OSCR

Can we identify people with Alzheimer's disease from examination of the eye? A bidirectional Mendelian randomization (MR) study.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Sensitivity analyses ↔ AD_Retina_MR_code.R, lines 92–131 · score 0.83 · weighted median, Horizontal pleiotropy, weighted mode, MR Egger, Steiger filtering, binary
  2. [2] § Methods › Study design ↔ AD_Retina_MR_code.R, lines 1–44 · score 0.60 · bidirectional MR, sample MR, exposure, GWAS, ocular, variants
  3. [3] § Results › Optic disc area (ODA) influences AD risk but the relationship is probably mediated by refractive error ↔ AD_Retina_MR_code.R, lines 92–131 · score 0.57 · MR Egger intercept, horizontal pleiotropy, CI, sensitivity, IVW, AD
  4. [4] § Results › AD may cause neurodegeneration of the inner retina but the evidence is weak ↔ AD_Retina_MR_code.R, lines 258–329 · score 0.50 · horizontal pleiotropy, Steiger filtering, sensitivity, IVW, MR, regression

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R · 329 lines · 13 KB · no license · 4 matches

  1. ### Script for analysing the causal risk factors of Alzheimer's disease identified from fundal imaging of the eye using bidirectional Mendelian Randomization (MR)
  2. ### TwoSampleMR package was applied for estimating the bidirectional effect between Alzheimer's disease and ocular features,
  3. ### For more information on commands/additional options see: https://mrcieu.github.io/TwoSampleMR/
  4. ## For simplicity, the code of bidirectional MR analysis between Alzheimer's disease and RNFL are provided here
  5. ######################################################################################################################################
  6. # Load packages
  7. ######################################################################################################################################
  8. rm(list=ls(all=TRUE)) #empties your R environment
  10. #Load packages
  11. install.packages("devtools")
  12. library(devtools)
  13. install_github("mrcieu/ieugwasr")
  14. library(ieugwasr)
  15. install_github("MRCIEU/TwoSampleMR")
  16. library(TwoSampleMR)
  17. install.packages("ggplot2")
  18. library(ggplot2)
  21. setwd("PATH_TO_FILE")
  23. ########################################################################################
  24. ########### Estimation of the causal effect of AD on ocular features ####################
  25. ################## AD on RNFL thickness ################################################
  27. #### For the direction of MR casual effect AD to Ocular features, we extracted GWAS summary data of Alzheimer's disease (Kunkle et al, Nature Genetics, 2019)
  28. ## from IEU OpenGWAS project (ID: "ieu-b-2") for the list of variants that showed best level of association after meta analysis of Stage 1 and Stage 2
  29. ## (from the published table 1: https://www.nature.com/articles/s41588-019-0358-2/tables/1). We also copied the column of effect allele from that published table
  30. ## as this column was missing in the summary data of AD in IEU OpenGWAS project.
  32. ############# Read exposure data (AD)
  33. ADx<-read_exposure_data(filename = "Kunkle_publised_gwas_AD_summary_data_with_maf.txt",
  34. snp_col = "MarkerName",
  35. beta_col = "Beta",
  36. se_col = "SE",
  37. effect_allele_col = "Effect_allele",
  38. other_allele_col = "Non_Effect_allele",
  39. eaf_col = "MAF",
  40. pval_col = "Pvalue",
  41. chr_col = "Chromosome",
  42. pos_col = "Position"
  43. )
  44. dim(ADx) # 21 SNPs
  48. #### these columns added to perform Steiger filtering for binary exposure (AD)
  49. ADx$exposure<-"AD"
  50. ADx$prevalence.exposure<-0.1
  51. ADx$ncase.exposure<-21982
  52. ADx$ncontrol.exposure<-41944
  53. ADx$units.exposure<-"log odds"
  56. ### no clumping performed as these variants were reported genome wide significant for AD (P<=5E-08)
