Hi,

I have a couple of queries relating to means modelling but more on the conceptual side rather than OpenMx scripting.

- This is just a general question. To test means models for eye traits ideally requires an assessment of Right vs Left measures, in addition to twin order and then zygosity. Most of the ophthalmic heritability papers I've read are haphazard in how they approach the R vs L issue. Some use one eye as the representative phenotype, others the mean of R and L, and some a Cholesky of both. If you test R and L means and find this constrained model is an acceptable fit compared to the saturated model, does that effectively mean there is nothing to be gained from using information from both eyes in a multivariate (cholesky) analysis? ie could you just take the mean of R and L eyes and obtain a similar result?

I'm not sure how much laterality is an issue with other twin researchers?

- The variables in my dataset are principal components of quantitative measures of optic nerve head shape. I want to use the PC scores as input variables in heritability analyses. I've found a strange problem when looking at the means of these PC scores (e.g. the first 6 PCs):

model:LEFT, means:expMeanLEFT

L_PC1 L_PC2 L_PC3 L_PC4 L_PC5 L_PC6

[1,] -0.002266072 -0.0002236406 0.001309996 -0.0003326820 0.0006076135 0.001867347

model:RIGHT, means:expMeanRIGHT

R_PC1 R_PC2 R_PC3 R_PC4 R_PC5 R_PC6

[1,] 0.002266055 0.0002236404 -0.001309996 0.0003326712 -0.0006076171 -0.001867343

When I try to run this, I get a red status. From what I can understand, because a PCA mean centres your data at zero, if you take two roughly equal sample sizes for a PC, the means will be opposite in sign, so that the grand mean is zero. The same also happens when I compare the means for twin 1 vs twin 2. Can you not fit a means model in this case? (sorry, if I've not explained it clearly).

Any thoughts/tips would be appreciated. Thanks in advance.

Paul Sanfilippo