OSCR

Somatic mosaicism in ALS and FTD identifies focal mutations associated with widespread degeneration.

Code ↔ Paper

11 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 11 matches
  1. [1] § Results › RNA-MosaicHunter identifies additional predicted deleterious somatic variants in bulk RNA-seq data of sALS cases ↔ pTDP43_quantification.R, lines 35–115 · score 0.75 · p.H566Y, p.R1962C, DYNC1H1, LMNA, neuronal, TDP
  2. [2] § Results › Predicted deleterious somatic variants have restricted regional distributions and are enriched in hypodiploid cells ↔ pTDP43_quantification.R, lines 35–115 · score 0.71 · p.L248F, pTDP, MATR3, TIA1, TARDBP, ALS2
  3. [3] § Results › Somatic variants in disease-relevant genes are enriched in ALS and FTD cases lacking pathogenic germline variants ↔ variant_burden_analysis.R, lines 107–154 · score 0.70 · variant burden, protein altering, ALS genes, related genes, FTD genes, background
  4. [4] § Results › Pathogenic germline variants in sALS and sFTD cases ↔ germline_variant_analysis.R, lines 44–94 · score 0.66 · C9orf72, repeat expansion, FTD genes, mutated, ANNOVAR, Missense
  5. [5] § Results › Identification of somatic SNVs and indels from MIP sequencing data ↔ spike_in_benchmarking.R, lines 61–146 · score 0.65 · RePlow, double called, spike, benchmark, precision, FPR
  6. [6] § Methods › Benchmarking with spike-in datasets ↔ spike_in_benchmarking.R, lines 61–146 · score 0.64 · RePlow, target VAFs, spike, benchmarking, Pisces, position
  7. [7] § Methods › Variant calling for germline variants ↔ variant_burden_analysis.R, lines 49–105 · score 0.61 · protein altering, ALS related, related genes, exonic, disease, variant
  8. [8] § Results › Pathogenic germline variants in sALS and sFTD cases ↔ variant_burden_analysis.R, lines 156–208 · score 0.59 · odds ratio, ALS genes, FTD genes, CI, disease, variants
  9. [9] § Methods › Computational prediction of variant deleteriousness ↔ germline_variant_analysis.R, lines 44–94 · score 0.56 · splice sites, ANNOVAR, Nonsense, missense, Mutation, germline
  10. [10] § Results › Identification of somatic SNVs and indels from MIP sequencing data ↔ somatic_variant_analysis.R, lines 1–62 · score 0.53 · validation VAFs, brain regions, somatic variant, synonymous, missense, identity
  11. [11] § Results › Pathogenic germline variants in sALS and sFTD cases ↔ germline_variant_analysis.R, lines 1–41 · score 0.52 · multiple pathogenic, multiple predicted, germline variants, expansion

