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

翻译：为探索复杂人类疾病中亲本起源效应的遗传印记与母体效应，研究者已开发出多种统计方法。然而现有方法存在两类局限：要么仅对这两种混杂的表观遗传效应之一进行建模，要么为避免参数过度化而采用强但非现实的人群假设。近期提出的部分似然方法（LIMEDSP）能基于不一致同胞对家庭数据识别两种表观遗传效应，且无需前述假设。理论与实证研究已证实其有效性与稳健性。鉴于LIMEDSP方法通过最大化部分似然获取参数估计，将其效率与全似然最大化方法进行比较具有重要研究价值。为克服全似然方法中参数过度化的难题，本研究提出基于不一致同胞对设计的蒙特卡洛期望最大化（MCEMDSP）方法，用于联合检测印记与母体效应。该方法将未知的婚配类型概率（即冗余参数）作为EM算法中的潜变量，采用蒙特卡洛采样对无法解析求解的期望函数进行数值逼近。模拟结果表明，尽管MCEMDSP算法计算耗时更长，但通常能以更高统计功效检测两种表观遗传效应，证明其可作为LIMEDSP方法的有效补充。