利用蒙特卡洛期望最大化算法检测基因组印记和母体效应
Detecting Imprinting and Maternal Effects Using Monte Carlo Expectation Maximization Algorithm
AI总结：
针对现有方法难以同时检测基因组印记和母体效应的问题，提出MCEM方法联合估计两种效应，虽耗时更长但功效更高、标准误更小，可作为LIME的补充。
摘要：
目前已开发出许多统计方法来探索基因组印记和母体效应，这两种效应是复杂人类疾病中亲本来源模式的成因。然而，其中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但不切实际的假设。一种最新的偏似然方法（LIME）能够基于病例对照家系数据识别这两种表观遗传效应，且无需这些假设。理论和实证研究已证明其有效性和稳健性。然而，由于LIME通过最大化偏似然来获得参数估计，将其效率与全似然最大化方法进行比较是很有意义的。为了克服使用全似然时过度参数化的困难，在本研究中我们提出了一种蒙特卡洛期望最大化（MCEM）方法，以联合检测基因组印记和母体效应。那些未知的交配型概率，即干扰参数，可以被视为EM算法中的潜在变量。蒙特卡洛样本被用于数值近似无法代数求解的期望函数。我们的模拟结果表明，尽管该MCEM算法相比LIME需要更长的计算时间，并且在某些模拟中可能产生更高的偏差，但它通常能以更高的功效和更小的标准误检测这两种表观遗传效应，这表明它可以作为LIME方法的一个良好补充。
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. 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.
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