Longitudinal prediction of DNA methylation to forecast epigenetic
  outcomes
Arthur Leroy Ai Ling Teh Frank Dondelinger Mauricio A. Alvarez Dennis Wang
Interrogating the evolution of biological changes at early stages of life
requires longitudinal profiling of molecules, such as DNA methylation, which
can be challenging with children. We introduce a probabilistic and longitudinal
machine learning framework based on multi-mean Gaussian processes (GPs),
accounting for individual and gene correlations across time. This method
provides future predictions of DNA methylation status at different individual
ages while accounting for uncertainty. Our model is trained on a birth cohort
of children with methylation profiled at ages 0-4, and we demonstrated that the
status of methylation sites for each child can be accurately predicted at ages
5-7. We show that methylation profiles predicted by multi-mean GPs can be used
to estimate other phenotypes, such as epigenetic age, and enable comparison to
other health measures of interest. This approach encourages epigenetic studies
to move towards longitudinal design for investigating epigenetic changes during
development, ageing and disease progression.