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Chapter 6
182
Supplementary materials
Genetic modeling - comparison of parsimonious models
Similarities among twin pairs are divided into similarities due to shared genetic factors (A) and shared environmental factors (C), while dissimilarities are ascribed to unique environmental influences and measurement error (E). Behavioral genetic modeling with the OpenMX package (Neale et al., 2016) in R (R Core Team, 2015) provides estimates of these A, C, and E components. For each of the 17 connections, four different models (ACE, AE (with C set to zero), CE (with A set to zero), and E (with A and C set to zero)) were estimated and a log likelihood was calculated. Each model was then compared to a more parsimonious model (e.g. ACE vs. AE; ACE vs. CE; AE vs. E and CE vs. E) by subtracting the log likelihoods, resulting in an estimate of the Log- Likelihood Ratio Test (LRT). Given that the LRT follows the χ2-distribution, an LRT<3.85 would indicate that the more parsimonious model has no worse fit to the data. The Akaike Information Criterion (AIC; Akaike (1974) was used to determine the best model for equally parsimonious non-nested models (i.e. AE and CE), with better model fit being indicated by a lower AIC. When ACE models show the best fit, both heritability, shared and unique environment are important contributors to explain the variance in the outcome variable. AE models indicate that genetic and unique environmental factors play a role; whilst CE models indicate influences of the shared environment and unique environment. If the E model has no worse fit than AE or CE models, variance in the outcome variable is accounted for by unique environmental factors and measurement error.