# Behavioral Genetics Models

## umxsexlim command-different results in each run for the exact same command

I am trying to conduct a multivariate sex-limitation model. I am not sure why, but when I run the exact same command I get different results in each run (and in some of the runs, the results don't really correspond with the raw data and don't make sense).

I assume that this is the result of an unstable solution. However, I don't get any warning messages that indicate there is anything wrong, or that could direct me what to do.

The script is attached. Do you have an idea what could be the problem?

## Bivariate ACE model with covariates for ordinal variables

I couldn't find any existing OpenMx codes to conduct bivariate genetic modelling for ordinal variables with covariates, so I've adapted Hermine Mae's twoACEvo.R code (bivariate ACE model for ordinal variables) by adding covariates to the code. I thought it was quite straightforward, but when I ran the code I got the following error:

Error in as.vector(data) :

no method for coercing this S4 class to a vector

## Inconsistency between global chi-square difference tests and t-tests

I've specified a multivariate ACE model and found that the z/t-tests produce substantially different p-values than the chi-square difference test per parameter constrained to zero. My recollection is that these tests should produce equivalent results but I recall reading somewhere that at relatively small n (here, n = 260 pairs), the global test may have more power. Does anyone know if this is likely correct or if there are methods papers address it? Could the difference be due to correlations among the z/t-tests? Not sure what's going on here.

## Problem to get the genetic correlations

I have fitted a 4-variate model and I have fixed A to 0 for one of these phenotypes. The model fits OK but I am struggled to obtain the genetic correlations.

This is the output from the model but as one of the A parameters is fixed to 0 I get these results:

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## ACE with moderator- warning in output

Mx starting optimization; number of parameters = 7

*** WARNING! ***

I am not sure I have found a solution that satisfies

Kuhn-Tucker conditions for a minimum.

NAG's IFAIL parameter is 6

Looks like I got stuck here. Check the following:

1. The model is correctly specified

2. Starting values are good

3. You are not already at the solution

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## Genetic correlation: two ways, two different estimates

I implemented two ways of calculating the genetic correlation between two phenotypes, estimated from twins (direct model of rg between A1 and A2, and Cholesky).

My two implementations are based on the path diagrams from H.H. Maes' slides 18 and 53 found at

https://vipbg.vcu.edu/media/course/HGEN619_2015/BivariateGeneticAnalysis2.pdf

I do not get the same estimates between the two methods (-0.26 vs 0.51). Below is the code with the reproducible fake data and results. The code is also attached.

## Ordered quantile normalization

Thanks in advance!

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## How to fix the genetic correlation larger than 1

In the ACE model, Ra=1.37 ,Rc=-0.04, Re=0.22.

Then I fitted ADE model, Ra=0.07, Rd=NA, Re=0.22.

The results of the goodness-of-fit test between saturated model and ACE or ADE are as follows.

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