Paper

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The authors' code

R · 454 lines · 15 KB · no license · 3 matches

  1. library(dplyr)
  2. library(ggplot2)
  3. library(lmerTest)
  5. PROJECT_ROOT <- "." # put your working directory
  6. setwd(PROJECT_ROOT)
  8. variant_count_matrix_per_indiv=read.table("data/variant_count_matrix_per_indiv.txt", header=T)
  9. variant_count_matrix_per_indiv$batch=factor(variant_count_matrix_per_indiv$batch, levels=c("1","2","3","4","5","6"))
  10. variant_count_matrix_per_indiv$clinical=factor(variant_count_matrix_per_indiv$clinical, levels=c("Control","ALS","FTD"))
  12. dat <- filter(variant_count_matrix_per_indiv, targetRegion == "Total_ND_genes")
  14. extract_clinical_effects <- function(data, response, mutType_label, targetRegion, sampleSet, conf.interval = 0.95) {
  15. fml <- reformulate(
  16. termlabels = c("clinical", "avgDepth", "sex", "pmi", "regionCnt", "(1|batch)"),
  17. response = response
  18. )
  20. fit <- lmerTest::lmer(fml, data = data, REML = FALSE)
  21. coefs <- summary(fit)$coefficients
  23. z <- qnorm(0.5 + conf.interval / 2)
  25. get_row <- function(term, label) {
  26. est <- coefs[term, "Estimate"]
  27. se <- coefs[term, "Std. Error"]
  28. p <- coefs[term, "Pr(>|t|)"]
  30. data.frame(
  31. estimate = as.numeric(est),
  32. ci_low = as.numeric(est - z * se),
  33. ci_high = as.numeric(est + z * se),
  34. pval = as.numeric(p),
  35. clinical = label,
  36. targetRegion = targetRegion,
  37. sampleSet = sampleSet,
  38. mutType = mutType_label,
  39. stringsAsFactors = FALSE
  40. )
  41. }
  43. bind_rows(
  44. get_row("clinicalFTD", "FTD"),
  45. get_row("clinicalALS", "ALS")
  46. )
  47. }
  49. mutTypes <- c(
  50. mutCnt_total = "All",
  51. mutCnt_exonic = "Exonic",
  52. mutCnt_functional = "Protein-altering",
  53. mutCnt_intronic = "Intronic",
  54. mutCnt_nonexonic = "Non-coding",
  55. mutCnt_nonfunctional = "Non-functional"
  56. )
  58. ci_merged <- bind_rows(lapply(names(mutTypes), function(resp) {
  59. extract_clinical_effects(dat, response = resp, mutType_label = mutTypes[[resp]], targetRegion = "Total_ND_gene", sampleSet = "Non_carrier")
  60. }))
  62. mutType_list <- c("All","Exonic","Protein-altering","Synonymous","Intronic","Non-coding","Non-functional","dFdS","dFdNE","dFdNF")
  64. ci_merged <- ci_merged %>%
  65. mutate(
  66. mutType = factor(mutType, levels = rev(mutType_list)),
  67. clinical = factor(clinical, levels = c("FTD","ALS"))
  68. )
  70. cbp <- c("FTD" = "#D55E00", "ALS" = "#604A64")
  72. ### Fig.4a
  73. ggplot(ci_merged, aes(x=estimate, y=mutType, color=clinical))+
  74. geom_point(position=position_dodge(0.5))+
  75. geom_errorbar(aes(xmin=ci_low, xmax=ci_high),width=.2,position=position_dodge(0.5))+
  76. geom_vline(xintercept=0, color="black", linetype="dashed")+
  77. theme_classic()+
  78. scale_color_manual(values = cbp)+
  79. xlab("Disease effect size")+
  80. ylab("Variant type")
  81. ggsave(file="plots/main/Fig_4_a.png", width=5, height=6)
  83. dat <- filter(variant_count_matrix_per_indiv, targetRegion == "ALS_related_genes")
  84. mutTypes_selected <- c(
  85. mutCnt_exonic = "Exonic",
  86. mutCnt_functional = "Protein-altering"
  87. )
  88. ci_merged=data.frame()
