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Publication - Dr Tom Gaunt

    Longitudinal analysis strategies for modelling epigenetic trajectories

    Citation

    Staley, JR, Suderman, M, Simpkin, A, Gaunt, T, Heron, J, Relton, C & Tilling, K, 2017, ‘Longitudinal analysis strategies for modelling epigenetic trajectories’. International Journal of Epidemiology.

    Abstract

    Background: DNA
    methylation levels are known to vary over time, and modelling these
    trajectories is crucial for our understanding of the biological relevance of
    these changes over time. However, due to the computational cost of fitting multilevel
    models across the epigenome, most trajectory modelling efforts to date have
    focussed on a subset of CpG sites identified through epigenome-wide association
    studies (EWAS) at individual time-points.



    Methods: We
    propose using linear regression across the repeated measures, estimating cluster
    robust standard errors using a sandwich estimator, as a less computationally
    intensive strategy than multilevel modelling. We compared these two
    longitudinal approaches, as well as three approaches based on EWAS (associated
    at baseline, at any time-point, and at all time-points), for identifying
    epigenetic change over time related to an exposure using simulations and by
    applying them to blood DNA methylation profiles from the Accessible Resource
    for Integrated Epigenomics Studies (ARIES).



    Results: Restricting
    association testing to EWAS at baseline identified a less complete set of
    associations than performing EWAS at each time-point or applying the
    longitudinal modelling approaches to the full dataset. Linear regression models
    with cluster robust standard errors identified similar sets of associations
    with almost identical estimates of effect as the multilevel models, while also
    being 74 times more efficient. Both longitudinal modelling approaches
    identified comparable sets of CpG sites in ARIES with an association with prenatal
    exposure to smoking (>70% agreement).



    Conclusions: Linear
    regression with cluster robust standard errors is an appropriate and efficient
    approach for longitudinal analysis of DNA methylation data.



    Full details in the University publications repository