UCSF

Aim

We examined whether variation in blood-based epigenome-wide association studies could be more completely explained by augmenting existing reference DNA methylation libraries.

Materials & methods

We compared existing and enhanced libraries in predicting variability in three publicly available 450K methylation datasets that collected whole-blood samples. Models were fit separately to each CpG site and used to estimate the additional variability when adjustments for cell composition were made with each library.

Results

Calculation of the mean difference in the CpG-specific residual sums of squares error between models for an arthritis, aging and metabolic syndrome dataset, indicated that an enhanced library explained significantly more variation across all three datasets (p < 10(-3)).

Conclusion

Pathologically important immune cell subtypes can explain important variability in epigenome-wide association studies done in blood.