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Characterising the motif composition and allele length distribution of <i>ZFHX3</i> GGC repeat expansions in amyotrophic lateral sclerosis

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  1. [1] § Results › Variability of repeat motif compositions and configurations in ZFHX3 ↔ 01_fig3_motif_composition.Rmd, lines 132–199 · score 0.58 · pure glycine, GGC motifs, motif composition, AGT, GAC, GGT

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The authors' code

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  1. ---
  2. title: "01_fig3_motif_composition"
  3. author: "Zoe Zussa"
  4. date: "2026-02-09"
  5. output: html_document
  6. ---
  8. This script is to recreate figure 3 from the publication "Characterising the motif composition and allele length distribution of ZFHX3 GGC repeat expansions in amyotrophic lateral sclerosis" by Zussa et al., 2026.
  10. Data to replicate this analysis is titled
  11. ZFHX3_Genotypes_Motif_Comp_Australian_ALS_2026-03-09.csv
  12. ZFHX3_Genotypes_Motif_Comp_MinE_ALS_Controls_2026-04-08.csv
  15. Load packages
  16. ```{r}
  18. library(tidyr)
  19. library(tidyverse)
  20. library(dplyr)
  21. library(data.table)
  22. library(plotrix) # needed for piecharts
  24. ```
  26. Load in data files
  27. ```{r}
  29. # Australian ALS motif compositions
  30. mq_motif<- fread("/Users/MQ10007635/Desktop/ZFHX3_Genotypes_Motif_Comp_Australian_ALS_2026-03-09.csv")
  32. # project MinE control motif compositions
  33. mine_motif<- fread("/Users/MQ10007635/Desktop/ZFHX3_Genotypes_Motif_Comp_MinE_ALS_Controls_2026-04-08.csv")
  35. ```
  37. Preparing data for plotting
  38. ```{r}
  40. # subset project MinE for controls with motif composition
  41. mine_motif <- subset(mine_motif, !is.na(motif_comp_allele_1))
  43. # pivot dfs
  44. mq_motif <- mq_motif %>%
  45. pivot_longer(
  46. cols = c(rep_allele_1, rep_allele_2, motif_comp_allele_1, motif_comp_allele_2),
  47. names_to = c(".value", "allele"),
  48. names_pattern = "(rep|motif_comp)_allele_(.)"
  49. ) %>%
  50. rename(seq = motif_comp) %>%
  51. select(rep, seq)
  53. mine_motif <- mine_motif %>%
  54. pivot_longer(
  55. cols = c(rep_allele_1, rep_allele_2, motif_comp_allele_1, motif_comp_allele_2),
  56. names_to = c(".value", "allele"),
  57. names_pattern = "(rep|motif_comp)_allele_(.)"
  58. ) %>%
  59. rename(seq = motif_comp) %>%
  60. select(rep, seq)
  62. # count how many times the motif composition occurs
  63. # counting case sequences
  64. counts_cases <- mq_motif %>%
  65. count(seq) %>%
  66. rename(seq_comp = seq, cases = n)
  68. # counting control sequences
  69. counts_controls <- mine_motif %>%
  70. count(seq) %>%
  71. rename(seq_comp = seq, controls = n)
  73. # combining counts
  74. counts_seq_comp <- full_join(
  75. counts_cases,
  76. counts_controls,
  77. by = "seq_comp"
  78. ) %>%
  79. mutate(
  80. cases = ifelse(is.na(cases), 0, cases),
  81. controls = ifelse(is.na(controls), 0, controls)
  82. )
  84. # get proportions of each motif per motif comp for pie charts
  85. # cases
  86. motif_list_cases <- mq_motif %>%
  87. # extract motifs structured like GGC(4)
  88. mutate(parts = str_extract_all(seq, "[A-Z]+\\(\\d+\\)")) %>%
  89. unnest(parts) %>%
  90. # separate motif and count
  91. extract(
  92. parts,
  93. into = c("motif", "count"),
  94. regex = "([A-Z]+)\\((\\d+)\\)",
  95. convert = TRUE
  96. ) %>%
  97. group_by(seq, motif) %>%
  98. # getting repeat size from count
  99. summarise(count = sum(count), .groups = "drop") %>%
  100. group_by(seq) %>%
  101. mutate(prop = count / sum(count)) %>%
  102. summarise(motifs = list(setNames(prop, motif))) %>%
  103. deframe()
  105. # extract out compositions without GGC motif for second column of pie charts
