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From aging to Alzheimer's disease: concordant brain DNA methylation changes in late life.

Code ↔ Paper

13 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 13 matches
  1. [1] § Results › Enrichment analysis highlighted the potential functional relevance of the aging-associated DNA methylation differences ↔ code/markdown/03b_cometh_dmr_meta_analysis.Rmd, lines 335–399 · score 0.93 · flanking bivalent TSS, ZNF genes repeats, weak transcription, poised TSS, active TSS, bivalent enhancer
  2. [2] § Results › Enrichment analysis highlighted the potential functional relevance of the aging-associated DNA methylation differences ↔ code/markdown/02b_association_meta_analysis.Rmd, lines 283–352 · score 0.92 · flanking bivalent TSS, ZNF genes repeats, weak transcription, poised TSS, active TSS, bivalent enhancer
  3. [3] § Results › Enrichment analysis highlighted the potential functional relevance of the aging-associated DNA methylation differences ↔ code/markdown/03b_cometh_dmr_meta_analysis.Rmd, lines 335–399 · score 0.70 · genic enhancers, flanking active, active TSS, quiescent, repressed, transcription
  4. [4] § Results › Enrichment analysis highlighted the potential functional relevance of the aging-associated DNA methylation differences ↔ code/markdown/02b_association_meta_analysis.Rmd, lines 283–352 · score 0.69 · genic enhancers, flanking active, active TSS, quiescent, repressed, transcription
  5. [5] § Methods › Pre-processing of DNA methylation data ↔ code/markdown/01b_preprocess_BDR_data.Rmd, lines 521–561 · score 0.64 · BMIQ normalization, wateRmelon, lumi, quantile, QC, beta
  6. [6] § Methods › Pre-processing of DNA methylation data ↔ code/markdown/01b_preprocess_Rosmap_data.Rmd, lines 521–561 · score 0.64 · BMIQ normalization, wateRmelon, lumi, quantile, QC, beta
  7. [7] § Methods › Validation of concordant brain DNAm differences in aging and AD neuropathology ↔ code/markdown/07c_MRS_KM_Plots.Rmd, lines 203–250 · score 0.63 · Cox model, survminer, survival, fit, status, ADNI
  8. [8] § Methods › Pre-processing of DNA methylation data ↔ code/markdown/01b_preprocess_BDR_data.Rmd, lines 399–419 · score 0.63 · DMRcate, rmSNPandCH, mafcut, cross, dist, BDR
  9. [9] § Methods › Pre-processing of DNA methylation data ↔ code/markdown/01b_preprocess_Rosmap_data.Rmd, lines 399–419 · score 0.63 · DMRcate, rmSNPandCH, mafcut, cross, dist, QC
  10. [10] § Results › Concordant brain DNAm is associated with AD progression in out-of-sample validation ↔ code/markdown/07c_MRS_KM_Plots.Rmd, lines 203–250 · score 0.61 · survival probabilities, Cox model, risk, status, MMSE, beta
  11. [11] § Methods › Region-based analysis ↔ code/markdown/03a_cometh_dmr.Rmd, lines 178–272 · score 0.54 · coMethDMR, Co methylated regions, contiguous, DMRs, CpGs
  12. [12] § Methods › Pre-processing of DNA methylation data ↔ code/R/CorticalClock.r, lines 138–213 · score 0.54 · cortical clock, predict age, human, cell, DNA, brain
  13. [13] § Methods › Correlations between DNA methylation in blood and brain samples ↔ code/markdown/04a_brain_blood_correlation.Rmd, lines 256–279 · score 0.52 · blood residuals, brain blood, London, correlations

