OSCR

GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer's disease.

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] § Methods › Expression quantitative trait methylation (eQTM) analysis ↔ eval/functional_mechanisms/eQTM_mapping_OPTIMA.R, lines 1–40 · score 0.94 · AD disease status, DNAm PCs, eQTM, CpGs, linear regressions, mRNA
  2. [2] § Methods › Human subjects ↔ prep/sample_qc/create_initial.ipynb, lines 58–105 · score 0.76 · Field IDs, dementia, QC, UKB, genotyped
  3. [3] § Methods › Methylation quantitative trait locus (meQTL) analysis ↔ eval/functional_mechanisms/eQTM_mapping_OPTIMA.R, lines 1–40 · score 0.70 · CpGs, linear regression, mRNA, PCs, OPTIMA, DNAm
  4. [4] § Methods › Comparison of GWAS results for imputed vs. WGS-derived STR variants ↔ eval/gSTR_vs_iSTR/iSTR_vs_gSTR.ipynb, lines 15–54 · score 0.67 · manually curated, allele frequencies, allele length, Spearman, Pearson, sum
  5. [5] § Results › Comparison of imputed vs. WGS-derived STRs ↔ eval/gSTR_vs_iSTR/iSTR_vs_gSTR.ipynb, lines 15–54 · score 0.65 · correlation coefficients, allele frequencies, allele lengths, Spearman, r2, Pearson
  6. [6] § Methods › Expression quantitative trait locus (eQTL) analysis ↔ eval/functional_mechanisms/eQTM_mapping_OPTIMA.R, lines 130–214 · score 0.64 · linear regression, mRNA, OPTIMA, covariate, models, transcriptome
  7. [7] § Methods › Human subjects ↔ prep/sample_qc/create_data.ipynb, lines 373–380 · score 0.64 · king cutoff, KING algorithm, kinship, IDs, QC, AD
  8. [8] § Methods › Fine mapping and conditional analyses ↔ eval/finemap/susie/run_susie.R, lines 68–134 · score 0.59 · SuSiE RSS, matrices, PIP, LD, locus, variants
  9. [9] § Methods › Genome-wide association analyses ↔ prep/sample_qc/create_initial.ipynb, lines 58–105 · score 0.58 · sequencing provider, field ID, WGS, genotype
  10. [10] § Methods › Fine mapping and conditional analyses ↔ eval/finemap/susie/eval_susie.py, lines 22–80 · score 0.56 · lead variant, SuSiE, posterior, probability, matrices, PIP
  11. [11] § Results › Delineating potential functional mechanisms of AD-associated STRs ↔ eval/functional_mechanisms/eQTM_mapping_OPTIMA.R, lines 43–107 · score 0.54 · eQTM, CpGs, mRNA, transcript, DNAm, genes
  12. [12] § Results › Discerning the drivers of STR-based GWAS signals ↔ eval/finemap/susie/run_susie.R, lines 68–134 · score 0.53 · SuSiE RSS, lead SNPs, PIP, log10, beta, locus
  13. [13] § Methods › Human subjects ↔ prep/sample_qc/create_data.ipynb, lines 304–315 · score 0.53 · White British, Asian, Chinese, Mixed, ethnic

