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O3‐05‐03: Multiple deletion copy number variants (CNVs) are associated with late‐onset Alzheimer's disease: The Alzheimer's disease genetics consortium
Author(s) -
Wang Weixin,
Lin Chiao-Feng,
Partch Amanda B.,
Valladares Otto,
Cantwell Laura,
Naj Adam C.,
Wang Li-San,
Schellenberg Gerard D.
Publication year - 2015
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2015.07.263
Subject(s) - copy number variation , genome wide association study , genetics , biology , single nucleotide polymorphism , structural variation , gene duplication , disease , allele , genotyping , genotype , medicine , genome , gene , pathology
Background:Alzheimer’s disease (AD) is characterized by progressive neuropathology and cognitive decline. Although the neuropathological manifestation of AD is well characterized in postmortem brain, little is known about the underlying risk factors or mechanism(s) involved in disease progression. We recently published the first epigenome-wide association studies (EWAS), demonstrating methylomic variation in AD brain and blood (Lunnon et al., 2014), with the most notable differences occurring in regions of the brain characterised by extensive neuropathology. Following on from these studies, we describe a network approach to identify modules of co-methylated loci associated with amyloid and tau burden using DNA from multiple brain regions in a cohort of 144 samples. Methods: DNA was extracted from 100mg tissue from a cohort of 144 individuals from the Mount Sinai NIH Brain and Tissue Repository (NBTR), with two brain regions (Prefrontal cortex-PFC and Superior temporal gyrus-STG) obtained from each individual. Genomic DNA was bisulfite treated using the Zymo EZ DNA methylation kit and samples were assessed using the Illumina Infinium Human Methylation 450K BeadChip. Following data normalisation and stringent quality control, we identifiedmethylomic variation associatedwith quantitativemeasures of neuropathology (amyloid/tau) using linear models, whilst controlling for the effects of age and gender. We used the R package “WGCNA” to identify groups of co-methylated genes that were associated with (a) neuropathological measures and (b) genetic variants associated with AD from recent large meta-analyses. Results: We identified many differentially methylate positions (DMPs) associated with disease in both the PFC and STG, replicating many of our previous findings. The strongest results were found with amyloid burden and braak staging, where we observed DMPs located in and near previously identified genes containing risk variants, including APOE. UsingWGCNA, we also identified multiple modules of co-methylated loci characterised by consistent associations with disease status and neuropathology measures, as well as polygenic risk status. Conclusions:This study provides further evidence for a role of epigenomic dysfunction in AD, identifying networks of co-methylated loci associated withvarious neuropathological traits. We demonstrate strong evidence for an interaction between genotype and DNA methylation at loci previously implicated in genetic studies of AD.