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Pathology and genetics in a global cohort of Parkinsonian Disorders

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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.

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  1. [1] § Methods › Statistical analyses ↔ Analyses/04_Path_Stages.R, lines 111 to 180 · score 0.88 · proportional odds logistic, mutation status, PD risk, Cox, regression, model · Report a mismatch
  2. [2] § Results › Genetic variation ↔ Analyses/04_Path_Stages.R, lines 111 to 180 · score 0.67 · GBA1 PD risk, GD causing, hazard, matched, survival, CI · Report a mismatch
  3. [3] § Methods › Neuropathological assessment and Genetic characterization ↔ Analyses/04_Path_Stages.R, lines 1 to 59 · score 0.65 · McKeith, LBD subtypes, brainstem, limbic, neocortical, Genetic · Report a mismatch
  4. [4] § Methods › Statistical analyses ↔ Analyses/05_Ancestry.R, lines 45 to 95 · score 0.57 · Chi square, pairwise, Fisher, death, age, mutation · Report a mismatch
  5. [5] § Methods › Statistical analyses ↔ Analyses/05_Ancestry.R, lines 45 to 95 · score 0.50 · Kruskal Wallis, Bonferroni, pairwise, ancestry · Report a mismatch

Paper

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The authors' code

R · 180 lines · 5.6 KB · MIT · 3 matches

  1. # 4. Pathological Stages
  2. # Generates visualizations and analyzes associations between pathological staging and genetic variants
  3. # Project: Clinical features, genetics, and pathology in a large series of movement disorder cases: a retrospective multi-ancestry brain bank cohort study
  5. # Last updated in October 2025
  7. ########### Stacked bar chart ###########
  8. # LBD SUBTYPE
  9. # Load libraries
  10. library(dplyr)
  11. library(stringr)
  12. library(ggplot2)
  13. library(RColorBrewer)
  14. library(scales)
  16. # Clean data
  17. df <- df %>%
  18. filter(
  19. !is.na(LBD_Subtype),
  20. !LBD_Subtype %in% c("No information available", "Not applicable", "Unclassifiable")
  21. ) %>%
  22. count(GENE_TYPE, LBD_Subtype) %>%
  23. dplyr::rename(Count = n) %>%
  24. group_by(GENE_TYPE) %>%
  25. mutate(Proportion = Count / sum(Count)) %>%
  26. ungroup()
  28. df$LBD_Subtype <- factor(df$LBD_Subtype, levels = c("None", "Amygdala", "Brainstem", "Limbic", "Neocortical"))
  30. # Define custom colors for McKeith stages
  31. custom_colors <- c(
  32. "None" = "#87CEEB", # sky blue
  33. "Amygdala" = "#4682B4", # steel blue
  34. "Brainstem" = "#FFE4E1", # misty rose
  35. "Limbic" = "#FFC6B3",
  36. "Neocortical" = "#FF7F50" # coral
  37. )
  39. # Plot
  40. p <- ggplot(df, aes(x = GENE_TYPE, y = Proportion, fill = LBD_Subtype)) +
  41. geom_bar(stat = "identity") +
  42. geom_text(aes(label = Percent_Label),
  43. position = position_stack(vjust = 0.5),
  44. size = 3, color = "black") +
  45. scale_fill_manual(values = custom_colors, name = "LBD Subtype") +
  46. labs(
  47. x = "Mutation Group",
  48. y = "Proportion of Cases",
  49. title = "Distribution of LBD subtype by Mutation Group Across all Pathological Diagnoses"
  50. ) +
  51. theme_minimal() +
  52. theme(
  53. panel.grid.major = element_blank(),
  54. panel.grid.minor = element_blank(),
  55. panel.background = element_blank(),
  56. plot.title = element_text(face = "bold"),
  57. axis.title.x = element_text(face = "bold"),
  58. axis.title.y = element_text(face = "bold")
  59. )
  62. # BRAAK NFT
  63. # Group into Braak NFT categories including stage 0
  64. df <- df %>%
  65. filter(!is.na(BRAAK_NFT)) %>%
  66. count(GENE_TYPE, BRAAK_NFT, name = "Count") %>%
  67. group_by(GENE_TYPE) %>%
  68. mutate(Proportion = Count / sum(Count)) %>%
  69. ungroup()
  71. # Set stage order: from no pathology to severe
  72. df$BRAAK_NFT <- factor(df$BRAAK_NFT,
  73. levels = c("0", "I–II", "III–IV", "V–VI"))