Paper

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The authors' code

R · 232 lines · 7.2 KB · GPL-3.0 · 4 matches

  1. ## This script is used to perform an eQTM mapping with known AD-CpGs. First,
  2. ## the CpGs and transcripts that have a selected distance to each other are
  3. ## determined. These are then used to calculate linear regressions. Correlation
  4. ## plots are also created.
  6. #.libPaths(.libPaths()[grepl("rtrack_env", .libPaths())])
  9. library(data.table)
  10. library(ggplot2)
  11. library(rlang)
  12. library(performance)
  13. library(rtracklayer)
  14. library(dplyr)
  15. library(openxlsx)
  17. args <- commandArgs(trailingOnly = TRUE)
  18. cis <- args[1] ## Maximum distance between CpGs and transcripts
  19. cis <- as.numeric(cis)
  20. out_path <- args[2] ## TODO change path to save results
  22. print(cis)
  25. DNAm <-read.csv("path/to/M.csv",
  26. header = TRUE, row.names = 1, check.names = FALSE) ## TODO Change Path to DNAm data
  27. cpgs <- read.csv("path/to/CpGs_gmelin.csv") ## TODO Change Path to CpGs to be tested
  30. DNAm <- DNAm[is.element(rownames(DNAm), cpgs[,1]),]
  32. RNA <- read.csv("path/to/G.csv",
  33. header = TRUE, row.names = 1, check.names = FALSE) ## TODO Change Path to mRNA data
  34. pheno <- read.csv("path/to/C.csv",
  35. header = TRUE, row.names = 1, check.names = FALSE) ## TODO Change Path to covariates. We used: age, sex, AD disease status, study, RIN, PMI, DNAm-PCs and mRNA-PCs
  39. annotation_g <- fread("path/to/G.bed6")
  40. annotation_m <- fread("path/to/M.bed6")
  43. ## prepare dataframe for pairs of CpGs and transcripts for the eQTM Mapping
  44. eQTMs <- data.frame(mt_id = character(),
  45. gt_id = character())
  47. ## Select CpGs and transcripts with the selected distance to each other
  48. for(i in 1:23){
  49. anno_g <- subset(annotation_g, chrom == i)
  50. anno_m <- subset(annotation_m, chrom == i)
  52. dnam <- anno_m[is.element(anno_m$name, row.names(DNAm)),]
  53. mrna <- anno_g[is.element(anno_g$name, row.names(RNA)),]
  56. if(nrow(dnam) != 0){
  57. for(j in 1:nrow(dnam)){
  58. for(n in 1:nrow(mrna)){
  60. ## CpG is located in the transcript
  61. if(mrna$chromStart[n] < dnam$chromStart[j] & dnam$chromStart[j] < mrna$chromEnd[n]){
  62. new_row <- data.frame(mt_id = dnam$name[j], gt_id = mrna$name[n])
  63. eQTMs <- rbind(eQTMs, new_row)
  64. }
  66. ## CpG is located in front of the transcript
  67. if(dnam$chromStart[j] < mrna$chromStart[n]){
  68. if(mrna$chromStart[n] - dnam$chromStart[j] < cis){
  69. new_row <- data.frame(mt_id = dnam$name[j], gt_id = mrna$name[n])
  70. eQTMs <- rbind(eQTMs, new_row)
  71. }
  72. }
  74. ## CpG is located behind the transcript
  75. if(dnam$chromStart[j] > mrna$chromEnd[n]){
  76. if(dnam$chromStart[j] - mrna$chromEnd[n] < cis){
  77. new_row <- data.frame(mt_id = dnam$name[j], gt_id = mrna$name[n])
  78. eQTMs <- rbind(eQTMs, new_row)
  79. }
  80. }
  81. }
  82. }
  83. }
  84. }
  88. print(c("Number of tests:", nrow(eQTMs)))
  91. ## add gene id and name
  92. anno_g <- fread("path/to/GTF_GRCh38_all_252989.txt") ## TODO change path to file with gene ID and name
  93. eQTMs <- merge(eQTMs, anno_g[,c("transcript_id", "gene_id", "gene_name")], by.x = "gt_id", by.y = "transcript_id") ## TODO change column names
  95. ## Prepare dataframe for results
  96. results <- data.frame(dnam = eQTMs$mt_id,
  97. rna = eQTMs$gt_id,
  98. gene_id = eQTMs$gene_id,
  99. gene_name = eQTMs$gene_name,
  100. outlier = numeric(length = nrow(eQTMs)),
  101. est = numeric(length = nrow(eQTMs)),
  102. std_error = numeric(length = nrow(eQTMs)),
  103. p = numeric(length = nrow(eQTMs)),
  104. q = numeric(length = nrow(eQTMs)),
  105. r = numeric(length = nrow(eQTMs)),
  106. rse = numeric(length = nrow(eQTMs)),
  107. vif = numeric(length = nrow(eQTMs)))
  110. ## function to remove outliers iteratively
  111. remove_outlier <- function(df, column, threshold = 4) {
