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Genetic landscape of adult executive function reveals a cell-type-specific developmental origin.

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  1. [1] § Results › Genetic correlations of TMA with cognitive traits and regional brain structures ↔ Phase1_Cognitive_Data_Clean/Aggregated_Cognitive_Data.Rmd, lines 35–77 · score 0.69 · Stroop Ink, Stroop box, symbol digit, reaction, cognitive

Paper

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The authors' code

R Markdown · 183 lines · 6.9 KB · no license · 1 match

  1. ---
  2. title: "Aggregated Cognitive Data"
  3. author: "Shafiq"
  4. date: "29/04/2022"
  5. output: html_document
  6. ---
  8. ```{r}
  9. library(data.table)
  10. library(dplyr)
  11. library(Hmisc) # plotting hist for data frame
  12. library(tidyr)
  13. library(tableone)
  14. library(corrplot)
  15. library(ggraph)
  16. library(ggplot2)
  17. library(ggcharts)
  18. library(car)
  19. library(semPlot)
  20. library(psych)
  21. library(knitr)
  22. library(kableExtra)
  23. library(MVN)
  24. library(magrittr)
  25. library(factoextra)
  26. library(FactoMineR)
  27. library(heatmaply)
  28. library(pca3d)
  29. library(rgl)
  30. library(scatterplot3d)
  31. library(ggplot2)
  32. ```
  35. # Reading all aggregated cognitive test data separately
  36. ## All times will be in mili-seconds
  37. ```{r Reading all CT data}
  38. # Reaction time
  39. rt <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Reaction_Time_Aggregated_Feb2022.txt")
  40. rt <- rt[,c(1:4)]
  41. names(rt) <- c("barcode","rt_correct_sum","rtMean_resp_time", "device_name2")
  43. # Quiz
  44. qz <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Quiz_Aggregated_Feb2022.txt")
  46. # Working memory:
  47. wm <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Working_memory_Aggregated_Feb2022.txt")
  49. # Pairing 7
  50. p7 <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Pairing7_Aggregated_Feb2022.txt")
  52. # Stroop Box
  53. sb <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Stroop_Box_Aggregated_Feb2022.txt")
  54. names(sb) <- c("barcode","sb_correct_sum","sbMean_resp_time", "device_name2", "sb_correct_length","sb_correct_prop")
  56. # Stroop Ink
  57. si <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Stroop_Ink_Aggregated_Feb2022.txt")
  58. names(si) <- c("barcode","si_correct_sum","siMean_resp_time", "device_name2", "si_correct_length","si_correct_prop")
  60. # Matrices
  61. mx <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Matrices_Aggregated_Feb2022.txt")
  63. # Vocabulary
  64. vy <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Vocabulary_Aggregated_Feb2022.txt")
  66. # Trails: Whole data
  67. ts <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Trails_Aggregated_Feb2022.txt")
  68. colnames(ts)
  69. ts <- ts[,c(1:4,7,8,11,12)]
  70. names(ts) <- c("barcode","ts_total_guesses","tsMean_resp_time", "device_name2",
  71. "ts_num_total_guesses","ts_num_Mean_resp_time","ts_alpha_total_guesses","ts_alpha_Mean_resp_time")
  74. # Symbol Digit
  75. sd <- fread("C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Symbol_Digit_Aggregated_Feb2022.txt")
  76. names(sd) <- c("barcode","sd_correct_sum","sdMean_resp_time", "device_name2", "sd_correct_length","sd_correct_prop")
  77. ```
  79. # Merging all data and save
  80. ```{r Merge all data}
  81. dfs <- Reduce(function(x,y) merge(x = x, y = y, by = c("barcode","device_name2"), all=TRUE),
  82. list(rt,qz,wm,p7,sb,si,mx,vy,ts,sd)) #21,169 participants
  84. dfs$barcode <- toupper(dfs$barcode)
  86. # one id is duplicated but missing information available in both rows; thereby aggregated
  87. duplicates <- dfs[(duplicated(dfs$barcode) | duplicated(dfs$barcode, fromLast=TRUE))]
  88. duplicates <- duplicates %>% replace(is.na(.), 0)
