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Genetic and epigenetic convergence at <i>MOBP</i> links ALS and PSP

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  1. [1] § Results › Fine mapping of MOBP locus confirms overlap between PSP and ALS but not FTD ↔ SuSiE Colocalisation GWAS.Rmd, lines 212 to 263 · score 0.58 · fine mapped, SuSiE, top SNP, PIP, credible, MOBP locus · Report a mismatch
  2. [2] § Methods › Co-localisation Analysis ↔ SuSiE Colocalisation GWAS.Rmd, lines 212 to 263 · score 0.53 · fine mapping, SuSiE, PIP, credible, trait, colocalisation · Report a mismatch

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The authors' code

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  1. ---
  2. title: "R Notebook"
  3. output: html_notebook
  4. ---
  5. ```{r}
  6. # Paths to LD matrix (r^2) and BIM file
  7. ld_path <- "/1KG.EUR.onList.refLD.ld"
  8. bim_path <- "/1KG.EUR.onList.reid.bim"
  10. LD <- as.matrix(read.table(ld_path, header = FALSE, check.names = FALSE))
  11. bim <- read.table(bim_path, header = FALSE, stringsAsFactors = FALSE)
  14. colnames(bim)[1:6] <- c("CHR","ID","CM","POS","A1","A2")
  16. ld_ids <- bim$ID
  17. stopifnot(nrow(LD) == length(ld_ids))
  18. rownames(LD) <- colnames(LD) <- ld_ids
  21. R_all <- LD
  22. R_all[R_all < 0] <- 0
  23. R_all <- sqrt(R_all)
  25. # LD-allele reference table
  26. ld_ref <- data.frame(
  27. snp = bim$ID,
  28. A1_ref = toupper(bim$A1),
  29. A2_ref = toupper(bim$A2)
  30. )
  31. ```
  35. ```{r}
  37. ```
  39. ```{r}
  40. library(dplyr)
  41. library(susieR)
  43. attach_ld_cc <- function(ds, trait_label) {
  46. idx <- match(ds$snp, ld_ref$snp)
  47. keep <- !is.na(idx)
  49. if (sum(keep) < 10) {
  50. warning(trait_label, ": only ", sum(keep), " SNPs match LD reference")
  51. }
  54. ds$snp <- ds$snp[keep]
  55. ds$beta <- ds$beta[keep]
  56. ds$varbeta <- ds$varbeta[keep]
  57. ds$MAF <- ds$MAF[keep]
  58. ds$A1 <- ds$A1[keep]
  59. ds$A2 <- ds$A2[keep]
  60. ds$REF <- ds$REF[keep]
  61. ds$ALT <- ds$ALT[keep]
  62. if (!is.null(ds$pos)) ds$pos <- ds$pos[keep]
  64. a1_ref <- ld_ref$A1_ref[idx[keep]]
  65. a2_ref <- ld_ref$A2_ref[idx[keep]]
  67. # flip where A1 is the other allele
  68. flip <- ds$A1 != a1_ref & ds$A1 == a2_ref
  69. ds$beta[flip] <- -ds$beta[flip]
  72. mismatch <- !( (ds$A1 == a1_ref & ds$A2 == a2_ref) |
  73. (ds$A1 == a2_ref & ds$A2 == a1_ref) )
  74. if (any(mismatch)) {
  75. message(trait_label, ": dropping ", sum(mismatch),
  76. " SNPs with incompatible alleles vs LD ref")
  77. keep2 <- !mismatch
  78. ds$snp <- ds$snp[keep2]
  79. ds$beta <- ds$beta[keep2]
  80. ds$varbeta <- ds$varbeta[keep2]
  81. ds$MAF <- ds$MAF[keep2]
  82. ds$A1 <- ds$A1[keep2]
  83. ds$A2 <- ds$A2[keep2]
  84. ds$REF <- ds$REF[keep2]
  85. ds$ALT <- ds$ALT[keep2]
  86. if (!is.null(ds$pos)) ds$pos <- ds$pos[keep2]
  87. idx <- idx[keep][keep2]
  88. } else {
  89. idx <- idx[keep]
  90. }
  92. # subset LD matrix
  93. ds$LD <- R_all[idx, idx]
  95. message(trait_label, ": attached LD for ", length(ds$snp), " SNPs")
  97. ds
  98. }
  100. runsusie_cc <- function(ds, L = 10, maxit = 10000) {
  101. z <- ds$beta / sqrt(ds$varbeta)
  103. susie_fit <- susie_rss(
