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Genetically predicted white matter microstructure mediates the relationship between risk factors and lacunar stroke.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Statistical analysis › MR sensitivity analysis ↔ 5-MR-Analysis_Risk_Factors_on_dMRI.R, lines 42–99 · score 0.75 · MR Egger regression, weighted median, weighted mode, MR RAPS, MR PRESSO, Outlier
  2. [2] § Methods › Statistical analysis › MR sensitivity analysis ↔ 5-MR-Analysis_Risk_Factors_on_dMRI.R, lines 42–99 · score 0.69 · weighted median, weighted mode, MR RAPS, MR Egger, MR PRESSO, SNP
  3. [3] § Methods › Statistical analysis › MR sensitivity analysis ↔ 4-MR-Analysis_dMRI_on_Lacunar.R, lines 1–40 · score 0.66 · MR Egger regression, weighted median, weighted mode, pleiotropy, IVW, SNP
  4. [4] § Methods › Statistical analysis › MR sensitivity analysis ↔ 3-MR-Analysis_Risk_Factors_on_Lacunar.R, lines 1–40 · score 0.57 · weighted median, weighted mode, MR Egger, pleiotropic, SNP, instruments
  5. [5] § Methods › Data sources › Exposures ↔ 1_MR-Preprocessing_Risk_Factors.R, lines 1–38 · score 0.57 · lipoprotein metabolism, FinnGen, hyperlipidaemia, risk factor, hypertension, Exposure

