Epigenome-wide analysis of DNA methylation from Alzheimer's patients and unaffected controls
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plots
qc
.gitignore
00_load_idats.R
01a_preprocess_methylation.R
01b_check_genotype_correlation_meth450k_vs_SNPChip.R
01c_check_negative_control_PCs.R
01d_pca.R
01e_subset_to_samples_for_analysis.R
01f_pull_genotypes.R
01g_create_demographic_table.R
01h_horvath_epigenetic_clock.R
02a_i_caseControl_regionalSpecific_probe_differences.R
02a_iii_caseControl_mainResultsModel_DMP_NeuN_sensitivity.R
02a_iv_caseControl_mainResultsModel_DMP_APOE_sensitivity.R
02a_v_normal_aging_controlsOnly.R
02a_vi_caseControl_noCRB_crossRegion_DMP.R
02b_merge_allRegion_stats.R
02c_compare_DMP_results_to_postmortem_AD_studies.R
02d_caseControl_ageAcceleration_and_Composition.R
02e_caseControl_boxplots.R
02f_caseControl_gene_set_enrichment.R
02g_caseControl_heatmaps_DMP.R
02h_sensitivity_posthoc_distribution_plots.R
03a_caseControl_DMR_analysis.R
03b_caseControl_DMR_plots.R
03c_caseControl_DMR_analysis_NeuN_sensitivity.R
04a_comparing_global_alz_DMP_stats_across_datasets.R
04b_check_case_control_stats_for_DMP_genes.R
04c_correlate_DNAm_with_gene_expression.R
04d_differential_gene_expression_boxplots.R
04e_replicability_of_top_DMPs.R
05a_aging_control_AD_Int.R
05c_aging_results_analysis.R
06a_genetic_risk_loci_DMP_results.R
06b_string_ppi_networks.R
06c_check_coexpression_networks.R
Gasparoni_et_al_2018_getGEO_01.R
Guide to Alzheimers Code.docx
Lunnon_2014_DMP_02.R
Lunnon_2014_getGEO_01.R
README.md
compare_n377_to_n380.R
double_checking.R
get_cpg_in_risk_loci.R
scrape_nature_genetics_lambert_et_al.R

README.md

Code used to generate results for the paper: Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer’s disease

Citation

Coming soon

Script summary

Importing idats, preprocessing, and exploratory data analysis

  • 00_load_idats.R: Import idat files into minfi (an RGset).
  • 01a_preprocess_methylation.R: Preprocess methylation array data (drop low-quality samples and probes, check QC).
  • 01b_check_genotype_correlation_meth450k_vs_SNPChip.R: Check correlation between SNP-chip and methylation array genotypes (~65 probes).
  • 01c_check_negative_control_PCs.R: Inspect principal component analysis of negative control probes.
  • 01d_pca.R: Principal component analysis of probes used in analysis.
  • 01e_subset_to_samples_for_analysis.R: Removal of samples not used in analysis.
  • 01f_pull_genotypes.R: [Not used in paper].
  • 01g_create_demographic_table.R: Create a demographic table (Supplemental Table 1).
  • 01h_horvath_epigenetic_clock.R: Compute the estimated DNAm age via Horvath's clock.

Probe-level analyses, results summary, and visualizations

  • 02a_i_caseControl_regionalSpecific_probe_differences.R: Case-control differential methylated probe (DMP) analysis stratified by brain region.
  • 02a_iii_caseControl_mainResultsModel_DMP_NeuN_sensitivity.R: Case-control DMP analysis, adjusting for estimates of neuronal proportion.
  • 02a_iv_caseControl_mainResultsModel_DMP_APOE_sensitivity.R: Case-control DMP analysis, adjusting APOE4 dosage.
  • 02a_v_normal_aging_controlsOnly.R: DMP effect of aging on DNAm in normal controls.
  • 02a_vi_caseControl_noCRB_crossRegion_DMP.R: [Not used in paper].
  • 02b_merge_allRegion_stats.R: Merging together statistics from multiple models.
  • 02c_compare_DMP_results_to_postmortem_AD_studies.R: Assessing replication of previously reported DMPs in our dataset.
  • 02d_caseControl_ageAcceleration_and_Composition.R: Case-control differences for DNAm age acceleration and for neuronal cell type proportions
  • 02e_caseControl_boxplots.R: Boxplots of top DMPs via various models.
  • 02f_caseControl_gene_set_enrichment.R: Enrichment of DNAm probes in genes.
  • 02g_caseControl_heatmaps_DMP.R: Heatmaps of top DMPs.
  • 02h_sensitivity_posthoc_distribution_plots.R: Posthoc distribution sensitivity plots.

Region-level analyses, results summary, and visualizations

  • 03a_caseControl_DMR_analysis.R: Run DNAm region-level case-control analysis.
  • 03b_caseControl_DMR_plots.R: Plot significant differentially methylated regions.
  • 03c_caseControl_DMR_analysis_NeuN_sensitivity.R: Run DMR case-control analysis with NeuN estimates adjustment.

Integrating gene-expression data and asessing replicability

  • 04a_comparing_global_alz_DMP_stats_across_datasets.R: Check correlation between statistics across datasets.
  • 04b_check_case_control_stats_for_DMP_genes.R: Pull differential gene expression results for case-control analysis.
  • 04c_correlate_DNAm_with_gene_expression.R: Correlate DNAm with gene expression of nearby genes (within 10kb either side).
  • 04d_replicability_of_top_DMPs.R: Check if top DMPs are present in Lunnon et al. 2014.

Checking involvement of aging

  • 05a_aging_control_AD_Int.R: Not included in paper.
  • 05c_aging_results_analysis.R: Comparing "normal" (unaffected-control) aging DNAm changes to AD-associated DNAm changes.

Functional and system-level analyses

  • 06a_genetic_risk_loci_DMP_results.R: Assess enrichment of DMPs in GWAS loci.
  • get_cpg_in_risk_loci.R: Determine which CpG probes lie within GWAS risk loci.
  • scrape_nature_genetics_lambert_et_al.R: Pull index SNP from 2014 AD-GWAS (Lambert et al.) to determine AD risk loci.
  • 06b_string_ppi_networks.R: Not included in paper.
  • 06c_check_coexpression_networks.R: Not included in paper.

Reprocessing data from Lunnon et al. (2014).

  • Lunnon_2014_getGEO_01.R: Download Lunnon et al data.
  • Lunnon_2014_DMP_02.R: Model case-control differences.