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Genomic insights into stroke recovery: cross-phenotype associations.

Code ↔ Paper

3 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 3 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Cross-phenotypic results › Cross-phenotypic SNPs of interest ↔ UpSet_Figure.R, the whole file · a weak match · score 0.85 · v3_PHQ8, v2_Delta_Grip, gene symbols, v4_PHQ8, v4_PTSD, v4_tMoCA
  2. [2] § Results › Cross-phenotypic results › Cross-phenotypic SNPs of interest ↔ GWAS_figures_and_tables.R, lines 255–326 · score 0.84 · v2_delta_grip, gene symbols, v4_PHQ8, v4_PTSD, v4_tMoCA, v3 phq8
  3. [3] § Results › Genome-wide association studies across phenotypes ↔ DeltaGrip_Investigation.R, the whole file · a weak match · score 0.52 · genomic inflation, delta grip, bp, MAF, Manhattan, QQ

Paper

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

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The authors' code

R · 47 lines · 1.8 KB · no license · 1 match

  1. library(tidyverse)
  2. library(data.table)
  3. library(ComplexUpset)
  4. library(patchwork)
  5. library(colorspace)
  7. my_pal <- qualitative_hcl(11, palette = "Dark 3")
  10. dt.cp_all <- read_csv("~/../Dropbox/Manuscripts/STRONG/STRONG_GWAS_P2PNetwork/Brain Submission/Revision/supplement_table_1_revised.csv")
  12. dt.ps <- dt.cp_all |>
  13. rename("v2_Delta_Grip" = `P_v2-v1_grip_ratio`,
  14. "v2_mRS_PoorOutcome"= P_v2_mrs_d2,
  15. "v2_SIS_ADL" = P_v2_sid_adl,
  16. "v2_PHQ8"= P_v2_phq8,
  17. "v3_PHQ8" = P_v3_phq8,
  18. "v3_PTSD"= P_v3_ptsd,
  19. "v4_PHQ8" = P_v4_phq8,
  20. "v4_PTSD" = P_v4_ptsd,
  21. "v4_tMoCA"= P_v4_tMoCa)
  23. phenotypes <- grep("^v\\d+_", colnames(dt.ps), value = T)
  26. dt.ps <- dt.ps |> mutate(across(matches("^v\\d+_"), function(x) x<5e-5))
  28. dt.ps.sub <- dt.ps |> filter(v2_Delta_Grip==T | v4_tMoCA==T)
  31. queries <- list(upset_query(set = "v4_tMoCA", fill = "purple"),
  32. upset_query(set = "v2_Delta_Grip", fill = "red"))
  34. p1 <- upset(dt.ps.sub, intersect = phenotypes, name = "Phenotypes", wrap = T, sort_sets = F,
  35. set_sizes = upset_set_size(),
  36. queries = queries,
  37. base_annotations = list(
  38. "Intersection size" = intersection_size(counts = T, mapping = aes(fill = Gene_Symbol)) +
  39. scale_y_continuous(n.breaks = 6) +
  40. labs(fill = "Gene", y = "Cross-Phenotype SNP (n)") +
  41. scale_fill_manual(values = my_pal))) +
  42. ggtitle(label = expression("Cross-Phenotype SNPs shared by 2+ uncorrelated phenotypes")) +
  43. theme(plot.title = element_text(size = 14))
  46. ggsave(p1, filename = "Cross-Phenotype-Scripts/upset_figure_gene_final.png", device = "png",
  47. dpi = 300, units = "mm", width = 160, height = 140)

UpSet_Figure.R at commit a0b528b, no license · at the source

Overview

Authors: Chad Aldridge1, Robynne Braun2, Livia Parodi3, Keith Lohse4, Matthew A Edwardson5, John W Cole2,6, Arne G Lindgren7, Fang-Chi Hsu8, Keith Keene9, Bradford Worrall1,10, Jonathan Rosand3, E Alison Holman11, Steven C Cramer12,13
ORCID iDs: Chad Aldridge, Keith Lohse
13 affiliations
  1. Department of Neurology, University of Virginia, Charlottesville, VA 22908, USA
  2. Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
  3. Department of Neurology, Center for Genomic Medicine, McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA 02114, USA
  4. Program in Physical Therapy, Department of Neurology, Washington University, St. Louis, MO 63108, USA
  5. Department of Neurology and Rehabilitation Medicine, Georgetown University, Washington, DC 20007, USA
  6. Department of Neurology, Baltimore Veterans Affairs Medical Center, Baltimore, MD 21201, USA
  7. Department of Neurology, Skåne University Hospital, Lund 29189, Sweden
  8. Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA
  9. Department of Biology, East Carolina University, Greenville, NC 27858, USA
  10. Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
  11. Sue & Bill Gross School of Nursing and Department of Psychological Science, University of California, Irvine, Irvine, CA 92697, USA
  12. Department of Neurology, University of California, Los Angeles, CA 90095-1769, USA
  13. California Rehabilitation Institute, Los Angeles, CA 90067, USA
Institutions: University of Virginia (United States); University of Maryland, Baltimore (United States); Massachusetts General Hospital (United States); Washington University in St. Louis (United States); Georgetown University (United States); Baltimore VA Medical Center (United States); Skåne University Hospital (Sweden); Wake Forest University (United States); East Carolina University (United States); University of California, Irvine (United States); University of California, Los Angeles (United States)
Journal: Brain communications, volume 8, issue 5, article fcag322
Dates: received 5 August 2025; accepted 12 August 2026; published online 26 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag322 · PMID 42695012 · PMCID PMC13540738 · OpenAlex W7204225448
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), stroke (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: GWAS, neuroplasticity, genomics, stroke, recovery
Topic: Stroke Rehabilitation and Recovery (Rehabilitation, Medicine), according to OpenAlex
Funding: NIH (R01NR015591, R01 NS114045); NS/NINDS NIH HHS; American Heart Association (23CSA1052295, VA-Merit BX004672-04); US Dept. of Veterans Affairs—Biomedical Laboratory Research and Development
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

