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Transcriptome-informed brain cartography of polygenic risk and association with brain structure in major psychiatric disorders.

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 · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › ENIGMA structural brain changes reference maps ↔ code/plot_brains.R, lines 21–85 · score 0.62 · accumbens, amygdala, caudate, hippocampus, pallidum, putamen
  2. [2] § Methods › TWAS and gene expression prediction ↔ code/ADHD_TWAS.sh, the whole file · a weak match · score 0.61 · PrediXcan, MultiXcan, brain tissue, v8, gene expression, GTEx
  3. [3] § Methods › TWAS and gene expression prediction ↔ code/ASD_TWAS.sh, the whole file · a weak match · score 0.61 · PrediXcan, MultiXcan, brain tissue, v8, gene expression, GTEx
  4. [4] § Methods › Gene expression and GEDAR calculation ↔ code/PTRS_estimation.R, lines 1–68 · score 0.57 · Human Protein Atlas, Desikan Killiany, DK, filtering, transcript, brains
  5. [5] § Methods › Gene expression and GEDAR calculation ↔ code/literature_gwas_analysis.R, lines 31–67 · score 0.57 · Human Protein Atlas, Desikan Killiany, DK, filtering, transcript, brains

Paper

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The authors' code

R · 288 lines · 9.5 KB · no license · 1 match

  1. library(ggseg) # subcortical data plot
  2. library(ggplot2) # general plotting
  3. library(tidyverse) # data science general
  4. library(readxl) # read excel files
  5. library(ggsegExtra) # additional ggseg stuff
  6. library(RColorBrewer) # nice colors
  7. library(fsbrain) # cortical plotting
  8. library(magick) # image manipulation
  12. # ----
  13. # Helper functions and path set
  14. zscore <- function(column){
  15. m <- mean(column)
  16. s <- sd(column)
  17. z <- ((column - m) / s)
  18. return(z)
  19. }
  21. setwd("/Users/alessiogiacomel/Dropbox/PhD/Analysis/transcriptomics_gio/MetaXcan_TWAS_Analysis/")
  22. base_path <- "/Users/alessiogiacomel/Dropbox/PhD/Analysis/transcriptomics_gio/MetaXcan_TWAS_Analysis/"
  23. figures_path <- "figures/"
  26. template_subjects_dir <- fsbrain::fsaverage.path()
  27. atlas <- 'aparc'
  28. template_subject <- 'fsaverage'
  30. # color palettes
  31. colFn_imaging_data3d <- colorRampPalette(rev(RColorBrewer::brewer.pal(11, name="RdBu")))
  32. colFn_imaging_data <- rev(RColorBrewer::brewer.pal(11, name="RdBu"))
  33. colFn_gene_data3d <- colorRampPalette(RColorBrewer::brewer.pal(11, name="PiYG"))
  34. colFn_gene_data <- RColorBrewer::brewer.pal(11, name="PiYG")
  35. # 3D plot options
  36. makecmap_options <-list('range'=c(-3,3),
  37. 'colFn'=colFn_imaging_data3d)
  38. makecmap_options2 <-list('range'=c(-2,2),
  39. 'colFn'=colFn_gene_data3d)
  40. # ----
  41. # Structural imaging
  45. data_file <- "data/ENIGMA/ENIGMA_structural_organised.xlsx"
  47. subcortical_data <- readxl::read_xlsx(data_file, sheet = 3)|>
  48. dplyr::mutate(Structure = stringr::str_to_lower(Structure)) |>
  49. dplyr::mutate(Structure = stringr::str_replace(Structure,
  50. "thalamusproper",
  51. "thalamus"))
  53. cortical_data <- readxl::read_xlsx(data_file, sheet = 2) |>
  54. dplyr::mutate(Structure = stringr::str_replace_all(
  55. stringr::str_replace_all(Structure,"M_",""),"_thickavg", ""))
  57. tdata <- dplyr::bind_rows(cortical_data, subcortical_data) |>