Paper

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The authors' code

R Markdown · 466 lines · 11 KB · MIT · 2 matches

  1. ---
  2. title: "Run meta analysis comparing Rosmap and BDR coMethDMR results"
  3. author:
  4. - David Lukacsovich^[University of Miami]
  5. - Lily Wang^[University of Miami]
  6. date: "`r format(Sys.time(), '%d %B %Y')`"
  7. output:
  8. rmarkdown::html_document:
  9. highlight: breezedark
  10. theme: lumen
  11. toc: true
  12. number_sections: true
  13. df_print: paged
  14. code_download: false
  15. toc_float:
  16. collapsed: yes
  17. toc_depth: 3
  18. editor_options:
  19. chunk_output_type: inline
  20. ---
  22. ```{r setup, include=FALSE}
  23. knitr::opts_chunk$set(echo = TRUE)
  24. ```
  26. # Set Up
  28. ## Load Libraries
  30. ```{r library}
  31. suppressMessages({
  32. library(dplyr)
  33. library(plyr)
  34. library(minfi)
  35. library(SummarizedExperiment)
  36. library(doParallel)
  37. if (Sys.info()["sysname"] == "Windows") {
  38. library(parallel)
  39. }
  40. library(coMethDMR)
  41. library(BiocParallel)
  42. library(IRanges)
  43. })
  45. base_dir <- "~/TBL Dropbox/David Lukacsovich"
  46. analysis_dir <- file.path(base_dir, "AD-Aging-brain-sample-analysis")
  47. code_dir <- file.path(analysis_dir, "code")
  48. dataset_dir <- file.path(code_dir, "DATASETS")
  49. rosmap_dir <- file.path(dataset_dir, "Rosmap")
  50. bdr_dir <- file.path(dataset_dir, "BDR")
  51. reference_dir <- file.path(code_dir, "Reference_Files")
  52. ```
  54. ## Read in Data
  56. ```{r read_data}
  57. rosmap_data <- readRDS(
  58. file.path(rosmap_dir, "06_cometh_data", "cometh_results.RDS")
  59. ) %>%
  60. group_by(.data$inputRegion) %>%
  61. dplyr::slice_min(order_by = .data$pvalue, n = 1)
  62. bdr_data <- readRDS(
  63. file.path(bdr_dir, "06_cometh_data", "cometh_results.RDS")
  64. ) %>%
  65. group_by(.data$inputRegion) %>%
  66. dplyr::slice_min(order_by = .data$pvalue, n = 1)
  67. ```
  69. ## Create Merged Data
  71. ```{r merge_data}
  72. cohort_df <- rosmap_data %>%
  73. dplyr::inner_join(bdr_data, by = "inputRegion", suffix = c("_ROSMAP", "_BDR"))
  74. ```
  76. # Run Meta Analysis
  78. ## Define Functions
  80. ```{r meta_functions, echo = FALSE}
  81. start_parallel <- function(parallel, cores) {
  82. if (parallel &&
  83. requireNamespace("doParallel", quietly = TRUE) &&
  84. requireNamespace("parallel", quietly = TRUE)) {
  85. if (Sys.info()["sysname"] == "Windows"){
  86. cluster <- parallel::makeCluster(cores)
  87. doParallel::registerDoParallel(cluster)
  88. } else {
  89. doParallel::registerDoParallel(cores)
  90. }
  91. } else {
  92. parallel = FALSE
  93. }
  95. parallel
  96. }
  98. stop_parallel <- function(parallel) {
  99. if (parallel &&
  100. requireNamespace("doParallel", quietly = TRUE) &&
  101. requireNamespace("parallel", quietly = TRUE)) {
  102. doParallel::stopImplicitCluster()
  103. }
  105. TRUE
  106. }
  108. get_direction <- function(estimate) {
  109. ifelse(
  110. is.na(estimate) | (estimate == 0),
  111. ".",
  112. ifelse(estimate > 0, "+", "-")
  113. )
  114. }
  115. ```
  117. ## Run Meta Analysis
  119. ```{r meta_run}
