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Phocaeicola vulgatus improves anxiety-like behavior by ameliorating amygdala neuroinflammation and the neurite impairment in IBS.

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  1. [1] § Materials and methods › Mendelian randomization ↔ R/format_mr_results2.R, lines 137–214 · score 0.65 · inverse variance weighted, weighted median, MR Egger, SNPs
  2. [2] § Materials and methods › Mendelian randomization ↔ R/mr.R, lines 882–921 · score 0.61 · instrumental variable, weighted median, modification, IVW, Mendelian, randomization

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The authors' code

R · 460 lines · 17 KB · other · 1 match

  1. #' Split outcome column
  2. #'
  3. #' This function takes the outcome column from the results generated by [mr()] and splits it into separate columns for 'outcome name' and 'id'.
  4. #'
  5. #' @param mr_res Results from [mr()].
  6. #'
  7. #' @export
  8. #' @return data frame
  9. split_outcome <- function(mr_res) {
  10. Pos <- grep("\\|\\|", mr_res$outcome) #the "||"" indicates that the outcome column was derived from summary data in MR-Base. Sometimes it won't look like this e.g. if the user has supplied their own outcomes
  11. if (sum(Pos) != 0) {
  12. Outcome <- as.character(mr_res$outcome[Pos])
  13. Vars <- strsplit(Outcome, split = "\\|\\|")
  14. Vars <- unlist(Vars)
  15. Vars <- trim(Vars)
  16. Trait <- Vars[seq(1, length(Vars), by = 2)]
  17. id <- Vars[seq(2, length(Vars), by = 2)]
  18. mr_res$outcome <- as.character(mr_res$outcome)
  19. mr_res$outcome[Pos] <- Trait
  20. }
  21. return(mr_res)
  22. }
  24. #' Split exposure column
  25. #'
  26. #' This function takes the exposure column from the results generated by [mr()] and splits it into separate columns for 'exposure name' and 'id'.
  27. #'
  28. #' @param mr_res Results from [mr()].
  29. #'
  30. #' @export
  31. #' @return data frame
  32. split_exposure <- function(mr_res) {
  33. Pos <- grep("\\|\\|", mr_res$exposure) #the "||"" indicates that the outcome column was derived from summary data in MR-Base. Sometimes it won't look like this e.g. if the user has supplied their own outcomes
  34. # Pos2<-grep("\\|\\|",mr_res$exposure,invert=T)
  35. # mr_res2 <-mr_res[Pos2,]
  36. # mr_res1<-mr_res[Pos,]
  37. if (sum(Pos) != 0) {
  38. Exposure <- as.character(mr_res$exposure[Pos])
  39. Vars <- strsplit(as.character(Exposure), split = "\\|\\|")
  40. Vars <- unlist(Vars)
  41. Vars <- trim(Vars)
  42. Trait <- Vars[seq(1, length(Vars), by = 2)]
  43. mr_res$exposure <- as.character(mr_res$exposure)
  44. mr_res$exposure[Pos] <- Trait
  45. }
  46. return(mr_res)
  47. }
  50. #' Generate odds ratios
  51. #'
  52. #' This function takes b and se from [mr()] and generates odds ratios and 95 percent confidence intervals.
  53. #'
  54. #' @param mr_res Results from [mr()].
  55. #'
  56. #' @export
  57. #' @return data frame
  58. generate_odds_ratios <- function(mr_res) {
  59. mr_res$lo_ci <- mr_res$b - 1.96 * mr_res$se
  60. mr_res$up_ci <- mr_res$b + 1.96 * mr_res$se
  61. mr_res$or <- exp(mr_res$b)
  62. mr_res$or_lci95 <- exp(mr_res$lo_ci)
  63. mr_res$or_uci95 <- exp(mr_res$up_ci)
  64. return(mr_res)
  65. }
  67. #' Subset MR-results on method
  68. #'
  69. #' This function takes MR results from [mr()] and restricts to a single method per exposure x disease combination.
  70. #'
  71. #' @param mr_res Results from [mr()].
  72. #' @param single_snp_method Which of the single SNP methods to use when only 1 SNP was used to estimate the causal effect? The default is `"Wald ratio"`.
  73. #' @param multi_snp_method Which of the multi-SNP methods to use when there was more than 1 SNPs used to estimate the causal effect? The default is `"Inverse variance weighted"`.
  74. #'
  75. #' @export
  76. #' @return data frame.
  77. subset_on_method <- function(
  78. mr_res,
  79. single_snp_method = "Wald ratio",