  58. dplyr::mutate_at(vars(ADHD, ASD, AN, BD, MDD, OCD, SCZ), zscore)
  60. area <- c('accumbens area',
  61. 'amygdala',
  62. 'caudate',
  63. 'hippocampus',
  64. 'pallidum',
  65. 'putamen',
  66. 'thalamus')
  68. sub_data <- tdata |>
  69. dplyr::filter(Structure %in% area)
  70. cort_data <- tdata |>
  71. dplyr::filter(!Structure %in% area)
  73. diseases <- c("ADHD", "ASD", "AN", "BD", "MDD", "OCD", "SCZ")
  75. for (dis in diseases){
  77. save_dis_folder <- paste0(figures_path, dis)
  78. if (!dir.exists(save_dis_folder)) {
  79. # If the folder does not exist, create it
  80. dir.create(save_dis_folder)
  81. cat("Folder created:", save_dis_folder, "\n")
  82. }
  84. sub_save <- paste0(base_path,save_dis_folder,"/",dis,"_subcortical_enigma.png")
  85. cort_save <- paste0(base_path, save_dis_folder,"/",dis,"_cortical_enigma.png")
  87. # Subcortical PLot
  88. some_data <- data.frame(
  89. region = sub_data$Structure,
  90. cohen = sub_data[[dis]],
  91. #hemi = rep("left", 6),
  92. #side = rep('coronal', 6),
  93. stringsAsFactors = FALSE)
  98. sub_plot <- ggplot() +
  99. geom_brain(atlas = aseg2,
  100. data = some_data,
  101. #mapping = aes(fill = cohen),
  102. hemi = "right",
  103. colour = 'black') +
  104. #scale_fill_gradientn(colors = colFn_imaging_data, breaks = c(-3, 0, 3), limits = c(-3,3), oob = scales::squish) +
  105. theme_brain() +
  106. theme(legend.position = "bottom",
  107. legend.title.position = "right",
  108. legend.title = element_text(hjust = .5, angle = 90),
  109. legend.text = element_text(hjust = .5),
  110. axis.text.x=element_blank(),
  111. axis.ticks.x=element_blank(),
  112. axis.text.y=element_blank(),
  113. axis.ticks.y=element_blank(),
  114. panel.background = element_rect(linetype = "solid", fill = "white"))
  117. ggsave(sub_save, plot = sub_plot)
  119. # Cortical plot
  121. # For the left hemi, we manually set data values for some regions.
  122. lh_region_value_list <- as.list(cort_data[[dis]])
  123. names(lh_region_value_list) <- cort_data$Structure
  128. cortical_structure <- vis.region.values.on.subject(template_subjects_dir,
  129. template_subject, atlas,
  130. surface = 'pial',
  131. lh_region_value_list,
  132. rh_region_value_list = NULL,
  133. makecmap_options = makecmap_options,
  134. draw_colorbar = TRUE)
  137. img <- export(cortical_structure, colorbar_legend="Z score",
  138. draw_colorbar = 'horizontal',
  139. view_angles = get.view.angle.names(angle_set = 'lh'),
  140. output_img = cort_save)
  142. rgl::close3d(dev = rgl::rgl.dev.list())
  145. cortical_img <- magick::image_read(cort_save)
  146. sub_img <- magick::image_read(sub_save) |>
  147. magick::image_scale("670x")
  149. img <- c(cortical_img, sub_img)
  151. stacked_img <- image_append(img, stack = TRUE)
  153. image_write(stacked_img,
  154. path = paste0(save_dis_folder,"/", dis, "_enigma.png"),
  155. format = "png")
  156. }
  158. # ----------
  159. # GENETIC TPRS PLOTS
  160. weight_scheme <- c("", "_pos", "_neg")
  161. thresholds <- c(10, 5, 1)
  163. for (dis in diseases){
  165. save_dis_folder <- paste0(figures_path, dis)
  166. if (!dir.exists(save_dis_folder)) {
  167. # If the folder does not exist, create it
  168. dir.create(save_dis_folder)
  169. cat("Folder created:", save_dis_folder, "\n")
  170. }
  172. for (weight in weight_scheme){