  120. datasets <- c("ROSMAP", "BDR")
  121. parallel <- start_parallel(TRUE, cores = 8)
  123. meta_df <- plyr::adply(
  124. .data = cohort_df,
  125. .margins = 1,
  126. .fun = function(region_data){
  128. est <- region_data[paste("estimate_bacon", datasets, sep = "_")] %>%
  129. as.numeric()
  131. direction <- paste0(get_direction(est), collapse = "")
  133. se <- region_data[paste("std_error_bacon", datasets, sep = "_")] %>%
  134. as.numeric()
  136. region_df <- data.frame(
  137. cohort = datasets,
  138. est = est,
  139. se = se,
  140. stringsAsFactors = FALSE
  141. )
  143. set.seed(23)
  144. f <- meta::metagen(
  145. TE = est,
  146. seTE = se,
  147. data = region_df
  148. )
  150. result <- tibble::tibble(
  151. inputRegion = region_data$inputRegion,
  152. estimate_bacon = f$TE.fixed,
  153. standard_error_bacon = f$seTE.fixed,
  154. pvalue_fixed_bacon = f$pval.fixed,
  155. pvalue_q = f$pval.Q,
  156. direction_bacon = direction
  157. )
  159. result
  160. } , .progress = "time",
  161. .parallel = parallel,
  162. .id = NULL
  163. )
  165. stop_parallel(parallel)
  166. ```
  168. ## Finalize P-Values
  170. ```{r meta_pvalues}
  171. meta_df <- meta_df %>%
  172. dplyr::mutate(
  173. pvalue_final_bacon = .data$pvalue_fixed_bacon
  174. ) %>%
  175. dplyr::mutate(
  176. fdr_bacon = stats::p.adjust(.data$pvalue_final_bacon, method = "fdr")
  177. )
  178. ```
  180. ## Add in Single-ton Regions
  182. ```{r singleton}
  183. # create singleton dataframe
  184. unique_df <- rosmap_data %>%
  185. dplyr::full_join(bdr_data, by = "inputRegion", suffix = c("_ROSMAP", "_BDR")) %>%
  186. dplyr::filter(!(.data$inputRegion %in% cohort_df$inputRegion))
  188. # add estimate bacon
  189. unique_df$estimate_bacon <- rowMaxs(
  190. unique_df[,c("estimate_BDR", "estimate_ROSMAP")] %>%
  191. as.matrix(),
  192. na.rm = TRUE
  193. )
  194. unique_df$pvalue_final_bacon <- rowMins(
  195. unique_df[,c("pvalue_bacon_BDR", "pvalue_bacon_ROSMAP")] %>%
  196. as.matrix(),
  197. na.rm = TRUE
  198. )
  199. unique_df$standard_error_bacon <- rowMins(
  200. unique_df[,c("std_error_bacon_BDR", "std_error_bacon_ROSMAP")] %>%
  201. as.matrix(),
  202. na.rm = TRUE
  203. )
  204. unique_df$direction_bacon <- paste0(
  205. get_direction(unique_df$estimate_bacon_ROSMAP),
  206. get_direction(unique_df$estimate_bacon_BDR)
  207. )
  209. # Line up columns
  210. columns <- colnames(meta_df)
  211. columns <- columns[!(columns %in% colnames(unique_df))]
  212. for (column in columns) {
  213. unique_df[,column] <- NA
  214. }
  215. unique_df <- unique_df[,colnames(meta_df)]
  217. # merge datasets
  218. meta_df <- rbind(
  219. meta_df,
  220. unique_df
  221. ) %>%
  222. dplyr::mutate(
  223. fdr_bacon_inclusive = stats::p.adjust(.data$pvalue_final_bacon, method = "fdr")
  224. ) %>%
  225. dplyr::arrange(.data$pvalue_final_bacon)
  226. ```
  228. # Annotate Regions
  230. ## Get Region Ranges
  232. ```{r annotate_region}
  233. region_df <- meta_df %>%
  234. dplyr::select("inputRegion") %>%
  235. tidyr::separate(
  236. .data$inputRegion,
  237. into = c("seqnames", "start", "end"),
  238. sep = ":|-",
  239. remove = FALSE
  240. ) %>%
  241. dplyr::mutate(