  75. # Add percent labels
  76. df <- df %>%
  77. mutate(Percent_Label = percent(Proportion, accuracy = 1))
  79. # Define custom colors
  80. braak_colors <- c(
  81. "0" = "#87CEEB", # pale blue (no pathology)
  82. "I–II" = "#FFE4E1",
  83. "III–IV" = "#FFC6B3",
  84. "V–VI" = "#FF7F50"
  85. )
  87. # Plot
  88. p_braak <- ggplot(df, aes(x = GENE_TYPE, y = Proportion, fill = BRAAK_NFT)) +
  89. geom_bar(stat = "identity") +
  90. geom_text(aes(label = Percent_Label),
  91. position = position_stack(vjust = 0.5),
  92. size = 3, color = "black") +
  93. scale_fill_manual(values = braak_colors, name = "Braak NFT Stage") +
  94. labs(
  95. x = "Mutation Group",
  96. y = "Proportion of Cases",
  97. title = "Distribution of Braak NFT Stages by Mutation in Individuals with Lewy Body Diseases"
  98. ) +
  99. theme_minimal() +
  100. theme(
  101. panel.grid.major = element_blank(),
  102. panel.grid.minor = element_blank(),
  103. panel.background = element_blank(),
  104. plot.title = element_text(face = "bold"),
  105. axis.title.x = element_text(face = "bold"),
  106. axis.title.y = element_text(face = "bold")
  107. )
  109. p_braak
  111. ########### Regressions ###########
  112. library(MASS)
  113. # Set reference
  114. df$Mutation_status <- relevel(as.factor(df$Mutation_status), ref = "NO MUTATION")
  116. # Fit ordinal logistic regression model using proportional odds logistic regression
  117. model <- polr(as.factor(Mckeith_coded) ~ Mutation_status + sex + age_at_death + DX1,
  118. data = df, method = "logistic")
  119. # The outcome Mckeith_coded is treated as an ordinal factor (e.g., stage 0 to 4).
  121. # Extract the model summary
  122. model_summary <- summary(model)
  124. ########### SURVIVAL ANALYSIS ###########
  125. library(survival)
  126. library(survminer)
  127. library(broom)
  129. # Make GENE_TYPE a factor with correct levels matching the palette
  130. df$GENE_TYPE <- factor(df$GENE_TYPE, levels = c("GBA1 PD RISK", "GBA1 GD CAUSING","LRRK2", "NO MUTATION"))
  132. # Create event variable
  133. df$event <- 1
  135. # Create survival object
  136. surv_obj <- Surv(time = df$DX1, event = df$event)
  137. surv_obj
  138. # Fit Kaplan-Meier model
  139. km_fit <- survfit(surv_obj ~ GENE_TYPE + AGE_ONSET, data = df)
  141. # Convert to dataframe using broom::tidy
  142. km_df <- tidy(km_fit)
  144. # Custom colors (named to match factor levels exactly)
  145. custom_colors <- c("GBA1 PD RISK" = "#FFA07A", "GBA1 GD CAUSING" = "#E5673C", "LRRK2" = "#FFE4E1","NO MUTATION" = "#4682B4")
  146. df$GENE_TYPE <- factor(df$GENE_TYPE, levels = names(custom_colors))
  147. surv_obj <- Surv(time = df$DX1, event = df$event)
  148. km_fit <- survfit(surv_obj ~ GENE_TYPE, data = df)
  149. km_df <- tidy(km_fit)
  151. # Clean strata and match colors
  152. km_df$GENE_TYPE <- gsub("GENE_TYPE=", "", km_df$strata)
  153. km_df$GENE_TYPE <- trimws(km_df$GENE_TYPE)
  154. km_df$GENE_TYPE <- factor(km_df$GENE_TYPE, levels = names(custom_colors))
  156. km_plot <- ggplot(km_df, aes(x = time, y = estimate, color = GENE_TYPE)) +
  157. geom_step(size = 1) +
  158. scale_color_manual(values = custom_colors) +
  159. xlim(0, 40) +
  160. labs(
  161. x = "Years since onset",
  162. y = "Survival Probability",
  163. color = "Gene Mutation",
  164. title = "Kaplan-Meier Survival by Mutation Status"
  165. ) +
  166. theme_minimal()
  168. km_plot
  170. # Set "NO MUTATION" as reference category
  171. df$GENE_TYPE <- relevel(as.factor(df$GENE_TYPE), ref = "NO MUTATION")
  173. # Kaplan-Meier log-rank test
  174. survdiff(Surv(DX1, event) ~ GENE_TYPE, data = df)
  176. # Cox proportional hazards model (adjusted for AGE_ONSET)
  177. cox_fit <- coxph(Surv(DX1, event) ~ GENE_TYPE + AGE_ONSET, data = df)
  179. # Summary output with HRs, 95% CI, p-values
  180. summary(cox_fit)