  89. ci_merged <- bind_rows(lapply(names(mutTypes_selected), function(resp) {
  90. extract_clinical_effects(dat, response = resp, mutType_label = mutTypes_selected[[resp]], targetRegion = "ALS_related_genes", sampleSet = "Non_carrier")
  91. }))
  93. dat <- filter(variant_count_matrix_per_indiv, targetRegion == "FTD_genes_AD_exclusive")
  94. mutTypes_selected <- c(
  95. mutCnt_exonic = "Exonic",
  96. mutCnt_functional = "Protein-altering"
  97. )
  98. ci_merged <- bind_rows(ci_merged, lapply(names(mutTypes_selected), function(resp) {
  99. extract_clinical_effects(dat, response = resp, mutType_label = mutTypes_selected[[resp]], targetRegion = "FTD_genes_AD_exclusive", sampleSet = "Non_carrier")
  100. }))
  101. ci_merged <- ci_merged %>%
  102. mutate(
  103. mutType = factor(mutType, levels = rev(mutType_list)),
  104. clinical = factor(clinical, levels = c("FTD","ALS"))
  105. )
  107. ### Fig.4b
  108. ggplot(ci_merged, aes(x=estimate, y=mutType, color=clinical))+
  109. geom_point(position=position_dodge(0.5))+
  110. geom_errorbar(aes(xmin=ci_low, xmax=ci_high),width=.2,position=position_dodge(0.5))+
  111. geom_vline(xintercept=0, color="black", linetype="dashed")+
  112. theme_classic()+
  113. theme(
  114. strip.background = element_rect(fill="lightgrey", color=NA),
  115. strip.text = element_text()
  116. ) +
  117. scale_color_manual(values = cbp)+
  118. xlab("Disease effect size")+
  119. ylab("Variant type")+
  120. facet_grid(
  121. cols = vars(targetRegion),
  122. scales = "free",
  123. labeller = labeller(
  124. targetRegion = c(
  125. "ALS_related_genes" = "ALS genes",
  126. "FTD_genes_AD_exclusive" = "FTD genes"
  127. )
  128. )
  129. )
  130. ggsave(file="plots/main/Fig_4_b.png", width=7, height=4)
  132. dat <- filter(variant_count_matrix_per_indiv, targetRegion == "ALS_related_genes")
  133. mutTypes_selected <- c(
  134. mutCnt_exonic = "Exonic",
  135. mutCnt_functional = "Protein-altering"
  136. )
  137. ci_merged=data.frame()
  138. ci_merged <- bind_rows(lapply(names(mutTypes_selected), function(resp) {
  139. extract_clinical_effects(dat, response = resp, mutType_label = mutTypes_selected[[resp]], targetRegion = "ALS_related_genes", sampleSet = "Non_carrier")
  140. }))
  142. dat <- filter(variant_count_matrix_per_indiv, targetRegion == "non_ALS_FTD_genes")
  143. mutTypes_selected <- c(
  144. mutCnt_exonic = "Exonic",
  145. mutCnt_functional = "Protein-altering"
  146. )
  147. ci_merged <- bind_rows(ci_merged, lapply(names(mutTypes_selected), function(resp) {
  148. extract_clinical_effects(dat, response = resp, mutType_label = mutTypes_selected[[resp]], targetRegion = "non_ALS_FTD_genes", sampleSet = "Non_carrier")
  149. }))
  150. ci_merged <- ci_merged %>%
  151. mutate(
  152. mutType = factor(mutType, levels = rev(mutType_list)),
  153. clinical = factor(clinical, levels = c("FTD","ALS"))
  154. )
  156. ### Extended Data Fig.5
  157. ggplot(ci_merged, aes(x=estimate, y=mutType, color=clinical))+
  158. geom_point(position=position_dodge(0.5))+
  159. geom_errorbar(aes(xmin=ci_low, xmax=ci_high),width=.2,position=position_dodge(0.5))+
  160. geom_vline(xintercept=0, color="black", linetype="dashed")+
  161. theme_classic()+
  162. theme(
  163. strip.background = element_rect(fill="lightgrey", color=NA),
