Nature CommunicationsGeneticsPeer-reviewed journal article
DNA methylation signatures of Alzheimer's disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types
Gemma L. ShirebyiD, Emma Louise DempsteriD, Stefania S S Policicchio, Rebecca G. SmithiD, Ehsan PishvaiD, Barry A. ChiozaiD, Jonathan P. DaviesiD, Joe BurrageiD, Katie LunnoniD, Dorothea Seiler VellameiD, Seth LoveiD, Alan J. ThomasiD, Keeley J. BrookesiD, Kevin MorganiD, Paul T. FrancisiD, Eilis J. HannoniD, Jonathan S. MilliD
Abstract
Alzheimers disease (AD) is a chronic neurodegenerative disease characterized by the progressive accumulation of amyloid-beta and neurofibrillary tangles of tau in the neocortex. Utilizing extensive neuropathology data from the Brains for Dementia Research (BDR) cohort we performed the most systematic epigenome-wide association study (EWAS) of multiple measures of AD neuropathology yet undertaken, profiling DNA methylation in two cortical regions from 631 donors. We meta-analyzed our results with those from previous studies of DNA methylation in AD cortex (total n = 2,013 donors), identifying 334 cortical differentially methylated positions (DMPs) associated with AD pathology including methylomic variation at novel loci not previously implicated in dementia. We subsequently characterized DNA methylation in purified nuclei populations - enriched for neurons, oligodendrocytes and microglia - exploring the extent to which cortex AD-associated DMPs reflect differences manifest in specific cell populations. We find that the majority of DMPs identified in bulk cortex tissue actually reflect DNA methylation differences occurring in non-neuronal cells, with dramatically increased effect sizes observed in microglia-enriched nuclei populations. Our study highlights the power of utilizing multiple measures of neuropathology to identify epigenetic signatures of AD and the importance of characterizing disease-associated variation in purified neural cell-types.
- organism: human
- assay: DNA methylation
- research focus: differential expression
- approach: statistical model
- Citations
- 128
- FWCI
- 7.67
- Field percentile
- top 1%
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