  242. start = as.integer(.data$start),
  243. end = as.integer(.data$end)
  244. )
  245. region_df$index <- 1:nrow(region_df)
  246. ```
  248. ## Illumina Annotation
  250. ```{r annotate_illumn}
  251. get_array_annotations <- function(array = c("HM450", "EPIC")) {
  252. array <- match.arg(array)
  254. if (array == "HM450"){
  255. minfi_object <- utils::data(
  256. "IlluminaHumanMethylation450kanno.ilmn12.hg19",
  257. package = "IlluminaHumanMethylation450kanno.ilmn12.hg19"
  258. )
  259. } else {
  260. minfi_object <- utils::data(
  261. "IlluminaHumanMethylationEPICanno.ilm10b4.hg19",
  262. package = "IlluminaHumanMethylationEPICanno.ilm10b4.hg19"
  263. )
  264. }
  266. anno_df <- minfi_object %>%
  267. minfi::getAnnotation() %>%
  268. GenomicRanges::makeGRangesFromDataFrame(
  269. start.field = "pos", end.field = "pos", keep.extra.columns = TRUE
  270. ) %>%
  271. as.data.frame() %>%
  272. dplyr::select(
  273. "Name",
  274. "seqnames",
  275. "start",
  276. "end",
  277. "UCSC_RefGene_Group",
  278. "UCSC_RefGene_Accession",
  279. "UCSC_RefGene_Name",
  280. "Relation_to_Island"
  281. )
  283. anno_df
  284. }
  286. get_illumina_annotations <- function() {
  287. anno_epic <- get_array_annotations(array = "EPIC")
  288. anno_450 <- get_array_annotations(array = "HM450") %>%
  289. dplyr::filter(!(.data$Name %in% anno_epic$Name))
  291. anno_df <- rbind(anno_epic, anno_450)
  293. anno_df
  294. }
  296. summarise_column <- function(data) {
  297. data <- data %>%
  298. stringr::str_split(";") %>%
  299. unlist() %>%
  300. unname() %>%
  301. unique()
  302. if (length(data) > 0) {
  303. data <- data[nchar(data) > 0]
  304. }
  306. data <- paste(data, collapse = ";")
  307. }
  309. anno_df <- get_illumina_annotations()
  310. anno_df$index <- 1:nrow(anno_df)
  311. anno_gr <- GenomicRanges::GRanges(
  312. seqnames = anno_df$seqnames,
  313. ranges = IRanges::IRanges(start = anno_df$start, end = anno_df$end)
  314. )
  315. region_gr <- GenomicRanges::GRanges(
  316. seqnames = region_df$seqnames,
  317. ranges = IRanges::IRanges(start = region_df$start, end = region_df$end)
  318. )
  319. overlap_df <- GenomicRanges::findOverlaps(region_gr, anno_gr) %>%
  320. as.data.frame()
  322. summ_df <- anno_df %>%
  323. dplyr::inner_join(overlap_df, by = c("index" = "subjectHits")) %>%
  324. dplyr::group_by(.data$queryHits) %>%
  325. dplyr::summarise(
  326. UCSC_RefGene_Group = summarise_column(.data$UCSC_RefGene_Group),
  327. UCSC_RefGene_Accession = summarise_column(.data$UCSC_RefGene_Accession),
  328. UCSC_RefGene_Name = summarise_column(.data$UCSC_RefGene_Name),
  329. Relation_to_Island = summarise_column(.data$Relation_to_Island)
  330. )
  331. region_df <- region_df %>%
  332. dplyr::left_join(summ_df, by=c("index" = "queryHits"))
  333. ```
  335. ## Chrommatin Annotation
  337. ```{r anootate_chromm}
  338. reference_df <- data.frame(
  339. abbreviation = c(
  340. '1_TssA',
  341. '2_TssAFlnk',
  342. '3_TxFlnk',
  343. '4_Tx',
  344. '5_TxWk',
  345. '6_EnhG',
  346. '7_Enh',
  347. '8_ZNF/Rpts',
  348. '9_Het',
  349. '10_TssBiv',
  350. '11_BivFlnk',
  351. '12_EnhBiv',
  352. '13_ReprPC',
  353. '14_ReprPCWk',
