DYRK1A and Parkinson's disease, facts and hypotheses.

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Paper

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The authors' code

R · 16 lines · 553 B · GPL-3.0

output$aboutOutput <- renderUI({
#get the about file from google drive and then download it
aboutFile <- drive_get(id = "1uuE9SF805HDqcCTUQCZhGJZ2IjOXlMfVaAb6B5X_2Qo")
data <- drive_download(aboutFile, path = "about.html", overwrite = T, verbose = F)
#read the lines from the file and format them
filelines <- readLines("about.html",warn=FALSE)
#insert "target='_blank'" attribut to all hyperlinks in the about html so that they open in a new tab
blankify <- gsub("<a","<a target='_blank'",filelines)
HTML(blankify)
})

About.R at commit 050e1a5, under GPL-3.0 · at the source

Overview

Authors: Laurent Meijer¹, Mattias F Lindberg¹, Gaëlle Hogrel¹, Bernard Khor²

ORCID iDs: Gaëlle Hogrel

Perha Pharmaceuticals, Presqu’île de Perharidy, 29680 Roscoff, France
Benaroya Research Institute, Seattle, WA 98101, USA

Institutions: Benaroya Research Institute

Journal: Neurobiology of disease, volume 225, article 107379

Dates: received 13 March 2026; accepted 31 March 2026; published online July 2026; in print July 2026

Type: Review · Language: English

License: CC BY-NC

Identifiers: DOI 10.1016/j.nbd.2026.107379 · PMID 41991085 · PMCID PMC13229842 · OpenAlex W7154279156

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), Alzheimer's / dementia (population), Parkinson's (population), cellular / molecular (subfield)

Methods: Preprocessing, Spectral & time-frequency, Statistics, Evoked potentials, Connectivity, Complexity, Physiology & signal measures, Machine learning

Keywords: DYRK1A, Parkinson's disease, Dementia with Lewy bodies, kinase inhibitors

MeSH: Parkinson Disease*, Protein Serine-Threonine Kinases*, Protein-Tyrosine Kinases*, Animals, Dyrk Kinases, Humans (* major topic)

Topic: Down syndrome and intellectual disability research (Public Health, Environmental and Occupational Health, Medicine), according to OpenAlex

Funding: European Innovation Council (190138295); Horizon 2020 Framework Programme (848077, GO-DS21); Fondation Jérôme Lejeune; European Commission; European Research Council

Citations: cited by 1 paper (Europe PMC); 1,380 references in the paper

Abstract

The dual-specificity, tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is intensively studied because of its implication in numerous human diseases (Down syndrome, Alzheimer’s disease, type 2 diabetes, myocardial infarction, various cancers and leukaemia, etc.). Several GWAS studies have identified DYRK1A as a risk factor for Parkinson’s disease (PD). DYRK1A indeed phosphorylates at least 20 proteins clearly involved in PD: AMPH, CASP9, DYN1, FOXO, GSK3B, MAP1B, MAPT, MEF2D, NFAT, TP53, PRKN, PLK2, RABs, RCAN1, SEPT4, SNCA, STAT3, SYNJ1, TOM70, WASL. Several other proteins involved in PD interact with DYRK1A: calpains, DSCAM, REST/NRSF, 14-3-3. DYRK1A is involved in axonal transport, neural stem cells proliferation and differentiation, and neuroinflammation. A few DYRK1A inhibitors have been tested on PD models, generally showing protective effects.

The overall picture provided by this comprehensive review on the links between DYRK1A and PD advocates for more fundamental studies to understand how DYRK1A participates to the onset and development of PD and dementia with Lewy bodies (DLB), two closely related disorders. It also encourages the evaluation of well-characterized pharmacological modulators of DYRK1A as therapeutic approaches to various aspects of PD and DLB.

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.

neurogenetics/GWAS_locus_browser

License: GPL-3.0

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: 050e1a57138fe72bc39ae14e68fbc9e48b3440ba, 6 May 2021

Languages: R (33), Jupyter (19), Python (6), JavaScript (5)

Size: 5,838 files, 63 scripts

Software Heritage: not archived

Found in: the text, “Genome-Wide Association Studies (GWAS) - polymor”

Holds: README, license file, 17 notebooks

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: pandas (22 files), data.table (16 files), tidyverse (15 files), NumPy (14 files), Plotly (3 files), ggplot2 (2 files), Matplotlib (2 files), seaborn (2 files), SciPy (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

