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- | [[http://goo.gl/forms/ | + | Ask questions at the [[http://openmx.psyc.virginia.edu/forums|OpenMx forums]] and the [[http:// |
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??? 2. In her folder from Monday, Hermine Maes provided many different scripts, which is absolutely great! Is is possible to give some explanation to these scripts and in what they differ (except of ACE, ADE & SAT which is quite clear)? | ??? 2. In her folder from Monday, Hermine Maes provided many different scripts, which is absolutely great! Is is possible to give some explanation to these scripts and in what they differ (except of ACE, ADE & SAT which is quite clear)? | ||
!!! MN: Hermine' | !!! MN: Hermine' | ||
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+ | Also, Hermine' | ||
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+ | 1a- Analysis for 2 zygosity groups without any covariates and with an age covariate [a] | ||
+ | 1b- Analysis for 5 groups without any covariates and with age covariate [a] | ||
+ | |||
+ | 2- Each set includes a saturated model (SAT with sub models testing assumptions), | ||
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+ | 3- Analyses for specific types of variables: continuous (c), binary (b), ordinal (o), mean/ | ||
+ | |||
+ | Two examples of the naming convention: | ||
+ | 1- The file called " | ||
+ | 2- The file called " | ||
??? 3. In classic Mx, we were able to include twin pairs with missing data. Can you clarify that we need to either drop twin pairs with any missing data or replace missing values with the mean? Are we not able to define a missing value and include that individual in the model? | ??? 3. In classic Mx, we were able to include twin pairs with missing data. Can you clarify that we need to either drop twin pairs with any missing data or replace missing values with the mean? Are we not able to define a missing value and include that individual in the model? | ||
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Yang, J., Bakshi, A., Zhu, Z., Hemani, G., Vinkhuyzen, A. A., Lee, S. H., . . . Visscher, P. M. (2015). Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nature Genetics, 47(10), 1114-1120. doi: | Yang, J., Bakshi, A., Zhu, Z., Hemani, G., Vinkhuyzen, A. A., Lee, S. H., . . . Visscher, P. M. (2015). Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nature Genetics, 47(10), 1114-1120. doi: | ||
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+ | MCK: Right, but note that the Yang 2015 paper used *imputed* SNPs that were thereby much rarer on average than the SNPs usually included on arrays. They picked up additional variation due to rare causal variants that were better tagged by these rare imputed SNPs. So they are picking up additional rare causal variant variation that would NOT usually have been picked up using GCTA just on SNPs on arrays. So this isn't equivalent to the usual way of running GCTA. In essence, if we had all the sequence variation (instead of just imputed SNPs), we'd be able to pick up 100% of variation due to both rare and common variants. | ||
??? 9. Could Matt or someone please expand on how to address ethnic heterogeneity in GCTA? On the slide on assumptions in estimating heritability, | ??? 9. Could Matt or someone please expand on how to address ethnic heterogeneity in GCTA? On the slide on assumptions in estimating heritability, | ||
- | !!! | + | !!! MCK: First off, you'd want to have a sample that is relatively ethnically homogeneous; |
??? 10. What exactly is the issue in using GREML with ascertained samples? Would it be appropriate for a continuous trait within a clinical group? | ??? 10. What exactly is the issue in using GREML with ascertained samples? Would it be appropriate for a continuous trait within a clinical group? | ||
!!! For background, see [[http:// | !!! For background, see [[http:// | ||
- | Rob K. says: I would tentatively answer your second question with a " | + | Rob K. says: I would tentatively answer your second question with a " |
??? 11. If we get a code Mx status RED, what do we need to do/consider (in general)? E.g., are our model estimates still reliable? | ??? 11. If we get a code Mx status RED, what do we need to do/consider (in general)? E.g., are our model estimates still reliable? | ||
- | !!! | + | !!! Rob K. says: Status RED means the optimizer is not certain it has found a minimum of the fitfunction. |
+ | * If you're analyzing ordinal data, sometimes a status RED is unavoidable without changing some [[http:// | ||
+ | * Use different start values. | ||
+ | * Try a different optimizer. | ||
+ | * Reparameterize your MxModel. | ||
+ | * Use [[http:// | ||
+ | |||
+ | Trying different start values is the most important thing. | ||
??? 12. For Ben's presentation, | ??? 12. For Ben's presentation, | ||
- | !!! | + | !!! Ben here - Here's the original LD Score MS: http:// |
- | ??? 13. Can we have a short summary of how the estimates from GCTA and LD regression | + | ??? |
+ | !!!MCK: I'll let Ben weigh in as well. But in essence, they should be two different ways of estimating the *same* parameter: SNP-heritability. I think that the differences in the literature are about what we'd expect given the SE's on the estimates. Ben - are there any systematic differences between the two? | ||
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+ | ??? 14. @Sarah where can we find the full syntax of this morning' | ||
!!! | !!! | ||
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+ | ??? 15. Where can researchers find publicly available twin data? | ||
+ | !!! One place a researcher can begin searching for publicly available data is the repository developed and managed by the Inter-university Consortium for Political and Social Research (ICPSR). | ||
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+ | As an aside, another publicly available resource towards developing harmonized measures of biomedical phenotypes is the PhenX toolbox (https:// | ||
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+ | MCK: Great question! Nick, Dorret, John?? Want to weight in? | ||
+ | Sadly (I think), twin research has not kept up with whole-genome research, where sharing of data is not only the norm, but also mandatory (for NIH funding at least). Would be scientifically useful if the same were true of twin research! | ||
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====== Questions/ | ====== Questions/ |