  106. motif_list_cases_noGGC <- lapply(motif_list_cases, function(x) {
  107. x2 <- x[names(x) != "GGC"]
  108. if(length(x2) == 0) return(NULL)
  109. x2 / sum(x2)
  110. })
  112. # controls
  113. motif_list_controls <- mine_motif %>%
  114. # extract motifs structured like GGC(4)
  115. mutate(parts = str_extract_all(seq, "[A-Z]+\\(\\d+\\)")) %>%
  116. unnest(parts) %>%
  117. # separate motif and count
  118. extract(
  119. parts,
  120. into = c("motif", "count"),
  121. regex = "([A-Z]+)\\((\\d+)\\)",
  122. convert = TRUE
  123. ) %>%
  124. group_by(seq, motif) %>%
  125. # getting repeat size from count
  126. summarise(count = sum(count), .groups = "drop") %>%
  127. group_by(seq) %>%
  128. mutate(prop = count / sum(count)) %>%
  129. summarise(motifs = list(setNames(prop, motif))) %>%
  130. deframe()
  132. # extract out compositions without GGC motif for second column of pie charts
  133. motif_list_controls_noGGC <- lapply(motif_list_controls, function(x) {
  134. x2 <- x[names(x) != "GGC"]
  135. if(length(x2) == 0) return(NULL)
  136. x2 / sum(x2)
  137. })
  139. # merging case and control
  140. all_seq <- counts_seq_comp$seq_comp
  142. # all motifs (first pie chart column)
  143. motif_list_all <- lapply(all_seq, function(s) {
  144. if(!is.null(motif_list_cases[[s]])) {
  145. motif_list_cases[[s]]
  146. } else if(!is.null(motif_list_controls[[s]])) {
  147. motif_list_controls[[s]]
  148. } else {
  149. NULL
  150. }
  151. })
  152. names(motif_list_all) <- all_seq
  154. # all motifs excluding GGC (second pie chart column)
  155. motif_list_all_noGGC <- lapply(all_seq, function(s) {
  156. if(!is.null(motif_list_cases_noGGC[[s]])) {
  157. motif_list_cases_noGGC[[s]]
  158. } else if(!is.null(motif_list_controls_noGGC[[s]])) {
  159. motif_list_controls_noGGC[[s]]
  160. } else {
  161. NULL
  162. }
  163. })
  164. names(motif_list_all_noGGC) <- all_seq
  166. # extracting repeat sizes from cases and controls
  167. repeat_all <- bind_rows(
  168. mq_motif %>% distinct(seq, rep),
  169. mine_motif %>% distinct(seq, rep)
  170. ) %>% distinct(seq, rep)
  172. repeat_size <- repeat_all$rep[
  173. match(all_seq, repeat_all$seq)]
  175. stopifnot(!any(is.na(repeat_size)))
  177. # ordering motif compositions by repeat size and then total counts
  178. ord <- order(
  179. repeat_size,
  180. counts_seq_comp$cases + counts_seq_comp$controls)
  182. counts_seq_comp <- counts_seq_comp[ord, ]
  183. seq_comp_names <- counts_seq_comp$seq_comp
  184. repeat_size <- repeat_size[ord]
  185. motif_list_all <- motif_list_all[seq_comp_names]
  186. motif_list_all_noGGC <- motif_list_all_noGGC[seq_comp_names]
  188. # identifying pure glycine sequences
  189. seqs_only_GGC_GGT <- sapply(motif_list_all, function(x) {
  190. non_canonical <- x[names(x) %in% c("AGT","AGC","GAC","AAT")]
  191. all(non_canonical == 0 | is.na(non_canonical))})
  193. # adding in asterisks to sequences extracted above (assists when plotting)
  194. seq_comp_names_marked <- seq_comp_names
  195. seq_comp_names_marked[seqs_only_GGC_GGT] <- paste0("*", seq_comp_names_marked[seqs_only_GGC_GGT])
  196. names_to_plot <- seq_comp_names_marked
  197. names_to_plot[startsWith(names_to_plot, "*")] <- ""
  199. ```
  201. Plotting figure 3
  202. ```{r}
  204. # log scale for counts
  205. plot_matrix <- rbind(
  206. Cases = log10(counts_seq_comp$cases + 1),
  207. Controls = log10(counts_seq_comp$controls + 1))
  209. # setting pie chart colous
  210. motif_colors <- c(
  211. GGC = "#AAAEB0",
  212. GGT = "#4D6291",
  213. AGT = "#9A68A4",
  214. AGC = "#9D2D52",
  215. GAC = "#006E4A",
  216. AAT = "#4F1259"
  217. )
  219. # starting plot
  220. pdf("ZFHX3_motif_comp.pdf", width = 15, height = 18)