  104. z = z,
  105. R = ds$LD,
  106. n = ds$N,
  107. L = L,
  108. coverage = 0.95,
  109. max_iter = maxit
  110. )
  112. susie_fit$snp <- ds$snp
  113. if (!is.null(ds$pos)) susie_fit$pos <- ds$pos
  114. susie_fit$label <- ds$label
  115. susie_fit
  116. }
  119. datasets$PD <- attach_ld_cc(datasets$PD, "PD")
  120. datasets$MSA <- attach_ld_cc(datasets$MSA, "MSA")
  121. datasets$AD <- attach_ld_cc(datasets$AD, "AD")
  122. datasets$CJD <- attach_ld_cc(datasets$CJD, "CJD")
  123. datasets$FTD <- attach_ld_cc(datasets$FTD, "FTD")
  125. susie_ALS <- runsusie_cc(dataset.als)
  126. susie_PSP <- runsusie_cc(dataset.psp)
  127. susie_PD <- runsusie_cc(datasets$PD)
  128. susie_MSA <- runsusie_cc(datasets$MSA)
  129. susie_AD <- runsusie_cc(datasets$AD)
  130. susie_CJD <- runsusie_cc(datasets$CJD)
  131. susie_FTD <- runsusie_cc(datasets$FTD)
  134. ```
  136. ```{r}
  137. library(dplyr)
  139. summarise_cs <- function(susie_fit, gwas_df, trait_label) {
  141. pip_tbl <- data.frame(
  142. SNP = susie_fit$snp,
  143. pip = susie_fit$pip,
  144. stringsAsFactors = FALSE
  145. )
  147. cs_list <- susie_fit$sets$cs
  149. if (is.null(cs_list)) {
  150. warning(trait_label, ": no credible sets found by SuSiE.")
  151. return(list(
  152. snp_level = NULL,
  153. cs_level = NULL
  154. ))
  155. }
  157. cs_tbl <- lapply(seq_along(cs_list), function(i) {
  158. snp_idx <- cs_list[[i]]
  159. data.frame(
  160. SNP = susie_fit$snp[snp_idx],
  161. cs_id = paste0("CS", i),
  162. stringsAsFactors = FALSE
  163. )
  164. }) %>% bind_rows()
  166. snp_level <- pip_tbl %>%
  167. left_join(cs_tbl, by = "SNP") %>%
  168. left_join(gwas_df %>% select(SNP, pos, P), by = "SNP") %>%
  169. mutate(trait = trait_label)
  171. cs_level <- snp_level %>%
  172. filter(!is.na(cs_id)) %>%
  173. group_by(trait, cs_id) %>%
  174. summarise(
  175. n_snps = n(),
  176. lead_SNP = SNP[which.max(pip)],
  177. max_pip = max(pip, na.rm = TRUE),
  178. min_pos = min(pos, na.rm = TRUE),
  179. max_pos = max(pos, na.rm = TRUE),
  180. .groups = "drop"
  181. ) %>%
  182. mutate(span_kb = (max_pos - min_pos) / 1e3)
  184. list(
  185. snp_level = snp_level,
  186. cs_level = cs_level
  187. )
  188. }
  190. psp_cs <- summarise_cs(susie_PSP, PSP_f, "PSP")
  191. als_cs <- summarise_cs(susie_ALS, ALS_f, "ALS")
  192. ftd_cs <- summarise_cs(susie_FTD, FTD_h, "FTD")
  193. ad_cs <- summarise_cs(susie_AD, AD_h, "AD")
  194. msa_cs <- summarise_cs(susie_MSA, MSA_h, "MSA")
  195. pd_cs <- summarise_cs(susie_PD, PD_h, "PD")
  196. cjd_cs <- summarise_cs(susie_CJD, CJD_f, "CJD")
  198. psp_cs$cs_level
  199. als_cs$cs_level
  200. ftd_cs$cs_level
  203. cs_all <- bind_rows(
  204. psp_cs$cs_level,
  205. als_cs$cs_level,
  206. ftd_cs$cs_level
  207. )
  209. cs_all
  211. ```
  212. ```{r}
  213. library(ggplot2)
  214. library(ggrepel)
  216. plot_cs_manhattan <- function(cs_obj, trait_label, title_prefix = NULL) {
  218. snps <- cs_obj$snp_level
  220. # credible set colour palette
  221. cs_ids <- sort(unique(na.omit(snps$cs_id)))
  222. n_cs <- length(cs_ids)
  224. cs_cols <- if (n_cs > 0) {
  225. setNames(RColorBrewer::brewer.pal(max(3, n_cs), "Set1")[1:n_cs], cs_ids)
  226. } else character(0)
