Stephanie Kim1,2, Melissa Eliot1, Devin C Koestler3, Eugene A Houseman4, James G Wetmur5, John K Wiencke6, Karl T Kelsey1,7. 1. Department of Epidemiology, Brown University School of Public Health, Providence, RI 02912, USA. 2. Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA. 3. Department of Biostatistics, University of Kansas Medical Center, Kansas City, KA 66160, USA. 4. Oregon State University College of Public Health & Human Sciences, Corvallis, OR 97331, USA. 5. Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 6. Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94158, USA. 7. Department of Laboratory Medicine & Pathology, Brown University, Providence, RI 02912, USA.
Abstract
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.
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.
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