Heritability of Cooperative Behavior in the strongTruststrong Game.pdfVIP

SUPPLEMENTARY ONLINE MATERIAL FOR Heritability of Cooperative Behavior in the Trust Game David Cesarini, Christopher T. Dawes, James H. Fowler, Magnus Johannesson, Paul Lichtenstein, Björn Wallace. Proceedings of the National Academy of Sciences 105 (10): 3721-3726 (11 March 2008) Methods Researchers have increasingly used Bayesian methods, implemented using Markov Chain Monte Carlo (MCMC) algorithms, to estimate the variance components in ACE models. The likelihood functions in genetic models often present computational challenges for maximum likelihood approaches because they contain high-dimension integrals that cannot be evaluated in closed form and thus must be evaluated numerically ( 1). MCMC algorithms evaluate the integrals using random draws rather than evaluating them analytically. Recent studies have successfully applied Bayesian methods to genetic models using binary data (1, 2), survival analysis (3), nonlinear developmental change and GxE interaction (4), item response theory (5), longitudinal models (6), and multivariate models for ordinal data ( 1). For a detailed discussion of Bayesian ACE models, readers should refer to van den Berg, Beem, and Boomsma (1). The ACE model can be specified as a mixed-effects censored linear regression model, or tobit model, where subject j is a member of family i choosing to send or return some fraction of their total endowment to the other player in the game. The model is defined as: * y ij = µ + ij * where the y ij is a latent variable that cannot be observed for values below zero and above one, µ is the population mean, and ij is the sum of genetic, shared environment, unshared environment random effects. T