  228. col_vec <- c("not_CS" = "grey80", cs_cols)
  230. snps$cs_id_plot <- ifelse(is.na(snps$cs_id), "not_CS", snps$cs_id)
  231. snps$neglogP <- -log10(snps$P)
  233. # top SNP labels (max pip per CS)
  234. top_snps <- snps %>%
  235. group_by(cs_id) %>%
  236. filter(pip == max(pip)) %>%
  237. ungroup() %>%
  238. filter(!is.na(cs_id))
  240. p <- ggplot(snps, aes(x = pos, y = neglogP)) +
  241. geom_point(aes(colour = cs_id_plot, size = pip), alpha = 0.9) +
  242. scale_colour_manual(values = col_vec, name = "Credible set") +
  243. scale_size(range = c(1.2, 4), name = "PIP") +
  244. geom_text_repel(
  245. data = top_snps,
  246. aes(label = SNP),
  247. size = 3,
  248. min.segment.length = 0
  249. ) +
  250. theme_classic(base_size = 14) +
  251. labs(
  252. title = paste0(title_prefix, trait_label, " fine-mapping (SuSiE)"),
  253. x = "Genomic position (chr 3, MOBP locus)",
  254. y = expression(-log[10](P))
  255. )
  257. p
  258. }
  259. p_psp <- plot_cs_manhattan(psp_cs, "PSP", "Credible sets: ")
  260. p_als <- plot_cs_manhattan(als_cs, "ALS", "Credible sets: ")
  261. p_ftd <- plot_cs_manhattan(ftd_cs, "FTD", "Credible sets: ")
  263. ```
  265. ```{r}
  266. plot_pip_track <- function(cs_obj, trait_label) {
  267. snps <- cs_obj$snp_level
  268. snps$cs_id_plot <- ifelse(is.na(snps$cs_id), "not_CS", snps$cs_id)
  269. cs_ids <- sort(unique(na.omit(snps$cs_id)))
  270. n_cs <- length(cs_ids)
  272. cs_cols <- if (n_cs > 0) {
  273. setNames(RColorBrewer::brewer.pal(max(3, n_cs), "Set1")[1:n_cs], cs_ids)
  274. } else character(0)
  275. col_vec <- c("not_CS" = "grey80", cs_cols)
  277. ggplot(snps, aes(x = pos, y = pip, colour = cs_id_plot)) +
  278. geom_point(size = 2, alpha = 0.9) +
  279. scale_colour_manual(values = col_vec, name = "Credible set") +
  280. theme_classic(base_size = 14) +
  281. labs(
  282. title = paste(trait_label, "posterior inclusion probabilities"),
  283. x = "Genomic position",
  284. y = "PIP"
  285. )
  286. }
  288. ```
  290. ```{r}
  291. library(dplyr)
  292. library(ggplot2)
  293. library(ggrepel)
  297. plot_pip_with_labels <- function(cs_obj, disease_label,
  298. top_n_per_cs = 3,
  299. min_pip = 0.01) {
  300. # cs_obj is the list from summarise_cs()
  301. snps <- cs_obj$snp_level %>%
  302. mutate(
  303. cs = ifelse(is.na(cs_id), "not_CS", cs_id),
  304. PIP = pip
  305. )
  307. # label only top K SNPs per CS with PIP >= min_pip
  308. lab_df <- snps %>%
  309. filter(cs != "not_CS", PIP >= min_pip) %>%
  310. group_by(cs) %>%
  311. slice_max(order_by = PIP,
  312. n = top_n_per_cs,
  313. with_ties = FALSE) %>%
  314. ungroup()
  316. ggplot(snps, aes(x = pos, y = PIP, colour = cs)) +
  317. geom_point(alpha = 0.9, size = 2) +
  318. geom_text_repel(
  319. data = lab_df,
  320. aes(label = SNP),
  321. size = 3,
  322. box.padding = 0.3,
  323. point.padding = 0.1,
  324. min.segment.length = 0,
  325. max.overlaps = Inf,
  326. colour = "black"
  327. ) +
  328. scale_colour_manual(
  329. values = c(
  330. "CS1" = "#d62728",
  331. "CS2" = "#1f77b4",
  332. "CS3" = "#2ca02c",
  333. "not_CS" = "grey75"
  334. ),
  335. name = "Credible set"
  336. ) +
  337. theme_classic(base_size = 13) +
  338. labs(