  80. multi_snp_method = "Inverse variance weighted"
  81. ) {
  82. dat <- subset(
  83. mr_res,
  84. (nsnp == 1 & method == single_snp_method) | (nsnp > 1 & method == multi_snp_method)
  85. )
  86. return(dat)
  87. }
  89. #' Combine all mr results
  90. #'
  91. #' This function combines results of [mr()], [mr_heterogeneity()], [mr_pleiotropy_test()] and [mr_singlesnp()] into a single data frame.
  92. #' It also merges the results with outcome study level characteristics in [available_outcomes()].
  93. #' If desired it also exponentiates results (e.g. if the user wants log odds ratio converted into odds ratios with 95 percent confidence intervals).
  94. #' The exposure and outcome columns from the output from [mr()] contain both the trait names and trait ids.
  95. #' The `combine_all_mrresults()` function splits these into separate columns by default.
  96. #'
  97. #' @param res Results from [mr()].
  98. #' @param het Results from [mr_heterogeneity()].
  99. #' @param plt Results from [mr_pleiotropy_test()].
  100. #' @param sin Results from [mr_singlesnp()].
  101. #' @param ao_slc Logical; if set to `TRUE` then outcome study level characteristics are retrieved from [available_outcomes()]. Default is `TRUE`.
  102. #' @param Exp Logical; if set to `TRUE` results are exponentiated. Useful if user wants log odds ratios expressed as odds ratios. Default is `FALSE`.
  103. #' @param split.exposure Logical; if set to `TRUE` the exposure column is split into separate columns for the exposure name and exposure ID. Default is `FALSE`.
  104. #' @param split.outcome Logical; if set to `TRUE` the outcome column is split into separate columns for the outcome name and outcome ID. Default is `FALSE`.
  105. #'
  106. #' @export
  107. #' @return data frame
  109. #
  110. # library(TwoSampleMR)
  111. # library(MRInstruments)
  113. # exp_dat <- extract_instruments(outcomes=c(2,300))
  115. # chd_out_dat <- extract_outcome_data(
  116. # snps = exp_dat$SNP,
  117. # outcomes = c(6,7,8,9)
  118. # )
  120. # dat <- harmonise_data(
  121. # exposure_dat = exp_dat,
  122. # outcome_dat = chd_out_dat
  123. # )
  125. # dat<-power.prune(dat,method.size=F)
  127. # Res<-mr(dat)
  128. # Het<-mr_heterogeneity(dat)
  129. # Plt<-mr_pleiotropy_test(dat)
  130. # Sin<-mr_singlesnp(dat)
  132. # All.res<-combine_all_mrresults(Res=Res,Het=Het,Plt=Plt,Sin=Sin)
  133. # All.res<-split_exposure(All.res)
  134. # All.res<-split_outcome(All.res)
  136. combine_all_mrresults <- function(
  137. res,
  138. het,
  139. plt,
  140. sin,
  141. ao_slc = TRUE,
  142. Exp = FALSE,
  143. split.exposure = FALSE,
  144. split.outcome = FALSE
  145. ) {
  146. het <- het[, c("id.exposure", "id.outcome", "method", "Q", "Q_df", "Q_pval")]
  148. # Convert all factors to character
  149. factors_to_character <- function(df) {
  150. df[] <- lapply(df, function(x) if (is.factor(x)) as.character(x) else x)
  151. df
  152. }
  153. res <- factors_to_character(res)
  154. het <- factors_to_character(het)
  155. sin <- factors_to_character(sin)
  157. sin <- sin[grep("[:0-9:]", sin$SNP), ]
  158. sin$method <- "Wald ratio"
  159. names(sin)[names(sin) == "p"] <- "pval"
  161. # Res<-Res[Res$method %in% c("MR Egger","Weighted median","Inverse variance weighted"),]
  163. #method is also the name of an argument in the method function. this prevents all.x argument from working. rename method column
  164. names(res)[names(res) == "method"] <- "Method"
  165. names(het)[names(het) == "method"] <- "Method"
  166. names(sin)[names(sin) == "method"] <- "Method"
  168. res <- merge(res, het, by = c("id.outcome", "id.exposure", "Method"), all.x = TRUE)
  169. res <- data.table::rbindlist(
  170. list(
  171. res,
  172. sin[, c(
  173. "exposure",
  174. "outcome",
  175. "id.exposure",
  176. "id.outcome",
  177. "SNP",
  178. "b",
  179. "se",
  180. "pval",
  181. "Method"
  182. )]
  183. ),