  173. if (weight == "") {
  174. save_n <- "wavg"
  175. } else {
  176. save_n <- weight
  177. }
  178. save_weigh_folder <- paste0(save_dis_folder, "/", save_n)
  179. if (!dir.exists(save_weigh_folder)) {
  180. # If the folder does not exist, create it
  181. dir.create(save_weigh_folder)
  182. cat("Folder created:", save_weigh_folder, "\n")
  183. }
  185. for (thr in thresholds){
  187. gdata_file <- paste0(base_path, "results/", dis, "/",
  188. dis, "_TPRS", weight, "_thr_", thr, ".tsv")
  190. gene_data <- readr::read_delim(file = gdata_file)|>
  191. dplyr::filter(hemisphere == "L") |>
  192. dplyr::filter(structure %in% c("cortex", "subcortex/brainstem")) |>
  193. dplyr::mutate(Z = zscore(weighted_avg))
  195. cort_gene <- gene_data |>
  196. dplyr::filter(structure == "cortex")
  198. sub_gene <- gene_data |>
  199. dplyr::filter(structure == "subcortex/brainstem") |>
  200. dplyr::mutate(label= stringr::str_replace(label,
  201. "thalamusproper",
  202. "thalamus proper")) |>
  203. dplyr::filter(label %in% area)
  206. # save paths
  207. sub_save <- paste0(base_path,save_weigh_folder,"/",dis,
  208. "_TPRS",weight,"_subcortical_thr_",thr ,".png")
  209. cort_save <- paste0(base_path,save_weigh_folder,"/",dis,
  210. "_TPRS",weight,"_cortical_thr_",thr ,".png")
  212. # Subcortical PLot
  213. some_data <- data.frame(
  214. region = sub_gene$label,
  215. cohen = sub_gene$Z,
  216. #hemi = rep("left", 6),
  217. #side = rep('coronal', 6),
  218. stringsAsFactors = FALSE)
  223. sub_plot <- ggplot() +
  224. geom_brain(atlas = aseg,
  225. data = some_data,
  226. mapping = aes(fill = cohen),
  227. hemi = "left",
  228. #side = "coronal",
  229. colour = 'black') +
  230. scale_fill_gradientn(colors = colFn_gene_data, breaks = c(-2, 0, 2),
  231. limits = c(-2,2), oob = scales::squish) +
  232. theme_brain() +
  233. theme(legend.position = "none",
  234. legend.title.position = "left",
  235. legend.title = element_text(hjust = .5, angle = 90),
  236. legend.text = element_text(hjust = .5),
  237. axis.text.x=element_blank(),
  238. axis.ticks.x=element_blank(),
  239. axis.text.y=element_blank(),
  240. axis.ticks.y=element_blank(),
  241. panel.background = element_rect(linetype = "solid", fill = "white"))
  244. ggsave(sub_save, plot = sub_plot)
  246. # Cortical
  247. lh_region_value_list <- as.list(cort_gene$Z)
  248. names(lh_region_value_list) <- cort_gene$label
  253. cortical_structure <- vis.region.values.on.subject(template_subjects_dir,
  254. template_subject, atlas,
  255. surface = 'pial',
  256. lh_region_value_list,
  257. rh_region_value_list = NULL,
  258. makecmap_options = makecmap_options2,
  259. draw_colorbar = TRUE)
  261. img <- export(cortical_structure, colorbar_legend="Z score",
  262. draw_colorbar = 'horizontal',
  263. view_angles = get.view.angle.names(angle_set = 'lh'),
  264. output_img = cort_save)
  266. rgl::close3d(dev = rgl::rgl.dev.list())
  268. cortical_img <- magick::image_read(cort_save)
  269. sub_img <- magick::image_read(sub_save) |>
  270. magick::image_scale("670x")
  272. img <- c(cortical_img, sub_img)
  274. stacked_img <- image_append(img, stack = TRUE)
  276. image_write(stacked_img,
  277. path = paste0(save_weigh_folder,"/", dis, "_TPRS",
  278. weight,"_thr_",thr,".png"),
  279. format = "png")
  281. }
  282. }
  283. }