  58. ## Source of RNFL summary data: https://www.ebi.ac.uk/gwas/efotraits/OBA_2050111
  59. ### read outcome data (RNFL)
  60. RNFL<-read_outcome_data(snps = ADx$SNP, filename = "GCST90014266_buildGRCh37.tsv", sep = "\t",
  61. snp_col = "variant_id",
  62. beta_col = "beta",
  63. se_col = "standard_error",
  64. effect_allele_col = "effect_allele",
  65. other_allele_col = "other_allele",
  66. eaf_col = "effect_allele_frequency",
  67. pval_col = "p_value",
  68. chr_col = "chromosome",
  69. pos_col = "base_pair_location"
  70. )
  73. #### these columns added to perform Steiger filtering for the outcome (RNFL)
  74. RNFL$outcome<-"RNFL"
  75. RNFL$units.outcome<-rep("SD", nrow(RNFL))
  76. RNFL$samplesize.outcome<-rep(31434, nrow(RNFL))
  78. ## checking if any of the IV are significantly associated to outcome of interst
  79. length(which(RNFL$pvalue.outcome<=5E-8))
  81. #### Harmonization ####################################
  82. dat1 <- harmonise_data(
  83. exposure_dat = ADx,
  84. outcome_dat = RNFL
  85. )
  87. #### Main MR analysis ###############################
  88. mr_results<-mr(dat1, method_list = c("mr_ivw", "mr_egger_regression", "mr_weighted_median", "mr_weighted_mode"))
  90. mr_results
  92. ###### adjusting the beta by multiplying 0.693 for MR with binary exposure ################
  93. mr_results$cbeta<-mr_results$b*0.693
  94. mr_results$cse<-mr_results$se*0.693
  95. mr_results$lcl<- mr_results$cbeta-1.96*mr_results$cse
  96. mr_results$ucl<- mr_results$cbeta+1.96*mr_results$cse
  97. results<-cbind.data.frame(mr_results$outcome,mr_results$exposure,mr_results$nsnp,mr_results$method,mr_results$cbeta,mr_results$lcl, mr_results$ucl,mr_results$pval)
  98. results
  99. names(results)<-c("Outcome", "Exposure", "N_SNPs", "MR_Methods", "Beta", "CI_Lower_Limit", "CI_Upper_Limit", "P_value")
  100. results
  103. #### Measuring the evidence of heterogeneity (significant heterogeneity in the SNP-exposure effects might be suggestive of pleiotropy)
  104. het <- mr_heterogeneity(dat1)
  105. het
  107. ########## measuring the evidence of horizontal pleiotropy (MR egger intercept), A significant intercept suggests signifciant pleiotropy.
  108. pleio <- mr_pleiotropy_test(dat1)
  109. pleio
  111. ######## confidence interval of MR egger inercept
  112. pleio$egger_intercept-1.96*pleio$se
  113. pleio$egger_intercept+1.96*pleio$se
  115. ########### performing single SNP analysis:
  117. ##### default single SNP analyses gives the wald ratio
  118. res_single <- mr_singlesnp(dat1)
  120. ####### leave one out analyses - by defalut uses IVW
  121. res_loo <- mr_leaveoneout(dat1)
  123. ####### performing MR Steiger filtering for additional sensitivity analysis
  125. ##### steiger filtering
  126. dat1_steiger<-steiger_filtering(dat1)
  127. dat1_AD_RNFL_steiger<-subset(dat1_steiger, dat1_steiger$steiger_dir==TRUE)
  129. #### MR analysis after Steiger filtering
  130. mr_results_steiger<-mr(dat1_AD_RNFL_steiger, method_list = c("mr_ivw", "mr_egger_regression", "mr_weighted_median", "mr_weighted_mode"))
  131. mr_results_steiger
  133. ####################################################### Generating MR plots ###################################################
  134. ### Generating a Scatter plot of main MR analysis
  135. p1<-mr_scatter_plot(mr_results,dat1)
  136. p1[[1]]
  137. ggsave(p1[[1]], file="AD_RNFL_scatter_plot.pdf", width=7, height=7)
  139. ### Generating a forest plot of each of the SNP effects
  140. p2<-mr_forest_plot(res_single)
  141. p2[[1]]
  142. ggsave(p2[[1]], file="AD_RNFL_forest_plot.pdf", width=7, height=7)
  144. # Generating a funnel plot to check asymmetry
  145. p3<-mr_funnel_plot(res_single)
  146. p3[[1]]
  147. ggsave(p3[[1]], file="AD_RNFL_main_funnel_plot.pdf", width=7, height=7)
