A meta-analysis of epigenome-wide association studies in Alzheimer’s disease highlights novel differentially methylated loci across cortex
Rebecca G. Smith+, Ehsan Pishva+, Gemma Shireby, Adam R. Smith, Janou A.Y. Roubroeks, Eilis Hannon, Gregory Wheildon, Diego Mastroeni, Gilles Gasparoni, Matthias Riemenschneider, Armin Giese, Andrew J. Sharp, Leonard Schalkwyk, Vahram Haroutunian, Wolfgang Viechtbauer, Daniel L.A. van den Hove, Michael Weedon, Jörn Walter, Paul D. Coleman, David A. Bennett, Philip L. De Jager, Jonathan Mill, Katie Lunnon*
+ Joint first author
* Corresponding author - k.lunnon@exeter.ac.uk
Epigenome-wide association studies of Alzheimer’s disease have highlighted neuropathology-associated DNA methylation differences, although existing studies have been limited in sample size and utilized different brain regions. Here, we combine data from six DNA methylomic studies of Alzheimer’s disease (N=1,453 unique individuals) to identify differential methylation associated with Braak stage in different brain regions and across cortex. We identify 236 CpGs in the prefrontal cortex, 95 CpGs in the temporal gyrus and ten CpGs in the entorhinal cortex at Bonferroni significance, with none in the cerebellum. Our cross-cortex meta-analysis (N=1,408 donors) identifies 220 CpGs associated with neuropathology, annotated to 121 genes, of which 84 genes have not been previously reported at this significance threshold. We have replicated our findings using two further DNA methylomic datasets consisting of a further > 600 unique donors. The meta-analysis summary statistics are available in our online data resource (www.epigenomicslab.com/ad-meta-analysis/).
Paper uploaded to Biorxiv (https://www.biorxiv.org/content/10.1101/2020.02.28.957894v1)/
This page contains scripts for the above paper
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Data quality control and harmonization.r - QC and harmonization for all samples included in analysis
- Loading the raw idat files
- Quality control
- Detecting samples with extreme intesities using the negative control probes as references
- Detecting samples with low background to signal ratio
- detecting the samples with the extreme mean intensity of methylated or unmethylated signals
- Detecting the samples with bisulfite conversion efficiency < 80%
- Detecting a mismatch between reported and predicted sex
- Uses SNP probes on the array to find genetic correlations between samples
- WateRmelon - pfilter
- Quantile normalization
- Removing the probes with evidence for cross-hybridizition
- CET
- Harmonisation
- Surrogate variables (SVs)armonisation
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Main Analyses.r - All analyses used
- Linear regression analyses were performed with respect to Braak stage
- Intra-tissue meta-analysis
- Fixed-effect inverse variance meta-analysis
- Cross-cortex AD-associated DMPs
- Comb-p
- AD GWAS enrichment analysis
- GO enrichment analysis
- Quantifying variance explained by PES and PRS
- Developing a classifier to predict Braak pathology
Statistical analysis was performed in R 3.5.2 and Bioconductor 3.8, Python and PLINK 1.9.
R Packages used were wateRmelon 1.26.0, minfi 1.28.4, CETS, sva 3.30.1, Meta 4.10.0, nlme 3.1.142, bacon 1.10.1, pROC 1.16.1, glmnet 2.0-18, PRSice 2.2.12.
Python package comb-p 33.1.1.
All programs and packages installed via Anaconda on cluster computing linux system
Operating System: CentOS Linux 7 (Core)
CPE OS Name: cpe:/o:centos:centos:7
Kernel: Linux 3.10.0-693.17.1.el7.x86_64
Architecture: x86-64
Installation depends on connection and system, however will install within two hours.
Run time for all analysis processes will be several hours