  221. par(mar = c(6,40,1,1))
  223. # replace 0 with NA so 0 values are not plotted
  224. # this gets rid of the weird line when values are equal to 0
  225. plot_matrix[plot_matrix == 0] <- NA
  227. # define consistent spacing
  228. gap_unit <- max(colSums(plot_matrix, na.rm = TRUE)) * 0.035
  230. # column positions
  231. pie_x1 <- -3 * gap_unit
  232. pie_x2 <- -2 * gap_unit
  233. repeat_x <- -1 * gap_unit
  235. # grouped bar chart with cases and controls
  236. bp <- barplot(
  237. plot_matrix,
  238. horiz = TRUE,
  239. beside = TRUE,
  240. names.arg = names_to_plot,
  241. las = 1,
  242. space = c(0,1),
  243. col = c("hotpink4","plum3"),
  244. cex.names = 1,
  245. xaxt = "n",
  246. xlim = c(
  247. pie_x1 - gap_unit *0.45,
  248. max(plot_matrix, na.rm = TRUE) * 1.1
  249. )
  250. )
  252. # adding x axis labels
  253. tick_counts <- c(0,1,2,5,10,20,50,100,500,1500) # manual scale
  254. axis(1, at = log10(tick_counts + 1), labels = tick_counts)
  256. # centering x axis label to bars not entire plot window
  257. usr <- par("usr") # returns plot boundaries
  258. x_center <- mean(c(0, usr[2]))# getting mean of max number
  259. mtext(
  260. "Counts of motif compositions (log)",
  261. side = 1,
  262. line = 3,
  263. at = x_center
  264. )
  266. # getting midpoint for each grouped bar
  267. bp_mid <- colMeans(bp)
  269. # bolding pure glycine compositions
  270. asterisk_idx <- which(startsWith(seq_comp_names_marked, "*"))
  271. normal_label_x <- par("usr")[1] -
  272. 0.01 * max(colSums(plot_matrix, na.rm = TRUE))
  274. text(
  275. x = normal_label_x,
  276. y = bp_mid[asterisk_idx],
  277. labels = seq_comp_names_marked[asterisk_idx],
  278. pos = 2,
  279. xpd = TRUE,
  280. font = 2,
  281. cex = 1,
  282. adj = 0
  283. )
  285. # plotting pie charts using midpoint for grouped bars
  286. for(i in seq_along(motif_list_all)){
  288. motifs_all <- motif_list_all[[i]]
  289. motifs_noGGC <- motif_list_all_noGGC[[i]]
  291. if(!is.null(motifs_all) && length(motifs_all) > 0){
  292. floating.pie(
  293. xpos = pie_x1,
  294. ypos = bp_mid[i],
  295. x = motifs_all,
  296. col = motif_colors[names(motifs_all)],
  297. radius = 0.090,
  298. startpos = 0
  299. )
  300. }
  302. if(!is.null(motifs_noGGC) && length(motifs_noGGC) > 0){
  303. floating.pie(
  304. xpos = pie_x2,
  305. ypos = bp_mid[i],
  306. x = motifs_noGGC,
  307. col = motif_colors[names(motifs_noGGC)],
  308. radius = 0.090,
  309. startpos = 0
  310. )
  311. }
  312. }
  314. # printing repeat sizes next to pie charts
  315. repeat_x <- mean(c(pie_x2, 0))
  316. text(x = repeat_x, y = bp_mid, labels = repeat_size, cex = 1.1, adj = 0.5)
  318. # setting motif legend
  319. motif_aas <- c(GGC="Gly", GGT="Gly", AGT="Ser", AGC="Ser", GAC="Asp", AAT="Asn")
  320. legend_labels <- paste0(names(motif_colors), " (", motif_aas[names(motif_colors)], ")")
  321. par(xpd = NA) # allow drawing in margins
  323. # plotting motif legend
  324. legend(
  325. x = par("usr")[1] - 0.6 * max(plot_matrix, na.rm = TRUE), # shift left
  326. y = min(bp) - 1.7, # shift down under y-axis labels
  327. legend = legend_labels,
  328. fill = motif_colors,
  329. title = "Motifs",
  330. bty = "o",
  331. cex = 1,
  332. ncol = 3,
  333. x.intersp = 0.2,
  334. y.intersp = 0.9
  335. )
  337. # plotting case and control figure legend
  338. legend(
  339. x = par("usr")[2] - 0.05 * diff(par("usr")[1:2]), # slightly left from right edge
  340. y = par("usr")[4] - 0.05 * diff(par("usr")[3:4]), # slightly down from top edge
  341. legend = c("Case", "Control"),
  342. fill = c("hotpink4", "plum3"),
  343. bty = "o",
  344. cex = 1,
  345. xjust = 1, # right-align the legend box at x
  346. yjust = 1 # top-align the legend box at y
  347. )
  349. dev.off()
  350. ```