65 files

About.R, R, 16 lines
DataProcessing/burden/ReadBurdenValuesScript.R, R, 123 lines
DataProcessing/codingvars/gen_coding_vars_ldlink.ipynb, Jupyter, 269 lines
DataProcessing/codingvars/gen_coding_vars_plink.ipynb, Jupyter, 375 lines
DataProcessing/codingvars/getRiskSNPProxies.R, R, 48 lines
DataProcessing/coexpression/coexpression_data.ipynb, Jupyter, 131 lines
DataProcessing/constraint/ReadConstraintValuesScript.py, Python, 54 lines
DataProcessing/diseasegene/DiseaseGeneEvidenceScript.py, Python, 70 lines
DataProcessing/diseasegene/disease_gene_file.ipynb, Jupyter, 151 lines
DataProcessing/evidence/merge_evidence.R, R, 36 lines
DataProcessing/expression/ReadExpressionValuesScript.R, R, 133 lines
DataProcessing/expression/exp_violin_plots.R, R, 94 lines
DataProcessing/expression/generateSingleCellPlots.R, R, 59 lines
DataProcessing/finemapping/.ipynb_checkpoints/finemapping_processing-checkpoint.ipynb, Jupyter, 418 lines
DataProcessing/finemapping/.ipynb_checkpoints/pd_finemap_summary_stats-checkpoint.ipynb, Jupyter, 1,187 lines
DataProcessing/finemapping/finemapping_processing.ipynb, Jupyter, 418 lines
DataProcessing/finemapping/pd_finemap_summary_stats.ipynb, Jupyter, 1,187 lines
DataProcessing/genelist/Harmonize_Summary_Statistics.ipynb, Jupyter, 242 lines
DataProcessing/genelist/Loci_Gene_List.ipynb, Jupyter, 214 lines
DataProcessing/locuszoom/make_interactive_stats.ipynb, Jupyter, 113 lines
DataProcessing/locuszoom/static_locus_zoom_plots.ipynb, Jupyter, 100 lines
DataProcessing/meta5nom/.ipynb_checkpoints/Meta5NominatedEvidenceScript-checkpoint.py, Python, 31 lines
DataProcessing/meta5nom/Meta5NominatedEvidenceScript.py, Python, 31 lines
DataProcessing/othersummarystats/collect_other_summary_stats.ipynb, Jupyter, 773 lines
DataProcessing/othersummarystats/risk_variant_pop_freq.ipynb, Jupyter, 243 lines
DataProcessing/pdgenes/.ipynb_checkpoints/PDGeneEvidenceScript-checkpoint.py, Python, 39 lines
DataProcessing/pdgenes/PDGeneEvidenceScript.py, Python, 39 lines
DataProcessing/phenovars/get_pheno_vars_file.ipynb, Jupyter, 243 lines
DataProcessing/pubmed/GeneCardsScrape.R, R, 70 lines
DataProcessing/pubmed/GenerateWordCloudPlots.R, R, 69 lines
DataProcessing/pubmed/pubmedHitData.R, R, 58 lines
DataProcessing/qtl/QTL_Generate_Data.ipynb, Jupyter, 366 lines
DataProcessing/qtl/QTL_Generate_Data_Cortex.ipynb, Jupyter, 225 lines
DataProcessing/qtl/QTL_Generate_Data_Psychencode.ipynb, Jupyter, 450 lines
DataProcessing/qtl/QTL_Proxy_SNPs_And_Plot_Info.ipynb, Jupyter, 760 lines
DataProcessing/qtl/getRiskSNPProxies.R, R, 20 lines
DataProcessing/qtl/plot_qtl_locuscompare.R, R, 191 lines
DataProcessing/qtl/plot_qtl_locuscompare_cortex.R, R, 196 lines
best_candidate_section.R, R, 41 lines
burden_section.R, R, 80 lines
coding_vars_section.R, R, 84 lines
coexpression_section.R, R, 79 lines
constraint_section.R, R, 80 lines
disease_gene_section.R, R, 80 lines
evidence_section.R, R, 226 lines
expression_section.R, R, 135 lines
finemap_section.R, R, 51 lines
global.R, R, 65 lines
literature_section.R, R, 160 lines
locus_zoom_section.R, R, 66 lines
pathways_section.R, R, 138 lines
pheno_vars_section.R, R, 49 lines
qtl_section.R, R, 430 lines
server.R, R, 87 lines
sidebar.R, R, 257 lines
summary_statistics_section.R, R, 282 lines
tutorial.R, R, 459 lines
ui.R, R, 174 lines
www/javascript/call_locus_zoom.js, JavaScript, 38 lines
www/javascript/google-analytics.js, JavaScript, 6 lines
www/javascript/jquery-ui.min-draggable+position.js, JavaScript, 7 lines
www/javascript/navigate.js, JavaScript, 18 lines
www/javascript/tutorial.js, JavaScript, 149 lines
LICENSE, License, 674 lines
README.md, Text, 113 lines

At the source: github.com/neurogenetics/GWAS_locus_browser

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.

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1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
63 scripts, each with its path and the digest of its content;
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Data

Datasets cited

geo:GPL1261, at NCBI GEO; found in the text, “DYRK1A expression in body, brain, substantia…”
portal.brain-map.org/gene-expression, at Allen Brain Map; found in the text, “DYRK1A expression in body, brain, substantia…”

Data availability

No data was used for the research described in the article.

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

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 4 keywords, 6 MeSH terms, 5 funders, 1371 references.

Cite

This paper

Meijer, L., Lindberg, M. F., Hogrel, G., & Khor, B. (2026). DYRK1A and Parkinson's disease, facts and hypotheses. Neurobiology of disease, 225, 107379. https://doi.org/10.1016/j.nbd.2026.107379

BibTeX

@article{meijer2026dyrk1a, author = {Meijer, Laurent and Lindberg, Mattias F and Hogrel, Gaëlle and Khor, Bernard}, title = {{DYRK1A and Parkinson's disease, facts and hypotheses}}, journal = {Neurobiology of disease}, year = {2026}, month = apr, volume = {225}, pages = {107379}, publisher = {Elsevier BV}, issn = {0969-9961}, doi = {10.1016/j.nbd.2026.107379}, url = {https://doi.org/10.1016/j.nbd.2026.107379}, pmid = {41991085}, pmcid = {PMC13229842} }

RIS

TY - JOUR AU - Meijer, Laurent AU - Lindberg, Mattias F AU - Hogrel, Gaëlle AU - Khor, Bernard TI - DYRK1A and Parkinson's disease, facts and hypotheses T2 - Neurobiology of disease J2 - Neurobiol Dis PY - 2026 DA - 2026/04/14 VL - 225 SP - 107379 SN - 0969-9961 PB - Elsevier BV DO - 10.1016/j.nbd.2026.107379 UR - https://doi.org/10.1016/j.nbd.2026.107379 LA - en ER -

CSL-JSON

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