A note on false positives and power in G × E modelling of twin data.
- Authors
- van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V
- Year
- 2012
- Journal
- Behavior genetics
- PMID
- 21748401
- DOI
- 10.1007/s10519-011-9480-3
- PMCID
- PMC3253285
The variance components models for gene-environment interaction proposed by Purcell in 2002 are widely used. In both the bivariate and the univariate parameterization of these models, the variance decomposition of trait T is a function of moderator M. We show that if M and T are correlated, and moderator M is correlated between twins as well, the univariate parameterization produces a considerable increase in false positive moderation effects. A simple extension of this univariate moderation model prevents this elevation of the false positive rate provided the covariance between M and T is itself not also subject to moderation. If the covariance between M and T varies as a function of M, then moderation effects observed in the univariate setting should be interpreted with care as these can have their origin in either moderation of the covariance between M and T or in moderation of the unique paths of T. We conclude that researchers should use the full bivariate moderation model to study the presence of moderation on the covariance between M and T. If such moderation can be ruled out, subsequent use of the extended univariate moderation model, as proposed in this paper, is recommended as this model is more powerful than the full bivariate moderation model.
a Full bivariate moderation model for a twin pair as proposed by Purcell (2002). Ac, Cc, and Ec are the variance components common to the moderator M and the trait T; Au, Cu, and Eu are the variance components unique to T. All latent variables have unit variance. Path loadings for M are denoted by am, cm, and em. The loadings of the cross-paths connecting M to T consist of parts that are unrelated to moderator M, i.e., ac, cc, and ec, and parts that depend on M via weights β ac, β cc, and β ec. Similarly, the loadings of the paths unique to T consist of parts that are unrelated to M, i.e., au, cu, and eu, and parts that depend on M via weights β a, β c, and β e. b Univariate moderation model for a twin pair as proposed by Purcell (2002). All latent variables have unit variance. Path loadings for T consist of parts that are unrelated to moderator M, i.e., a, c, and e, and parts that depend on M via weights β a, β c, and β e. M is also included in the means model of T, where β 0 denotes the intercept and β 1 the regression weight of T on M
LLM interpretation
This figure presents two structural equation modeling (SEM) diagrams illustrating moderation models for twin pairs. Panel (a) shows a full bivariate moderation model where latent variance components (A, C, E) are shared between a moderator (M) and a trait (T), with path loadings expressed as functions of the moderator. Panel (b) shows a simplified univariate moderation model where the trait (T) is influenced by latent components and the moderator (M) via both path loadings and a means model.
a–c Bivariate models in which the correlation of ~.24 between moderator M and trait T either exclusively runs via A (a), exclusively via C (b), or exclusively via E (c). The variance of both trait T and moderator M are for 40% due to A, for 30% to C, and for 30% due to E. All latent variables have unit variance
LLM interpretation
This figure consists of three structural equation model diagrams (a, b, and c) illustrating different pathways for the correlation between moderator M and trait T. Each diagram shows latent variables (A, C, and E) contributing to observed variables (M1, T1, T2, M2) for twins, with paths indicating variance weights (e.g., .4, .3, .3). The models differ by which latent variable—A in (a), C in (b), or E in (c)—mediates the correlation between the moderator and the trait.
PP-plots for the univariate moderation model in Simulation study 1, in which T and M are correlated exclusively via A (upper part), exclusively via C (middle part), or exclusively via E (lower part). Deviations from the 45° line show whether the use of the regular χ2(1) test would result in conservative (above the line) or liberal (below the line) decision. % hits denotes the percentage of likelihood-ratio tests smaller than the critical value 3.84 (i.e., significant given α = .05). A hit rate of .05 is expected given α = .05, and given that moderation effects were absent in the data. Hit rates outside the .04–.06 range should be considered incorrect (i.e., significantly too low or too high)
LLM interpretation
This figure consists of a 3x3 grid of P-P plots comparing observed versus expected cumulative probabilities for a univariate moderation model across different simulation conditions. The x-axis represents "Observed cum prob" and the y-axis represents "Expected cum prob," with a 45° dashed line indicating the expected distribution. The bottom row (drop Ba and drop Bc) shows significant downward deviations from the diagonal and high hit rates (0.53 and 0.55), while the other plots remain closer to the diagonal with lower hit rates ranging from 0.05 to 0.09.
PP-plots for the extended univariate moderation model in Simulation study 2, in which T and M are correlated exclusively via A (upper part), exclusively via C (middle part), or exclusively via E (lower part). Deviations from the 45° line show whether the use of the regular χ2(1) test would result in conservative (above the line) or liberal (below the line) decision. % hits denotes the percentage of likelihood-ratio tests smaller than the critical value 3.84 (i.e., significant given α = .05). A hit rate of .05 is expected given α = .05, and given that moderation effects were absent in the data. Hit rates outside the .04–.06 range should be considered incorrect (i.e., significantly too low or too high)
LLM interpretation
This figure consists of a 3x3 grid of P-P plots comparing observed versus expected cumulative probabilities for an extended univariate moderation model. The rows represent different correlation paths (A, C, and E), and the columns represent different dropped variables (Ba, Bc, and Be). All plots show data points closely following the 45° diagonal line, with reported hit rates ranging from 0.04 to 0.05, indicating that the $\chi^2(1)$ test results are consistent with the expected $\alpha = .05$ level.
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