  184. fill = TRUE,
  185. use.names = TRUE
  186. )
  187. data.table::setDF(res)
  189. if (ao_slc) {
  190. ao <- available_outcomes()
  191. names(ao)[names(ao) == "nsnp"] <- "nsnps.outcome.array"
  192. res <- merge(
  193. res,
  194. ao[, !names(ao) %in% c("unit", "priority", "sd", "path", "note", "filename", "access", "mr")],
  195. by.x = "id.outcome",
  196. by.y = "id"
  197. )
  198. }
  200. res$nsnp[is.na(res$nsnp)] <- 1
  202. for (i in unique(res$id.outcome)) {
  203. wald_snps <- paste(
  204. res$SNP[res$id.outcome == i & res$Method == "Wald ratio"],
  205. collapse = "; "
  206. )
  207. res$SNP[res$id.outcome == i & res$Method != "Wald ratio"] <- wald_snps
  208. }
  210. if (Exp) {
  211. res$or <- exp(res$b)
  212. res$or_lci95 <- exp(res$b - res$se * 1.96)
  213. res$or_uci95 <- exp(res$b + res$se * 1.96)
  214. }
  216. # add intercept test from MR Egger
  217. plt <- plt[, c("id.outcome", "id.exposure", "egger_intercept", "se", "pval")]
  218. plt$Method <- "MR Egger"
  219. names(plt)[names(plt) == "egger_intercept"] <- "intercept"
  220. names(plt)[names(plt) == "se"] <- "intercept_se"
  221. names(plt)[names(plt) == "pval"] <- "intercept_pval"
  223. res <- merge(res, plt, by = c("id.outcome", "id.exposure", "Method"), all.x = TRUE)
  225. if (split.exposure) {
  226. res <- split_exposure(res)
  227. }
  229. if (split.outcome) {
  230. res <- split_outcome(res)
  231. }
  233. Cols <- c(
  234. "Method",
  235. "outcome",
  236. "exposure",
  237. "nsnp",
  238. "b",
  239. "se",
  240. "pval",
  241. "intercept",
  242. "intercept_se",
  243. "intercept_pval",
  244. "Q",
  245. "Q_df",
  246. "Q_pval",
  247. "consortium",
  248. "ncase",
  249. "ncontrol",
  250. "pmid",
  251. "population"
  252. )
  254. res <- res[, c(names(res)[names(res) %in% Cols], names(res)[which(!names(res) %in% Cols)])]
  256. # names(ResSNP)<-tolower(names(ResSNP))
  257. return(res)
  258. }
  260. #' Power prune
  261. #'
  262. #' When there are duplicate summary sets for a particular exposure-outcome combination, this function keeps the
  263. #' exposure-outcome summary set with the highest expected statistical power.
  264. #' This can be done by dropping the duplicate summary sets with the smaller sample sizes.
  265. #' Alternatively, the pruning procedure can take into account instrument strength and outcome sample size.
  266. #' The latter is useful, for example, when there is considerable variation in SNP coverage between duplicate summary sets
  267. #' (e.g. because some studies have used targeted or fine mapping arrays).
  268. #' If there are a large number of SNPs available to instrument an exposure,
  269. #' the outcome GWAS with the better SNP coverage may provide better power than the outcome GWAS with the larger sample size.
  270. #'
  271. #' @param dat Results from [harmonise_data()].
  272. #' @param method Should the duplicate summary sets be pruned on the basis of sample size alone (`method = 1`)
  273. #' or a combination of instrument strength and sample size (`method = 2`)? Default set to `1`.
  274. #' When set to 1, the duplicate summary sets are first dropped on the basis of the outcome sample size (smaller duplicates dropped).
  275. #' If duplicates are still present, remaining duplicates are dropped on the basis of the exposure sample size (smaller duplicates dropped).
  276. #' When method is set to `2`, duplicates are dropped on the basis of instrument strength
  277. #' (amount of variation explained in the exposure by the instrumental SNPs) and sample size,
  278. #' and assumes that the SNP-exposure effects correspond to a continuous trait with a normal distribution (i.e. exposure cannot be binary).
  279. #' The SNP-outcome effects can correspond to either a binary or continuous trait. If the exposure is binary then `method=1` should be used.
  280. #'
  281. #' @param dist.outcome The distribution of the outcome. Can either be `"binary"` or `"continuous"`. Default set to `"binary"`.