  339. title = paste(disease_label, "posterior inclusion probabilities"),
  340. x = "Genomic position (chr3, MOBP locus)",
  341. y = "PIP"
  342. )
  343. }
  345. p_pip_psp <- plot_pip_with_labels(psp_cs, "PSP")
  346. p_pip_als <- plot_pip_with_labels(als_cs, "ALS")
  347. p_pip_ftd <- plot_pip_with_labels(ftd_cs, "FTD")
  349. library(patchwork)
  351. p_pip_psp + p_pip_als + p_pip_ftd +
  352. plot_layout(ncol = 1)
  354. ```
  357. ```{r}
  358. library(dplyr)
  359. library(tidyr)
  362. all_cs_snps <- tibble(SNP = unique(c(psp_cs1, als_cs1, ftd_cs1)))
  364. # presence/absence for each disease
  365. cs_membership <- all_cs_snps %>%
  366. mutate(
  367. PSP = SNP %in% psp_cs1,
  368. ALS = SNP %in% als_cs1,
  369. FTD = SNP %in% ftd_cs1
  370. )
  371. cs_membership
  373. ```
  375. ```{r}
  376. library(dplyr)
  377. library(GenomicRanges)
  378. library(ggplot2)
  381. mobp_gff <- gencode_gff %>%
  382. filter(gene_name == "MOBP")
  385. unique(mobp_gff$seqid)
  386. unique(mobp_gff$strand)
  389. mobp_exons_raw <- mobp_gff %>%
  390. filter(type == "exon") %>%
  391. select(seqid, start, end, strand)
  394. gr_exons <- GRanges(
  395. seqnames = mobp_exons_raw$seqid,
  396. ranges = IRanges(start = mobp_exons_raw$start,
  397. end = mobp_exons_raw$end)
  398. )
  400. gr_exons_merged <- reduce(gr_exons)
  401. mobp_exons_merged <- as.data.frame(gr_exons_merged) %>%
  402. rename(seqid = seqnames) %>%
  403. select(seqid, start, end)
  406. gene_start <- min(mobp_exons_merged$start)
  407. gene_end <- max(mobp_exons_merged$end)
  410. ggplot() +
  411. # gene backbone = intron line
  412. geom_segment(
  413. aes(x = gene_start, xend = gene_end, y = 0, yend = 0),
  414. linewidth = 0.6,
  415. colour = "grey40"
  416. ) +
  417. # merged exons as boxes on the backbone
  418. geom_rect(
  419. data = mobp_exons_merged,
  420. aes(xmin = start, xmax = end,
  421. ymin = -0.1, ymax = 0.1),
  422. fill = "grey20"
  423. ) +
  424. coord_cartesian(
  425. xlim = c(gene_start - 2000, gene_end + 2000), # add a bit of padding
  426. ylim = c(-0.3, 0.3),
  427. expand = FALSE
  428. ) +
  429. theme_classic(base_size = 12) +
  430. labs(
  431. x = "Genomic position on chr3",
  432. y = NULL,
  433. title = "MOBP gene structure (merged exons across transcripts)"
  434. ) +
  435. theme(
  436. axis.text.y = element_blank(),
  437. axis.ticks.y = element_blank()
  438. )
  440. ```
  441. ```{r}
  442. library(dplyr)
  443. library(GenomicRanges)
  444. library(ggplot2)
  447. mobp_exons <- gencode_gff %>%
  448. filter(gene_name == "MOBP", type == "exon")
  451. gr <- GRanges(
  452. seqnames = mobp_exons$seqid,
  453. ranges = IRanges(start = mobp_exons$start, end = mobp_exons$end)
  454. )
  456. gr_merged <- reduce(gr)
  458. mobp_exons_merged <- as.data.frame(gr_merged) %>%
  459. transmute(start = start, end = end)
  462. gene_start <- min(mobp_exons_merged$start)
  463. gene_end <- max(mobp_exons_merged$end)
  466. p_strip <- ggplot() +
  467. # intron backbone
  468. geom_segment(aes(x = gene_start, xend = gene_end, y = 0, yend = 0),
  469. linewidth = 0.6, colour = "grey40") +
  470. # exons (merged)
  471. geom_rect(data = mobp_exons_merged,
