Heritability estimates from human twin data by incorporating historical prior information

Bayesian methods are commonly used in some analyses of human genetic data, such as segregation and linkage analyses, but they are not typically used f or analyses of human twin data. In this paper we develop a scheme for a Bay esian analysis of human twin data. We develop prior elicitation schemes to incorporate historical information. We consider three prior schemes: fully informative, semi-informative and noninformative. We use Markov chain Monte Carlo sampling algorithms to facilitate Bayesian computation and provide d etailed implementation schemes. We also develop model diagnostics for asses sing the goodness of fit of twin models. Using a simulation study, we show that if the purpose of the study is to estimate the intraclass correlations or heritability in twin studies, then the semi-informative prior is as inf ormative as the fully informative prior. Finally, a real data example is us ed to illustrate the proposed methodologies.