  164. strip.text = element_text()
  165. ) +
  166. scale_color_manual(values = cbp)+
  167. xlab("Disease effect size")+
  168. ylab("Variant type")+
  169. facet_grid(
  170. cols = vars(targetRegion),
  171. scales = "free",
  172. labeller = labeller(
  173. targetRegion = c(
  174. "ALS_related_genes" = "ALS genes",
  175. "non_ALS_FTD_genes" = "non-ALS/FTD genes"
  176. )
  177. )
  178. )
  179. ggsave(file="plots/extendedDataFig/ExtDataFig_5.png", width=7, height=4)
  181. dat <- filter(variant_count_matrix_per_indiv, targetRegion == "Total_ND_genes")
  183. summary_tbl <- dat %>%
  184. group_by(clinical) %>%
  185. summarise(
  186. total_F = sum(mutCnt_functional, na.rm = TRUE),
  187. total_NE = sum(mutCnt_nonexonic, na.rm = TRUE),
  188. .groups = "drop"
  189. )
  191. ctrl_vals <- filter(summary_tbl, clinical == "Control")
  193. odds_tbl <- summary_tbl %>%
  194. filter(clinical %in% c("ALS","FTD")) %>%
  195. mutate(
  196. odds_ratio = (total_F / total_NE) /
  197. (ctrl_vals$total_F / ctrl_vals$total_NE),
  198. Log10_OR = log10(odds_ratio)
  199. ) %>%
  200. select(Clinical = clinical, Log10_OR)
  202. ### Extended Data Fig.4a
  203. ggplot(odds_tbl, aes(x=Clinical, y=Log10_OR))+
  204. geom_bar(position=position_dodge(0.7),width=.6, stat="identity", fill="darkgrey")+
  205. theme_classic()+
  206. ylab("Log10(Odds ratio)")+
  207. xlab("Clinical")
  208. ggsave(file="plots/extendedDataFig/ExtDataFig_4_a.png", width=3, height=6)
  210. log_ratio_conf_interval <- function(X, Y,
  211. confidence_level = 0.95,
  212. n_bootstrap = 1000,
  213. pseudocount = 0.01,
  214. seed = 1) {
  215. stopifnot(length(X) == length(Y))
  217. X <- X + pseudocount
  218. Y <- Y + pseudocount
  219. n <- length(X)
  221. log_ratio_hat <- log10(mean(X / Y, na.rm = TRUE))
  223. set.seed(seed)
  224. idx <- replicate(n_bootstrap, sample.int(n, size = n, replace = TRUE))
  225. boot_stats <- apply(idx, 2, function(ii) log10(mean((X[ii] / Y[ii]), na.rm = TRUE)))
  227. alpha <- 1 - confidence_level
  228. ci <- as.numeric(stats::quantile(boot_stats, probs = c(alpha/2, 1 - alpha/2), na.rm = TRUE))
  230. list(
  231. log_ratio = log_ratio_hat,
  232. lower_log = ci[1],
  233. upper_log = ci[2],
  234. confidence_level = confidence_level,
  235. n = n
  236. )
  237. }
  239. dat <- variant_count_matrix_per_indiv %>%
  240. filter(targetRegion == "Total_ND_genes") %>%
  241. mutate(clinical = factor(clinical, levels = c("Control","ALS","FTD")))
  243. run_one_group <- function(df, clinical_label) {
  244. res <- log_ratio_conf_interval(
  245. X = df$mutCnt_functional,
  246. Y = df$mutCnt_nonexonic,
  247. confidence_level = 0.95,
  248. n_bootstrap = 1000,
  249. pseudocount = 0.01,
  250. seed = 1
  251. )
  253. data.frame(
  254. estimate = res$log_ratio,
  255. ci_low = res$lower_log,
  256. ci_high = res$upper_log,
  257. pval = NA_real_,
  258. clinical = clinical_label,
  259. targetRegion = "Total_ND_gene",
  260. sampleSet = "Non_carrier",
  261. mutType = "log10_dFdNE",
  262. n = res$n,
  263. stringsAsFactors = FALSE
  264. )
  265. }
  267. ci_merged <- bind_rows(
  268. run_one_group(filter(dat, clinical == "ALS"), "ALS"),
  269. run_one_group(filter(dat, clinical == "FTD"), "FTD"),