  472. aes(xmin = start, xmax = end, ymin = -0.1, ymax = 0.1),
  473. fill = "grey20") +
  474. coord_cartesian(
  475. xlim = c(gene_start - 2000, gene_end + 2000),
  476. ylim = c(-0.3, 0.3),
  477. expand = FALSE
  478. ) +
  479. theme_classic(base_size = 12) +
  480. theme(
  481. axis.title.y = element_blank(),
  482. axis.text.y = element_blank(),
  483. axis.ticks.y = element_blank()
  484. ) +
  485. labs(
  486. x = "Genomic position (chr3)",
  487. title = "MOBP gene structure (merged exons)"
  488. )
  490. p_strip
  492. ```
  494. ```{r}
  495. library(dplyr)
  498. psp_cs1 <- psp_cs$snp_level %>%
  499. filter(cs_id == "CS1") %>%
  500. pull(SNP) %>%
  501. unique()
  503. als_cs1 <- als_cs$snp_level %>%
  504. filter(cs_id == "CS1") %>%
  505. pull(SNP) %>%
  506. unique()
  508. ftd_cs1 <- ftd_cs$snp_level %>%
  509. filter(cs_id == "CS1") %>%
  510. pull(SNP) %>%
  511. unique()
  514. all_cs_snps <- tibble(SNP = unique(c(psp_cs1, als_cs1, ftd_cs1)))
  517. cs_membership <- all_cs_snps %>%
  518. mutate(
  519. PSP = SNP %in% psp_cs1,
  520. ALS = SNP %in% als_cs1,
  521. FTD = SNP %in% ftd_cs1
  522. )
  525. pos_lookup <- bind_rows(
  526. ALS_f %>% select(SNP, pos),
  527. PSP_f %>% select(SNP, pos),
  528. FTD_h %>% select(SNP, pos)
  529. ) %>%
  530. distinct(SNP, .keep_all = TRUE)
  532. cs_snps_pos <- cs_membership %>%
  533. left_join(pos_lookup, by = "SNP")
  536. cs_snps_pos <- cs_snps_pos %>%
  537. mutate(
  538. group = case_when(
  539. PSP & ALS & !FTD ~ "Shared PSP+ALS",
  540. ALS & !PSP & !FTD ~ "ALS only",
  541. PSP & !ALS & !FTD ~ "PSP only",
  542. FTD & !PSP & !ALS ~ "FTD only",
  543. TRUE ~ "Other"
  544. )
  545. )
  547. gene_start <- min(mobp_exons_merged$start)
  548. gene_end <- max(mobp_exons_merged$end)
  549. mobp_exons_merged # from the code that made your strip
  551. library(ggplot2)
  552. library(ggrepel)
  555. snp_y <- 0.25
  557. min_pos <- min(cs_snps_pos$pos, na.rm = TRUE)
  559. left_lim <- min(min_pos, gene_start) - 2000 # include upstream FTD
  560. right_lim <- gene_end + 2000 # always keep full gene
  562. p_mobp_cs <- ggplot() +
  563. # intron backbone
  564. geom_segment(
  565. aes(x = gene_start, xend = gene_end, y = 0, yend = 0),
  566. linewidth = 0.6,
  567. colour = "grey40"
  568. ) +
  569. # exons
  570. geom_rect(
  571. data = mobp_exons_merged,
  572. aes(xmin = start, xmax = end, ymin = -0.1, ymax = 0.1),
  573. fill = "grey20"
  574. ) +
  575. # SNP stems
  576. geom_segment(
  577. data = cs_snps_pos,
  578. aes(x = pos, xend = pos, y = 0, yend = 0.25, colour = group),
  579. linewidth = 0.5
  580. ) +
  581. # SNP points
  582. geom_point(
  583. data = cs_snps_pos,
  584. aes(x = pos, y = 0.25, colour = group),
  585. size = 2
  586. ) +
  587. # labels for shared PSP+ALS
  588. ggrepel::geom_text_repel(
  589. data = cs_snps_pos ,
  590. aes(x = pos, y = 0.25, label = SNP, colour = group),
  591. nudge_y = 0.1,
  592. size = 3,
  593. show.legend = FALSE
  594. ) +
  595. scale_colour_manual(
  596. values = c(
  597. "Shared PSP+ALS" = "#E31A1C",
  598. "ALS only" = "#1F78B4",
  599. "FTD only" = "#33A02C"
  600. ),
  601. name = "CS1 membership"
  602. ) +
  603. coord_cartesian(
