidemiology Unit, Queensland Institute of Medical Research \_,-._/ | 300 Herston Rd, Brisbane, Queensland 4029, Australia GPG 4D0B994A v