  149. # generating a leave one out plot to test whether any one SNP is driving any pleiotropy or asymmetry in the estimates
  150. p4<-mr_leaveoneout_plot(res_loo)
  151. p4[[1]]
  152. ggsave(p4[[1]], file="AD_RNFL_main_leaveoneout_plot.pdf", width=7, height=7)
  155. ########################################################################################
  156. ########### Estimtation of the causal effect of ocular features on AD ####################
  157. ################## RNFL thickness on AD ################################################
  160. ## Source of RNFL summary data: https://www.ebi.ac.uk/gwas/efotraits/OBA_2050111
  161. ### read exposure data (RNFL)
  162. RNFL<-read_exposure_data(filename = "GCST90014266_buildGRCh37.tsv", sep = "\t",
  163. snp_col = "variant_id",
  164. beta_col = "beta",
  165. se_col = "standard_error",
  166. effect_allele_col = "effect_allele",
  167. other_allele_col = "other_allele",
  168. eaf_col = "effect_allele_frequency",
  169. pval_col = "p_value",
  170. chr_col = "chromosome",
  171. pos_col = "base_pair_location"
  172. )
  174. #### these columns added to perform Steiger filtering for the exposure (RNFL)
  175. RNFL$exposure<-"RNFL"
  176. RNFL$units.exposure<-rep("SD", nrow(RNFL))
  177. RNFL$samplesize.exposure<-rep(31434, nrow(RNFL))
  178. head(RNFL)
  180. ##### selecting the SNPs associated with RNFL with p<5E-08
  181. RNFL1<-subset(RNFL, RNFL$pval.exposure<=5E-8)
  183. #### clumping to get independantly associated SNPs with RNFL thickness
  184. RNFL2<- clump_data(RNFL1)
  186. #### Read outcome data using the IEU OpenGWAS project code: "ieu-b-2"
  187. AD1<-extract_outcome_data(snps = RNFL2$SNP, outcomes = "ieu-b-2")
  189. #### checking if any of the IV are significantly associated to outcome of interst
  190. length(which(AD1$pvalue.outcome<=5E-8))
  192. #### these columns are newly added to perform Steiger filtering for binary outcome (AD)
  193. AD1$prevalence.outcome<-0.1
  194. AD1$ncase.outcome<-21982
  195. AD1$ncontrol.outcome<-41944
  196. AD1$units.outcome<-"log odds"
  198. ##################### extracting the EAF column from the european reference pannel (1000Genome project) as this column is missing in IEU OpendGWAS dataset
  199. ###### eaf column is missing, so need to extract from the 1000 genome project for the European population
  201. data1<-AD1[, c("SNP","chr.outcome", "pos.outcome", "effect_allele.outcome", "other_allele.outcome", "beta.outcome")]
  203. data1$ID<-paste(data1$chr.outcome, data1$pos.outcome, sep = ":")
  204. head(data1)
  206. ### read the 1000Genome file for European ancestry
  207. #library("data.table")
  208. Euro_Genome<-fread("1000Genome_European_referece_snps_maf.txt", sep = "\t")
  209. head(Euro_Genome)
  210. dim(Euro_Genome)
  212. data12<-merge(data1, Euro_Genome, by = "ID")
  213. dim(data12)
  214. head(data12)
  216. ### checking the allele and converting maf accordingly
  217. data12$eaf.outcome<-"NA"
  218. data123 <- within(data12, {
  219. # Check if Effect_Allele matches Minor_Allele
  220. match_effect_minor <- effect_allele.outcome == ALT & other_allele.outcome == REF
  221. # If they do not match, switch the minor allele (ALT is the minor allele and ALTfreq is provided) to effect allele
  222. minor_allele <- ifelse(match_effect_minor, ALT, effect_allele.outcome)
  223. major_allele <- ifelse(match_effect_minor, REF, other_allele.outcome)
  224. # Update Effect_Allele_Frequency from 1-MAF (which is basically ALTfreq) if they didn't match
  225. eaf.outcome <- ifelse(match_effect_minor, eaf.outcome, 1-MAF)
  226. # Update Effect_Allele_Frequency from Minor_Allele_Frequency if they match
  227. eaf.outcome <- ifelse(!match_effect_minor, eaf.outcome, MAF)
  228. })
  229. head(data123)
  230. eafdata<-data123[, c("SNP", "eaf.outcome")]
  231. AD2<-merge(AD1, eafdata, by = "SNP")