Paper

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The authors' code

R · 235 lines · 9.4 KB · no license · 2 matches

  1. # List of risk factors to analyze
  2. risk_factors <- c("Hypertension", "Type2Diabetes", "Lipoprotein_Metabolism")
  4. # List of all IDP traits (assuming you have 288 dMRI traits files)
  5. idp_files <- list.files(pattern = "exp_dat_IDP_.*\\.csv")
  7. # Initialize dataframes to store final results
  8. significant_results <- data.frame()
  9. heterogeneity_results <- data.frame()
  10. pleiotropy_results <- data.frame()
  11. directionality_results <- data.frame()
  13. # Set significance threshold (adjust this if necessary)
  14. significance_threshold <- 0.05 / (length(risk_factors) * length(idp_files)) # Bonferroni correction
  16. # Loop through each risk factor
  17. for (risk_factor in risk_factors) {
  19. # Load preprocessed data for the current risk factor
  20. risk_factor_file <- paste0("exp_dat_", risk_factor, ".csv")
  21. risk_dat <- read.csv(risk_factor_file)
  23. # Loop through each IDP trait file
  24. for (i in seq_along(idp_files)) {
  25. idp_file <- idp_files[i]
  27. # Load preprocessed data for the current IDP trait
  28. idp_dat <- read.csv(idp_file)
  30. # Extract the IDP name from the file name
  31. idp_name <- sub(".*exp_dat_IDP_(.*)\\.csv", "\\1", idp_file)
  33. # Harmonize exposure (risk factor) and outcome (IDP trait) data
  34. dat <- harmonise_data(
  35. exposure_dat = risk_dat,
  36. outcome_dat = idp_dat
  37. )
  39. # Filter the data for valid instruments (where mr_keep == TRUE)
  40. valid_dat <- dat[dat$mr_keep == TRUE, ]
  42. # Check if there are enough valid SNPs after filtering
  43. if (is.null(valid_dat) || nrow(valid_dat) < 2) {
  44. cat("Not enough SNPs in valid instruments for MR analysis between", risk_factor, "and", idp_name, "\n")
  45. next
  46. }
  48. # Perform MR analysis
  49. res <- mr(valid_dat, method_list = c("mr_ivw", "mr_egger_regression", "mr_weighted_mode", "mr_weighted_median"))
  52. # Extract IVW method p-value
  53. ivw_pval <- res[res$method == "Inverse variance weighted", "pval"]
  55. # Perform MR-RAPS analysis
  56. mr_raps_result <- mr.raps(valid_dat$beta.exposure, valid_dat$beta.outcome, valid_dat$se.exposure, valid_dat$se.outcome)
  58. # Organize MR-RAPS results
  59. mr_raps_df <- data.frame(
  60. id.exposure = unique(valid_dat$id.exposure),
  61. id.outcome = unique(valid_dat$id.outcome),
  62. outcome = unique(valid_dat$outcome),
  63. exposure = unique(valid_dat$exposure),
  64. method = "mr_raps",
  65. nsnp = nrow(valid_dat),
  66. b = mr_raps_result$beta.hat,
  67. se = mr_raps_result$beta.se,
  68. pval = mr_raps_result$beta.p.value
  69. )
  71. # Combine MR and MR-RAPS results
  72. res <- rbind(res, mr_raps_df)
  74. # Perform MR-PRESSO outlier detection if enough instruments are available
  75. if (nrow(valid_dat) >= 2) {
  76. tryCatch({
  77. presso_result <- run_mr_presso(dat = valid_dat)
  79. # Create dataframes for MR-PRESSO results
  80. mr_presso_df <- data.frame(
  81. analysis = c("Raw", "Outlier-corrected"),
  82. causal_estimate = presso_result[[1]]$`Main MR results`$`Causal Estimate`,
  83. sd = presso_result[[1]]$`Main MR results`$Sd,
  84. t_stat = presso_result[[1]]$`Main MR results`$`T-stat`,
  85. p_value = presso_result[[1]]$`Main MR results`$`P-value`
  86. )
  88. global_test_df <- data.frame(
  89. rss_obs = presso_result[[1]]$`MR-PRESSO results`$`Global Test`$RSSobs,
  90. p_value = presso_result[[1]]$`MR-PRESSO results`$`Global Test`$Pvalue
  91. )
  93. outlier_test_df <- presso_result[[1]]$`MR-PRESSO results`$`Outlier Test`
  95. distortion_test_df <- data.frame(
  96. outliers_indices = presso_result[[1]]$`MR-PRESSO results`$`Distortion Test`$`Outliers Indices`,
  97. distortion_coefficient = presso_result[[1]]$`MR-PRESSO results`$`Distortion Test`$`Distortion Coefficient`,
  98. p_value = presso_result[[1]]$`MR-PRESSO results`$`Distortion Test`$Pvalue
  99. )
  101. # Combine all MR-PRESSO results into a list
  102. result_list <- list(
  103. MR_PRESSO_results = mr_presso_df,
  104. MR_PRESSO_global_test = global_test_df,
  105. MR_PRESSO_outlier_test = outlier_test_df,
  106. MR_PRESSO_distortion_test = distortion_test_df
  107. )
  109. # Save each dataframe in the result list to a CSV file
  110. file_prefix <- gsub(" ", "_", idp_name)
  111. risk_factor_prefix <- gsub(" ", "_", risk_factor) # Ensure risk factor name has no spaces
  113. for (name in names(result_list)) {
  114. write.csv(result_list[[name]],
  115. file = paste0(i, "_", name, "_", file_prefix, "_", risk_factor_prefix, ".csv"),
  116. row.names = FALSE)
  117. }
  119. # Extract Outlier-corrected results
  120. main_mr_results <- presso_result[[1]]$`Main MR results`
