Institute of Statistics and Big Data, Renmin University of China, 100872, Beijing, China.
Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China.
Nat Commun. 2019 Jul 15;10(1):3113. doi: 10.1038/s41467-019-10864-z.
In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. Current approaches to the association detection claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not at aggregate level and can suffer from low statistical power. Here, we propose a statistical method, HIgh REsolution (HIRE), which not only improves the power of association detection at aggregate level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types.
在全基因组关联研究中,每个样本的测量信号是来自不同细胞类型的甲基化谱的混合物。目前用于关联检测的方法是在总体水平上声称胞嘧啶-磷酸-鸟嘌呤 (CpG) 位点是否与表型相关联,并且可能存在统计功效低的问题。在这里,我们提出了一种统计方法,即高分辨率(HIRE),它不仅可以提高与现有方法相比的总体关联检测的功效,还可以检测个体细胞类型的风险-CpG 位点。