Abstract
BackgroundWith the help of epigenome-wide association studies (EWAS), increasing knowledge on the role of epigenetic mechanisms such as DNA methylation in disease processes is obtained. In addition, EWAS aid the understanding of behavioral and environmental effects on DNA methylation. In terms of statistical analysis, specific challenges arise from the characteristics of methylation data. First, methylation β-values represent proportions with skewed and heteroscedastic distributions. Thus, traditional modeling strategies assuming a normally distributed response might not be appropriate. Second, recent evidence suggests that not only mean differences but also variability in site-specific DNA methylation associates with diseases, including cancer. The purpose of this study was to compare different modeling strategies for methylation data in terms of model performance and performance of downstream hypothesis tests. Specifically, we used the generalized additive models for location, scale and shape (GAMLSS) framework to compare beta regression with Gaussian regression on raw, binary logit and arcsine square root transformed methylation data, with and without modeling a covariate effect on the scale parameter.ResultsUsing simulated and real data from a large population-based study and an independent sample of cancer patients and healthy controls, we show that beta regression does not outperform competing strategies in terms of model performance. In addition, Gaussian models for location and scale showed an improved performance as compared to models for location only. The best performance was observed for the Gaussian model on binary logit transformed β-values, referred to as M-values. Our results further suggest that models for location and scale are specifically sensitive towards violations of the distribution assumption and towards outliers in the methylation data. Therefore, a resampling procedure is proposed as a mode of inference and shown to diminish type I error rate in practically relevant settings. We apply the proposed method in an EWAS of BMI and age and reveal strong associations of age with methylation variability that are validated in an independent sample.ConclusionsModels for location and scale are promising tools for EWAS that may help to understand the influence of environmental factors and disease-related phenotypes on methylation variability and its role during disease development.
Highlights
With the help of epigenome-wide association studies (EWAS), increasing knowledge on the role of epigenetic mechanisms such as DNA methylation in disease processes is obtained
High-throughput array technologies such as the Illumina Infinium HumanMethylation450k BeadChip [10] have enabled epigenome-wide association studies (EWAS) to explore the relationship between phenotypes and DNA methylation in large population-based studies [2]
This was not as clearly observed for the beta regression model, which performed considerably worse than the variable scale Gaussian regression models on raw or transformed data
Summary
With the help of epigenome-wide association studies (EWAS), increasing knowledge on the role of epigenetic mechanisms such as DNA methylation in disease processes is obtained. We used the generalized additive models for location, scale and shape (GAMLSS) framework to compare beta regression with Gaussian regression on raw, binary logit and arcsine square root transformed methylation data, with and without modeling a covariate effect on the scale parameter. As typical for proportion data, their distributions display substantial heteroscedasticity [11] They tend to show smaller variances when located near the boundaries 0 and 1 as compared to the center of the unit interval. When using β-values as response variable in a statistical regression model, traditional modeling strategies that assume a normally distributed response might be inappropriate [12] To solve this problem, it has been proposed to use the less heteroscedastic M-values for Gaussian regression, which are approximately equal to binary logit transformed β-values [13]. Improved performance of beta regression as compared to traditional approaches was reported [18], a thorough comparison of these competing approaches for the purpose of univariate screening for genome-wide phenotype-methylation associations is lacking
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