1] Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. [2] Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
Nat Methods. 2013 Oct;10(10):949-55. doi: 10.1038/nmeth.2632.
Epigenome-wide association studies (EWAS) hold promise for the detection of new regulatory mechanisms that may be susceptible to modification by environmental and lifestyle factors affecting susceptibility to disease. Epigenome-wide screening methods cover an increasing number of CpG sites, but the complexity of the data poses a challenge to separating robust signals from noise. Appropriate study design, a detailed a priori analysis plan and validation of results are essential to minimize the danger of false positive results and contribute to a unified approach. Epigenome-wide mapping studies in homogenous cell populations will inform our understanding of normal variation in the methylome that is not associated with disease or aging. Here we review concepts for conducting a stringent and powerful EWAS, including the choice of analyzed tissue, sources of variability and systematic biases, outline analytical solutions to EWAS-specific problems and highlight caveats in interpretation of data generated from samples with cellular heterogeneity.
全基因组表观遗传关联研究（EWAS）有望发现新的调控机制，这些机制可能容易受到影响疾病易感性的环境和生活方式因素的改变。全基因组表观遗传筛选方法涵盖了越来越多的 CpG 位点，但数据的复杂性给从噪声中分离稳健信号带来了挑战。适当的研究设计、详细的先验分析计划和结果验证对于最小化假阳性结果的危险并有助于统一方法至关重要。在同质细胞群中进行全基因组图谱研究将有助于我们了解与疾病或衰老无关的甲基组的正常变异。在这里，我们回顾了进行严格而有力的 EWAS 的概念，包括分析组织的选择、变异性和系统偏差的来源，概述了针对 EWAS 特定问题的分析解决方案，并强调了对来自具有细胞异质性的样本生成的数据进行解释时的注意事项。