04_Path_Stages.R at commit f755f53, under MIT · at the source

Overview

Authors: Lesley Y. Wu1, Tessa du Toit1, Tatiana Georgiades1, Eleanor J. Stafford1, Kristin Levine2, Zih-Hua Fang3, Simona Jasaityte1, Ana-Luisa Gil Martinez1, Patrick Cullinane4, Eduardo De Pablo Fernandez4, Cornelis Blauwendraat5, Andrew B. Singleton5, Sonja W. Scholz6,7, Bryan J. Traynor7,8, Nicholas Wood1,9, John Hardy10,9, Patrick Chinnery11, Henry Houlden1, Richard Cain12, Claire Troakes13
and 20 other authorsViorica Chelban1, Geidy E. Serrano14, Djordje Gveric15, Catriona McLean16, Seth Love12, Andrew King13, Andrew C. Robinson17, Federico Roncaroli17, Claire Shepherd18,19, Glenda Halliday18,20, Laura Parkkinen21, Christopher M Morris22, Colin Smith23, Thomas G. Beach14, Steve Gentleman15, Thomas T. Warner4, Tammaryn Lashley10, Zane Jaunmuktane1,4,9, Raquel Real1,9, Huw R Morris1,9
23 affiliations
  1. Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
  2. Data Tecnica International, Washington, DC, USA
  3. The German Center for Neurodegenerative Diseases, Tübingen, Germany
  4. Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
  5. Global Parkinson’s Genetics Program, Chevy Chase, MD, USA
  6. Neurodegenerative Diseases Research Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
  7. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
  8. Neuromuscular Diseases Research Section, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
  9. Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
  10. Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
  11. Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
  12. University of Bristol, Bristol, Horfield, United Kingdom
  13. London Neurodegenerative Diseases Brain Bank, King’s College London, London, UK
  14. Department of Pathology, Banner Sun Health Research Institute, Sun City, AZ, USA
  15. Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
  16. Victorian Brain Bank, Victoria, Australia
  17. Geoffrey Jefferson Brain Research Centre, Division of Neuroscience, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
  18. Neuroscience Research Australia, Randwick, Sydney, Australia
  19. Department of Biomedical Science, Faculty of Health and Medicine, University of New South Wales, Sydney, Australia
  20. School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
  21. Department of Neuropathology and The Queen’s College, University of Oxford, Oxford, UK
  22. Newcastle Brain Tissue Resource, NIHR Newcastle Biomedical Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK
  23. Academic Department of Neuropathology, Institute of Neurological and Cardiovascular Research, University of Edinburgh, Edinburgh, UK
Institutions: UCL Queen Square Institute of Neurology (United Kingdom); Data Tecnica International (United States) (United States); German Center for Neurodegenerative Diseases (Germany); National Institute of Neurological Disorders and Stroke (United States); National Institutes of Health (United States); Johns Hopkins University (United States); National Institute on Aging (United States); Aligning Science Across Parkinson's (United States); University of Cambridge (United Kingdom); University of Bristol (United Kingdom); King's College London (United Kingdom); Banner Sun Health Research Institute (United States); Imperial College London (United Kingdom); University of Manchester (United Kingdom); Neuroscience Research Australia (Australia); UNSW Sydney (Australia); The University of Sydney (Australia); University of Oxford (United Kingdom); NIHR Newcastle Biomedical Research Centre (United Kingdom); Newcastle University (United Kingdom); University of Edinburgh (United Kingdom)
Journal: medRxiv (preprint)
Dates: published online 26 March 2026
Type: Preprint · Language: English
License: CC BY
Identifiers: DOI 10.64898/2026.03.23.26348322 · OpenAlex W7140407606
Open access: green, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), Alzheimer's / dementia (population), Parkinson's (population), clinical / translational (subfield)
Methods: Statistics, Machine learning, Connectivity
Topic: Parkinson's Disease Mechanisms and Treatments (Clinical Neurology, Medicine), according to OpenAlex
Funding: U.S. Department of Health and Human Services; Michael J. Fox Foundation for Parkinson's Research (U24 NS072026, 05-901, ASAP-000478, 211002); Arizona Biomedical Research Commission (U24 NS072026, 05-901, 211002, 05–901, P30 AG019610); UK Research and Innovation; National Institute for Health and Care Research; National Institutes of Health (U24NS07-2026, 05-901, 211002, ZIA NS003154, P30AG072980, P30AG019610, AG019610, NS072026); Medical Research Council (MR/T018569/1); NIHR Newcastle Biomedical Research Centre
Citations: not cited yet (Europe PMC); 42 references in the paper

