  Numerous statistical methods have been developed to explore genomic
imprinting and maternal effects, which are causes of parent-of-origin patterns
in complex human diseases. Most of the methods, however, either only model one
of these two confounded epigenetic effects, or make strong yet unrealistic
assumptions about the population to avoid over-parameterization. A recent
partial likelihood method (LIMEDSP ) can identify both epigenetic effects based
on discordant sibpair family data without those assumptions. Theoretical and
empirical studies have shown its validity and robustness. As LIMEDSP method
obtains parameter estimation by maximizing partial likelihood, it is
interesting to compare its efficiency with full likelihood maximizer. To
overcome the difficulty in over-parameterization when using full likelihood,
this study proposes a discordant sib-pair design based Monte Carlo Expectation
Maximization (MCEMDSP ) method to detect imprinting and maternal effects
jointly. Those unknown mating type probabilities, the nuisance parameters, are
considered as latent variables in EM algorithm. Monte Carlo samples are used to
numerically approximate the expectation function that cannot be solved
algebraically. Our simulation results show that though this MCEMDSP algorithm
takes longer computation time, it can generally detect both epigenetic effects
with higher power, which demonstrates that it can be a good complement of
LIMEDSP method
