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Numerous statistical methods have been developed to explore genomicimprinting and maternal effects, which are causes of parent-of-origin patternsin complex human diseases. However, most of them either only model one of thesetwo confounded epigenetic effects, or make strong yet unrealistic assumptionsabout the population to avoid over-parameterization. A recent partiallikelihood method (LIME) can identify both epigenetic effects based oncase-control family data without those assumptions. Theoretical and empiricalstudies have shown its validity and robustness. However, because LIME obtainsparameter estimation by maximizing partial likelihood, it is interesting tocompare its efficiency with full likelihood maximizer. To overcome thedifficulty in over-parameterization when using full likelihood, in this studywe propose a Monte Carlo Expectation Maximization (MCEM) method to detectimprinting and maternal effects jointly. Those unknown mating typeprobabilities, the nuisance parameters, can be considered as latent variablesin EM algorithm. Monte Carlo samples are used to numerically approximate theexpectation function that cannot be solved algebraically. Our simulationresults show that though this MCEM algorithm takes longer computational time,and can give higher bias in some simulations compared to LIME, it can generallydetect both epigenetic effects with higher power and smaller standard errorwhich demonstrates that it can be a good complement of LIME method.

Detecting Imprinting and Maternal Effects Using Monte Carlo Expectation Maximization Algorithm