  604. xlim = c(left_lim, right_lim),
  605. ylim = c(-0.3, 0.5),
  606. expand = FALSE
  607. ) +
  608. theme_classic(base_size = 12) +
  609. theme(
  610. axis.title.y = element_blank(),
  611. axis.text.y = element_blank(),
  612. axis.ticks.y = element_blank()
  613. ) +
  614. labs(
  615. x = "Genomic position (chr3)",
  616. title = "CS1 credible SNPs across MOBP locus"
  617. )
  619. p_mobp_cs
  622. ```
  623. ```{r}
  624. library(dplyr)
  625. library(tidyr)
  626. library(stringr)
  629. all_cs1_snps <- union(union(psp_cs_snps, als_cs1), ftd_cs1)
  632. summary_tbl <- tibble(SNP = all_cs1_snps) %>%
  633. mutate(
  634. PSP = ifelse(SNP %in% psp_cs1, "✓", ""),
  635. ALS = ifelse(SNP %in% als_cs1, "✓", ""),
  636. FTD = ifelse(SNP %in% ftd_cs1, "✓", "")
  637. )
  639. summary_tbl <- summary_tbl %>%
  640. mutate(
  641. Group = case_when(
  642. PSP == "✓" & ALS == "✓" ~ "Shared PSP+ALS",
  643. ALS == "✓" & PSP == "" ~ "ALS only",
  644. FTD == "✓" ~ "FTD only",
  645. TRUE ~ "Other"
  646. )
  647. )
  650. get_pip_df <- function(cs_obj, label){
  651. cs_obj %>%
  652. select(SNP, PIP) %>%
  653. rename(!!paste0(label, "_PIP") := PIP)
  654. }
  656. pip_psp <- get_pip_df(psp_cs, "PSP")
  657. pip_als <- get_pip_df(als_cs, "ALS")
  658. pip_ftd <- get_pip_df(ftd_cs, "FTD")
  661. summary_tbl <- summary_tbl %>%
  662. left_join(pip_psp, by = "SNP") %>%
  663. left_join(pip_als, by = "SNP") %>%
  664. left_join(pip_ftd, by = "SNP")
  667. summary_tbl <- summary_tbl %>%
  668. arrange(desc(Group), SNP)
  670. summary_tbl
  672. ```
  674. ```{r}
  675. library(dplyr)
  679. psp_cs1_df <- psp_cs$snp_level %>%
  680. filter(cs_id == "CS1") %>%
  681. transmute(
  682. SNP,
  683. pos,
  684. PSP_PIP = pip,
  685. PSP_CS1 = TRUE
  686. )
  688. als_cs1_df <- als_cs$snp_level %>%
  689. filter(cs_id == "CS1") %>%
  690. transmute(
  691. SNP,
  692. pos,
  693. ALS_PIP = pip,
  694. ALS_CS1 = TRUE
  695. )
  697. ftd_cs1_df <- ftd_cs$snp_level %>%
  698. filter(cs_id == "CS1") %>%
  699. transmute(
  700. SNP,
  701. pos,
  702. FTD_PIP = pip,
  703. FTD_CS1 = TRUE
  704. )
  706. cs1_summary <- psp_cs1_df %>%
  707. full_join(als_cs1_df, by = c("SNP", "pos")) %>%
  708. full_join(ftd_cs1_df, by = c("SNP", "pos"))
  711. cs1_summary <- cs1_summary %>%
  712. mutate(
  713. PSP_CS1 = if_else(is.na(PSP_CS1), FALSE, PSP_CS1),
  714. ALS_CS1 = if_else(is.na(ALS_CS1), FALSE, ALS_CS1),
  715. FTD_CS1 = if_else(is.na(FTD_CS1), FALSE, FTD_CS1),
  716. PSP_PIP = if_else(is.na(PSP_PIP), 0, PSP_PIP),
  717. ALS_PIP = if_else(is.na(ALS_PIP), 0, ALS_PIP),
  718. FTD_PIP = if_else(is.na(FTD_PIP), 0, FTD_PIP)
  719. )
  723. cs1_summary <- cs1_summary %>%
  724. mutate(
  725. Group = case_when(
  726. PSP_CS1 & ALS_CS1 & !FTD_CS1 ~ "Shared PSP+ALS",
  727. ALS_CS1 & !PSP_CS1 & !FTD_CS1 ~ "ALS only",
  728. FTD_CS1 & !PSP_CS1 & !ALS_CS1 ~ "FTD only",
  729. TRUE ~ "Other"
  730. ),
  731. PSP = if_else(PSP_CS1, "✓", ""),
  732. ALS = if_else(ALS_CS1, "✓", ""),
  733. FTD = if_else(FTD_CS1, "✓", "")
  734. ) %>%
  735. select(
  736. SNP, pos,
  737. PSP, ALS, FTD,
  738. PSP_PIP, ALS_PIP, FTD_PIP,
  739. Group
  740. ) %>%
  741. arrange(Group, SNP)
  743. cs1_summary
  745. ```