Stroke is a major cause of long-term disability with variable recovery. While clinical factors such as initial severity play a role, genetic factors are increasingly recognized as important contributors to stroke recovery. Genotype studies are generally focused on a single post-stroke behavioural domain, but some genes might relate to broad mechanisms of plasticity. This study therefore aimed to identify cross-phenotypic genetic variants associated across two or more stroke recovery domains. DNA from Stroke, Stress, Rehabilitation, and Genetics study participants was genotyped, resulting in 9 814 610 variants. In order to examine cross-phenotypic results, we first conducted genome-wide association studies on the six recovery domains: motor (grip force), cognition (Telephone Montreal Cognitive Assessment), depression (Patient Health Questionnaire-8), stress (Primary Care Post-Traumatic Stress Disorder Screen), functional status (Stroke Impact Scale-Activities of Daily Living), and disability (modified Rankin Scale 0–2 versus 3–6), some of which were tested longitudinally, yielding nine phenotypes. Models were adjusted for age, sex, initial severity (NIH Stroke Scale score), and ancestry. Cross-phenotype associations were identified by evaluating single nucleotide polymorphisms (SNPs) associated (P < 5e-5) with multiple phenotypes. To determine how these genetic variants may relate to biological mechanisms of recovery, we conducted gene enrichment analyses. Participants (n = 565, 59% male) had mild-moderate initial stroke severity (median acute NIH Stroke Scale score = 4). After accounting for the correlation structure among the nine phenotypes, we observed 319 cross-phenotypic SNPs, 3.45 times the expected number. Five of the cross-phenotypic SNPs were linked to genes relevant to neural development, function and plasticity, e.g. ERICH1 (rs11778883-C), FOX3 (rs55726768-G), LIFR-AS1 (rs76401391-T), RPS6KA2 (rs113518460-C) and TUBGCP2 (rs147150392-C), as were enrichments in RAB5–EEA1, CTNNA1–CTNNB1, CIN85–SH3GL2 and ELMO1–DOCK2 complexes. Multiple gene enrichments were found, e.g. Stroke Impact Scale-Activities of Daily Living and Patient Health Questionnaire 8 at 3 months were enriched for CREB phosphorylation, which is important for long-term potentiation. We identified cross-phenotypic SNPs associated with multiple behavioural domains of stroke recovery. Some of these genes encode, or regulate, druggable proteins. These genetic factors are not well captured by clinical or neuroimaging assessments and so provide a unique window into stroke recovery. These findings, if validated, suggest that some genes may be broadly important to stroke recovery.

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 3 matches between paragraphs and lines of code.

CMAldridge/Cross-Phenotype-GWAS

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a0b528b01f0e8b2e1c13650b6270918ac75dc147, 3 August 2026
Languages: R (8)
Size: 9 files, 8 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (7 files), tidyverse (7 files), patchwork (2 files), ComplexHeatmap (1 file), ggplot2 (1 file), psych (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/CMAldridge/Cross-Phenotype-GWAS

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

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;
  • 8 scripts, each with its path and the digest of its content;
  • 3 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

The GWAS summary statistics for the nine phenotypes can be found at the Cerebrovascular Disease Knowledge Portal (https://cd.hugeamp.org/) where they can be downloaded. Code generated for this study can be found at the GitHub repository https://github.com/CMAldridge/Cross-Phenotype-GWAS upon publication. STRONG clinical and genotype individual-level data will be released upon reasonable request to the corresponding author, S.C.C. In the future, the clinical and genotyped data will be available at the NIH’s dbGaP platform.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 4 funders, 64 references.

Cite

This paper

Aldridge, C., Braun, R., Parodi, L., Lohse, K., Edwardson, M. A., Cole, J. W., Lindgren, A. G., Hsu, F.-C., Keene, K., Worrall, B., Rosand, J., Holman, E. A., & Cramer, S. C. (2026). Genomic insights into stroke recovery: cross-phenotype associations. Brain communications, 8(5), fcag322. https://doi.org/10.1093/braincomms/fcag322

BibTeX

@article{aldridge2026genomic,
author = {Aldridge, Chad and Braun, Robynne and Parodi, Livia and Lohse, Keith and Edwardson, Matthew A and Cole, John W and Lindgren, Arne G and Hsu, Fang-Chi and Keene, Keith and Worrall, Bradford and Rosand, Jonathan and Holman, E Alison and Cramer, Steven C},
title = {{Genomic insights into stroke recovery: cross-phenotype associations}},
journal = {Brain communications},
year = {2026},
month = aug,
volume = {8},
number = {5},
pages = {fcag322},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag322},
url = {https://doi.org/10.1093/braincomms/fcag322},
pmid = {42695012},
pmcid = {PMC13540738}
}

RIS

TY - JOUR
AU - Aldridge, Chad
AU - Braun, Robynne
AU - Parodi, Livia
AU - Lohse, Keith
AU - Edwardson, Matthew A
AU - Cole, John W
AU - Lindgren, Arne G
AU - Hsu, Fang-Chi
AU - Keene, Keith
AU - Worrall, Bradford
AU - Rosand, Jonathan
AU - Holman, E Alison
AU - Cramer, Steven C
TI - Genomic insights into stroke recovery: cross-phenotype associations
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/08/26
VL - 8
IS - 5
SP - fcag322
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag322
UR - https://doi.org/10.1093/braincomms/fcag322
LA - en
ER -

CSL-JSON

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