  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. However, most of them 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 (LIME) can identify both epigenetic effects based on
case-control family data without those assumptions. Theoretical and empirical
studies have shown its validity and robustness. However, because LIME 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, in this study
we propose a Monte Carlo Expectation Maximization (MCEM) method to detect
imprinting and maternal effects jointly. Those unknown mating type
probabilities, the nuisance parameters, can be 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 MCEM algorithm takes longer computational time,
and can give higher bias in some simulations compared to LIME, it can generally
detect both epigenetic effects with higher power and smaller standard error
which demonstrates that it can be a good complement of LIME method.
