Behavioral Genetics Models

Hello

I have run the common Pathways multivariate model for my data. When I total up the specific genetic variance and genetic variance from the latent variable to each of the phenotypes, I am getting less than 100%. I am also getting less than what the model says is the total genetic variance (from stA in the model attached). Is there anything I am doing wrong?

(output for one of my variables:

stA 0.3060186 (overall heritability)
stE 0.6939814

specific heritability (stas^2) 0.07233587
specific environment (stes^2) 0.3732820

latent heritability contribution (h2c) 0.1749177

Hi guys, I wrote up a moderation model script, looking a several scripts from the Boulder workshops as reference. When running it, I'm getting large swings in -2LL and parameter estimates with start value and bound changes, so there has be an identification problem in my model specification, but after staring at it for a hour - I still can't see where.

Hello

Some of the variables in my dataset do not satisfy the assumption that the data should be normally distributed. So I am unable to rationally run parametric SEM analyses.

I have attempted to transform the data to approximate a normal distribution using cumulative distribution functions, log10 and square root transformations, but with no success. These transformations slightly shifts the skewness or squeezes the data together, however the data remains immune to normality.

1. What steps can be taken when the normality assumptions cannot be met?

Hello

I have been checking whether the equal variances assumption can be applied to my data for twins 1 and 2 and for MZ and DZ twins. It appears that I can't make this assumption.

1. If I am not able to constrain the variances for twins 1 and 2, how do I deal with this? Do I just leave the model so that the variances for each twin are included separately in creating the covariance (ie as in the model below)? (script below)

I have a couple of questions relating to adding covariates to the twin model and testing for their relevance

When a nested model differs to a previous model, but is worse (ie has a higher -2LL) how do we assess its significance? This happened in adding in a variable as a covariate. As it makes the model worse do we then exclude it as a covariate? For example – with the script for Full DASS the covariate of Developmental when doing the covariate check after constraining the means was a larger -2LL producing a negative difference.

Many thanks

Script

Hi, everyone.

I want to add covarites to multivariate ACE model. I looked through former workshops's materials and noticed that there was MX script for this. I'd like to ask is there corresponding OpenMx script?

Thanks!
Ting

Greetings -- After a few weeks of lurking, I have finally joined the board and I'm braving my first post! I've been struggling with a script that I know should be super simple and am hoping to get some assistance. I'm relatively new to R and to twin modeling, so it's entirely possible that I'm missing something very very basic.

My goal: I want to calculate the variance components for a simple dichotomous variable in which twins can answer in one of two ways -- yes or no. I want to run and compare ACE, AE, and CE models.

Hi,

I have heard that it is now possible to run a model estimating genetic and environmental effects with twin data that includes separate variables with binary and continuous (or ordinal?) coding.

I have been running a thresholds model with age and sex as covariates (which I will attach) and I would like to include the same behavioural measures from different data sets in the one model.

I want to transform the results of a correlated-factor model into multivariate Cholesky form.

Loehlin (1996) [1] gives the formulae for a bivariate case, but I wonder if anyone has a reference or pseudo-code for the multivariate case?

My end goal is to set the correlations between factors in such a way as to be equivalent to dropping paths in a Cholesky.

[1] Loehlin, J. C. (1996). The Cholesky approach: A cautionary note. Behavior Genetics, 26(1), 65-69.