SuSiE Colocalisation GWAS.Rmd at commit e0ce8e7, no license · at the source

Overview

Authors: Katherine Fodder1, Megha Murthy2, Rohan de Silva2,3, Towfique Raj4,5,6,7, Kurt Farrell5,6,7,8,9,10, Jack Humphrey4,5,6,7, Conceição Bettencourt1
ORCID iDs: Katherine Fodder, Towfique Raj, Conceição Bettencourt
  1. Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
  2. Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
  3. Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK
  4. Department of Genetics and Genomic Sciences, Icahn Genomics Institute, Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  5. Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  6. Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  7. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  8. Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  9. Department of Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  10. Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Institutions: UCL Queen Square Institute of Neurology (United Kingdom); Icahn School of Medicine at Mount Sinai (United States)
Journal: bioRxiv (preprint)
Dates: published online 27 March 2026
Type: Preprint · Language: English
License: CC BY
Identifiers: DOI 10.64898/2026.03.25.714147 · OpenAlex W7141512504
Open access: green, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), other condition (population), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics
Topic: Amyotrophic Lateral Sclerosis Research (Clinical Neurology, Medicine), according to OpenAlex
Funding: BrightFocus Foundation; CurePSP; Target ALS; Georgia Clinical and Translational Science Alliance (UL1TR004419); National Institutes of Health (U01-AG068880, R01-NS116006, R01-AG054005, UL1 TR004419, AG066514, NIA R01-AG054005, RF1-AG065926); Tau Consortium; Medical Research Council (MR/N013867/1); National Institute on Aging (K01AG070326, U01 AG068880, R21-AG063130, R01- AG054005, NIA R21-AG063130, RF1-AG065926); National Institute of Neurological Disorders and Stroke (R01 NS116006); National Center for Advancing Translational Sciences (UL1TR004419); Reta Lila Weston Trust for Medical Research
Citations: not cited yet (Europe PMC); 48 references in the paper

Abstract

Myelin oligodendrocyte basic protein (MOBP) is an abundant oligodendrocyte gene implicated in multiple neurodegenerative diseases. Genetic variation at the MOBP locus has been associated with risk for progressive supranuclear palsy (PSP), amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTD), corticobasal degeneration (CBD), Alzheimer’s disease (AD), Lewy body dementia (LBD), and Creutzfeldt–Jakob disease (CJD). Epigenetically, MOBP promoter hypermethylation and reduced expression have been reported in multiple system atrophy (MSA). Although MOBP is thought to play a role in oligodendrocyte morphology and myelin structure, how genetic and epigenetic variation at this locus influences gene regulation and contributes to disease risk remains poorly understood across neurodegenerative disorders.