  89. duplicate_sample_merged <- duplicates %>% group_by(barcode, device_name2) %>% summarise_all(sum) #%>% relocate(ct_attempted, .after = last_col())
  91. dfs_without_duplicated_sample <- dfs[which(!dfs$barcode=="SP00300148173G",)] #21,167
  93. dfs <- merge(dfs_without_duplicated_sample,duplicate_sample_merged, all=TRUE) #21,168
  95. My_Theme = theme(
  96. axis.title.x = element_text(size = 14, color = "blue", face = "bold"),
  97. axis.text.x = element_text(size = 10,color = "black", face = "bold"),
  98. axis.title.y = element_text(size = 14, color = "blue", face = "bold"),
  99. axis.text.y = element_text(size = 10, color = "black", face = "bold"),
  100. axis.line = element_line(),
  101. panel.grid.major = element_blank(),
  102. panel.grid.minor = element_blank(),
  103. panel.border = element_blank(),
  104. panel.background = element_blank())
  106. ggplot(dfs, aes(x=device_name2)) +
  107. geom_bar(width=0.4, colour = "#1F3552", fill = "#4271AE")+
  108. ylab("Number of volunteers (n=21,168)") +
  109. xlab("Devices") + My_Theme
  112. fwrite(dfs, file="C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/All_Cogtests_Aggregated_Phase1.txt", col.names=TRUE, row.names = FALSE, sep="\t")
  113. ```
  115. # Main varriables from the tests
  116. ```{r}
  117. rel_vars <- dfs %>%
  118. select_if(grepl("barcode|correct_sum|Mean_resp|max_correct|guesses|vy_correct_prop", names(.)))
  120. rel_vars <- rel_vars[,c(1,3:5,8,10,12,13,14,19,21,22)]
  122. names(rel_vars) <- c("barcode","RT","QZ","WM",
  123. "PR","SB","SI","MX",
  124. "VY","TM_Num","TM_Alpha","SD")
  126. rel_vars <- rel_vars %>% mutate_if(is.integer,as.numeric)
  128. library(Hmisc)
  129. hist.data.frame(rel_vars[,-1])
  131. rel_vars$RT_l10 <- log10(rel_vars$RT)
  132. rel_vars$WM_l10 <- log10(rel_vars$WM)
  133. rel_vars$PR_l10 <- log10(rel_vars$PR)
  134. rel_vars$SB_l10 <- log10(rel_vars$SB)
  135. rel_vars$SI_l10 <- log10(rel_vars$SI)
  136. rel_vars$TM_Num_l10 <- log10(rel_vars$TM_Num)
  137. rel_vars$TM_Alpha_l10 <- log10(rel_vars$TM_Alpha)
  140. # Reversing scores for QZ, VY, WM, MX and SD tests to have same scoring
  141. rel_vars$QZ_rev <- max(rel_vars$QZ, na.rm = TRUE) - rel_vars$QZ
  142. rel_vars$VY_rev <- max(rel_vars$VY ,na.rm = TRUE) - rel_vars$VY
  143. rel_vars$WM_l10_rev <- max(rel_vars$WM_l10, na.rm = TRUE) - rel_vars$WM_l10
  144. rel_vars$MX_rev <- max(rel_vars$MX, na.rm = TRUE) - rel_vars$MX
  145. rel_vars$SD_rev <- max(rel_vars$SD, na.rm = TRUE) - rel_vars$SD
  147. cogt <- rel_vars[,c(1,20,22:24,21,13,15:19)]
  148. ```
  151. # This is for G-11 (combined all 11 tests)
  152. ```{r G-11}
  153. cogt_full <- na.omit(cogt)
  154. g11.pr <- prcomp(cogt_full[,-1], center = TRUE, scale = TRUE)
  155. g11_pcs <- as.data.frame(g11.pr$x)
  156. g11 <- as.data.frame(g11_pcs$PC1)
  157. names(g11) <- "g_11"
  158. get_eigenvalue(g11.pr)
  159. ```
  161. # G-6 including PR,TM tests, RT, SI, SB (6 tests)
  162. ```{r G-6, PR TM tests RT SI SB}
  163. g6.pr <- prcomp(cogt_full[,c(7:12)], center = TRUE, scale = TRUE)
  164. g6_pcs <- as.data.frame(g6.pr$x)
  165. g6 <- as.data.frame(g6_pcs$PC1)
  166. names(g6) <- "g_6"
  167. ```
  169. # G-4 including QZ, WM, MX and SD
  170. ```{r G-4, QZ WM MX SD}
  171. g4.pr <- prcomp(cogt_full[,c(2:5)], center = TRUE, scale = TRUE)
  172. g4_pcs <- as.data.frame(g4.pr$x)
  173. g4 <- as.data.frame(g4_pcs$PC1) # first PCs as general intelligence (explains 41.4% variance)
  174. names(g4) <- "g_4"
  175. ```
  178. # Combine PCs to CT tests data and make complete data frame
  179. ```{r, Combine all these PCs to dataset}
  180. cogt_full_pcs <- cbind(cogt_full,g11,g6,g4)
  181. main_cogs_pc <- merge(cogt, cogt_full_pcs, all = TRUE)
  182. fwrite(main_cogs_pc, file="C:/Gene_cognition_shafiq/02_Cleaning_Cognitive_data/Aggregated_Clean_Cognitive_Data/Main_cognitive_varriable_includingG.txt", col.names=TRUE, row.names = FALSE, sep="\t")
  183. ```

