OSCR

Setd1a Loss-of-function Disrupts Epigenetic Regulation of Ribosomal Genes via Altered DNA Methylation.

Code ↔ Paper

4 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 4 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Cell Type Deconvolution ↔ R/runNoiseSimulations.R, the whole file · a weak match · score 0.88 · bulk tissue, cellular composition, Reference DNA methylation, CETYGO score, DNA methylation profile, reference cell
  2. [2] § Methods › Cell Type Deconvolution ↔ R/estimateCellCountsWithError.R, lines 1–71 · score 0.76 · cellular composition, frontal cortex, hyper, hypo, deconvolution, error
  3. [3] § Methods › DNA Methylation Profiling ↔ dataExplorationScripts/exploreBScon.r, lines 129–168 · score 0.61 · ENmix, IDAT, Infinium, detection, bisulfite, intensities
  4. [4] § Methods › DNA Methylation Profiling ↔ qcScripts/calcMouseMethQCmetrics.r, lines 186–242 · score 0.56 · probes failing, ENmix, detection, intensities, QC, matrix

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 96 lines · 4 KB · Artistic-2.0 · 1 match

  1. #' Perform simulations to test deconvolution model with constructed bulk
  2. #' tissue profiles.
  3. #'
  4. #' Function to implement the Houseman deconvolution method for a provided
  5. #' set reference data and apply it to a series of bulk profiles
  6. #' constructed from user-provided proportions. The function first selects
  7. #' the sites that will be used for the deconvolutions. It then generates
  8. #' test bulk profiles which are weighted sums of reference profiles.
  9. #' Finally it calculates estimates of the cellular composition and the
  10. #' CETYGO score for each simulated bulk profile.
  11. #'
  12. #' We recommend that the training reference data and the test reference
  13. #' data (which is used to construct bulk profiles for testing) are
  14. #' distinct. Note that no normalisation is performed as part of this
  15. #' function, it is recommended that data is thouroughly QC'd prior to
  16. #' this analysis and your perfferred normalisation method applied.
  17. #'
  18. #' @param trainBetas A matrix of reference DNA methylation profiles,
  19. #' where rows are sites and columns are samples. Note you need multiple
  20. #' samples from the same cell type.
  21. #' @param trainCellTypes A vector of which cell types to include in the
  22. #' deconcolution model.
  23. #' @param trainCellInd A vector indicating which cell type each column in
  24. #' trainBetas comes from.
  25. #' @param testBetas A matrix with DNA methylation levels for reference cell
  26. #' types to construct the bulk tissue profiles from. Format is one column per
  27. #' cell type. Requires the same number of rows as trainBetas and in the same
  28. #' order.
  29. #' @param matrixSimProp A matrix of proportions to combine reference cell
  30. #' types. Each row represents a different combination of cell types. Number
  31. #' of columns must match the number of columns in testBetas, unless the last
  32. #' column is the proportion of "Noise", and must be labelled as such.
  33. #' @return A matrix with predicted cellular compositions and CETYGO score for
  34. #' each simulated bulk tissue profile.
  35. #' @export
  36. #'
  37. #' @examples
  38. #' # create mean DNAm levels for 100 sites
  39. #' set.seed(1327)
  40. #' meanBetas <- runif(100, min = 0, max = 1)
  41. #' # generate cell type diffs
  42. #' meanCTDiff <- rnorm(100, mean = 0, sd = 0.2)
  43. #' # create reference training data
  44. #' refBetas <- cbind(
  45. #' matrix(meanBetas +
  46. #' rnorm(500, mean = 0, sd = 0.01),
  47. #' nrow = 100, byrow = FALSE
  48. #' ),
  49. #' matrix(meanBetas + rnorm(500, mean = 0, sd = 0.01) +
  50. #' meanCTDiff, nrow = 100, byrow = FALSE)
  51. #' )
  52. #' # force to lie between 0 and 1
  53. #' refBetas[refBetas < 0] <- runif(sum(refBetas < 0), 0, 0.05)
  54. #' refBetas[refBetas > 1] <- runif(sum(refBetas > 1), 0.95, 1)
  55. #' rownames(refBetas) <- paste0("cg", 1:100)
  56. #' # create test data
  57. #' testBetas <- cbind(meanBetas, meanBetas + meanCTDiff) +
  58. #' rnorm(200, mean = 0, sd = 0.01)
  59. #' # force to lie between 0 and 1
  60. #' testBetas[testBetas < 0] <- runif(sum(testBetas < 0), 0, 0.05)
  61. #' testBetas[testBetas > 1] <- runif(sum(testBetas > 1), 0.95, 1)
  62. #' rownames(testBetas) <- paste0("cg", 1:100)
  63. #' simProps <- matrix(data = c(0.5, 0.5, 0.2, 0.8), ncol = 2)
  64. #' colnames(simProps) <- c("A", "B")
  65. #'
  66. #' runNoiseSimulations(
  67. #' trainBetas = refBetas,
  68. #' trainCellTypes = c("A", "B"),
  69. #' trainCellInd = c(rep("A", 5), rep("B", 5)),
  70. #' testBetas = testBetas,
  71. #' matrixSimProp = simProps
  72. #' )
  73. #'
  74. runNoiseSimulations <- function(trainBetas, trainCellTypes,
  75. trainCellInd, testBetas, matrixSimProp) {
  76. if (!identical(rownames(trainBetas), rownames(testBetas))) {
  77. stop("Rows of training and test data are not identical")
  78. }
  79. ## fit model
  80. model <- pickCompProbesMatrix(
  81. rawbetas = trainBetas,
  82. cellTypes = trainCellTypes,
  83. cellInd = trainCellInd,
  84. numProbes = 50,
  85. probeSelect = "auto"
  86. )
  88. ## create test data
  89. testBulkBetas <- createBulkProfiles(
  90. testBetas[rownames(model$coef), ],
  91. matrixSimProp
  92. )
  93. ## do cellular prediction with error
  94. predProp <- projectCellTypeWithError(testBulkBetas, model$coef)
  95. return(predProp)
  96. }