01_fig3_motif_composition.Rmd at commit 494d51c, no license · at the source

Overview

Authors: Zoe N. Zussa1, Andrew N. Smith1, Joke J.F.A van Vugt2, Daniel S. O’Shaughnessy1, Natalie Grima1, Sandrine Chan Moi Fat1, Ian P. Blair1, Dominic B Rowe1,3, Roger Pamphlett4, Garth A. Nicholson5,6, Matthew C.K Kiernan7, Wouter van Rheenen2, Jan Veldink2, Project MinE ALS sequencing consortium, Kelly L. Williams1, Lyndal Henden1
ORCID iDs: Zoe N. Zussa, Andrew N. Smith, Joke J.F.A van Vugt, Daniel S. O’Shaughnessy, Natalie Grima, Sandrine Chan Moi Fat, Ian P. Blair, Dominic B Rowe, Roger Pamphlett, Garth A. Nicholson, Matthew C.K Kiernan, Wouter van Rheenen, Jan Veldink, Kelly L. Williams, Lyndal Henden
  1. Macquarie University Motor Neuron Disease Research Centre, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
  2. Department of Neurology, UMC Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
  3. Macquarie University Health Neurology, Macquarie University, Sydney, NSW, Australia
  4. Department of Neuropathology, The University of Sydney, Sydney, NSW, Australia
  5. Molecular Medicine Laboratory, Concord Repatriation General Hospital, Sydney, NSW 2139, Australia
  6. Northcott Neuroscience Laboratory, ANZAC Research Institute, Sydney, NSW 2139, Australia
  7. Neuroscience Research Australia, and the University of NSW, Sydney, NSW, Australia
Institutions: Macquarie University (Australia); University Medical Center Utrecht (Netherlands); The University of Sydney (Australia); Concord Repatriation General Hospital (Australia); Anzac Research Institute (Australia); Neuroscience Research Australia, and the University of NSW, Sydney, NSW, Australia
Journal: medRxiv (preprint)
Dates: published online 10 March 2026
Type: Preprint
License: CC BY-NC
Identifiers: DOI 10.64898/2026.03.09.26347973 · OpenAlex W7134938635
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Statistics
Topic: Genetic Neurodegenerative Diseases (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: FightMND; Health~Holland; Macquarie University; Stichting ALS Nederland; Medical Research Council; National Health and Medical Research Council
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Background and objectives: A pathogenic GGC repeat expansion in the zinc finger homeobox 3 (ZFHX3) gene, encoding a pure polyglycine tract, is the cause of spinocerebellar ataxia type 4 (SCA4). Intermediate expansions of other SCA loci contribute to the risk of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease involving the progressive loss of motor neurons. There is increasing awareness of the role of short tandem repeat (STR) motif composition and configuration in disease pathogenicity. Given the genetic pleiotropy between ALS and SCA, this study aimed to evaluate whether ZFHX3 GGC expansions were associated with ALS and to characterise repeat motif composition.

Methods: ExpansionHunter v5 was used to genotype ZFHX3 GGC repeat sizes in short-read whole genome sequencing data from people with ALS and healthy controls of European ancestry. Repeat sizes were visually inspected using REViewer v2. Repeat motif configurations of Australian ALS cases and healthy controls were manually derived from REViewer images. Receiver operating characteristic (ROC) curve analysis and Youden’s J statistic were performed to find a candidate repeat size threshold for association testing. Fisher’s exact tests were performed to evaluate the associations of repeat size and motif composition with disease status.

Results: Analysis of 5,785 people with ALS and 7,982 healthy controls found no association between ZFHX3 GGC repeat expansions and disease risk. Fifty unique repeat motif compositions were identified across 802 people with ALS and 800 healthy controls. Of these, eleven distinct configurations coded a pure polyglycine tract which, when expanded, is canonical to SCA4, though no association with ALS was found.

Discussion: Although no association was observed between ZFHX3 GGC repeat expansions and ALS, this study established the dynamic nature of ZFHX3 repeat motif composition and configuration. Unique motif compositions were identified both within and between repeat sizes, including the presence of pure polyglycine repeats. Consideration of repeat motif composition and configuration, in addition to repeat allele length, may be important for assessing neurodegenerative disease risk.