Aggregated_Cognitive_Data.Rmd at commit 9403d00, no license · at the source

Overview

Authors: Md Shafiqur Rahman1,2, Azra Frkatović-Hodžić3, Jelle van den Ameele1,4, Steven M. Hill2,5, Nathalie Kingston6,7, John R. Bradley6,8, Brian D. M. Tom2, Patrick F. Chinnery1,4,6
ORCID iDs: Md Shafiqur Rahman, Steven M. Hill, Nathalie Kingston, John R. Bradley, Brian D. M. Tom, Patrick F. Chinnery
  1. Department of Clinical Neurosciences, University of Cambridge,Cambridge, UK
  2. MRC Biostatistics Unit, University of Cambridge,Cambridge, UK
  3. Genos Glycoscience Research Laboratory,Zagreb, Croatia
  4. MRC Mitochondrial Biology Unit, University of Cambridge,Cambridge, UK
  5. Present Address: Cancer Research UK National Biomarker Centre, University of Manchester,Manchester, UK
  6. National Institute for Health and Care Research BioResource,Cambridge, UK
  7. Dept of Haematology, Cambridge University,Cambridge, UK
  8. Department of Medicine, University of Cambridge,Cambridge, UK
Institutions: University of Cambridge (United Kingdom); Genos (Croatia) (Croatia); University of Manchester (United Kingdom); National Institute for Health and Care Research (United Kingdom)
Journal: Nature communications, volume 17, issue 1, article 5953
Dates: received 14 January 2025; accepted 24 March 2026; published online 2 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-71738-9 · PMID 42069759 · PMCID PMC13342582 · OpenAlex W7159944498
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cognitive (subfield)
Methods: Statistics, Machine learning, Preprocessing
Keywords: Neurological disorders, Development
MeSH: Brain*, Executive Function*, Adult, Cognition, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide (* major topic)
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: UK Medical Research Council (MC_UU_00028/7); LifeArc (10748)
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Executive function is an essential cognitive domain for typical human behavior which is disrupted in neurodevelopmental and neurodegenerative disorders, but little is known about its underlying molecular basis. To address this, we perform genome-wide association studies (GWAS) using three different measures of executive function in UK Biobank (N = 84,238) and NIHR BioResource’s Genes and Cognition (N = 9932) study participants, followed by a meta-analysis. The trail-making alphanumeric (TMA) measure is the most heritable phenotype (h²=7-26%), associated with 18 independent loci that exhibit a similar direction of effect in both cohorts. Across these loci, in-silico follow-up implicates 178 genes, of which NT5DC2 and RP11-579E24.2 are independently replicated prior to meta-analysis. TMA is linked to pan-cerebral differences in brain structure, with brain-enriched genes showing a biphasic expression profile from early development through to later life. Our data implicate specific cell types, histone modifications and butyrophilin immunoglobulin family proteins as potential targets for promoting cognitive resilience.

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 1 match between paragraphs and lines of code.

shafiqnoa/genes-and-cognition-phase-1

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 9403d008a29f7151fecbff398a89b4f15e38995a, 29 January 2024
Languages: R (11)
Size: 12 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 11 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (11 files), tidyverse (11 files), ggpubr (10 files), patchwork (10 files), ggplot2 (9 files), psych (2 files), car (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
12 files
At the source: github.com/shafiqnoa/Genes-and-Cognition-Phase-1/tree/main/Phase1_Cognitive_Data_Clean

Code availability

All software used in this study is publicly available. The codes used for cognitive data cleaning are available on GitHub (https://github.com/shafiqnoa/Genes-and-Cognition-Phase-1/tree/main/Phase1_Cognitive_Data_Clean).

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

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;
  • 11 scripts, each with its path and the digest of its content;
  • 1 match 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

Datasets cited

Data availability

GWAS summary statistics generated in this study have been deposited in the Zenodo database (doi: 10.5281/zenodo.11066096).73 NIHR Bioresource holds individual-level genetic and phenotypic data for G&C study participants, which can be accessed through https://bioresource.nihr.ac.uk/using-our-bioresource/. Individual-level genetic and phenotypic data for UKB can be accessed through https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. Other data relevant to this study are provided in the article or included in the Supplementary Information.

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 keywords, 11 MeSH terms, 2 funders, 70 references.

Cite

This paper

Rahman, M. S., Frkatović-Hodžić, A., van den Ameele, J., Hill, S. M., Kingston, N., Bradley, J. R., Tom, B. D. M., & Chinnery, P. F. (2026). Genetic landscape of adult executive function reveals a cell-type-specific developmental origin. Nature communications, 17(1), 5953. https://doi.org/10.1038/s41467-026-71738-9

BibTeX

@article{rahman2026genetic,
author = {Rahman, Md Shafiqur and Frkatović-Hodžić, Azra and van den Ameele, Jelle and Hill, Steven M. and Kingston, Nathalie and Bradley, John R. and Tom, Brian D. M. and Chinnery, Patrick F.},
title = {{Genetic landscape of adult executive function reveals a cell-type-specific developmental origin}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {5953},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71738-9},
url = {https://doi.org/10.1038/s41467-026-71738-9},
pmid = {42069759},
pmcid = {PMC13342582}
}

RIS

TY - JOUR
AU - Rahman, Md Shafiqur
AU - Frkatović-Hodžić, Azra
AU - van den Ameele, Jelle
AU - Hill, Steven M.
AU - Kingston, Nathalie
AU - Bradley, John R.
AU - Tom, Brian D. M.
AU - Chinnery, Patrick F.
TI - Genetic landscape of adult executive function reveals a cell-type-specific developmental origin
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/02
VL - 17
IS - 1
SP - 5953
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71738-9
UR - https://doi.org/10.1038/s41467-026-71738-9
LA - en
ER -

CSL-JSON

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