  354. '15_Quies'
  355. ),
  356. state = c(
  357. "Active TSS",
  358. "Flanking Active TSS",
  359. "Transcr. at gene 5' and 3'",
  360. "Strong transcription",
  361. "Weak transcription",
  362. "Genic enhancers",
  363. "Enhancers",
  364. "ZNF genes & repeats",
  365. "Heterochromatin",
  366. "Bivalent/Poised TSS",
  367. "Flanking Bivalent TSS/Enh",
  368. "Bivalent Enhancer",
  369. "Repressed PolyComb",
  370. "Weak Repressed PolyComb",
  371. "Quiescent/Low"
  372. )
  373. )
  375. chromm_df <- read.table(file.path(reference_dir, "E073_15_coreMarks_mnemonics.bed.gz"), sep = "\t") %>%
  376. dplyr::rename(seqnames = "V1", start = "V2", end = "V3", abbreviation = "V4") %>%
  377. dplyr::left_join(reference_df, by = c("abbreviation"))
  378. chromm_df$index <- 1:nrow(chromm_df)
  380. region_gr <- GenomicRanges::GRanges(
  381. seqnames = region_df$seqnames,
  382. ranges = IRanges::IRanges(start = region_df$start, end = region_df$end)
  383. )
  385. chromm_gr <- GenomicRanges::GRanges(
  386. seqnames = chromm_df$seqnames,
  387. ranges = IRanges::IRanges(start = chromm_df$start, end = chromm_df$end)
  388. )
  390. overlap_df <- GenomicRanges::findOverlaps(region_gr, chromm_gr) %>%
  391. as.data.frame()
  393. summ_df <- chromm_df %>%
  394. dplyr::left_join(overlap_df, by = c("index" = "subjectHits")) %>%
  395. dplyr::group_by(.data$queryHits) %>%
  396. dplyr::summarise(state = paste(unique(.data$state), collapse = ";"))
  397. region_df <- region_df %>%
  398. dplyr::left_join(summ_df, by=c("index" = "queryHits"))
  399. ```
  401. ## Re-Organize Columns
  403. ```{r labels_sort}
  404. region_df <- region_df %>%
  405. dplyr::select(-c("seqnames", "start", "end", "index"))
  406. meta_df <- meta_df %>%
  407. dplyr::left_join(region_df, by = "inputRegion")
  409. columns <- colnames(meta_df)
  410. annotate_columns <- colnames(region_df)
  411. dataset_columns <- c()
  412. renamed_columns <- c()
  414. for (dataset in datasets) {
  415. label <- paste0(".", dataset)
  416. target_columns <- columns[stringr::str_ends(columns, label)]
  417. new_columns <- paste0(
  418. dataset,
  419. "_",
  420. stringr::str_replace(target_columns, label, "")
  421. )
  423. dataset_columns <- c(dataset_columns, target_columns)
  424. renamed_columns <- c(renamed_columns, new_columns)
  425. }
  427. general_columns <- columns[
  428. (!(columns %in% dataset_columns) &
  429. !(columns %in% annotate_columns)
  430. )
  431. ]
  433. meta_df <- meta_df[,c(annotate_columns, general_columns, dataset_columns)]
  434. colnames(meta_df) <- c(annotate_columns, general_columns, renamed_columns)
  435. ```
  437. ## Show Significant Results
  439. ```{r show_signif}
  440. meta_df %>%
  441. dplyr::filter(.data$fdr_bacon < 1e-3)
  442. ```
  444. # Save
  446. ```{r save}
  447. write.csv(
  448. meta_df,
  449. file = file.path(
  450. analysis_dir,
  451. "analysis-results",
  452. "meta_analysis",
  453. "meta_analysis_cometh_dmr_bacon.csv"
  454. ),
  455. row.names = FALSE
  456. )
  457. ```
  459. # Session Information
  461. <details>
  462. <summary>**Session Info**</summary>
  463. ```{r session}
  464. sessionInfo()
  465. ```
  466. </details>