  233. #################################################################################################################
  235. #### Harmonization ####################################
  236. dat2 <- harmonise_data(
  237. exposure_dat = RNFL2,
  238. outcome_dat = AD2
  239. )
  241. #### main MR analysis
  242. mr_results<-mr(dat2, method_list = c("mr_ivw", "mr_egger_regression", "mr_weighted_median", "mr_weighted_mode"))
  243. mr_results
  245. #Converting the estimatd beta to odds ratio and estimating 95% confidence interval
  246. or<-generate_odds_ratios(mr_results)
  247. or
  248. orresults<-cbind.data.frame(or$outcome,or$exposure, or$nsnp, or$method, or$or, or$or_lci95, or$or_uci95, or$pval)
  249. orresults
  250. names(orresults)<-c("Outcome", "Exposure", "N_SNPs", "MR_Methods", "OR", "CI_Lower_Limit", "CI_Upper_Limit", "P_value")
  251. orresults
  253. # Runing some sensitivity analyses
  254. # measuring the evidence of heterogeneity in the genetic effects
  255. het <- mr_heterogeneity(dat2)
  258. # Measuring the evidence of horizontal pleiotropy
  259. pleio <- mr_pleiotropy_test(dat2)
  261. ########### performing single SNP analysis:
  263. ##### default single SNP analyses gives the wald ratio
  264. res_single <- mr_singlesnp(dat2)
  266. ####### leave one out analyses - by defalut uses IVW
  267. res_loo <- mr_leaveoneout(dat2)
  269. ####### performing MR Steiger filtering for additional sensitivity analysis
  271. ##### steiger filtering
  272. dat2_steiger<-steiger_filtering(dat2)
  273. dat1_RNFL_AD_steiger<-subset(dat2_steiger, dat2_steiger$steiger_dir==TRUE)
  275. #### MR analysis after Steiger filtering
  276. mr_results_steiger<-mr(dat2_RNFL_AD_steiger, method_list = c("mr_ivw", "mr_egger_regression", "mr_weighted_median", "mr_weighted_mode"))
  277. mr_results_steiger
  279. ####################################################### Generating MR plots ###################################################
  280. ### Generating a Scatter plot of main MR analysis
  281. p1<-mr_scatter_plot(mr_results,dat2)
  282. p1[[1]]
  283. ggsave(p1[[1]], file="RNFL_AD_scatter_plot.pdf", width=7, height=7)
  285. ### Generating a forest plot of each of the SNP effects
  286. p2<-mr_forest_plot(res_single)
  287. p2[[1]]
  288. ggsave(p2[[1]], file="RNFL_AD_forest_plot.pdf", width=7, height=7)
  290. # Generating a funnel plot to check asymmetry
  291. p3<-mr_funnel_plot(res_single)
  292. p3[[1]]
  293. ggsave(p3[[1]], file="RNFL_AD_funnel_plot.pdf", width=7, height=7)
  295. # generating a leave one out plot to test whether any one SNP is driving any pleiotropy or asymmetry in the estimates
  296. p4<-mr_leaveoneout_plot(res_loo)
  297. p4[[1]]
  298. ggsave(p4[[1]], file="RNFL_AD_leaveoneout_plot.pdf", width=7, height=7)
  301. ################ F statistic #########################################
  303. ### selecting the SNPs applied in the main MR analysis
  304. dat_mr<-subset(dat2, dat2$mr_keep==TRUE)
  306. #### defining the vector values from the exposure data
  308. eaf <- dat_mr[,"eaf.exposure"]
  309. b <- dat_mr[,"beta.exposure"]
  310. se <- dat_mr[,"se.exposure"]
  311. p <- dat_mr[,"pval.exposure"]
  312. n <- dat_mr[,"samplesize.exposure"]
  313. snp <- dat_mr[,"SNP"]
  315. # Converting EAF to MAF where necessary
  316. maf <- ifelse(eaf > 0.5, 1 - eaf, eaf)
  318. # Calculating per SNP R2
  319. r2 <- (2 * b^2 * maf * (1 - maf)) / ((2 * b^2 * maf * (1 - maf)) + (se^2 * (2 * n) * maf * (1 - maf)))
  321. # Individual F-stats
  322. k <- 1
  323. F <- r2 * (n - 1 - k) / ((1 - r2) * k)
  325. # Overall R2 and F-stats
  326. k <- length(snp)
  327. all_r2 <- sum(r2)
  328. all_F <- all_r2 * (mean(n) - 1 - k) / ((1 - all_r2) * k)
  329. ########################################################################################################