  270. run_one_group(filter(dat, clinical == "Control"), "Control")
  271. ) %>%
  272. mutate(
  273. clinical = factor(clinical, levels = c("Control","ALS","FTD"))
  274. )
  276. cbp_clinical <- c("Control" = "lightgray", "ALS" = "#604A64", "FTD" = "#D55E00")
  278. cbp_clinical <- c("lightgray","#604A64","#D55E00")
  279. ci_merged$clinical=factor(ci_merged$clinical, levels=rev(c("Control","ALS","FTD")))
  281. ### Extended Data Fig.4b
  282. ggplot(ci_merged, aes(x=estimate, y=mutType, color=clinical))+
  283. geom_point(position=position_dodge(0.5))+
  284. geom_errorbar(aes(xmin=ci_low, xmax=ci_high),width=.2,position=position_dodge(0.5))+
  285. geom_vline(xintercept=0, color="black", linetype="dashed")+
  286. theme_classic()+
  287. scale_color_manual(values = rev(cbp_clinical))+
  288. xlab("log10(nonsynonymous/noncoding)")+
  289. ylab("Clinical")+
  290. theme(axis.text.y = element_blank(), axis.ticks.y=element_blank())
  291. ggsave(file="plots/extendedDataFig/ExtDataFig_4_b.png", width=8, height=6)
  294. variant_count_matrix_per_brainRegion=read.table("data/variant_count_matrix_per_brain_region.txt", header=T)
  295. mutTypes <- c("All","Exonic","Functional")
  296. brainRegion_order = c("SC","CB","OC","AC","BA4","BA6","PFC")
  297. variant_count_matrix_per_brainRegion$batch=factor(variant_count_matrix_per_brainRegion$batch, levels=c("1","2","3","4","5","6"))
  298. variant_count_matrix_per_brainRegion$clinical=factor(variant_count_matrix_per_brainRegion$clinical, levels=c("Control","ALS","FTD"))
  299. variant_count_matrix_per_brainRegion$mutType = factor(variant_count_matrix_per_brainRegion$mutType, levels=mutTypes)
  300. variant_count_matrix_per_brainRegion$brainRegion = factor(variant_count_matrix_per_brainRegion$brainRegion, levels=brainRegion_order)
  302. dat <- filter(variant_count_matrix_per_brainRegion, targetRegion == "ALS_FTD_genes")
  304. extract_clinical_effects_per_brain_region <- function(data, response, mutType_label, brainRegion_label, targetRegion, sampleSet, conf.interval = 0.95) {
  305. sub <- data %>%
  306. filter(targetRegion == !!targetRegion,
  307. mutType == !!mutType_label,
  308. brainRegion == !!brainRegion_label)
  310. if (nrow(sub) < 3 || dplyr::n_distinct(sub$clinical) < 2) return(NULL)
  312. sub$clinical <- factor(sub$clinical, levels = c("Control", "ALS", "FTD"))
  314. fml <- reformulate(
  315. termlabels = c("clinical", "avgDepth", "sex", "pmi", "(1|batch)"),
  316. response = response
  317. )
  319. z <- qnorm(0.5 + conf.interval / 2)
  321. out <- tryCatch({
  322. fit <- lmerTest::lmer(fml, data = sub)
  323. coefs <- summary(fit)$coefficients
  325. get_row <- function(term, label) {
  326. if (!term %in% rownames(coefs)) return(NULL)
  328. est <- coefs[term, "Estimate"]
  329. se <- coefs[term, "Std. Error"]
  330. p <- coefs[term, "Pr(>|t|)"]
  332. data.frame(
  333. estimate = as.numeric(est),
  334. ci_low = as.numeric(est - z * se),
  335. ci_high = as.numeric(est + z * se),
  336. pval = as.numeric(p),
  337. clinical = label,
  338. mutType = mutType_label,
  339. brainRegion = brainRegion_label,
  340. targetRegion = targetRegion,
  341. sampleSet = sampleSet,
  342. stringsAsFactors = FALSE
  343. )
  344. }
  346. dplyr::bind_rows(
  347. get_row("clinicalFTD", "FTD"),