03b_cometh_dmr_meta_analysis.Rmd at commit f40145f, under MIT · at the source

Overview

Authors: David Lukacsovich1, Juan I. Young2,3, Lissette Gomez3, Michael A. Schmidt2,3, Wei Zhang1, Brian W. Kunkle2,3, Xi Steven Chen1,4, Eden R. Martin2,3, Lily Wang1,2,3,4,5
ORCID iDs: Lily Wang
  1. Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine,Miami, FL 33136 USA
  2. Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine,Miami, FL 33136 USA
  3. John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine,Miami, FL 33136 USA
  4. Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine,Miami, FL 33136 USA
  5. Soffer Clinical Research Ctr, University of Miami School of Medicine,Miami, USA 1120 NW 14th St, FL 33136
Institutions: University of Miami (United States)
Journal: Genome medicine, volume 18, issue 1, article 126
Dates: received 13 February 2025; accepted 5 June 2026; published online 16 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s13073-026-01698-8 · PMID 42304519 · PMCID PMC13505181 · OpenAlex W4411410983
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.17.25329345
Status: code verified
Categories: genetics / omics (modality), histology / microscopy (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Aging, Alzheimer’s disease, DNA methylation, Epigenetics, Biomarkers
MeSH: Aging*, Alzheimer Disease*, Brain*, DNA Methylation*, Aged, Aged, 80 and over, CpG Islands, Epigenesis, Genetic, Female, Genome-Wide Association Study, Humans, Male (* major topic)
Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute on Aging (R01AG062634); NINDS (R61NS135587)
Citations: not cited yet (Europe PMC); 117 references in the paper

Abstract

Background: Aging is the strongest risk factor for Alzheimer’s disease (AD), but the molecular connections between aging and AD remain unclear. DNA methylation (DNAm) is implicated in both processes.

Methods: We conducted a meta-analysis of DNAm in prefrontal cortex from two independent postmortem cohorts: the Religious Orders Study and Memory and Aging Project (ROSMAP) and Brains for Dementia Research (BDR). Age-associated CpG sites were identified using cohort-specific linear models adjusted for neuronal proportion, sex, and batch, followed by meta-analysis. We computed epigenetic age acceleration in brain samples as delta-age (DNAmAge − chronological age), and compared clinically diagnosed AD with cognitively unimpaired participants. Functional analyses included genomic feature enrichment, pathway analysis, brain-blood DNAm correlation, and colocalization with genome-wide association study (GWAS) loci. Prognostic relevance of age-associated CpGs was tested in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset using Cox proportional hazards models for disease progression.

Results: We identified 3264 CpG sites associated with aging; most were hypermethylated and enriched in promoters and CpG islands, and involved genes related to immune regulation and metabolism. Comparison with AD neuropathology-associated methylation showed substantial overlap, with nearly all shared CpGs and regions showing concordant directional changes. Cortical epigenetic age acceleration was higher in ROSMAP participants with clinical AD than in cognitively unimpaired individuals after covariates adjustment, and this association persisted when the cortical clock was restricted to the aging-associated CpGs identified here, suggesting that acceleration in AD is attributable to age-related CpGs. Several CpGs showed significant brain-blood methylation correlations or were linked to AD GWAS risk loci through colocalization analyses. In ADNI, among 33 candidate CpGs selected for concordant aging- and AD-associated changes in cortex and significant brain-blood methylation correlations, baseline methylation at one CpG (cg10752406 in AZU1 promoter) was associated with progression at a 5% false discovery rate after covariate adjustment.

