蒙特卡洛期望最大化算法检测不一致同胞对数据中的印记效应和母体效应
Monte Carlo Expectation-Maximization algorithm to detect imprinting and maternal effects for discordant sib-pair data
AI总结：
针对不一致同胞对数据，提出MCEMDSP方法联合检测印记和母体效应，通过蒙特卡洛近似EM算法处理过度参数化，虽耗时更长但检测功效更高，可补充LIMEDSP方法。
摘要：
大量统计方法已被开发用于探索基因组印记和母体效应，这些效应是复杂人类疾病中亲本起源模式的成因。然而，大多数方法要么仅对这两种混杂的表观遗传效应之一进行建模，要么为避免过度参数化而对群体做出强烈且不切实际的假设。最近的一种部分似然方法（LIMEDSP）可以在没有这些假设的情况下，基于不一致同胞对家庭数据识别这两种表观遗传效应。理论和实证研究已证明其有效性和稳健性。由于LIMEDSP方法通过最大化部分似然来获得参数估计，因此比较其与全似然最大化器的效率是有意义的。为了克服使用全似然时过度参数化的困难，本研究提出了一种基于不一致同胞对设计的蒙特卡洛期望最大化（MCEMDSP）方法，以联合检测印记效应和母体效应。那些未知的交配型概率，即干扰参数，在EM算法中被视为潜在变量。使用蒙特卡洛样本对无法代数求解的期望函数进行数值近似。我们的模拟结果表明，尽管MCEMDSP算法需要更长的计算时间，但它通常能以更高的功效检测这两种表观遗传效应，这表明它可以作为LIMEDSP方法的良好补充。
英文摘要：
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
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