Abstract

Importance: Accurate diagnosis of neurodegenerative movement disorders is challenging because of a lack of in vivo biomarkers, overlapping clinical features and a delay in the emergence of pathognomonic features.

Objective: To evaluate clinicopathological correlation, diagnostic accuracy, genetic association with pathology, and ancestry-related differences in a multi-ancestry brain bank cohort.

Design: Multicentre retrospective autopsy cohort study on donors enrolled between 1985 - 2024.

Setting: 11 academic brain banks in the UK, US and Australia

Participants: Brain donors identified from participating brain banks with available brain tissue and a clinical diagnosis of Parkinson’s disease, Parkinson’s disease dementia, dementia with Lewy bodies, progressive supranuclear palsy, corticobasal syndrome, multiple system atrophy, or neurologically normal controls.

Exposure: Genetic variant carrier status and clinical diagnostic category.

Main outcome: Clinical diagnostic accuracy; Lewy body and Alzheimer’s disease pathology burden; survival; association with genetic variants and genetically inferred ancestry.

Results: We studied 3,353 brain donors (1281 [38.2%] female, mean [SD] age at death, 76.8 [10.6] years). Misdiagnosis rates for movement disorders ranged approximately from 10% - 20%. Clinical diagnoses of dementia with parkinsonism (PDD/DLB) were more strongly associated with Lewy body pathology than Parkinson’s disease without dementia (OR = 1·96, 95% CI = 1·30 - 3·04, p = 7·2e-04). Lewy pathology was identified in 4% of neurologically normal controls. Alzheimer’s disease co-pathology was present in 40% of cases with Lewy body disease. GBA1 variant carriers exhibited greater Lewy body burden compared with noncarriers (OR = 1·94, 95% CI = 1·24 - 3·03, p = 0·01) or LRRK2 carriers (OR = 7·44, 95% CI = 2·16 - 25·64, p = 0·01). Pathological diagnoses differed by ancestry, with South Asian donors more likely to have progressive supranuclear palsy pathology and Ashkenazi Jewish donors more likely to have Lewy body disease (p < 0.0001), independent of GBA1 and LRRK2 mutation status.

Conclusion and Relevance: Our findings highlight the value of integrating genetic and pathological data to improve diagnostic accuracy. The high prevalence of Alzheimer’s disease co-pathology and ancestry-related differences in pathology point to the need for biologically informed diagnostic tools. These results support the integration of genetically and pathologically stratified approaches, correlating pathology with in vivo biomarkers, for future therapeutic trials.

Funding: Medical Research Council, Global Parkinson’s Genetic Program/Aligning Science Across Parkinson’s

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

Repositories

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GP2code/MD-GAP-GP2-CPC

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f755f53919efb5226a934f8f741b3c52375013ba, 22 December 2025
Languages: R (5)
Size: 7 files, 5 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), broom (1 file), data.table (1 file), ggplot2 (1 file), survival (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
7 files
At the source: github.com/GP2code/MD-GAP-GP2-CPC

Zenodo 18020958

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), broom (1 file), data.table (1 file), ggplot2 (1 file), survival (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
7 files
At the source: dx.doi.org/10.5281/zenodo.18020958

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Data

Datasets cited

Data and code availability

Data used in the preparation of this article were obtained from the Global Parkinson’s Genetics Program (GP2; https://gp2.org). Specifically, we used Tier 2 data from GP2 release 10 (https://doi.org/10.5281/zenodo.15748014). Pathological staging and co-pathology data is in Release 11. See eTable 11 for key resources used in this project. All code generated for this article, and the identifiers for all software programs and packages used, are available on GitHub (https://github.com/GP2code/MD-GAP-GP2-CPC) and were given a persistent identifier via Zenodo (10.5281/zenodo.18020958 (http://dx.doi.org/10.5281/zenodo.18020958)).

