Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies.
Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies.
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DOI：
10.1155/2022/7843990
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发表时间：
2022
影响因子：
--
通讯作者：
Xu L
中科院分区：
文献类型：
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作者：
Guo Y;Yuan Z;Liang Z;Wang Y;Wang Y;Xu L
遗传变异之间的相互作用（上位性）在模型系统中普遍存在，可以显着影响进化适应、遗传图谱和精准医疗工作。在本文中，我们提出了一种上位检测方法，称为 EpiMIC（通过最大信息系数（MIC）进行上位检测）。 MIC 是一种有前途的双变量依赖性测量，明确设计用于快速平等地探索各种功能类型并在相同范围内解释和比较它们。大多数上位性检测方法对遗传变异之间的关联形式做出假设，导致统计性能有限。基于这样的概念：如果两个 SNP 不相互作用，那么它们在所有样本中且仅在某些情况下的联合分布不应有显着差异。我们开发了一种统计量，利用 MIC 的差异作为上位信号，并将其与排列重采样策略相结合来估计统计量的经验分布。模拟和真实世界数据集的结果表明，EpiMIC 优于以前识别不同遗传程度上位性的方法。
Interactions between genetic variants (epistasis) are ubiquitous in the model system and can significantly affect evolutionary adaptation, genetic mapping, and precision medical efforts. In this paper, we proposed a method for epistasis detection, called EpiMIC (epistasis detection through a maximal information coefficient (MIC)). MIC is a promising bivariate dependence measure explicitly designed for rapidly exploring various function types equally and for interpreting and comparing them on the same scale. Most epistasis detection approaches make assumptions about the form of the association between genetic variants, resulting in limited statistical performance. Based on the notion that if two SNPs do not interact, their joint distribution in all samples and in only cases should not be substantially different. We developed a statistic that utilizes the difference of MIC as a signal of epistasis and combined it with a permutation resampling strategy to estimate the empirical distribution of our statistic. Results of simulation and real-world data set showed that EpiMIC outperformed previous approaches for identifying epistasis at varying degrees of heredity.