  282. #'
  283. #' @export
  284. #' @return data.frame with duplicate summary sets removed
  286. power_prune <- function(dat, method = 1, dist.outcome = "binary") {
  287. # dat[,c("eaf.exposure","beta.exposure","se.exposure","samplesize.outcome","ncase.outcome","ncontrol.outcome")]
  288. if (method == 1) {
  289. id.sets <- paste(split_exposure(dat)$exposure, split_outcome(dat)$outcome)
  290. id.set.unique <- unique(id.sets)
  291. dat$id.set <- as.numeric(factor(id.sets))
  292. L <- vector("list", length(id.set.unique))
  293. for (i in seq_along(id.set.unique)) {
  294. # print(i)
  295. print(paste(
  296. "finding summary set for --",
  297. id.set.unique[i],
  298. "-- with largest sample size",
  299. sep = ""
  300. ))
  301. dat1 <- dat[id.sets == id.set.unique[i], ]
  302. id.subset <- paste(dat1$exposure, dat1$id.exposure, dat1$outcome, dat1$id.outcome)
  303. id.subset.unique <- unique(id.subset)
  304. dat1$id.subset <- as.numeric(factor(id.subset))
  305. ncase <- dat1$ncase.outcome
  306. if (is.null(ncase)) {
  307. ncase <- NA
  308. }
  309. if (anyNA(ncase)) {
  310. ncase <- dat1$samplesize.outcome
  311. if (dist.outcome == "binary") {
  312. warning(paste(
  313. "dist.outcome set to binary but case sample size is missing. Will use total sample size instead but power pruning may be less accurate"
  314. ))
  315. }
  316. }
  317. if (anyNA(ncase)) {
  318. stop("sample size missing for at least 1 summary set")
  319. }
  320. # Permute dat1 and ncase identically; sort() would drop NA values and
  321. # misalign ncase from dat1's rows.
  322. ncase_ord <- order(ncase, decreasing = TRUE)
  323. dat1 <- dat1[ncase_ord, ]
  324. # id.expout<-paste(split_exposure(dat)$exposure,split_outcome(dat)$outcome)
  325. ncase <- ncase[ncase_ord]
  326. # dat1$power.prune.ncase<-"drop"
  327. # dat1$power.prune.ncase[ncase==ncase[1]]<-"keep"
  328. dat1 <- dat1[ncase == ncase[1], ]
  329. nexp <- dat1$samplesize.exposure
  330. nexp_ord <- order(nexp, decreasing = TRUE)
  331. dat1 <- dat1[nexp_ord, ]
  332. nexp <- nexp[nexp_ord]
  333. # dat1$power.prune.nexp<-"drop"
  334. # dat1$power.prune.nexp[nexp==nexp[1]]<-"keep"
  335. # dat1$power.prune<-"drop"
  336. # dat1$power.prune[dat1$power.prune.ncase=="keep" & dat1$power.prune.nexp == "keep"]<-"keep"
  337. # dat1<-dat1[,!names(dat1) %in% c("power.prune.ncase","power.prune.nexp")]
  338. # dat1[,c("samplesize.exposure","ncase.outcome","exposure","outcome")]
  339. dat1 <- dat1[nexp == nexp[1], ]
  340. L[[i]] <- dat1
  341. }
  342. dat <- data.table::rbindlist(L)
  343. dat <- dat[, !names(dat1) %in% c("id.set", "id.subset")]
  344. # if (drop.duplicates == T) {
  345. # dat<-dat[dat$power.prune=="keep",]
  346. # }
  347. return(dat)
  348. }
  350. if (method == 2) {
  351. id.sets <- paste(split_exposure(dat)$exposure, split_outcome(dat)$outcome)
  352. id.set.unique <- unique(id.sets)
  353. dat$id.set <- as.numeric(factor(id.sets))
  354. L <- vector("list", length(id.set.unique))
  355. for (i in seq_along(id.set.unique)) {
  356. dat1 <- dat[id.sets == id.set.unique[i], ]
  357. # unique(dat1[,c("exposure","outcome")])
  358. id.subset <- paste(dat1$exposure, dat1$id.exposure, dat1$outcome, dat1$id.outcome)
  359. id.subset.unique <- unique(id.subset)
  360. dat1$id.subset <- as.numeric(factor(id.subset))
  361. L1 <- vector("list", length(id.subset.unique))
  362. for (j in seq_along(id.subset.unique)) {
  363. # print(j)
  364. print(paste("identifying best powered summary set: ", id.subset.unique[j], sep = ""))
  365. dat2 <- dat1[id.subset == id.subset.unique[j], ]
  366. p <- dat2$eaf.exposure #effect allele frequency
  367. # b<-abs(dat2$beta.exposure) # effect of SNP on risk factor
  368. se <- dat2$se.exposure
  369. z <- dat2$beta.exposure / dat2$se.exposure
  370. n <- dat2$samplesize.exposure
  371. b <- z / sqrt(2 * p * (1 - p) * (n + z^2))
