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• W4387496871 abstract "Across psychiatric disorders, comorbidity is very common. Clinically, two patients presenting with the same two disorders may have one or the other recognized as primary or secondary. Most current statistical models for covariance between relatives typically involve a one-model-for-all perspective where the causality is either unidirectional or bidirectional. A mixture model, where for some individuals disorder X causes disorder Y, while in others the causation is reversed, is similar to the clinical attribution of primary vs. secondary disorders in comorbid individuals. Using a series of simulations, we explore the potential of the mixture Direction of Causation (DoC) twin model to detect and model heterogeneity due to varying causal direction. Given two traits, X and Y, cross-sectional data observed in monozygotic and dizygotic twins can be used to test certain hypotheses concerning the causal relationship between the two traits such as in the classical DoC twin model. The finite mixture DoC model adds mixture components to the DoC twin model by defining class components that differ in direction of causation. Model capability was tested under various simulations that vary the proportion of concordant and discordant twins for causal direction, class component means, trait heritability, causal effect size, and genetic confounding. Relative entropy index was calculated and used to evaluate accuracy in classifying individuals. When data simulated under the mixture DoC model are modeled, the mixture DoC models show more parsimony through lower AIC values than the classical DoC and the bidirectional DoC models. For data generated with no heterogeneity, or with bidirectional causation, the mixture DoC model has a less parsimonious fit than the homogeneity models. Simulations evaluating the accuracy of posterior probability-based classifications determined that entropy highly depend on differences in phenotypic means between the primary and secondary disorders. Larger causal effect size also results in improved entropy values. The mixture DoC twin model addresses potential population heterogeneity due to individual differences in causal direction, through integrating the DoC twin model and finite mixture modeling. Even at low amounts of heterogeneity, the mixture model exhibits more parsimony than the unidirectional and bidirectional DoC models. As with all models, the mixture DoC has some limitations. Similar to the DoC model, the assumptions of random mating, no genotype-environment interactions, and no genotype by environment covariance are made, though these may be tested by improving design or adding polygenic scores. Both the DoC model and the mixture DoC model are susceptible to measurement error. The DoC model, like other finite mixture models, requires large sample sizes to obtain accurate classification. Despite these limitations, the analyses performed show that the model can correctly recover both the parameter estimates and mixture proportions." @default.
• W4387496871 created "2023-10-11" @default.
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• W4387496871 date "2023-10-01" @default.
• W4387496871 modified "2023-10-12" @default.
• W4387496871 title "16. INTEGRATING FINITE MIXTURE MODELING WITH DIRECTION OF CAUSATION TWIN MODEL" @default.
• W4387496871 doi "https://doi.org/10.1016/j.euroneuro.2023.08.127" @default.
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