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Abstract
The behavior of epigenetic mechanisms in the brain is obscured by tissue heterogeneity and disease-related histological changes. Not accounting for these confounders leads to biased results. We develop a statistical methodology that estimates and adjusts for celltype composition by decomposing neuronal and non-neuronal differential signal. This method provides a conceptual framework for deconvolving heterogeneous epigenetic data from postmortem brain studies. We apply it to find cell-specific differentially methylated regions between prefrontal cortex and hippocampus. We demonstrate the utility of the method on both Infinium 450k and CHARM data.
Highlights
The brain is a good example of highly specialized and diverse functions arising from the same genetic program
Estimation of mixture proportions We measured DNA methylation profiles for dorsolateral prefrontal cortex (DLPFC), hippocampal formation (HF), and superior temporal gyrus (STG) samples dissected from frozen brains of normal individuals using the comprehensive high-throughput arrays for relative methylation (CHARM) technique [23]
Neuronal (NeuN+) and nonneuronal (NeuN-) fractions from DLPFC, HF, and STG were collected for downstream processing and methylation analysis with CHARM (Additional File 1, Figure S1)
Summary
The brain is a good example of highly specialized and diverse functions arising from the same genetic program. Epigenetic mechanisms copy information other than the sequence itself during cell division, such as DNA methylation and chromatin arrangements [1]. Epigenetics is an attractive substrate for understanding specialized brain function and its disruption in disease. An example of an epigenetic mechanism is DNA methylation, which at CpG dinucleotides is heritable during cell division, because that sequence is recognized by a DNA methyltransferase on newly replicated strands. Neurological diseases have been linked to mutations in DNA methyltransferases [6] and methyl-CpG-binding proteins [7]
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