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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Related Experiment Video

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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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Implementing a method for studying longitudinal DNA methylation variability in association with age.

Yunzhang Wang1, Nancy L Pedersen1, Sara Hägg1

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

Epigenetics
|September 26, 2018
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Summary

Epigenetic drift in aging involves DNA methylation variability. This study introduces a new method to analyze longitudinal DNA methylation data, identifying 570 age-associated methylation sites, many linked to nervous system development.

Keywords:
DNA methylation variabilityaginglongitudinal study

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Area of Science:

  • Epigenetics
  • Genomics
  • Aging Research

Background:

  • Interindividual variability in DNA methylation is a key aspect of epigenetic drift during aging.
  • Previous studies focused on cross-sectional data, leaving longitudinal analysis of methylation variability unexplored.
  • Understanding age-related changes in DNA methylation patterns is crucial for aging research.

Purpose of the Study:

  • To develop and validate a method for estimating DNA methylation variability in longitudinal datasets.
  • To identify CpG sites exhibiting age-related changes in methylation variability using longitudinal data.
  • To investigate the functional enrichment of genes associated with age-varying methylation sites.

Main Methods:

  • Conducted a simulation study to evaluate methods for longitudinal methylation variability estimation.
  • Applied a linear mixed-effects model with random intercepts, validated by the Breusch-Pagan test, for robust analysis.
  • Performed an epigenome-wide association study (EWAS) on 1011 longitudinal samples from 385 individuals over 18 years.

Main Results:

  • The developed method accurately estimated changes in interindividual DNA methylation variability over time.
  • Identified 570 CpG sites significantly associated with age-related changes in methylation variability (P < 1.3 × 10⁻⁷).
  • Gene regions of the identified loci were significantly enriched for nervous system development functions.

Conclusions:

  • A novel statistical method for analyzing longitudinal DNA methylation variability has been established.
  • The study successfully identified specific CpG sites and associated genes that change in methylation variability with age.
  • Findings highlight the role of age-varying DNA methylation in nervous system development and aging.