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

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Recorded: type, language, journal, dates, 40 authors, 8 funders, 41 references.

Cite

This paper

Wu, L. Y., du Toit, T., Georgiades, T., Stafford, E. J., Levine, K., Fang, Z.-H., Jasaityte, S., Martinez, A.-L. G., Cullinane, P., De Pablo Fernandez, E., Blauwendraat, C., Singleton, A. B., Scholz, S. W., Traynor, B. J., Wood, N., Hardy, J., Chinnery, P., Houlden, H., Cain, R., . . . Morris, H. R. (2026). Pathology and genetics in a global cohort of Parkinsonian Disorders. medRxiv (preprint). https://doi.org/10.64898/2026.03.23.26348322

BibTeX

@article{wu2026pathology,
author = {Wu, Lesley Y. and du Toit, Tessa and Georgiades, Tatiana and Stafford, Eleanor J. and Levine, Kristin and Fang, Zih-Hua and Jasaityte, Simona and Martinez, Ana-Luisa Gil and Cullinane, Patrick and De Pablo Fernandez, Eduardo and Blauwendraat, Cornelis and Singleton, Andrew B. and Scholz, Sonja W. and Traynor, Bryan J. and Wood, Nicholas and Hardy, John and Chinnery, Patrick and Houlden, Henry and Cain, Richard and Troakes, Claire and Chelban, Viorica and Serrano, Geidy E. and Gveric, Djordje and McLean, Catriona and Love, Seth and King, Andrew and Robinson, Andrew C. and Roncaroli, Federico and Shepherd, Claire and Halliday, Glenda and Parkkinen, Laura and Morris, Christopher M and Smith, Colin and Beach, Thomas G. and Gentleman, Steve and Warner, Thomas T. and Lashley, Tammaryn and Jaunmuktane, Zane and Real, Raquel and Morris, Huw R},
title = {{Pathology and genetics in a global cohort of Parkinsonian Disorders}},
journal = {medRxiv (preprint)},
year = {2026},
month = mar,
publisher = {medRxiv},
doi = {10.64898/2026.03.23.26348322},
url = {https://doi.org/10.64898/2026.03.23.26348322}
}

RIS

TY - JOUR
AU - Wu, Lesley Y.
AU - du Toit, Tessa
AU - Georgiades, Tatiana
AU - Stafford, Eleanor J.
AU - Levine, Kristin
AU - Fang, Zih-Hua
AU - Jasaityte, Simona
AU - Martinez, Ana-Luisa Gil
AU - Cullinane, Patrick
AU - De Pablo Fernandez, Eduardo
AU - Blauwendraat, Cornelis
AU - Singleton, Andrew B.
AU - Scholz, Sonja W.
AU - Traynor, Bryan J.
AU - Wood, Nicholas
AU - Hardy, John
AU - Chinnery, Patrick
AU - Houlden, Henry
AU - Cain, Richard
AU - Troakes, Claire
AU - Chelban, Viorica
AU - Serrano, Geidy E.
AU - Gveric, Djordje
AU - McLean, Catriona
AU - Love, Seth
AU - King, Andrew
AU - Robinson, Andrew C.
AU - Roncaroli, Federico
AU - Shepherd, Claire
AU - Halliday, Glenda
AU - Parkkinen, Laura
AU - Morris, Christopher M
AU - Smith, Colin
AU - Beach, Thomas G.
AU - Gentleman, Steve
AU - Warner, Thomas T.
AU - Lashley, Tammaryn
AU - Jaunmuktane, Zane
AU - Real, Raquel
AU - Morris, Huw R
TI - Pathology and genetics in a global cohort of Parkinsonian Disorders
T2 - medRxiv (preprint)
J2 - medRxiv
PY - 2026
DA - 2026/03/26
PB - medRxiv
DO - 10.64898/2026.03.23.26348322
UR - https://doi.org/10.64898/2026.03.23.26348322
LA - en
ER -

CSL-JSON

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]
}
}

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