Comparison of Infinium MethylationEPIC v2.0 to v1.0 for human population epigenetics: considerations for addressing EPIC version differences in DNA methylation-based tools

Author:

Zhuang Beryl C.ORCID,Jude Marcia Smiti,Konwar ChainiORCID,Ryan Calen P.,Whitehead JoanneORCID,Engelbrecht Hannah-RuthORCID,MacIsaac Julia L.ORCID,Dever Kristy,Toan Tran Khanh,Korinek Kim,Zimmer Zachary,Huffman Kim M.,Lee Nanette R.,McDade Thomas W.,Kuzawa Christopher W.,Belsky Daniel W.,Kobor Michael S.ORCID

Abstract

AbstractBackgroundThe recently launched DNA methylation profiling platform, Illumina MethylationEPIC BeadChip Infinium microarray v2.0 (EPICv2), is highly correlated with measurements obtained from its predecessor MethylationEPIC BeadChip Infinium microarray v1.0 (EPICv1). However, the concordance between the two versions in the context of DNA methylation-based tools, including cell type deconvolution algorithms, epigenetic clocks, and inflammation and lifestyle biomarkers has not yet been investigated.FindingsWe profiled DNA methylation on both EPIC versions using matched venous blood samples from individuals spanning early to late adulthood across three cohorts. On combining the DNA methylomes of the cohorts, we observed that samples primarily clustered by the EPIC version they were measured on. Within each cohort, when we calculated cell type proportions, epigenetic age acceleration (EAA), rate of aging estimates, and biomarker scores for the matched samples on each version, we noted significant differences between EPICv1 and EPICv2 in the majority of these estimates. These differences were not significant, however, when estimates were adjusted for EPIC version or when EAAs were calculated separately for each EPIC version.ConclusionsOur findings indicate that EPIC version differences predominantly explain DNA methylation variation and influence estimates of DNA methylation-based tools, and therefore we recommend caution when combining cohorts run on different versions. We demonstrate the importance of calculating DNA methylation-based estimates separately for each EPIC version or accounting for EPIC version either as a covariate in statistical models or by using version correction algorithms.

Publisher

Cold Spring Harbor Laboratory

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