plot_brains.R at commit e045bc5, no license · at the source

Overview

Authors: Alessio Giacomel1,2,3, Timothy R. Powell4, Rodrigo R. R. Duarte4,5, Giovanna Nordio1, Steve C. R. Williams1, Federico Turkheimer1,6, Mattia Veronese1,7, Daniel Martins1,8,9, Danai Dima1,10
ORCID iDs: Alessio Giacomel, Timothy R. Powell, Rodrigo R. R. Duarte, Steve C. R. Williams, Federico Turkheimer, Mattia Veronese, Daniel Martins, Danai Dima
  1. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London,London, UK
  2. Department of Child and Adolescent Psychiatry, University Hospital, Goethe University,Frankfurt am Main, Germany
  3. Cooperative Brain Imaging Centre (COBIC), Goethe University,Frankfurt am Main, Germany
  4. Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London,London, UK
  5. Department of Medical & Molecular Genetics, Faculty of Life Sciences & Medicine, King’s College London,London, UK
  6. The Institute for Human and Synthetic Minds, King’s College London,London, UK
  7. Department of Information Engineering, University of Padova,Padova, Italy
  8. Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto,Porto, Portugal
  9. RISE-Health Network (Neurosciences thematic line), Faculty of Medicine, University of Porto,Porto, Portugal
  10. Department of Psychology and Neuroscience, School of Health and Medical Sciences, City St George’s, University of London,London, UK
Institutions: King's College London (United Kingdom); Goethe University Frankfurt (Germany); University of Padua (Italy); Universidade do Porto (Portugal); University of London (United Kingdom)
Journal: Molecular psychiatry, volume 31, issue 7, pages 3965-3977
Dates: received 25 June 2025; accepted 12 February 2026; published online 6 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41380-026-03497-4 · PMID 41792457 · PMCID PMC13268975 · OpenAlex W7134045537
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), depression (population), autism (population), schizophrenia / psychosis (population), ADHD (population), bipolar (population), cellular / molecular (subfield)
Methods: Statistics, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging
Keywords: Biomarkers, Psychiatric disorders, Neuroscience, Molecular biology
MeSH: Brain*, Mental Disorders*, Anorexia Nervosa, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder, Bipolar Disorder, Brain Mapping, Gene Expression Profiling, Genetic Risk Score, Genome-Wide Association Study, Humans, Major Depressive Disorder, Multifactorial Inheritance, Obsessive-Compulsive Disorder, Schizophrenia, Transcriptome (* major topic)
Topic: Tryptophan and brain disorders (Biological Psychiatry, Neuroscience), according to OpenAlex
Funding: National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

Psychiatric disorders are complex, polygenic conditions characterized by patterned structural brain alterations. Whether these changes reflect transcriptional dysregulation driven by genetic risk remains unclear. We introduce a novel imaging-transcriptomics framework that integrates transcriptome-wide association studies (TWAS) with brain transcriptomic atlases to predict macroscale structural brain abnormalities across seven disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), anorexia nervosa (AN), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia (SCZ). We generated disorder-related Gene Expression-based Disorder Associated Risk (GEDAR) maps and assessed their spatial correlation with observed brain alterations thereby establishing a structured approach to map polygenic transcriptional risk onto macroscale brain phenotypes. We found significant transcriptomic-anatomical correlations in MDD (cortical and subcortical), SCZ (subcortical), and ADHD (subcortical), indicating that regional transcriptional vulnerability might contribute to varying extents to the anatomical expression of genetic risk in these disorders. Pathway enrichment analysis on genetically predicted differentially expressed genes for those disorders where we found spatial correlations between GEDAR maps and observed structural changes revealed immune-related processes as dominant in MDD and SCZ, and neurodevelopmental pathways in ADHD. Importantly, spatial transcriptomic-anatomical alignment did not scale with between-disorder differences in heritability, pointing instead toward additional influences like developmental timing or environmental interactions. These findings underscore the potential and limitations of imaging transcriptomics as a framework for bridging the gap between genetic architecture and systems-level brain changes in psychiatric disorders.

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 5 matches between paragraphs and lines of code.

alegiac95/METAXCAN_TWAS_ANALYSIS

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: e045bc51961b0425f51bbdf54672d59222cfa8b9, 20 October 2025
Languages: R (9), Shell (7), Python (4)
Size: 1,076 files, 20 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (9 files), ggplot2 (6 files), NumPy (4 files), pandas (3 files), ComplexHeatmap (2 files), netneurotools (2 files), patchwork (2 files), SciPy (2 files), statsmodels (2 files), BrainSpace (1 file), circlize (1 file), clusterProfiler (1 file), ggseg (1 file), Matplotlib (1 file), neuromaps (1 file), NiBabel (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
21 files
At the source: github.com/alegiac95/METAXCAN_TWAS_ANALYSIS

Code availability

Code and reproducible workflows for GEDAR computation, spatial statistics (including spin permutations), enrichment analyses, and figure generation are available at https://github.com/alegiac95/METAXCAN_TWAS_ANALYSIS. A versioned snapshot will be archived with a DOI upon acceptance to ensure long-term accessibility.