Conclusions: Aging-associated DNAm changes in prefrontal cortex overlap with AD neuropathology-related changes and are involved in accelerated epigenetic aging in clinical AD. Our study provides valuable insights into the epigenetic landscape of aging and its implications for AD.

Supplementary Information: The online version contains supplementary material available at 10.1186/s13073-026-01698-8.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 13 matches between paragraphs and lines of code.

TransBioInfoLab/AD-aging-brain-samples-analysis

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f40145fc73a05a651f88e3beadc7d611243c8c59, 12 February 2025
Languages: R (33)
Size: 35 files, 33 scripts
Software Heritage: not archived
Found in: the references
Holds: README, license file, 30 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (30 files), ggplot2 (14 files), ggpubr (2 files), survival (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
35 files
At the source: github.com/TransBioInfoLab/AD-aging-brain-samples-analysis

The paper's code and data availability statement is in the Data section.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

Datasets cited

Data availability

All datasets analyzed in this study are publicly available or available through established data-access procedures. ROSMAP data can be accessed from the AD Knowledge Portal [26] (accession: syn3157275) and are described in prior publications [24]. BDR DNAm data can be accessed from the Gene Expression Omnibus database (GEO; accession: GSE197305) and are described in Smith et al. [45]. The London matched brain-blood dataset can be accessed from GEO (accession: GSE59685) and is described in prior publications [31, 32]. The ADNI dataset can be accessed from http://adni.loni.usc.edu, and the ADNI DNAm data are described in Vasanthakumar et al. [33]. The three cerebellum DNAm datasets can be accessed from GEO (accessions: GSE134379, GSE105109, and GSE59685) and are described in prior publications [29–31]. The scripts for the analysis performed in this study can be accessed through the project GitHub repository [117].

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 12 MeSH terms, 2 funders, 116 references.

Cite

This paper

Lukacsovich, D., Young, J. I., Gomez, L., Schmidt, M. A., Zhang, W., Kunkle, B. W., Chen, X. S., Martin, E. R., & Wang, L. (2026). From aging to Alzheimer's disease: concordant brain DNA methylation changes in late life. Genome medicine, 18(1), 126. https://doi.org/10.1186/s13073-026-01698-8

BibTeX

@article{lukacsovich2026aging,
author = {Lukacsovich, David and Young, Juan I. and Gomez, Lissette and Schmidt, Michael A. and Zhang, Wei and Kunkle, Brian W. and Chen, Xi Steven and Martin, Eden R. and Wang, Lily},
title = {{From aging to Alzheimer's disease: concordant brain DNA methylation changes in late life}},
journal = {Genome medicine},
year = {2026},
month = jun,
volume = {18},
number = {1},
pages = {126},
publisher = {BMC},
issn = {1756-994X},
doi = {10.1186/s13073-026-01698-8},
url = {https://doi.org/10.1186/s13073-026-01698-8},
pmid = {42304519},
pmcid = {PMC13505181}
}

RIS

TY - JOUR
AU - Lukacsovich, David
AU - Young, Juan I.
AU - Gomez, Lissette
AU - Schmidt, Michael A.
AU - Zhang, Wei
AU - Kunkle, Brian W.
AU - Chen, Xi Steven
AU - Martin, Eden R.
AU - Wang, Lily
TI - From aging to Alzheimer's disease: concordant brain DNA methylation changes in late life
T2 - Genome medicine
J2 - Genome Med
PY - 2026
DA - 2026/06/16
VL - 18
IS - 1
SP - 126
SN - 1756-994X
PB - BMC
DO - 10.1186/s13073-026-01698-8
UR - https://doi.org/10.1186/s13073-026-01698-8
LA - en
ER -

CSL-JSON

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"family": "Lukacsovich",
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"volume": "18",
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"PMCID": "PMC13505181",
"ISSN": "1756-994X",
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"date-parts": [
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}
}

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