AD_Retina_MR_code.R at commit 529020f, no license · at the source

Overview

Authors: Humayun Kiser1,2, Ashley Budu-Aggrey3,4, Jessica N. Cooke Bailey5,6,7, Ana Villaplana-Velasco8, Miguel O. Bernabeu9,10, Xiaofan Jiang11,12, Christopher G. Owen13, Jonathan L. Haines6,7, Louis R. Pasquale14, Stuart MacGregor15, Xiaoyi Raymond Gao16, Janey L. Wiggs17, Chen Jiang18, Hélène Choquet18,19, NEIGHBORHOOD consortium, International Glaucoma Genetics Consortium, UK Biobank Eye and Vision Consortium, George Davey Smith3,4, Patrick G. Kehoe1, Neil M. Davies20,21,22, Aimee L. Hanson3,4, Emma L. Anderson3,22, Denize Atan23,24
ORCID iDs: Humayun Kiser, Ashley Budu-Aggrey, Jessica N. Cooke Bailey, Miguel O. Bernabeu, Christopher G. Owen, Louis R. Pasquale, Janey L. Wiggs, Chen Jiang, George Davey Smith, Patrick G. Kehoe, Neil M. Davies, Emma L. Anderson, Denize Atan
24 affiliations
  1. Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, UK
  2. Department of Statistics, Comilla University, Cumilla, Bangladesh
  3. Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
  4. Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
  5. Center for Health Disparities, Department of Pharmacology & Toxicology, Brody School of Medicine, East Carolina University, Greenville, NC, 27834, USA
  6. Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
  7. Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
  8. Baillie Gifford Pandemic Science Hub, Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
  9. Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
  10. The Bayes Centre, The University of Edinburgh, Edinburgh, Scotland, UK
  11. UCL Institute of Ophthalmology, London. UK
  12. UCL Great Ormond Street Institute of Child Health, University College London, London, UK
  13. Population Health Research Institute, City St. George’s, University of London, London, UK
  14. Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY USA
  15. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
  16. Departments of Ophthalmology and Visual Science and Biomedical Informatics, Division of Human Genetics, The Ohio State University, Columbus, OH 43212, USA
  17. Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
  18. Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA
  19. Department of Health Systems Science Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA
  20. Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7034 Trondheim, Norway
  21. Department of Statistical Science, University College London, London, UK
  22. Division of Psychiatry, University College London, London, UK
  23. School of Neuroscience and Psychology, University of Bristol, Bristol, UK
  24. Bristol Eye Hospital, University Hospitals Bristol & Weston NHS Foundation Trust, Bristol, UK
Institutions: Comilla University (Bangladesh); University of Bristol (United Kingdom); MRC Integrative Epidemiology Unit; Medical Research Council (United Kingdom); East Carolina University (United States); Case Western Reserve University (United States); Centre for Inflammation Research (United Kingdom); The Queen's Medical Research Institute (United Kingdom); University of Edinburgh (United Kingdom); University College London (United Kingdom); City St George's, University of London (United Kingdom); Icahn School of Medicine at Mount Sinai (United States); QIMR Berghofer Medical Research Institute (Australia); The Ohio State University (United States); Massachusetts Eye and Ear Infirmary (United States); Harvard University (United States); Kaiser Permanente (United States); Kaiser Permanente Bernard J. Tyson School of Medicine (United States); Norwegian University of Science and Technology (Norway); Bristol Eye Hospital (United Kingdom); University Hospitals Bristol and Weston NHS Foundation Trust (United Kingdom)
Journal: The journal of prevention of Alzheimer's disease, volume 13, issue 8, article 100635
Dates: received 19 November 2025; accepted 22 June 2026; published online 10 July 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.tjpad.2026.100635 · PMID 42430965 · PMCID PMC13380462 · OpenAlex W7167930327
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population)
Keywords: Alzheimer’s disease, Vasculature, Retina, Optic disc, Mendelian randomization
MeSH: Alzheimer Disease*, Mendelian Randomization Analysis*, Genome-Wide Association Study, Humans, Male, Tomography, Optical Coherence (* major topic)
Topic: Glaucoma and retinal disorders (Ophthalmology, Medicine), according to OpenAlex
Funding: Medical Research Council (MC_UU_00032/1&9); Fight for Sight (SGA18_011); Norwegian Research Council (295989); NIH (R01 EY015473, R01 EY022305, R01 EY033829)
Citations: not cited yet (Europe PMC); 32 references in the paper