  121. outlier_corrected <- main_mr_results[main_mr_results$`MR Analysis` == 'Outlier-corrected', ]
  123. causal_estimate <- outlier_corrected$`Causal Estimate`
  124. sd <- outlier_corrected$Sd
  125. p_value <- outlier_corrected$`P-value`
  127. # Extract p-values from the Outlier Test
  128. outlier_test <- presso_result[[1]]$`MR-PRESSO results`$`Outlier Test`
  129. outlier_test$Pvalue <- as.numeric(gsub("[^0-9.]", "", outlier_test$Pvalue))
  131. # Calculate the number of outliers with Pvalue < 0.05
  132. nsnp_outlier <- sum(outlier_test$Pvalue < 0.05, na.rm = TRUE)
  134. # Calculate the effective number of instruments
  135. nsnp_effective <- nrow(valid_dat) - nsnp_outlier
  137. # Create MR-PRESSO Outlier-corrected results dataframe
  138. mr_presso_outlier_corrected <- data.frame(
  139. id.exposure = unique(valid_dat$id.exposure),
  140. id.outcome = unique(valid_dat$id.outcome),
  141. outcome = unique(valid_dat$outcome),
  142. exposure = unique(valid_dat$exposure),
  143. method = "MR-PRESSO_Outlier_corrected",
  144. nsnp = nsnp_effective,
  145. b = causal_estimate,
  146. se = sd,
  147. pval = p_value
  148. )
  150. # Add MR-PRESSO Outlier-corrected results to res dataframe
  151. res <- rbind(res, mr_presso_outlier_corrected)
  153. }, error = function(e) {
  154. message("Error in MR-PRESSO analysis: ", e$message)
  155. })
  156. } else {
  157. message("Not enough instrumental variables for MR-PRESSO analysis. Skipping this step.")
  158. }
  160. # Generate odds ratios
  161. or_df <- generate_odds_ratios(res)
  163. # Merge results and odds ratios
  164. result <- merge(res, or_df, by = c("id.exposure", "id.outcome", "outcome", "exposure", "method", "nsnp", "b", "se", "pval"))
  166. # Calculate overall R^2 and F-statistic
  167. overall_R2 <- sum(valid_dat$R2)
  168. overall_R2.easy <- sum(valid_dat$R2.easy)
  170. k <- nrow(valid_dat) # Number of instruments
  171. overall_F_statistic <- (overall_R2 * (N - k - 1)) / (k * (1 - overall_R2))
  173. # Add overall R^2 and F-statistic to result
  174. result$overall_R2 <- overall_R2
  175. result$overall_F_statistic <- overall_F_statistic
  177. # Save MR results to a CSV file
  178. output_file <- paste0("res_", risk_factor, "_on_", idp_name, ".csv")
  179. write.csv(result, file = output_file, row.names = FALSE)
  181. # Check if any "Inverse variance weighted" method p-values are significant
  182. if (any(res$method == "Inverse variance weighted" & res$pval < significance_threshold)) {
  183. res$risk_factor <- risk_factor # Add risk factor name column
  184. res$idp_trait <- idp_name # Add IDP trait column
  185. res$i <- i # Add loop index column
  186. significant_results <- rbind(significant_results, res)
  187. }
  189. # Visualization: scatter plot
  190. p1 <- mr_scatter_plot(res, valid_dat)
  191. ggsave(p1[[1]], file = paste0("scatter_", risk_factor, "_on_", idp_name, ".pdf"), width = 7, height = 7)
  193. # Single SNP analysis: forest plot
  194. res_single <- mr_singlesnp(valid_dat)
  195. p2 <- mr_forest_plot(res_single)
  196. ggsave(p2[[1]], file = paste0("forest_", risk_factor, "_on_", idp_name, ".pdf"), width = 7, height = 7)
  198. # Leave-one-out analysis: leave-one-out plot
  199. res_loo <- mr_leaveoneout(valid_dat)
  200. p3 <- mr_leaveoneout_plot(res_loo)
  201. ggsave(p3[[1]], file = paste0("loo_", risk_factor, "_on_", idp_name, ".pdf"), width = 7, height = 7)
  203. # Funnel plot
  204. p4 <- mr_funnel_plot(res_single)
  205. ggsave(p4[[1]], file = paste0("funnel_", risk_factor, "_on_", idp_name, ".pdf"), width = 7, height = 7)
  207. # Additional analyses:
  208. # Heterogeneity test
  209. het <- mr_heterogeneity(valid_dat)
  210. het$risk_factor <- risk_factor
  211. het$idp_trait <- idp_name
  212. het$i <- i # Add loop index column
  213. heterogeneity_results <- rbind(heterogeneity_results, het)
  215. # Pleiotropy test
  216. pleio <- mr_pleiotropy_test(valid_dat)
  217. pleio$risk_factor <- risk_factor
  218. pleio$idp_trait <- idp_name
  219. pleio$i <- i # Add loop index column
  220. pleiotropy_results <- rbind(pleiotropy_results, pleio)
  222. # Add loop index column, risk factor name, ivw_pval to Steiger directionality test
  223. steiger_df$risk_factor <- risk_factor
  224. steiger_df$idp_trait <- idp_name
  225. steiger_df$ivw_pval <- ivw_pval
  226. steiger_df$i <- i
  227. directionality_results <- rbind(directionality_results, steiger_df)
  228. }
  229. }
  231. # Save significant results to a CSV file
  232. write.csv(significant_results, "significant_results.csv", row.names = FALSE)
  233. write.csv(heterogeneity_results, "heterogeneity_results.csv", row.names = FALSE)
  234. write.csv(pleiotropy_results, "pleiotropy_results.csv", row.names = FALSE)
  235. write.csv(directionality_results, "directionality_results.csv", row.names = FALSE)

