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Measuring cell-type specific differential methylation in human brain tissue

Carolina M Montaño12, Rafael A Irizarry3, Walter E Kaufmann4, Konrad Talbot5, Raquel E Gur5, Andrew P Feinberg6 and Margaret A Taub7*

Author Affiliations

1 Medical Scientist Training Program, Johns Hopkins University School of Medicine, 1830 E Monument Street, Baltimore, MD 21205, USA

2 Predoctoral Training Program in Human Genetics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD 21205, USA

3 Dana Farber Cancer Institute, Department of Biostatistics and Computational Biology, 450 Brookline Avenue, Boston, MA 02215, USA

4 Department of Neurology, Boston Children's Hospital and Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA

5 Department of Psychiatry, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA

6 Center for Epigenetics, Johns Hopkins University School of Medicine, 855 N Wolfe Street, Baltimore, MD 21205, USA

7 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA

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Genome Biology 2013, 14:R94  doi:10.1186/gb-2013-14-8-r94

Published: 30 August 2013

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.

Keywords:
DNA methylation; epigenetics; differentially methylated region; brain region; cell-type heterogeneity; deconvolution; NeuN; neuron; glia; postmortem brain; fluorescence activated cell sorting