Additional file 1: of Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array
Creators: name: Mansell, Georgina
name: Gorrie-Stone, Tyler
name: Yanchun Bao
name: Kumari, Meena
name: Schalkwyk, Leonard
name: Mill, Jonathan
name: Hannon, Eilis
Publisher: name: figshare
Year: 2019
Subjects: subject: Biophysics
subject: Biochemistry
subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Molecular Biology
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Developmental Biology
subject: Cancer
subject: Hematology
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences

description: Figure S1. Distribution of minimum p-values from 1000 null EWAS simulations. The red line at 9.42 × 10–8 is the 5th percentile point and represents the 5% FWER. Figure S2. The four assumptions of linear regression (described in the left boxes), were tested using R package gvlma with four statistical tests (described in the right boxes) with arrows matching the assumptions to the relevant test. Gvlma calculates a p-value for each test, where the null hypothesis is that the assumption(s) hold true, and the alternative hypothesis is that they do not (i.e. a significant p-value indicates that the assumption(s) are violated). Figure S3. Quantile-Quantile plots of the 5 tests of the assumptions of linear regression. Plotted are the observed (x-axis) against expected (y-axis) –log10(p-value) from the (a) global (b) skewness (c) kurtosis (d) link function and (e) heteroskedasticity tests performed in the R gvlma package for all DNA methylation sites. Under the null distribution, of no significant associations, all points would be expected to lie on the red line at y = x. The observed data show a dramatic inflation of p-values smaller than expected by chance in all 5 plots indicating that many DNA methylation sites fail these statistical tests for the assumptions of linear regression. Figure S4. Scatterplots of –log10(p-value) against mean DNA methylation level from the (a) global (b) skewness (c) kurtosis (d) link function and (e) heteroskedasticity tests performed in the R gvlma package for all DNA methylation sites. Each point represents a single site, and the color of the point represents the density of points plotted (low density in grey to high density in yellow). Figure S5. Scatterplots of DNA methylation standard deviation against –log10(p-values) from the (a) global (b) skewness (c) kurtosis (d) link function and (e) heteroskedasticity tests performed in the R gvlma package. Each point represents a single DNA methylation site, and the color of the points represents the density of points plotted (low density in grey to high density in yellow). Figure S6. Boxplots of –log10(p-value) for each of the 5 tests: (a) global (b) skewness (c) kurtosis (d) link function and (e) heteroscedasticity separated by site variability status. DNA methylation sites were defined as variable if the range of their middle 80% of values, calculated as the 90th percentile (P90) minus the 10th percentile (P10) was greater than 5%. Each boxplot is colored by their mean –log10(p-value), from light yellow (lowest –log10(p-value)) to red (highest –log10(p-value). Sites with a p-value of 0 (i.e. p