  372. if (anyNA(dat2$ncase.outcome)) {
  373. stop(paste("number of cases missing for summary set: ", id.subset.unique[j], sep = ""))
  374. }
  375. n.cas <- dat2$ncase.outcome
  376. n.con <- dat2$ncontrol.outcome
  377. var <- 1 # variance of risk factor assumed to be 1
  378. r2 <- 2 * b^2 * p * (1 - p) / var
  379. if (anyNA(r2)) {
  380. warning(
  381. "beta or allele frequency missing for some SNPs, which could affect accuracy of power pruning"
  382. )
  383. }
  384. r2 <- r2[!is.na(r2)]
  385. # k<-length(p[!is.na(p)]) #number of SNPs in the instrument / associated with the risk factor
  386. # n<-min(n) #sample size of the exposure/risk factor GWAS
  387. r2sum <- sum(r2) # sum of the r-squares for each SNP in the instrument
  388. # F<-r2sum*(n-1-k)/((1-r2sum*k )
  389. if (dist.outcome == "continuous") {
  390. iv.se <- 1 / sqrt(mean(dat2$samplesize.outcome, na.rm = TRUE) * r2sum) #standard error of the IV should be proportional to this
  391. }
  392. if (dist.outcome == "binary") {
  393. if (anyNA(n.cas) || anyNA(n.con)) {
  394. warning(
  395. "dist.outcome set to binary but number of cases or controls is missing. Will try using total sample size instead but power pruning will be less accurate"
  396. )
  397. iv.se <- 1 / sqrt(mean(dat2$samplesize.outcome, na.rm = TRUE) * r2sum)
  398. } else {
  399. iv.se <- 1 / sqrt(mean(n.cas, na.rm = TRUE) * mean(n.con, na.rm = TRUE) * r2sum) #standard error of the IV should be proportional to this
  400. }
  401. }
  402. # Power calculations to implement at some point
  403. # iv.se<-1/sqrt(unique(n.cas)*unique(n.con)*r2sum) #standard error of the IV should be proportional to this
  404. # n.outcome<-unique(n.con+n.cas)
  405. # ratio<-unique(n.cas/n.con)
  406. # sig<-alpha #alpha
  407. # b1=log(or) # assumed log odds ratio
  408. # power<-pnorm(sqrt(n.outcome*r2sum*(ratio/(1+ratio))*(1/(1+ratio)))*b1-qnorm(1-sig/2))
  409. dat2$iv.se <- iv.se
  410. # dat2$power<-power
  411. L1[[j]] <- dat2
  412. }
  413. L[[i]] <- data.table::rbindlist(L1)
  414. }
  415. dat2 <- data.table::rbindlist(L)
  416. dat2 <- dat2[order(dat2$id.set, dat2$iv.se), ]
  417. id.sets <- unique(dat2$id.set)
  418. id.keep <- vector("list", length(id.sets))
  419. for (i in seq_along(id.sets)) {
  420. # print(i)
  421. # print(id.sets[i])
  422. id.temp <- unique(dat2[dat2$id.set == id.sets[i], c("id.set", "id.subset")])
  423. id.keep[[i]] <- paste(id.temp$id.set, id.temp$id.subset)[1]
  424. }
  426. dat2$power.prune <- "drop"
  427. dat2$power.prune[paste(dat2$id.set, dat2$id.subset) %in% id.keep] <- "keep"
  428. # if (drop.duplicates == T) {
  429. dat2 <- dat2[dat2$power.prune == "keep", ]
  430. # }
  431. dat2 <- dat2[, !names(dat2) %in% c("iv.se", "power.prune", "id.set", "id.subset")]
  432. dat <- dat2
  433. # dat2[,c("exposure","outcome","iv.se","power","id.set","id.subset","power.prune")]
  435. # unique(dat2[order(dat2$id.set,dat2$id.subset),c("samplesize.exposure","ncase.outcome","exposure","outcome","iv.se","power","id.set","id.subset")])
  437. # dat2[dat2$id.set==1,c("iv.se","id.set","id.subset")]
  439. return(dat)
  440. }
  441. }
  443. #' Size prune
  444. #'
  445. #' Whens there are duplicate summary sets for a particular exposure-outcome combination,
  446. #' this function drops the duplicates with the smaller total sample size
  447. #' (for binary outcomes, the number of cases is used instead of total sample size).
  448. #'
  449. #' @param dat Results from [harmonise_data()].
  450. #'
  451. #' @export
  452. #' @return data frame
  454. size.prune <- function(dat) {
  455. dat$ncase[is.na(dat$ncase)] <- dat$samplesize[is.na(dat$ncase)]
  456. dat <- dat[order(dat$ncase, decreasing = TRUE), ]
  457. id.expout <- paste(dat$exposure, dat$outcome)
  458. id.keep <- id.expout[!duplicated(data.frame(dat$exposure, dat$originalname.outcome))]
  459. dat <- dat[id.expout %in% id.keep, ]
  460. }