  348. get_row("clinicalALS", "ALS")
  349. )
  350. }, error = function(e) {
  351. NULL
  352. })
  353. out
  354. }
  356. run_lmer_by_mutType_brainRegion <- function(data, response = "mutCnt", sampleSet, targetRegion, conf.interval = 0.95) {
  357. combos <- data %>%
  358. distinct(mutType, brainRegion)
  360. bind_rows(lapply(seq_len(nrow(combos)), function(i) {
  361. extract_clinical_effects_per_brain_region(
  362. data = data,
  363. response = response,
  364. mutType_label = combos$mutType[i],
  365. brainRegion_label = combos$brainRegion[i],
  366. sampleSet = sampleSet,
  367. targetRegion = targetRegion,
  368. conf.interval = conf.interval
  369. )
  370. }))
  371. }
  373. ci_merged <- run_lmer_by_mutType_brainRegion(
  374. data = dat,
  375. response = "mutCnt",
  376. sampleSet = "Non_carrier",
  377. targetRegion = "ALS_FTD_genes"
  378. )
  380. ci_merged <- ci_merged %>%
  381. mutate(
  382. mutType = recode(mutType,"Functional" = "Protein-altering"),
  383. clinical = factor(clinical, levels = c("FTD","ALS"))
  384. )
  386. ### Fig.4c
  387. ggplot(ci_merged, aes(x=estimate, y=brainRegion, color=clinical))+
  388. geom_point(position=position_dodge(0.5))+
  389. geom_errorbar(aes(xmin=ci_low, xmax=ci_high),width=.2,position=position_dodge(0.5))+
  390. geom_vline(xintercept=0, color="black", linetype="dashed")+
  391. theme_classic()+
  392. theme(
  393. strip.background = element_rect(fill="lightgrey", color=NA),
  394. strip.text = element_text()
  395. ) +
  396. scale_color_manual(values = cbp)+
  397. xlab("Disease effect size")+
  398. ylab("Tissue region")+
  399. facet_grid(
  400. cols = vars(mutType),
  401. scales = "free",
  402. labeller = labeller(
  403. mutType = c(
  404. "All" = "All",
  405. "Exonic" = "Exonic",
  406. "Protein-altering" = "Protein-altering"
  407. )
  408. )
  409. )
  410. ggsave(file="plots/main/Fig_4_c.png", width=6, height=8)
  413. dat <- filter(variant_count_matrix_per_brainRegion, targetRegion == "Total_ND_genes")
  415. ci_merged <- run_lmer_by_mutType_brainRegion(
  416. data = dat,
  417. response = "mutCnt",
  418. sampleSet = "Non_carrier",
  419. targetRegion = "Total_ND_genes"
  420. )
  422. ci_merged <- ci_merged %>%
  423. mutate(
  424. mutType = recode(mutType,"Functional" = "Protein-altering"),
  425. clinical = factor(clinical, levels = c("FTD","ALS"))
  426. )
  428. ### Extended Data Fig.6
  429. ggplot(ci_merged, aes(x=estimate, y=brainRegion, color=clinical))+
  430. geom_point(position=position_dodge(0.5))+
  431. geom_errorbar(aes(xmin=ci_low, xmax=ci_high),width=.2,position=position_dodge(0.5))+
  432. geom_vline(xintercept=0, color="black", linetype="dashed")+
  433. theme_classic()+
  434. theme(
  435. strip.background = element_rect(fill="lightgrey", color=NA),
  436. strip.text = element_text()
  437. ) +
  438. scale_color_manual(values = cbp)+
  439. xlab("Disease effect size")+
  440. ylab("Tissue region")+
  441. facet_grid(
  442. cols = vars(mutType),
  443. scales = "free",
  444. labeller = labeller(
  445. mutType = c(
  446. "All" = "All",
  447. "Exonic" = "Exonic",
  448. "Protein-altering" = "Protein-altering"
  449. )
  450. )
  451. )
  452. ggsave(file="plots/extendedDataFig/ExtDataFig_6.png", width=6, height=8)

