BackgroundMany different methods exist to adjust for variability in cell-type mixture proportions when analyzing DNA methylation studies. Here we present the result of an extensive simulation study, built on cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data, to compare the performance of eight methods including the most commonly used approaches.ResultsWe designed a rich multi-layered simulation containing a set of probes with true associations with either binary or continuous phenotypes, confounding by cell type, variability in means and standard deviations for population parameters, additional variability at the level of an individual cell-type-specific sample, and variability in the mixture proportions across samples. Performance varied quite substantially across methods and simulations. In particular, the number of false positives was sometimes unrealistically high, indicating limited ability to discriminate the true signals from those appearing significant through confounding. Methods that filtered probes had consequently poor power. QQ plots of p values across all tested probes showed that adjustments did not always improve the distribution. The same methods were used to examine associations between smoking and methylation data from a case–control study of colorectal cancer, and we also explored the effect of cell-type adjustments on associations between rheumatoid arthritis cases and controls.ConclusionsWe recommend surrogate variable analysis for cell-type mixture adjustment since performance was stable under all our simulated scenarios.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0935-y) contains supplementary material, which is available to authorized users.

Background:This study explored state-related tendencies in DNA methylation in people with anorexia nervosa. Methods: We measured genome-wide DNA methylation in 75 women with active anorexia nervosa (active), 31 women showing stable remission of anorexia nervosa (remitted) and 41 women with no eating disorder (NED). We also obtained postintervention methylation data from 52 of the women from the active group. Results: Comparisons between members of the active and NED groups showed 58 differentially methylated sites (Q < 0.01) that corresponded to genes relevant to metabolic and nutritional status (lipid and glucose metabolism), psychiatric status (serotonin receptor activity) and immune function. Methylation levels in members of the remitted group differed from those in the active group on 265 probes that also involved sites associated with genes for serotonin and insulin activity, glucose metabolism and immunity. Intriguingly, the direction of methylation effects in remitted participants tended to be opposite to those seen in active participants. The chronicity of Illness correlated (usually inversely, at Q < 0.01) with methylation levels at 64 sites that mapped onto genes regu lating glutamate and serotonin activity, insulin function and epigenetic age. In contrast, body mass index increases coincided (at Q < 0.05) with generally increased methylation-level changes at 73 probes associated with lipid and glucose metabolism, immune and inflammatory processes, and olfaction. Limitations: Sample sizes were modest for this type of inquiry, and findings may have been subject to uncontrolled effects of medication and substance use. Conclusion: Findings point to the possibility of reversible epigenetic alterations in anorexia nervosa, and suggest that an adequate pathophysiological model would likely need to include psychiatric, metabolic and immune components.