5-MR-Analysis_Risk_Factors_on_dMRI.R at commit 811cc4e, no license · at the source

Overview

Authors: Jie Zhang1,2,3, Min Wu2,4, Douglas Neville3, Yue Zou5, Kaisi Ren1, Qing Ye1, Shuchang Zhong1, Haiying Xiang1, Wenshi Wang1, Xiangming Ye1, Benyan Luo2, Li Zhang1
ORCID iDs: Jie Zhang, Min Wu, Douglas Neville, Xiangming Ye, Benyan Luo, Li Zhang
  1. Centre for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
  2. Department of Neurology, Brain Medical Centre, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
  3. Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, Institute of Neurology, University College London, London, UK
  4. Wellcome Centre for Integrative Neuroimaging, FMRIB, Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
  5. Department for Clinical and Movement Neurosciences, Institute of Neurology, University College London, London, UK
Institutions: Wellcome Centre for Human Neuroimaging (United Kingdom); Zhejiang Provincial People's Hospital (China); Hangzhou Medical College (China); First Affiliated Hospital Zhejiang University (China); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); Zhejiang University (China); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom); MRC Brain Network Dynamics Unit (United Kingdom); Medical Research Council (United Kingdom)
Journal: Stroke and vascular neurology, volume 11, issue 1, article e004208
Dates: received 5 March 2025; accepted 4 June 2025; published online 26 June 2025
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1136/svn-2025-004208 · PMID 40571410 · PMCID PMC13019051 · OpenAlex W4411686917
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), other condition (population), stroke (population), clinical / translational (subfield)
Methods: Statistics, fMRI & imaging
Keywords: Stroke, Magnetic Resonance Imaging, Risk Factors, Blood Pressure, Genetics
MeSH: Hypertension*, Leukoencephalopathies*, Stroke, Lacunar*, White Matter*, Aged, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Mendelian Randomization Analysis, Middle Aged, Phenotype, Predictive Value of Tests, Risk Assessment, Risk Factors (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Medical Science and Technology Project of Zhejiang Province (2021KY457, 2023KY481, 2024KY695, 2024KY771); Key Research and Development Program of Zhejiang Province (2024C03040); National Natural Science Foundation of China (National Science Foundation of China) (82272592)
Citations: cited by 1 paper (Europe PMC); 52 references in the paper

Abstract

Background and purpose: Lacunar stroke is a complex, multifactorial disease with significant genetic underpinnings. However, the mechanisms through which genetic predispositions and risk factors contribute to its pathogenesis remain poorly understood. We investigated whether genetically predicted white matter (WM) microstructure mediates causal relationships between risk factors and lacunar stroke.

