An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies

Abstract

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.

Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies

“…In these scenarios, latent variables obtained by a decomposition of the methylation data can be used as a proxy for cell-type proportions. McGregor et al [2] performed an extensive simulation study comparing one reference-based and seven latent variable methods. Although not always the best method, the reference-based method performed well.…”

“…As the recommendation by McGregor and colleagues [2] was based mainly on simulated data, we studied SVA in two large-scale empirical studies. The first involved 1149 Dutch subjects (825 cases with depression and 324 controls) aged 18–65 years [4] and the second 1448 Swedish subjects (774 schizophrenia cases and 674 controls) aged 25–92 years [5, 6].…”

eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data

“…The search for non-genetic factors is more complicated due to the many confounding factors, in particular, genetic heterogeneity between individuals and cell-type heterogeneity in accessible sample material (e.g., whole blood). Nonetheless, significant progress has been made following the introduction of systematic epigenome-wide association studies (EWAS) in 2011 (Rakyan et al., 2011), including correction of cell-composition effects (Houseman et al., 2012, Zou et al., 2014, McGregor et al., 2016, Houseman et al., 2016). Since then, over 250 EWAS have been conducted (Michels et al., 2013).…”

Cell type-specific DNA methylation in neonatal cord tissue and cord blood: a 850K-reference panel and comparison of cell types

“…Second, and the most widely used approach requires the use of a reference panel assembled from DNA methylation profiles of the tissue’s constituent cell types [5,6]. When a reference panel is not available, the last approach is to use a reference-free method, but this approach is computationally intense and less accurate [7–11]. …”