Skip to content

Latest commit

 

History

History
199 lines (145 loc) · 12.1 KB

README.md

File metadata and controls

199 lines (145 loc) · 12.1 KB
Raw

Cross-tissue meta-analysis of blood and brain epigenome-wide association studies in Alzheimer’s disease

Tiago C. Silva, Juan I. Young, Lanyu Zhang, Lissette Gomez, Michael A. Schmidt, Achintya Varma, Xi Chen, Eden R. Martin, Lily Wang

Citing this repository

DOI

Description

This github repository includes scripts used for the analyses in the above manuscript.

In this work, we performed a meta-analysis of two large independent blood-based epigenome-wide association studies, the ADNI and AIBL studies, and identified 5 CpGs mapped to the SPIDR, CDH6 genes, and intergenic regions that were significantly associated with AD diagnosis. Furthermore, to identify blood-based DNA methylation markers that also change with underlying neuropathology in the brain, we next performed a cross-tissue meta-analysis by combining these blood DNA methylation datasets with four additional DNA methylation datasets, which included a total of 1030 brain prefrontal cortex samples. Our findings provide a useful resource for future biomarker studies in AD.

1. Study cohorts, Preprocessing of DNA methylation data, Single Cohort analysis

File Dataset Link
code/ADNI/ADNI_SAS.Rmd ADNI Link to the script
code/ADNI/ADNI_DMR_analysis_SAS.Rmd ADNI Link to the script
code/ADNI/GLMM_models_ADNIdata_all.sas ADNI Link to the script
code/AIBL/AIBL.Rmd AIBL Link to the script
code/AIBL/AIBL_DMR.Rmd AIBL Link to the script
code/Matched_data_ADNI/matched_RNA_DNAm_data_and_residuals.R ADNI Link to the script
code/Clinical/clinical_info.Rmd ADNI & AIBL Link to the script

2. Blood samples meta-analysis

File Link
code/meta-analysis/meta-analysis-two-cohorts_glm.Rmd Link to the script
code/meta-analysis/meta-analysis-two-cohorts_glm_DMR.Rmd Link to the script

2.1 Blood samples meta-analysis results

File Link
meta_analysis_glm_fixed_effect_ADNI_and_AIBL_AD_vs_CN_single_cpg_annotated.csv https://github.com/TransBioInfoLab/AD-meta-analysis-blood/blob/main/results/meta_analysis_glm_fixed_effect_ADNI_and_AIBL_AD_vs_CN_single_cpg_annotated.csv

3. Cross-tissue meta-analysis

File Link
code/cross_tissue_meta_analysis/cross_tissue_meta_analysis.Rmd Link to the script
code/cross_tissue_meta_analysis/cross_tissue_meta_analysis_DMR.Rmd Link to the script

3.1 Cross-tissue meta-analysis results

File Link
cross_tissue_meta_analysis_glm_using_AD_vs_CN_single_cpg.csv https://github.com/TransBioInfoLab/AD-meta-analysis-blood/blob/main/results/cross_tissue_meta_analysis_glm_using_AD_vs_CN_single_cpg.csv

4. Functional annotation of significant methylation differences

File Link
code/annotations/create_great_annotation.R Link to the script
code/annotations/annotate_enhancer.R Link to the script

5. Correlations between methylation levels of significant CpGs and DMRs in AD with expressions of nearby genes

File Link
code/DNAm_vs_RNA/Blood_ADNI_RNA_vs_DMR.R Link to the script
code/DNAm_vs_RNA/Blood_ADNI_RNA_vs_cpg.R Link to the script
code/DNAm_vs_RNA/Brain_ROSMAP_RNA_vs_DMR.R Link to the script
code/DNAm_vs_RNA/Brain_ROSMAP_RNA_vs_cpg.R Link to the script

6. MethReg integrative analysis

File Link
code/MethReg/Blood_MethReg_DMR_cpg.R Link to the script
code/MethReg/Blood_MethReg_DMR_median.R Link to the script
code/MethReg/Blood_MethReg_cpg.R Link to the script
code/MethReg/Brain_MethReg_cpg.R Link to the script

7. Integrative analysis of DNA methylation differences in the brain and blood with transcriptome-wide gene expressions

File Link
code/TWAS_pathway_analysis/TWAS_approach_blood.R Link to the script
code/TWAS_pathway_analysis/TWAS_approach_brain.R Link to the script
code/TWAS_pathway_analysis/jaccard_idx.R Link to the script

8. Correlation and overlap with genetic susceptibility loci

File Link
code/Overlap_with_AD_associated_genetics_loci/Overlap_with_AD_associated_genetics_loci.R Link to the script

9. Correlation of AD-associated CpGs and DMRs methylation levels in blood and brain samples

File Link
code/MethReg/Blood_MethReg_DMR_cpg.R Link to the script

10. Out-of-sample validations of AD-associated DNAm differences in an external cohort

File Link
code/validation/out_of_sample_validation.Rmd Link to the script

For reproducible research

The following R packages are required:

if (!requireNamespace("BiocManager", quietly = TRUE)){
  install.packages("BiocManager")
}
BiocManager::install(version = "3.13",ask = FALSE) # Install last version of Bioconductor

list.of.packages <- c(
  "bacon",                                        
  "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 4.1.0 (2021-05-18)
 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

Acknowledgement

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

References

  1. 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).

  2. 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).