Here, we investigated whether shared or disease-specific genetic mechanisms at MOBP converge on altered DNA methylation and expression across neurodegenerative disorders. We analysed MOBP variants using summary statistics from recent GWAS for ALS, PSP, FTD, LBD, PD, MSA, AD, and CJD. Colocalisation (COLOC and SuSiE-coloc) was used to test whether disease-associated variants overlapped between diseases, and with oligodendrocyte expression quantitative trait loci (eQTLs) and bulk brain methylation quantitative trait loci (mQTLs). To further investigate mQTL effects at this locus, rs1768208, a variant previously associated with PSP, was genotyped in an overlapping brain methylation cohort, allowing direct testing of genotype–methylation associations in frontal white matter tissue.

ALS and PSP GWAS demonstrated strong association at MOBP, with most strongly associated SNPs (e.g. rs631312, rs616147, rs1768208) shared between both disorders. Colocalisation analyses indicated high posterior probability that ALS and PSP share the same causal variant, with weaker overlap with FTD. mQTL colocalisation highlighted cg15069948, located near an exon junction within MOBP, as strongly colocalising with the ALS/PSP risk variants. In complementary tissue analyses, rs1768208-T carriers showed hypomethylation at cg15069948 in PSP brains. No genotype-methylation effects were detected in MSA or Parkinson’s disease.

Together with prior evidence of promoter hypermethylation and reduced expression in MSA, our findings identify cg15069948 as a regulatory methylation site linking ALS/PSP risk variants to altered MOBP methylation, and support MOBP dysregulation as a shared feature of neurodegeneration. However, the underlying mechanisms appear disease-specific, highlighting the complexity of involvement of this gene across neurodegenerative disorders.

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 2 matches between paragraphs and lines of code.

CBettencourtLab/MOBPAnalysis

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: e0ce8e7e53a02528aa6718f3feec05730e32e1b4, 25 August 2026
Languages: Shell (3), R (2)
Size: 10 files, 5 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), patchwork (2 files), STAR (2 files), tidyverse (2 files), pheatmap (1 file), reshape2 (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
6 files
At the source: github.com/CBettencourtLab/MOBPAnalysis

Code Availability

All code used for analysis will be made available upon publication in a publicly accessible GitHub repository: https://github.com/CBettencourtLab/MOBPAnalysis.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 scripts, each with its path and the digest of its content;
  • 2 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

Datasets cited

Data Availability

ALS, MSA, AD, CJD and PD GWAS summary statistics were accessed through the GWAS Catalog (accession numbers GCST90027164, GCST90406924, GCST90027158, GCST90001389 and GCST009325 respectively). FTD GWAS summary statistics were accessed from the UCL Research Data Depository (https://doi.org/10.5522/04/25600692.v1.). eQTL summary statistics were accessed through https://zenodo.org/records/7276971. mQTL summary statistics were provided by the study authors (27).

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, dates, 7 authors, 11 funders, 48 references.

Cite

This paper

Fodder, K., Murthy, M., de Silva, R., Raj, T., Farrell, K., Humphrey, J., & Bettencourt, C. (2026). Genetic and epigenetic convergence at <i>MOBP</i> links ALS and PSP. bioRxiv (preprint). https://doi.org/10.64898/2026.03.25.714147

BibTeX

@article{fodder2026genetic,
author = {Fodder, Katherine and Murthy, Megha and de Silva, Rohan and Raj, Towfique and Farrell, Kurt and Humphrey, Jack and Bettencourt, Conceição},
title = {{Genetic and epigenetic convergence at <i>MOBP</i> links ALS and PSP}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.25.714147},
url = {https://doi.org/10.64898/2026.03.25.714147}
}

RIS

TY - JOUR
AU - Fodder, Katherine
AU - Murthy, Megha
AU - de Silva, Rohan
AU - Raj, Towfique
AU - Farrell, Kurt
AU - Humphrey, Jack
AU - Bettencourt, Conceição
TI - Genetic and epigenetic convergence at <i>MOBP</i> links ALS and PSP
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/27
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.25.714147
UR - https://doi.org/10.64898/2026.03.25.714147
LA - en
ER -

CSL-JSON

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"id": "10.64898/2026.03.25.714147",
"type": "article",
"title": "Genetic and epigenetic convergence at <i>MOBP</i> links ALS and PSP",
"container-title": "bioRxiv (preprint)",
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"family": "Fodder",
"given": "Katherine"
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{
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],
"container-title-short": "bioRxiv",
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"language": "en",
"issued": {
"date-parts": [
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2026,
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27
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}

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