Behavioral Genetics Models

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.

CI is confusing
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ACE with moderator-help interpreting results
1. Are the estimates of T,U,V the moderated components? (often denoted by betas)
2. If the estimate for a certain parameter is not zero, yet the CI contains zero- does this imply that this component is not significant?
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Error in output
Error found in rectangular data; record number 1 :
1004 -999 -999 -999 -999 -999
We have a problem whose error code is 176
and which I ran across at line number 38
of your input script
RE FILE=AGE11DATA.DAT
~~~
An error has occurred while reading a rectangular file
Make sure that you *don't* have a FORMAT at the beginning
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Difference between optimizers
Here is the sat part, if it helps:
#sat model, rmz, rdz:
PathMZ <- mxMatrix("Lower",nrow = 2,ncol = 2,free = TRUE,values=starting_value_pathcoeff,name = "PathMZ")#cholesky for pos definitness
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direct comparison between traits' heritability with no raw data
I am trying to conduct a meta-analysis on the heritability of two traits that are different, yet related (I found six studies in which the twins were measured in both traits).
From what I know, in order to directly compare between the two traits heritability, I need to enter them in the same model, and for that, I need either the raw data or the full correlation matrix (and I have neither).

Multivariate ACE Vs. Multilevel Model
I'm trying to understand the relationship between multivariate ACE models (e.g., Cholesky model fit to two phenotypes) and multilevel behavior genetic models. Is there a formal comparison somewhere?
My sense is that multivariate ACE models conflate within and between pair variance. A and C could capture within and between effects while E captures the within pair effects. E will map right onto the within pair effect in the multilevel model (MLM). Is all this correct?
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