variant_burden_analysis.R at commit 02fba49, no license · at the source

Overview

Authors: Zinan Zhou1,2,3, Junho Kim1,2,3,4, August Yue Huang1,2,3, Matthew Nolan5, Junseok Park1,2,3, Ryan Doan1,3, Taehwan Shin1,2,3, Michael B Miller1,6, Mingyun Bae1,2,3, Boxun Zhao1,2,3, Jinhyeong Kim4, Brian Chhouk1,2,3, Katherine Morillo1,2,3, Rebecca C Yeh1,2,3, Connor Kenny1,2,3, Jennifer E Neil1,2,3,7, Chao-Zong Lee5, Takuya Ohkubo8,9, John Ravits9, Olaf Ansorge10, Lyle W Ostrow11, Clotilde Lagier-Tourenne5, Eunjung Alice Lee1,2,3, Christopher A Walsh1,2,3,7
ORCID iDs: Zinan Zhou, August Yue Huang, Junseok Park, Michael B Miller, Boxun Zhao, Jinhyeong Kim, Takuya Ohkubo, Olaf Ansorge, Lyle W Ostrow, Clotilde Lagier-Tourenne, Eunjung Alice Lee, Christopher A Walsh
  1. Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA USA
  2. Manton Center for Orphan Disease, Boston Children’s Hospital, Boston, MA USA
  3. Department of Pediatrics, Harvard Medical School, Boston, MA USA
  4. Department of Biological Sciences, Sungkyunkwan University, Suwon, South Korea
  5. Department of Neurology, The Sean M. Healey and AMG Center for ALS at Mass General, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
  6. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
  7. Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA USA
  8. Department of Neurology, Yokohama City Minato Red Cross Hospital, Yokohama, Japan
  9. Department of Neurosciences, School of Medicine, University of California, San Diego, La Jolla, CA USA
  10. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
  11. Department of Neurology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA USA
Institutions: Boston Children's Hospital (United States); Harvard University (United States); Manton Center for Orphan Disease Research (United States); Sungkyunkwan University (South Korea); Massachusetts General Hospital (United States); Brigham and Women's Hospital (United States); Howard Hughes Medical Institute (United States); University of California San Diego (United States); Yokohama Municipal Minato Red Cross Hospital (Japan); University of Oxford (United Kingdom); Temple University (United States)
Journal: Nature genetics, volume 58, issue 5, pages 1019-1029
Dates: received 7 November 2023; accepted 11 March 2026; published online 15 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41588-026-02570-6 · PMID 41986690 · PMCID PMC13175891 · OpenAlex W7154514855
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: fMRI & imaging
Keywords: DNA sequencing, Neuromuscular disease, Dementia, Medical genetics
MeSH: Amyotrophic Lateral Sclerosis*, Frontotemporal Dementia*, Mosaicism*, Mutation*, Brain, C9orf72 Protein, High-Throughput Nucleotide Sequencing, Humans (* major topic)
Topic: Amyotrophic Lateral Sclerosis Research (Neurology, Medicine), according to OpenAlex
Funding: NIA NIH HHS (K08 AG065502, R01 AG088082, R56 AG079857, K01 AG051791, DP2 AG072437, R01 AG070921); American Heart Association (American Heart Association, Inc.) (23CDA1046074); U.S. Department of Defense (United States Department of Defense) (W81XWH2010028); National Research Foundation of Korea (NRF) (RS-2023-00217881, RS-2025-02215360); NINDS NIH HHS (UH3 NS132138, R01 NS032457, UG3 NS132138)
Citations: cited by 1 paper (Europe PMC); 113 references in the paper

Abstract

Although mutations in many genes cause familial amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), most cases are sporadic (sALS and sFTD) with unclear etiology. Here we tested whether somatic mutations contribute to sALS and sFTD by deep targeted sequencing of 88 neurodegeneration-related genes in postmortem brain and spinal cord samples from 399 sporadic cases and 144 controls. Predicted deleterious somatic variants in ALS/FTD genes were observed in 2.1% of sporadic cases lacking deleterious germline variants. These variants occurred at very low allele fractions (typically <2%) and were often focal and enriched in disease-affected regions. Analysis of bulk RNA-sequencing data from an additional cohort identified deleterious somatic variants in DYNC1H1 and LMNA, genes associated with pediatric motor neuron degeneration. Targeted long-read sequencing further identified one sFTD case with de novo somatic C9orf72 repeat expansions. Together, these findings suggest that rare, focal somatic variants can contribute to sALS and sFTD and drive widespread neurodegeneration.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

AugustHuang/RNA-MosaicHunter

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: d3caac84e2b5515e68e1f0d2fe43ab006ed34cfe, 16 September 2025
Languages: Java (46), R (1)
Size: 122 files, 47 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 (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
48 files
At the source: github.com/AugustHuang/RNA-MosaicHunter

kimjh607/ALS-FTD-somatic-mosaicism

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 02fba49d34d9d220149a1fabe7dcfeb25abd6bdc, 18 February 2026
Languages: R (7)
Size: 59 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (7 files), tidyverse (6 files), reshape2 (2 files), lmerTest (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
7 files
At the source: github.com/kimjh607/ALS-FTD-somatic-mosaicism

Zenodo 18682277

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (7 files), tidyverse (6 files), reshape2 (2 files), lmerTest (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
7 files
At the source:

Code availability

The source code and default configuration file of RNA-MosaicHunter have been published and are available at https://github.com/AugustHuang/RNA-MosaicHunter. Other custom analysis scripts used in this study are publicly available on GitHub (https://github.com/kimjh607/ALS-FTD-somatic-mosaicism) and archived on Zenodo (10.5281/zenodo.18682277)113.

