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                                Heritability of aggression following social evaluation
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 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. To investigate whether the more parsimonious AE model (with C fixed to zero), CE model (with A fixed to zero) or E model (with both A and C fixed to zero) showed a better fit to the data, we subtracted the log-likelihood of the AE and CE models from the log-likelihood of the ACE model and the fit of the E model from the fit of the AE or CE models to get an estimate of the Log-likelihood Ratio Test (LRT). In most circumstances LRT follows the X2 distribution, with 3.84 as a critical value at p=.05, thus a LRT>3.84 indicates a significantly worse fit of the data. In addition, we used the Akaike Information Criterion (AIC; Akaike (1974)) a standardized model-fit metric, to compare the different models. Lower AIC values indicate a better model fit. 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.
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