  113. repeat {
  114. mean_val <- mean(df[[column]], na.rm = TRUE)
  115. sd_val <- sd(df[[column]], na.rm = TRUE)
  117. new_df <- df[abs(df[[column]] - mean_val) <= threshold * sd_val, ]
  119. if (nrow(new_df) == nrow(df)) {
  120. break
  121. }
  123. df <- new_df
  124. }
  126. return(df)
  127. }
  130. ## For loop to remove the outliers, calculate the linear regressions and create the correlation plots
  131. print("Start linear regressions")
  132. for (i in 1:nrow(results)) {
  134. ## extract cpg and transcript for regression
  135. dnam <- as.data.frame(t(DNAm[results$dnam[i],]))
  136. dnam$id <- row.names(dnam)
  137. mrna <- as.data.frame(t(RNA[results$rna[i],]))
  138. mrna$id <- row.names(dnam)
  139. pheno$id <- row.names(pheno)
  142. ## merge DNAm, mRNA and covariate data data for regression and plotting
  143. plot_data <- merge(dnam, mrna, by= "id")
  144. plot_data <- merge(plot_data, pheno, by = "id")
  146. ## remove outliers
  147. n_with_outlier <- nrow(plot_data)
  148. plot_data <- remove_outlier(plot_data, 2) ## remove outliers based on DNAm
  149. plot_data <- remove_outlier(plot_data, 3) ## remove outliers based on mRNA
  150. n_without_outlier <- nrow(plot_data)
  153. ## z-standatization for better comparability of the different data sets
  154. if(!all(plot_data[,3] == 0)){ ## Z-standadization is not possible if all values are 0
  156. plot_data[,2] <- scale(plot_data[,2])
  157. plot_data[,3] <- scale(plot_data[,3])
  159. ## linear regression with covariates
  160. lm_model <- lm(as.formula(paste(names(plot_data)[3], "~", names(plot_data)[2],
  161. "+age+sex+status+pmi_h+rin+PC1.x+PC2+PC3+PC1.y")), ## TODO change covariates
  162. data = plot_data)
  165. results$est[i] <- summary(lm_model)$coefficients[2,1]
  166. results$std_error[i] <- summary(lm_model)$coefficients[2,2]
  167. results$p[i] <- summary(lm_model)$coefficients[2,4]
  168. results$rse[i] <- summary(lm_model)$sigma ## residual standard error
  169. results$vif[i] <- check_collinearity(lm_model)$VIF[1] ## variance inflation factor
  170. results$outlier[i] <- n_with_outlier - n_without_outlier ## number of outliers
  171. results$r[i] <- round(cor(plot_data[2], plot_data[3], method = "pearson"),4)
  173. ## create correlation plots
  174. if(!is.na(results$p[i])){
  175. plot_data$status <- ifelse(plot_data$status == 1,"AD","HC")
  176. R <- round(cor(plot_data[2], plot_data[3], method = "pearson"),4) ## calculate Pearson correlation coefficient
  178. cor_plot <- ggplot(plot_data, aes_string(x = names(plot_data)[2], y = names(plot_data)[3])) +
  179. geom_point(aes(color = status)) + ## Color of the dots indicates whether AD or HC
  180. geom_smooth(method = "lm", se = FALSE, color = "black")+ ## regression line
  181. annotate("text", x = min(plot_data[,2])+0.75, y = max(plot_data[,3])+0.25, label = paste("R=", R))+ ## pearson correlation coefficient
  182. theme_minimal() +
  183. labs(color = "Disease status") +
  184. scale_color_manual(
  185. values = c("AD" = "#813513", "HC" = "#32584B"))
  187. ggsave(
  188. filename = paste0("plots/OPTIMA_corplot_", as.character(cis), "_", ## TODO: change file name
  189. names(plot_data)[2], "_", names(plot_data)[3], ".png"),
  190. plot = cor_plot,
  191. width = 6, height = 4)
  192. }
  193. }
  194. }
  196. ## remove invalid p-values so that an FDR correction can be performed
  197. results <- results[!is.na(results$p),]
  198. results <- results[results$p != 0,]
  200. ## perform FDR correction
  201. results$q <- p.adjust(results$p, method = "fdr")
  203. ## Add mRNA or lncRNA
  204. results <- results %>%
  205. left_join(
  206. anno_g %>%
  207. select(transcript_id, transcript_biotype) %>%
  208. distinct(transcript_id, .keep_all = TRUE),
  209. by = c("rna" = "transcript_id")
  210. )
  213. ## Save results
  214. write.csv(results, out_path, quote = FALSE, row.names = FALSE)

