Format

Send to

Choose Destination
Epigenetics. 2018;13(8):866-874. doi: 10.1080/15592294.2018.1521222. Epub 2018 Oct 2.

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

Author information

1
a Department of Medical Epidemiology and Biostatistics , Karolinska Institutet , Stockholm , Sweden.

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.

KEYWORDS:

DNA methylation variability; aging; longitudinal study

PMID:
30251590
PMCID:
PMC6291263
DOI:
10.1080/15592294.2018.1521222
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Taylor & Francis Icon for PubMed Central
Loading ...
Support Center