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Updated: Jun 13, 2026
DNA Methylation: Bisulphite Modification and Analysis
Published on: October 21, 2011
A comparison of differential DNA methylation analysis methods for continuous outcomes: implications for epigenetic
Oladejo Ahmodu1, Gaurav Bhatti1, Adi L Tarca1,2,3
1Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA.
Background:
Univariate methods are widely employed in epigenome-wide association studies to identify CpGs associated with phenotypic traits. However, their performance has not been thoroughly evaluated.
Methods:
We compared commonly used methods- limma, Spearman's correlation (SC), and quantile regression (QR)- for analysis of methylation changes with gestational and individual age across multiple cohorts. The comparison was based on reproducibility, genomic location distribution, and predictive accuracy of CpGs identified as differentially methylated.
Results:
Limma identified more consistent gestational age-associated CpGs (n = 1,846) than SC (n = 1,409; p = 3.25e-15) and QR (n = 1,145; p < 2.2e-16). CpGs selected by limma and SC were more clustered within chromosomes than QR, as determined by nearest neighbor index analysis (p < 0.05). For gestational age prediction using top 100 features, random forest and elastic net yielded more accurate models (lower RMSE) for limma and SC compared to QR (all p < 0.05). With top 10,000 features, random forest again favored limma and SC over QR, while elastic net performed comparably across methods. Similar results were obtained for analysis of individual age.
Conclusion:
The findings of this study indicate that the choice of differential methylation analysis method impacts CpG-level reproducibility, within chromosome co-location, and predictive accuracy. Overall, limma offers a strong balance of reproducibility and predictive value.