format_mr_results2.R at commit d4df219, under other · at the source

Overview

Authors: Jiacheng Wu1, Jing He1, Kailu Zhang2, Xiaoming Liu2, Pengcheng Yang1, Dongke Wang1, Zhiyue Xu1, Wei Qian1, Lei Zhang1, Ziqiao Lei2, Xiaohua Hou1,3, Bai Tao1,4
ORCID iDs: Xiaoming Liu, Bai Tao
  1. Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022 China
  2. Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
  3. Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014 China
  4. Department of Gastroenterology, Tianyou Hospital, College of Medicine, Wuhan University of Science and Technology, Wuhan, 430064 China
Institutions: Wuhan Union Hospital (China); Huazhong University of Science and Technology (China); Central Hospital of Wuhan (China); Wuhan University of Science and Technology (China)
Journal: Translational psychiatry, volume 16, issue 1, article 422
Dates: received 3 December 2025; accepted 22 May 2026; published online 15 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-04142-y · PMID 42289401 · PMCID PMC13493998 · OpenAlex W7164734559
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), mouse (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Biomarkers, Psychiatric disorders, Molecular neuroscience
MeSH: Amygdala*, Anxiety*, Irritable Bowel Syndrome*, Adult, Animals, Disease Models, Animal, Female, Gastrointestinal Microbiome, Humans, Magnetic Resonance Imaging, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Gastrointestinal motility and disorders (Gastroenterology, Medicine), according to OpenAlex
Funding: Natural Science Foundation of Hubei Province (Hubei Provincial Natural Science Foundation) (2023AFB807); National Natural Science Foundation of China (National Science Foundation of China) (92268108, 81900477)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