Abstract

Background: Neurodegeneration in Alzheimer’s disease (AD) is thought to be driven by amyloid-beta and tau deposition in the cerebral vasculature and brain. As the eye is an extension of the central nervous system, this study aimed to determine which neurovascular and neuroretinal changes in the eye are caused by AD rather than associations of the disease.

Methods: Bidirectional two-sample univariable and multivariable Mendelian randomization (MR) methods were applied. Instrumental variables were derived from genome-wide association studies (GWAS) of AD and the following ocular features: thickness measurements of central macula (MT), retinal nerve fibre layer (mRNFL), ganglion cell-inner plexiform layer (mGCIPL), outer nuclear layer (ONL), inner segment layer (IS), and outer segment (OS) from macular region OCT scans; arteriolar tortuosity (AT), venular tortuosity (VT), venular width (VW), fractal dimension (FD), vertical cup-to-disc ratio (VCDR), optic cup area (OCA), and optic disc area (ODA) derived from other imaging methods.

Results: There was strong evidence that genetic liability to AD affected the retinal vasculature by specifically increasing AT (β = 0.007;95%CI=0.002,0.011;p-value=0.005) in UK Biobank participants (n=52,798). AD may influence the mRNFL (β=-0.047,95%CI=-0.119,0.023,p-value=0.18) and mGCIPL (β=-0.061;95%CI=-0.14,0.025,p-value=0.16) of the inner retina and OS layer (β = 0.044;95%CI=-0.0001,0.08;p-value=0.05) but the evidence was weak. Multivariable MR analysis showed that a causal relationship between optic disc area and AD (OR=0.76;95%CI=0.62,0.93,p-value=0.009) was probably mediated by refractive error.

Conclusion: Early cerebrovascular signs of AD may be detected by examination of the eye. Further investigation is required to determine the clinical utility of eye screening for dementia.

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 4 matches between paragraphs and lines of code.

HuKiser/Retinal_features_AD_MR

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 529020f4cb0e4e3e965b8d8d31a2c7a723bfed77, 14 July 2025
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: the text, “Study design”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/HuKiser/Retinal_features_AD_MR

bulik/ldsc

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 2fdeeb3b44379408794154993dbd6101b8946b7e, 16 January 2026
Languages: Python (19), MATLAB (4), Perl (1), R (1)
Size: 1,093 files, 25 scripts
Software Heritage: archived
Found in: the text, “Genetic correlation between ocular traits”
Holds: README, license file, environment (environment.yml, requirements.txt, setup.py), tests
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (18 files), pandas (11 files), SciPy (5 files), BEDTools (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
27 files
At the source: github.com/bulik/ldsc/

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:

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

The genetic instruments used to perform the MR analyses in this study are provided in the supplementary material. All GWAS summary data used in this study are publicly available or can be provided by the authors upon request.

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 2, 28 September 2026

  • Authors: added Ashley Budu-Aggrey (0000-0002-8911-2492); Jessica N. Cooke Bailey (0000-0002-4001-8702); Miguel O. Bernabeu (0000-0002-6456-3756); Christopher G. Owen (0000-0003-1135-5977); Janey L. Wiggs (0000-0003-1890-3278); Chen Jiang (0000-0002-0336-4518); George Davey Smith (0000-0002-1407-8314); removed Ashley Budu-Aggrey; Jessica N. Cooke Bailey; Miguel O. Bernabeu; Christopher G. Owen; Janey L. Wiggs; Chen Jiang; George Davey Smith

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 21 authors, 5 keywords, 6 MeSH terms, 4 funders, 32 references.

