direct comparison between traits' heritability with no raw data
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lior abramson
Joined: 07/21/2017
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Dear forum
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).
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).
I would like to ask: Does anyone know a way to perform a direct statistical comparison between the heritability estimates of these traits, given that I only have information about the cross-twin-within-traits correlations, but not about the cross-twin-cross-trait correlations (e.g., the correlation between one twin 1 in trait 1 and the second twin in trait 2, separately for MZ and DZ twins)? Is a descriptive comparison between the heritability estimates of the traits the only option?
Thank you very much for your help,
Lior
Given that you are just
So the difference in heritabilities would be 2*(rM1 - rM2 - rD1 +rD2). Be sure to read up on the limitations and assumptions of the Falconer formula.
Then to calculate the standard error for this value, that’s 2*sqrt(Var_rM1 + Var_rM2 -2*Cov_rM1rM2 + Var_rD1 + Var_rD2 -2*Cov_rD1rD2)
The issue, as you note, is that the covariance terms require estimates of not only the cross-twin within-trait correlations but also the within-twin within-trait and cross-twin cross-trait correlations. See Equations. 6: https://journals.sagepub.com/doi/pdf/10.1177/01466219922031239?casa_token=oACj12LtQeAAAAAA:SFVBEqelbBixTa1SC1_bqWn7GNzUJUziW75UCOuOKxSpqhZnKvDcLz-BNsuwsrTCnslrW-IaTTo
Given that this is only being used to calculate a standard error and not the point estimate, it may be reasonable to estimate a plausible value. For example rXtwinXtrait ≈ rWtwinXtrait * mean(rXtwinWtrait1, rXtwinWtrait2). You could also examine a few larger or smaller correlation estimates to illustrate potential implications of this assumption on SE width.
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