Sammendrag
DNA methylation is the most widely studied epigenetic mark in humans and plays an essential role in
normal biological processes as well as in disease
development. More focus has recently been placed
on understanding functional aspects of methylation, prompting the development of methods to
investigate the relationship between heterogeneity
in methylation patterns and disease risk. However,
most of these methods are limited in that they
use simplified models that may rely on arbitrarily
chosen parameters, they can only detect differentially methylated regions (DMRs) one at a time, or
they are computationally intensive. To address these
shortcomings, we present a wavelet-based method
called ‘Wavelet Screening’ (WS) that can perform an
epigenome-wide association study (EWAS) of thousands of individuals on a single CPU in only a matter
of hours. By detecting multiple DMRs located near
each other, WS identifies more complex patterns that
can differentiate between different methylation profiles. We performed an extensive set of simulations to
demonstrate the robustness and high power of WS,
before applying it to a previously published EWAS
dataset of orofacial clefts (OFCs). WS identified 82
associated regions containing several known genes
and loci for OFCs, while other findings are novel and
warrant replication in other OFCs cohorts.
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