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, with 1 match between paragraphs and lines of code.

mq-mnd/grp_williams/zfhx3_analysis_publication_2026

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 494d51cde152943bb1f3fb9b34cc82f79d9ec34f, 8 April 2026
Languages: R (2)
Size: 6 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
3 files
At the source: gitlab.com/mq-mnd/grp_williams/zfhx3_analysis_publication_2026

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;
  • 2 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

Individual-level ZFHX3 repeat allele size data for 5,785 people with ALS and 7,982 healthy controls plus individual-level motif composition data for the subset of 802 Australian cases with ALS and 800 healthy controls is available at Zenodo (DOI: 10.5281/zenodo.18931240).

Code written in R is available in R Markdown workbooks in a GitLab repository: https://gitlab.com/mq-mnd/grp_williams/zfhx3_analysis_publication_2026.

Reproduced under the paper's license (CC BY-NC), 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, journal, dates, 16 authors, 6 funders, 34 references.

Cite

This paper

Zussa, Z. N., Smith, A. N., van Vugt, J. J., O’Shaughnessy, D. S., Grima, N., Moi Fat, S. C., Blair, I. P., Rowe, D. B., Pamphlett, R., Nicholson, G. A., Kiernan, M. C., van Rheenen, W., Veldink, J., Project MinE ALS sequencing consortium, Williams, K. L., & Henden, L. (2026). Characterising the motif composition and allele length distribution of <i>ZFHX3</i> GGC repeat expansions in amyotrophic lateral sclerosis. medRxiv (preprint). https://doi.org/10.64898/2026.03.09.26347973

BibTeX

@article{zussa2026characterising,
author = {Zussa, Zoe N. and Smith, Andrew N. and van Vugt, Joke J.F.A and O’Shaughnessy, Daniel S. and Grima, Natalie and Moi Fat, Sandrine Chan and Blair, Ian P. and Rowe, Dominic B and Pamphlett, Roger and Nicholson, Garth A. and Kiernan, Matthew C.K and van Rheenen, Wouter and Veldink, Jan and {Project MinE ALS sequencing consortium} and Williams, Kelly L. and Henden, Lyndal},
title = {{Characterising the motif composition and allele length distribution of <i>ZFHX3</i> GGC repeat expansions in amyotrophic lateral sclerosis}},
journal = {medRxiv (preprint)},
year = {2026},
month = mar,
publisher = {medRxiv},
doi = {10.64898/2026.03.09.26347973},
url = {https://doi.org/10.64898/2026.03.09.26347973}
}

RIS

TY - JOUR
AU - Zussa, Zoe N.
AU - Smith, Andrew N.
AU - van Vugt, Joke J.F.A
AU - O’Shaughnessy, Daniel S.
AU - Grima, Natalie
AU - Moi Fat, Sandrine Chan
AU - Blair, Ian P.
AU - Rowe, Dominic B
AU - Pamphlett, Roger
AU - Nicholson, Garth A.
AU - Kiernan, Matthew C.K
AU - van Rheenen, Wouter
AU - Veldink, Jan
AU - Project MinE ALS sequencing consortium
AU - Williams, Kelly L.
AU - Henden, Lyndal
TI - Characterising the motif composition and allele length distribution of <i>ZFHX3</i> GGC repeat expansions in amyotrophic lateral sclerosis
T2 - medRxiv (preprint)
J2 - medRxiv
PY - 2026
DA - 2026/03/10
PB - medRxiv
DO - 10.64898/2026.03.09.26347973
UR - https://doi.org/10.64898/2026.03.09.26347973
ER -

CSL-JSON

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"id": "10.64898/2026.03.09.26347973",
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[3] doi:10.1155/humu/4225263
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In common: genetics / omics, other condition, author Garth A. Nicholson
[4] doi:10.1038/s41467-026-73902-7 [code]
GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer's disease.
Journal: Nature communications
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[5] doi:10.1016/j.mocell.2026.100363 [code]
Tandem repeats in human brain evolution and disease susceptibility.
Journal: Molecules and cells
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[6] doi:10.1038/s41380-026-03574-8
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Journal: Molecular psychiatry
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[7] doi:10.1038/s41467-026-72598-z [code]
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[8] doi:10.1093/braincomms/fcag322 [code]
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Journal: Brain communications
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[9] doi:10.1371/journal.pcbi.1014422 [code]
Deciphering cell type-specific causal genetic effects on brain imaging-derived phenotypes and disorders with single-cell Mendelian randomization.
Journal: PLoS computational biology
In common: data.table, tidyverse, genetics / omics, 1 reference
[10] doi:10.1186/s13195-026-02036-1 [code]
Genetic drivers of progression in Alzheimer's disease are distinct from disease risk.
Journal: Alzheimer's research & therapy
In common: data.table, tidyverse, genetics / omics, 1 reference

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