Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array

作者：Georgina Mansell, T.J. Gorrie-Stone, Yanchun Bao, Meena Kumari, Leonard C. Schalkwyk, Jonathan Mill, Eilís Hannon · 发表于：BMC Genomics · 年份：2019 · DOI：10.1186/s12864-019-5761-7 · 被引用次数：399 · 研究领域：Epigenetics and DNA Methylation、Genetic Associations and Epidemiology、Genetic Syndromes and Imprinting

There has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies. We quantified DNA methylation in the Understanding Society cohort ( n = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p -values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10 − 8 . Next, we tested whether the assumptions of linear regression are violated by DNA methylation data and found that the majority of sites do not satisfy the assumption of normal residuals. Neverthe...