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 61 scripts, each with its path and the digest of its content;
  • 11 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 bulk RNA-seq data generated by the NYGC ALS Consortium are available through controlled access through the Target ALS Data Portal (https://dataengine.targetals.org/). Access requires acceptance of the Target ALS Data Use Agreement and submission of a data access request through the portal application process. Additional details are provided in the Target ALS Data Portal User Manual. The MIP-based targeted sequencing data and long-read sequencing data generated in this study have been deposited in dbGaP under accession phs003530, with access governed by human participant privacy regulations. Germline and somatic variants identified and validated in this study are listed in the Supplementary Tables.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 24 authors, 4 keywords, 8 MeSH terms, 5 funders, 111 references.

Cite

This paper

Zhou, Z., Kim, J., Huang, A. Y., Nolan, M., Park, J., Doan, R., Shin, T., Miller, M. B., Bae, M., Zhao, B., Kim, J., Chhouk, B., Morillo, K., Yeh, R. C., Kenny, C., Neil, J. E., Lee, C.-Z., Ohkubo, T., Ravits, J., . . . Walsh, C. A. (2026). Somatic mosaicism in ALS and FTD identifies focal mutations associated with widespread degeneration. Nature genetics, 58(5), 1019-1029. https://doi.org/10.1038/s41588-026-02570-6

BibTeX

@article{zhou2026somatic,
author = {Zhou, Zinan and Kim, Junho and Huang, August Yue and Nolan, Matthew and Park, Junseok and Doan, Ryan and Shin, Taehwan and Miller, Michael B and Bae, Mingyun and Zhao, Boxun and Kim, Jinhyeong and Chhouk, Brian and Morillo, Katherine and Yeh, Rebecca C and Kenny, Connor and Neil, Jennifer E and Lee, Chao-Zong and Ohkubo, Takuya and Ravits, John and Ansorge, Olaf and Ostrow, Lyle W and Lagier-Tourenne, Clotilde and Lee, Eunjung Alice and Walsh, Christopher A},
title = {{Somatic mosaicism in ALS and FTD identifies focal mutations associated with widespread degeneration}},
journal = {Nature genetics},
year = {2026},
month = apr,
volume = {58},
number = {5},
pages = {1019--1029},
publisher = {Nature Portfolio},
issn = {1061-4036},
doi = {10.1038/s41588-026-02570-6},
url = {https://doi.org/10.1038/s41588-026-02570-6},
pmid = {41986690},
pmcid = {PMC13175891}
}

RIS

TY - JOUR
AU - Zhou, Zinan
AU - Kim, Junho
AU - Huang, August Yue
AU - Nolan, Matthew
AU - Park, Junseok
AU - Doan, Ryan
AU - Shin, Taehwan
AU - Miller, Michael B
AU - Bae, Mingyun
AU - Zhao, Boxun
AU - Kim, Jinhyeong
AU - Chhouk, Brian
AU - Morillo, Katherine
AU - Yeh, Rebecca C
AU - Kenny, Connor
AU - Neil, Jennifer E
AU - Lee, Chao-Zong
AU - Ohkubo, Takuya
AU - Ravits, John
AU - Ansorge, Olaf
AU - Ostrow, Lyle W
AU - Lagier-Tourenne, Clotilde
AU - Lee, Eunjung Alice
AU - Walsh, Christopher A
TI - Somatic mosaicism in ALS and FTD identifies focal mutations associated with widespread degeneration
T2 - Nature genetics
J2 - Nat Genet
PY - 2026
DA - 2026/04/15
VL - 58
IS - 5
SP - 1019
EP - 1029
SN - 1061-4036
PB - Nature Portfolio
DO - 10.1038/s41588-026-02570-6
UR - https://doi.org/10.1038/s41588-026-02570-6
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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