Estimating fetal and maternal genetic contributions to premature birth from multiparous pregnancy histories of twins using MCMC and maximum-likelihood approaches

Abstract

The analysis of genetic and environmental contributions to preterm birth is not straightforward in family studies, as etiology could involve both maternal and fetal genes. Markov Chain Monte Carlo (MCMC) methods are presented as a flexible approach for defining user-specified covariance structures to handle multiple random effects and hierarchical dependencies inherent in children of twin (COT) studies of pregnancy outcomes. The proposed method is easily modified to allow for the study of gestational age as a continuous trait and as a binary outcome reflecting the presence or absence of preterm birth. Estimation of fetal and maternal genetic factors and the effect of the environment are demonstrated using MCMC methods implemented in WinBUGS and maximum likelihood methods in a Virginia COT sample comprising 7,061 births. In summary, although the contribution of maternal and fetal genetic factors was supported using both outcomes, additional births and/or extended relationships are required to precisely estimate both genetic effects simultaneously. We anticipate the flexibility of MCMC methods to handle increasingly complex models to be of particular relevance for the study of birth outcomes.