eQTM_mapping_OPTIMA.R at commit b314ffa, under GPL-3.0 · at the source

Overview

Authors: David Gmelin1, Olena Ohlei1,2, M Muaaz Aslam1, Marit P Junge1, Laura Parkkinen3, Kristina Mullin4, Dmitry Prokopenko4,5, Christina M Lill2,6, Rudolph E Tanzi4,5, Valerija Dobricic1, Lars Bertram1
ORCID iDs: Olena Ohlei, Marit P Junge, Laura Parkkinen, Dmitry Prokopenko, Christina M Lill, Rudolph E Tanzi, Lars Bertram
  1. Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
  2. Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
  3. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
  4. Genetics and Aging Research Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA USA
  5. Harvard Medical School, Boston, MA USA
  6. Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
Institutions: University of Lübeck (Germany); University of Münster (Germany); University of Oxford (United Kingdom); Massachusetts General Hospital (United States); Harvard University (United States); Imperial College London (United Kingdom)
Journal: Nature communications, volume 17, issue 1, article 4968
Dates: received 26 February 2025; accepted 19 May 2026; published online 4 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73902-7 · PMID 42243151 · PMCID PMC13237113 · OpenAlex W7163523562
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity
Keywords: Structural variation, Alzheimer's disease, Genome-wide association studies
MeSH: Alzheimer Disease*, Genetic Predisposition to Disease*, Genome-Wide Association Study*, Microsatellite Repeats*, Aged, Female, Genotype, Humans, Male, Polymorphism, Single Nucleotide, Whole Genome Sequencing (* major topic)
Topic: Genetic Neurodegenerative Diseases (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Alzheimer Society; National Institute for Health and Care Research
Citations: cited by 3 papers (Europe PMC); 64 references in the paper

Abstract

GWAS typically focus on SNPs, often excluding complex genetic variants, such as short tandem repeats. Here, we report the results of GWAS analyses systematically assessing the role of short tandem repeats, both imputed and directly genotyped by whole genome sequencing, on risk for Alzheimer’s disease in a large collection of ~330,000 individuals (3287 cases; 47,048 Alzheimer’s disease-by-proxy cases, 283,111 controls) from the UK biobank. Using short tandem repeat genotype data, we identify 15 independent loci showing evidence for genome-wide significant association with Alzheimer’s disease risk. While most identified loci had already been highlighted by SNP-based GWAS, we detect short tandem repeat-based signals near the genes SNX32 (chr. 11q13) and WSB1 (chr. 17q11). In addition, we delineate several other loci where short tandem repeats (and not SNPs) either represent the lead signal (ABCA7) or make substantial contributions to the SNP-driven associations (HLA-DRB1, MINDY/ADAM10, and APOE). Heritability analyses estimate that short tandem repeats account for at least 3% of the total phenotypic variance of Alzheimer’s disease in this dataset. Aligning our top short tandem repeats with DNA methylation and transcriptome profiles from human brain samples suggests that several short tandem repeats may unfold their effects by impacting gene expression.

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

Repositories

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

dgmelin/ukb_ad_str_gwas

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b314ffa387c2c16979516f43bb1032470bd39e4f, 9 April 2026
Languages: Shell (11), Jupyter (8), Python (7), R (4)
Size: 50 files, 30 scripts
Software Heritage: not archived
Found in: the text, “Application of the analysis code”
Holds: README, license file, 8 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (14 files), NumPy (10 files), seaborn (10 files), Matplotlib (9 files), SciPy (5 files), BCFtools (2 files), data.table (2 files), ggplot2 (2 files), tidyverse (2 files), easystats (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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32 files
At the source: github.com/dgmelin/ukb_ad_str_gwas

Zenodo 19484860

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Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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32 files
At the source:

Zenodo 8365671

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Tools: NumPy (4 files), NetworkX (3 files)
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gymrek-lab/ensembletr

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
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Commit: dbda15bcf92c2416fb011d1337ddfead1b4d77b6, 30 April 2025
Languages: Python (10), Shell (3)
Size: 24 files, 13 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, environment (poetry.lock, pyproject.toml), tests
Not found: license file, CITATION.cff, continuous integration, documentation
Tools: NumPy (3 files), NetworkX (2 files), SAMtools (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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14 files
At the source: github.com/gymrek-lab/EnsembleTR/tree/v1.0.1

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

Tracing map

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What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 86 scripts, each with its path and the digest of its content;
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Data

