Implementing a method for studying longitudinal DNA methylation variability in association with age.

Abstract:

:Interindividual variability of DNA methylation is a mechanism of the epigenetic drift in aging. Studies on cross-sectional data have discovered a change in methylation variability in association with age. However, thus far, no method explored DNA methylation variability in longitudinal data, which was the aim of this study. First, we performed a simulation study to explore methods for estimating methylation variability in longitudinal data. Second, an epigenome-wide association study (EWAS) on 1011 longitudinal samples (385 individuals followed up to 18 years) was performed to identify age-varying methylation sites using these methods. Following Breusch-Pagan test of heteroscedasticity, we showed that a linear regression model, where the residuals were used in a mixed effect model with a random intercept, properly estimated the change of interindividual variability over time. Our EWAS identified 570 CpG sites where methylation variability was significantly associated with age (P < 1.3 × 10-7). Gene regions of identified loci were enriched in nervous system development functions. In conclusion, we provide a method for analyzing methylation variability in longitudinal data and further identified age-varying methylation loci in a longitudinal analysis using these methods.

journal_name

Epigenetics

journal_title

Epigenetics

authors

Wang Y,Pedersen NL,Hägg S

doi

10.1080/15592294.2018.1521222

subject

Has Abstract

pub_date

2018-01-01 00:00:00

pages

866-874

issue

8

eissn

1559-2294

issn

1559-2308

journal_volume

13

pub_type

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