Cite

This paper

Kiser, H., Budu-Aggrey, A., Bailey, J. N. C., Villaplana-Velasco, A., Bernabeu, M. O., Jiang, X., Owen, C. G., Haines, J. L., Pasquale, L. R., MacGregor, S., Gao, X. R., Wiggs, J. L., Jiang, C., Choquet, H., NEIGHBORHOOD consortium, International Glaucoma Genetics Consortium, UK Biobank Eye and Vision Consortium, Smith, G. D., Kehoe, P. G., Davies, N. M., Hanson, A. L., . . . Atan, D. (2026). Can we identify people with Alzheimer's disease from examination of the eye? A bidirectional Mendelian randomization (MR) study. The journal of prevention of Alzheimer's disease, 13(8), 100635. https://doi.org/10.1016/j.tjpad.2026.100635

BibTeX

@article{kiser2026can,
author = {Kiser, Humayun and Budu-Aggrey, Ashley and Bailey, Jessica N. Cooke and Villaplana-Velasco, Ana and Bernabeu, Miguel O. and Jiang, Xiaofan and Owen, Christopher G. and Haines, Jonathan L. and Pasquale, Louis R. and MacGregor, Stuart and Gao, Xiaoyi Raymond and Wiggs, Janey L. and Jiang, Chen and Choquet, Hélène and {NEIGHBORHOOD consortium, International Glaucoma Genetics Consortium, UK Biobank Eye and Vision Consortium} and Smith, George Davey and Kehoe, Patrick G. and Davies, Neil M. and Hanson, Aimee L. and Anderson, Emma L. and Atan, Denize},
title = {{Can we identify people with Alzheimer's disease from examination of the eye? A bidirectional Mendelian randomization (MR) study}},
journal = {The journal of prevention of Alzheimer's disease},
year = {2026},
month = jul,
volume = {13},
number = {8},
pages = {100635},
publisher = {Elsevier},
issn = {2274-5807},
doi = {10.1016/j.tjpad.2026.100635},
url = {https://doi.org/10.1016/j.tjpad.2026.100635},
pmid = {42430965},
pmcid = {PMC13380462}
}