Datasets cited

Data availability

Individual-level genetic and phenotypic data used in this study are available under restricted access due to the legal terms of the UK Biobank Material Transfer Agreement from the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/register). Access to the individual-level genetic data from all participants can be obtained by qualified researchers by applying directly to the UK Biobank. The individual-level imputed STR data generated in this study will be returned to the UK Biobank. Access can be obtained by approved researchers through the UK Biobank as a ‘Returned Dataset’ (under application ID 81874) once UKB’s internal processing is complete. The raw individual-level genetic data are protected and are not publicly available due to data privacy laws. The SNP-STR imputation panel is available in the Zenodo database under accession code 10.5281/zenodo.8365671. Access to the OPTIMA dataset is restricted due to data protection regulations. Principal investigators can be contacted with data access requests via their research group portal (https://www.pharm.ox.ac.uk/research/groups/smith-group-oxford-project-to-investigate-memory-and-ageing-optima-and-b-vitamin-research-group), subject to the execution of a standard data use agreement. GWAS summary statistics generated in this study have been deposited in the Zenodo database under accession code 10.5281/zenodo.17908176. Source data for the main figures are provided with this paper, while source data for the Supplementary figures are included in the Zenodo repository. The previously published GWAS summary statistics from Bellenguez et al.3 used in this study are available in the GWAS Catalog database under accession code GCST90027158 http://www.ebi.ac.uk/gwas/publications/35379992. Summary statistics from Jansen et al.24 have been retrieved from 10.1038/s41588-018-0311-9. Source data are provided with this paper.

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

Versions

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Version 2, 28 September 2026

  • Funding: added Alzheimer Society; National Institute for Health and Care Research

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 3 keywords, 11 MeSH terms, 62 references.

Cite

This paper

Gmelin, D., Ohlei, O., Aslam, M. M., Junge, M. P., Parkkinen, L., Mullin, K., Prokopenko, D., Lill, C. M., Tanzi, R. E., Dobricic, V., & Bertram, L. (2026). GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer's disease. Nature communications, 17(1), 4968. https://doi.org/10.1038/s41467-026-73902-7

BibTeX

@article{gmelin2026gwas,
author = {Gmelin, David and Ohlei, Olena and Aslam, M Muaaz and Junge, Marit P and Parkkinen, Laura and Mullin, Kristina and Prokopenko, Dmitry and Lill, Christina M and Tanzi, Rudolph E and Dobricic, Valerija and Bertram, Lars},
title = {{GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer's disease}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {4968},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73902-7},
url = {https://doi.org/10.1038/s41467-026-73902-7},
pmid = {42243151},
pmcid = {PMC13237113}
}

RIS

TY - JOUR
AU - Gmelin, David
AU - Ohlei, Olena
AU - Aslam, M Muaaz
AU - Junge, Marit P
AU - Parkkinen, Laura
AU - Mullin, Kristina
AU - Prokopenko, Dmitry
AU - Lill, Christina M
AU - Tanzi, Rudolph E
AU - Dobricic, Valerija
AU - Bertram, Lars
TI - GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer's disease
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/04
VL - 17
IS - 1
SP - 4968
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73902-7
UR - https://doi.org/10.1038/s41467-026-73902-7
LA - en
ER -

CSL-JSON

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[3] doi:10.1038/s41467-026-72598-z [code]
Functional impact of genetic background on variable expressivity in neurodevelopmental disorders.
Journal: Nature communications
In common: BCFtools, SAMtools, NetworkX, 8 other tools, 1 reference
[4] doi:10.1002/alz.71271 [code]
Intracellular protein GBF1 displays significant associations with amyloid pathology in Alzheimer's disease.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: BCFtools, SAMtools, ggplot2, 5 other tools, Alzheimer's / dementia, cellular / molecular, 3 references
[5] doi:10.1093/bioinformatics/btag592 [code]
Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.
Journal: Bioinformatics (Oxford, England)
In common: BCFtools, SAMtools, NetworkX, 8 other tools, genetics / omics
[6] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: BCFtools, SAMtools, NetworkX, 8 other tools, cellular / molecular
[7] doi:10.1016/j.mocell.2026.100363 [code]
Tandem repeats in human brain evolution and disease susceptibility.
Journal: Molecules and cells
In common: SAMtools, NetworkX, NumPy, cellular / molecular, 5 references
[8] doi:10.1038/s41586-026-10345-6 [code]
Population-scale repeat expansions elucidate disease risk and brain atrophy.
Journal: Nature
In common: BCFtools, SAMtools, data.table, 6 other tools, genetics / omics, 1 reference
[9] doi:10.1038/s41467-026-71790-5 [code]
Recurrent DNA break clusters drive replication-stress-induced copy number variants and genome diversification.
Journal: Nature communications
In common: BCFtools, SAMtools, data.table, 7 other tools, genetics / omics, cellular / molecular
[10] doi:10.1093/molbev/msag035 [code]
Mammalian mitochondrial DNA accumulates insertions and deletions with age in energetically demanding tissues.
Journal: Molecular biology and evolution
In common: BCFtools, SAMtools, NetworkX, 6 other tools, genetics / omics, cellular / molecular

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