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;
  • 20 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

All GWAS summary statistics used in this study were obtained from the publicly available resources of the Psychiatric Genomics Consortium (https://www.med.unc.edu/pgc). Neuroimaging data were derived from meta-analytic results published by the ENIGMA consortium (http://enigma.ini.usc.edu). Gene expression data were sourced from the Allen Human Brain Atlas (https://human.brain-map.org/). GTEx panels used for the TWAS analyses and methods originating this dataset are available at https://www.gtexportal.org/home/. Meta-analytical estimates of gene differential regulation in post-mortem brain samples and heritability estimates data are available from the corresponding sources cited within the main text and supplementary materials.

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

Versions

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

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 4 keywords, 16 MeSH terms, 1 funder, 92 references.

Cite

This paper

Giacomel, A., Powell, T. R., Duarte, R. R. R., Nordio, G., Williams, S. C. R., Turkheimer, F., Veronese, M., Martins, D., & Dima, D. (2026). Transcriptome-informed brain cartography of polygenic risk and association with brain structure in major psychiatric disorders. Molecular psychiatry, 31(7), 3965-3977. https://doi.org/10.1038/s41380-026-03497-4

BibTeX

@article{giacomel2026transcriptome,
author = {Giacomel, Alessio and Powell, Timothy R. and Duarte, Rodrigo R. R. and Nordio, Giovanna and Williams, Steve C. R. and Turkheimer, Federico and Veronese, Mattia and Martins, Daniel and Dima, Danai},
title = {{Transcriptome-informed brain cartography of polygenic risk and association with brain structure in major psychiatric disorders}},
journal = {Molecular psychiatry},
year = {2026},
month = mar,
volume = {31},
number = {7},
pages = {3965--3977},
publisher = {Springer Nature},
issn = {1359-4184},
doi = {10.1038/s41380-026-03497-4},
url = {https://doi.org/10.1038/s41380-026-03497-4},
pmid = {41792457},
pmcid = {PMC13268975}
}

RIS

TY - JOUR
AU - Giacomel, Alessio
AU - Powell, Timothy R.
AU - Duarte, Rodrigo R. R.
AU - Nordio, Giovanna
AU - Williams, Steve C. R.
AU - Turkheimer, Federico
AU - Veronese, Mattia
AU - Martins, Daniel
AU - Dima, Danai
TI - Transcriptome-informed brain cartography of polygenic risk and association with brain structure in major psychiatric disorders
T2 - Molecular psychiatry
J2 - Mol Psychiatry
PY - 2026
DA - 2026/03/06
VL - 31
IS - 7
SP - 3965
EP - 3977
SN - 1359-4184
PB - Springer Nature
DO - 10.1038/s41380-026-03497-4
UR - https://doi.org/10.1038/s41380-026-03497-4
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41380-026-03497-4",
"type": "article-journal",
"title": "Transcriptome-informed brain cartography of polygenic risk and association with brain structure in major psychiatric disorders",
"container-title": "Molecular psychiatry",
"author": [
{
"family": "Giacomel",
"given": "Alessio"
},
{
"family": "Powell",
"given": "Timothy R."
},
{
"family": "Duarte",
"given": "Rodrigo R. R."
},
{
"family": "Nordio",
"given": "Giovanna"
},
{
"family": "Williams",
"given": "Steve C. R."
},
{
"family": "Turkheimer",
"given": "Federico"
},
{
"family": "Veronese",
"given": "Mattia"
},
{
"family": "Martins",
"given": "Daniel"
},
{
"family": "Dima",
"given": "Danai"
}
],
"container-title-short": "Mol Psychiatry",
"volume": "31",
"issue": "7",
"page": "3965-3977",
"DOI": "10.1038/s41380-026-03497-4",
"PMID": "41792457",
"PMCID": "PMC13268975",
"ISSN": "1359-4184",
"publisher": "Springer Nature",
"URL": "https://doi.org/10.1038/s41380-026-03497-4",
"language": "en",
"issued": {
"date-parts": [
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2026,
3,
6
]
]
}
}

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In common: ComplexHeatmap, pheatmap, patchwork, 6 other tools, genetics / omics, cellular / molecular, 6 references
[10] doi:10.1038/s41467-026-71719-y [code]
Brain functional-structural gradient coupling reflects development, behavior and genetic influences.
Journal: Nature communications
In common: BrainSpace, patchwork, NiBabel, 6 other tools, 7 references

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