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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. Most of the methods, however, either only model oneof these two confounded epigenetic effects, or make strong yet unrealisticassumptions about the population to avoid over-parameterization. A recentpartial likelihood method (LIMEDSP ) can identify both epigenetic effects basedon discordant sibpair family data without those assumptions. Theoretical andempirical studies have shown its validity and robustness. As LIMEDSP methodobtains parameter estimation by maximizing partial likelihood, it isinteresting to compare its efficiency with full likelihood maximizer. Toovercome the difficulty in over-parameterization when using full likelihood,this study proposes a discordant sib-pair design based Monte Carlo ExpectationMaximization (MCEMDSP ) method to detect imprinting and maternal effectsjointly. Those unknown mating type probabilities, the nuisance parameters, areconsidered as latent variables in EM algorithm. Monte Carlo samples are used tonumerically approximate the expectation function that cannot be solvedalgebraically. Our simulation results show that though this MCEMDSP algorithmtakes longer computation time, it can generally detect both epigenetic effectswith higher power, which demonstrates that it can be a good complement ofLIMEDSP method

Monte Carlo Expectation-Maximization algorithm to detect imprinting and maternal effects for discordant sib-pair data