Methods: Data from genome-wide association studies were used to perform two-sample Mendelian randomisation (MR) analyses. Genetic variants associated with risk factors (n=34 461–898 130), lacunar stroke (n=232 596) and eight MRI-derived WM microstructural metrics across 48 tracts (n=20 859–20 860) were analysed. Univariable MR assessed causal effects of risk factors on lacunar stroke. Two-step MR analysis evaluated mediation roles of WM microstructure, whereas multivariable MR accounted for confounders.

Results: Hypertension was identified as the strongest risk factor for lacunar stroke (OR=1.38; 95% CI: 1.28 to 1.50, p=4.43×10−15). Only hypertension showed a significant causal association with genetically predicted WM microstructure. Elevated mean diffusivity (MD), isotropic volume fraction (ISOVF) and the tertiary eigenvalue in the anterior limb of the internal capsule (ALIC) were independently linked to increased lacunar stroke risk, beyond the influence of WM hyperintensities, dilated perivascular spaces and brain volume. Mediation analysis suggested that hypertension-induced lacunar stroke was partially mediated through bilateral MD and left ISOVF in the ALIC, with mediation proportions of 23.70%–33.44%.

Conclusions: Hypertension may contribute to lacunar stroke pathogenesis in part through WM microstructure alterations, particularly in the ALIC. MD and ISOVF in the ALIC may serve as structural brain reserves and early biomarkers of hypertension-induced pathophysiology associated with lacunar stroke.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

jzhang-neuro/MR_WM_LS

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 811cc4ed790bdc6dc98307d0dbf28a68fe9769c7, 22 September 2024
Languages: R (7)
Size: 9 files, 7 scripts
Software Heritage: not archived
Found in: the text, “MR sensitivity analysis”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), ggplot2 (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
8 files
At the source: github.com/jzhang-neuro/MR_WM_LS

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 7 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

Data are available in a public, open access repository.

Reproduced under the paper's license (CC BY-NC), 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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 16 MeSH terms, 3 funders, 43 references.

Cite

This paper

Zhang, J., Wu, M., Neville, D., Zou, Y., Ren, K., Ye, Q., Zhong, S., Xiang, H., Wang, W., Ye, X., Luo, B., & Zhang, L. (2026). Genetically predicted white matter microstructure mediates the relationship between risk factors and lacunar stroke. Stroke and vascular neurology, 11(1), e004208. https://doi.org/10.1136/svn-2025-004208

BibTeX

@article{zhang2026genetically,
author = {Zhang, Jie and Wu, Min and Neville, Douglas and Zou, Yue and Ren, Kaisi and Ye, Qing and Zhong, Shuchang and Xiang, Haiying and Wang, Wenshi and Ye, Xiangming and Luo, Benyan and Zhang, Li},
title = {{Genetically predicted white matter microstructure mediates the relationship between risk factors and lacunar stroke}},
journal = {Stroke and vascular neurology},
year = {2026},
month = mar,
volume = {11},
number = {1},
pages = {e004208},
publisher = {BMJ Publishing Group},
issn = {2059-8688},
doi = {10.1136/svn-2025-004208},
url = {https://doi.org/10.1136/svn-2025-004208},
pmid = {40571410},
pmcid = {PMC13019051}
}

RIS

TY - JOUR
AU - Zhang, Jie
AU - Wu, Min
AU - Neville, Douglas
AU - Zou, Yue
AU - Ren, Kaisi
AU - Ye, Qing
AU - Zhong, Shuchang
AU - Xiang, Haiying
AU - Wang, Wenshi
AU - Ye, Xiangming
AU - Luo, Benyan
AU - Zhang, Li
TI - Genetically predicted white matter microstructure mediates the relationship between risk factors and lacunar stroke
T2 - Stroke and vascular neurology
J2 - Stroke Vasc Neurol
PY - 2026
DA - 2026/03/04
VL - 11
IS - 1
SP - e004208
SN - 2059-8688
PB - BMJ Publishing Group
DO - 10.1136/svn-2025-004208
UR - https://doi.org/10.1136/svn-2025-004208
LA - en
ER -

CSL-JSON

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