MRCIEU/TwoSampleMR

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d4df21929fdabeb2f89686ffaad0db5f56973d64, 25 September 2026
Languages: R (68)
Size: 238 files, 68 scripts
Software Heritage: not archived
Found in: the text, “Mendelian randomization”
Holds: README, license file, CITATION.cff, environment (DESCRIPTION), tests, continuous integration, documentation, 8 notebooks
Tools: data.table (17 files), ggplot2 (10 files), tidyverse (5 files), cowplot (2 files), psych (2 files), car (1 file), glmnet (1 file), randomForest (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
71 files
At the source: github.com/MRCIEU/TwoSampleMR

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;
  • 68 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

Data links

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to a dataset: NCBI

Read it in the paper: doi.org/10.1038/s41398-026-04142-y.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 keywords, 13 MeSH terms, 2 funders, 68 references.

Cite

This paper

Wu, J., He, J., Zhang, K., Liu, X., Yang, P., Wang, D., Xu, Z., Qian, W., Zhang, L., Lei, Z., Hou, X., & Tao, B. (2026). Phocaeicola vulgatus improves anxiety-like behavior by ameliorating amygdala neuroinflammation and the neurite impairment in IBS. Translational psychiatry, 16(1), 422. https://doi.org/10.1038/s41398-026-04142-y

BibTeX

@article{wu2026phocaeicola,
author = {Wu, Jiacheng and He, Jing and Zhang, Kailu and Liu, Xiaoming and Yang, Pengcheng and Wang, Dongke and Xu, Zhiyue and Qian, Wei and Zhang, Lei and Lei, Ziqiao and Hou, Xiaohua and Tao, Bai},
title = {{Phocaeicola vulgatus improves anxiety-like behavior by ameliorating amygdala neuroinflammation and the neurite impairment in IBS}},
journal = {Translational psychiatry},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {422},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04142-y},
url = {https://doi.org/10.1038/s41398-026-04142-y},
pmid = {42289401},
pmcid = {PMC13493998}
}

RIS

TY - JOUR
AU - Wu, Jiacheng
AU - He, Jing
AU - Zhang, Kailu
AU - Liu, Xiaoming
AU - Yang, Pengcheng
AU - Wang, Dongke
AU - Xu, Zhiyue
AU - Qian, Wei
AU - Zhang, Lei
AU - Lei, Ziqiao
AU - Hou, Xiaohua
AU - Tao, Bai
TI - Phocaeicola vulgatus improves anxiety-like behavior by ameliorating amygdala neuroinflammation and the neurite impairment in IBS
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/06/15
VL - 16
IS - 1
SP - 422
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04142-y
UR - https://doi.org/10.1038/s41398-026-04142-y
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41398-026-04142-y",
"type": "article-journal",
"title": "Phocaeicola vulgatus improves anxiety-like behavior by ameliorating amygdala neuroinflammation and the neurite impairment in IBS",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Wu",
"given": "Jiacheng"
},
{
"family": "He",
"given": "Jing"
},
{
"family": "Zhang",
"given": "Kailu"
},
{
"family": "Liu",
"given": "Xiaoming"
},
{
"family": "Yang",
"given": "Pengcheng"
},
{
"family": "Wang",
"given": "Dongke"
},
{
"family": "Xu",
"given": "Zhiyue"
},
{
"family": "Qian",
"given": "Wei"
},
{
"family": "Zhang",
"given": "Lei"
},
{
"family": "Lei",
"given": "Ziqiao"
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{
"family": "Hou",
"given": "Xiaohua"
},
{
"family": "Tao",
"given": "Bai"
}
],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "422",
"DOI": "10.1038/s41398-026-04142-y",
"PMID": "42289401",
"PMCID": "PMC13493998",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-04142-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
15
]
]
}
}

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

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