Tiago C. Silva, Wei Zhang, Juan I. Young, Lissette Gomez, Michael A. Schmidt, Achintya Varma, X. Steven Chen, Eden R. Martin, Lily Wang
This github repository includes scripts used for the analyses in the above manuscript.
Method In this work, we performed a sex-specific meta-analysis of two large independent blood-based epigenome-wide association studies, the ADNI and AIBL studies, with a total of 1284 whole blood samples (633 female samples and 651 male samples). Within each dataset, we used two complementary analytical strategies, a sex-stratified analysis that examined methylation to AD associations in male and female samples separately, and a methylation-by-sex interaction analysis that compared the magnitude of these associations between different sexes. After adjusting for age, estimated immune cell type proportions, batch effects, and correcting for inflation, the inverse-variance fixed-effects meta-analysis model was used to identify the most consistent DNAm differences across datasets. In addition, we also evaluated the performance of the sex-specific methylation-based risk prediction models for AD diagnosis using an independent external dataset.
Results In the sex-stratified analysis, we identified 2 CpGs, mapped to the PRRC2A and RPS8 genes, significantly associated with AD in females at a 5% false discovery rate, and an additional 25 significant CpGs (21 in females, 4 in males) at P-value < 1×10-5. In methylation-by-sex interaction analysis, we identified 5 significant CpGs at P-value < 10-5. Out-of-sample validations using the AddNeuroMed dataset showed in females, the best logistic prediction model included age, estimated immune cell-type proportions, and methylation risk scores (MRS) computed from 9 of the 23 CpGs identified in AD vs. CN analysis that are also available in AddNeuroMed dataset (AUC = 0.74, 95% CI: 0.65 - 0.83). In males, the best logistic prediction model included only age and MRS computed from 2 of the 5 CpGs identified in methylation-by-sex interaction analysis that are also available in the AddNeuroMed dataset (AUC = 0.70, 95% CI: 0.56 - 0.82). Overall, our results show that the DNA methylation differences in AD are largely distinct between males and females. As sex is a strong factor underlying phenotypic variability in AD, the results of our study are particularly relevant for a better understanding of the epigenetic architecture that underlie AD and for promoting precision medicine in AD.
| File | Dataset | Link |
|---|---|---|
| ADNI/ADNI_SAS_bySex.Rmd | ADNI | Link to the script |
| ADNI/GLMM_models_ADNI.sas | ADNI | Link to the script |
| AIBL/AIBL_bySex.Rmd | AIBL | Link to the script |
| Matched_data_ADNI/matched_RNA_DNAm_data_and_residuals_bySex.R | ADNI | Link to the script |
| Clinical/clinical_info.Rmd | ADNI, AIBL, AddNeuroMed | Link to the script |
| Clinical/clinical_info_brain.Rmd | GASPARONI, LONDON, MtSinai, ROSMAP | Link to the script |
| File | Link |
|---|---|
| meta-analysis/meta-analysis-two-cohorts_glm_by_sex.Rmd | Link to the script |
| meta-analysis-two-cohorts-interaction-glm.Rmd | Link to the script |
| Result | File | Link |
|---|---|---|
| Female | MALE_meta_analysis_glm_fixed_effect_ADNI_and_AIBL_AD_vs_CN_single_cpg.csv | Link |
| Male | FEMALE_meta_analysis_glm_fixed_effect_ADNI_and_AIBL_AD_vs_CN_single_cpg.csv | Link |
| Interaction | meta_analysis_glm_fixed_effect_ADNI_and_AIBL_AD_vs_CN_interaction_single_cpg.csv | Link |
| File | Link |
|---|---|
| cross_tissue_meta_analysis/cross_tissue_meta_analysis_male.Rmd | Link to the script |
| cross_tissue_meta_analysis/cross_tissue_meta_analysis_female.Rmd | Link to the script |
| Result | File | Link |
|---|---|---|
| Male | MALE_cross_tissue_meta_analysis_glm_using_AD_vs_CN_single_cpg.csv | Link |
| Female | FEMALE_cross_tissue_meta_analysis_glm_using_AD_vs_CN_single_cpg.csv | Link |
4. Correlations between methylation levels of significant CpGs and DMRs in AD with expressions of nearby genes
| File | Link |
|---|---|
| DNAm_vs_RNA/Blood_ADNI_RNA_vs_cpg_bySex.R | Link to the script |
| DNAm_vs_RNA/Brain_ROSMAP_RNA_vs_cpg_bySex.R | Link to the script |
5. Out-of-sample validations of AD-associated DNAm differences in an external cohort - Methylation_Risk_scores
| File | Link |
|---|---|
| Methylation_Risk_scores/Methylation_risk_scores_both.Rmd | Link to the script |
| File | Link |
|---|---|
| mQTL_analysis/mQTL_analysis.R | Link to the script |
The following R packages are required:
if (!requireNamespace("BiocManager", quietly = TRUE)){
install.packages("BiocManager")
}
BiocManager::install(version = "3.14",ask = FALSE) # Install last version of Bioconductor
list.of.packages <- c(
"bacon",
"EpiSmokEr",
"DMRcate",
"doParallel",
"dplyr",
"DT",
"EpiDISH",
"ExperimentHub",
"fgsea",
"GenomicRanges",
"GEOquery",
"ggpubr",
"ggrepel",
"gridExtra",
"gt",
"GWASTools",
"IlluminaHumanMethylationEPICanno.ilm10b4.hg19",
"lubridate",
"lumi",
"meta",
"metap",
"MethReg",
"minfi",
"missMethyl",
"mygene",
"plyr",
"readr",
"readxl",
"ReMapEnrich",
"RPMM",
"RVenn",
"sm",
"stats",
"SummarizedExperiment",
"tidyr",
"wateRmelon",
"writexl"
)
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) BiocManager::install(new.packages)
devtools::install_github("igordot/msigdbr")For ADNIMERGE, download it from https://ida.loni.usc.edu/: Merged ADNI 1/GO/2 Packages for R
install.packages("/path/to/ADNIMERGE_0.0.1.tar.gz", repos = NULL, type = "source")The platform information are:
version R version 4.2.0 (2022-04-22)
os macOS Big Sur 11.4
system x86_64, darwin17.0
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2021-07-12 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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Vasanthakumar, A. et al. Harnessing peripheral DNA methylation differences in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to reveal novel biomarkers of disease. Clin Epigenetics 12, 84 (2020).
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Ellis, K.A. et al. Enabling a multidisciplinary approach to the study of ageing and Alzheimer's disease: an update from the Australian Imaging Biomarkers and Lifestyle (AIBL) study. Int Rev Psychiatry 25, 699-710 (2013).