RIS

TY - JOUR
AU - Kiser, Humayun
AU - Budu-Aggrey, Ashley
AU - Bailey, Jessica N. Cooke
AU - Villaplana-Velasco, Ana
AU - Bernabeu, Miguel O.
AU - Jiang, Xiaofan
AU - Owen, Christopher G.
AU - Haines, Jonathan L.
AU - Pasquale, Louis R.
AU - MacGregor, Stuart
AU - Gao, Xiaoyi Raymond
AU - Wiggs, Janey L.
AU - Jiang, Chen
AU - Choquet, Hélène
AU - NEIGHBORHOOD consortium, International Glaucoma Genetics Consortium, UK Biobank Eye and Vision Consortium
AU - Smith, George Davey
AU - Kehoe, Patrick G.
AU - Davies, Neil M.
AU - Hanson, Aimee L.
AU - Anderson, Emma L.
AU - Atan, Denize
TI - Can we identify people with Alzheimer's disease from examination of the eye? A bidirectional Mendelian randomization (MR) study
T2 - The journal of prevention of Alzheimer's disease
J2 - J Prev Alzheimers Dis
PY - 2026
DA - 2026/07/10
VL - 13
IS - 8
SP - 100635
SN - 2274-5807
PB - Elsevier
DO - 10.1016/j.tjpad.2026.100635
UR - https://doi.org/10.1016/j.tjpad.2026.100635
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.tjpad.2026.100635",
"type": "article-journal",
"title": "Can we identify people with Alzheimer's disease from examination of the eye? A bidirectional Mendelian randomization (MR) study",
"container-title": "The journal of prevention of Alzheimer's disease",
"author": [
{
"family": "Kiser",
"given": "Humayun"
},
{
"family": "Budu-Aggrey",
"given": "Ashley"
},
{
"family": "Bailey",
"given": "Jessica N. Cooke"
},
{
"family": "Villaplana-Velasco",
"given": "Ana"
},
{
"family": "Bernabeu",
"given": "Miguel O."
},
{
"family": "Jiang",
"given": "Xiaofan"
},
{
"family": "Owen",
"given": "Christopher G."
},
{
"family": "Haines",
"given": "Jonathan L."
},
{
"family": "Pasquale",
"given": "Louis R."
},
{
"family": "MacGregor",
"given": "Stuart"
},
{
"family": "Gao",
"given": "Xiaoyi Raymond"
},
{
"family": "Wiggs",
"given": "Janey L."
},
{
"family": "Jiang",
"given": "Chen"
},
{
"family": "Choquet",
"given": "Hélène"
},
{
"literal": "NEIGHBORHOOD consortium, International Glaucoma Genetics Consortium, UK Biobank Eye and Vision Consortium"
},
{
"family": "Smith",
"given": "George Davey"
},
{
"family": "Kehoe",
"given": "Patrick G."
},
{
"family": "Davies",
"given": "Neil M."
},
{
"family": "Hanson",
"given": "Aimee L."
},
{
"family": "Anderson",
"given": "Emma L."
},
{
"family": "Atan",
"given": "Denize"
}
],
"container-title-short": "J Prev Alzheimers Dis",
"volume": "13",
"issue": "8",
"page": "100635",
"DOI": "10.1016/j.tjpad.2026.100635",
"PMID": "42430965",
"PMCID": "PMC13380462",
"ISSN": "2274-5807",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.tjpad.2026.100635",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
10
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s43587-026-01106-1 [code]
Repurposing drugs for the prevention of vascular dementia using evidence from drug target Mendelian randomization.
Journal: Nature aging
In common: Alzheimer's / dementia, genetics / omics, 1 reference, 3 authors
[2] doi:10.1038/s41467-026-71682-8 [code]
GWAS meta-analysis of cerebrospinal fluid Alzheimer's biomarkers reveals loci regulating lipids, brain volume and autophagy.
Journal: Nature communications
In common: BEDTools, ggplot2, pandas, 2 other tools, Alzheimer's / dementia, genetics / omics, 4 references
[3] 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: BEDTools, ggplot2, pandas, 2 other tools, Alzheimer's / dementia, 1 reference
[4] doi:10.1038/s41380-026-03571-x [code]
Convergent coexpression reveals shared biological mechanisms underlying common and rare variant risk in six neuropsychiatric disorders.
Journal: Molecular psychiatry
In common: BEDTools, ggplot2, pandas, 2 other tools, genetics / omics, 1 reference
[5] doi:10.1038/s41467-026-73428-y [code]
Regional heterogeneity in phenotypic and genetic associations between bone and brain in humans.
Journal: Nature communications
In common: BEDTools, ggplot2, pandas, 2 other tools, genetics / omics, 1 reference
[6] doi:10.1038/s41467-026-72164-7 [code]
Multivariate genetic analysis reveals three distinct pathological dimensions in musculoskeletal disorders.
Journal: Nature communications
In common: BEDTools, ggplot2, pandas, 2 other tools, genetics / omics, 1 reference
[7] doi:10.1038/s41467-026-73902-7 [code]
GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer's disease.
Journal: Nature communications
In common: ggplot2, pandas, SciPy, 1 other tool, Alzheimer's / dementia, genetics / omics, 2 references
[8] doi:10.1016/j.isci.2026.116412 [code]
KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease.
Journal: iScience
In common: BEDTools, ggplot2, pandas, 2 other tools, genetics / omics, 1 reference
[9] doi:10.1038/s43856-026-01707-2 [code]
Decreased amyloid-related structure-function coupling in preclinical Alzheimer's disease.
Journal: Communications medicine
In common: pandas, SciPy, NumPy, Alzheimer's / dementia, 3 references
[10] doi:10.1038/s43587-026-01207-x [code]
A microprotein atlas of the human frontal cortex in Alzheimer's disease.
Journal: Nature aging
In common: BEDTools, ggplot2, pandas, 2 other tools, Alzheimer's / dementia, genetics / omics

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.