runNoiseSimulations.R at commit 78a050c, under Artistic-2.0 · at the source

Overview

Authors: Nicholas E Clifton1, Stefania Policicchio1, Emma M Walker1, Isabel Castanho1, Matthew L Bosworth2, Kirtikesav S Saravanaraj1, Joe Burrage1, Jeremy Hall2,3, Emma L Dempster1, Eilis Hannon1, Anthony R Isles2, Jonathan Mill1
ORCID iDs: Nicholas E Clifton, Matthew L Bosworth, Eilis Hannon, Anthony R Isles
  1. Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
  2. Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
  3. Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, United Kingdom
Institutions: University of Exeter (United Kingdom); Cardiff University (United Kingdom)
Journal: Schizophrenia bulletin, volume 52, issue 2, article sbaf091
Dates: published online 12 June 2025; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/schbul/sbaf091 · PMID 40500874 · PMCID PMC12996923 · OpenAlex W4411195871
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), schizophrenia / psychosis (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions
Keywords: methylation, SETD1A, genes, DNA, ribosomes, mitochondria, schizophrenia, rare variant
MeSH: DNA Methylation*, Epigenesis, Genetic*, Frontal Lobe*, Haploinsufficiency*, Histone-Lysine N-Methyltransferase*, Schizophrenia*, Animals, Loss of Function Mutation, Male, Mice (* major topic)
Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Wellcome Trust (WT101650MA)
Citations: cited by 7 papers (Europe PMC); 55 references in the paper

Abstract

Background and Hypothesis: SETD1A, a histone methyltransferase, is implicated in schizophrenia through rare loss-of-function mutations. While SETD1A regulates gene expression via histone H3K4 methylation, its influence on broader epigenetic dysregulation remains incompletely understood. We explored the hypothesis that SETD1A haploinsufficiency contributes to neurodevelopmental disruptions associated with schizophrenia risk via alterations in DNA methylation.

Study Design: We profiled DNA methylation in the frontal cortex of Setd1a+/− mice across prenatal and postnatal development using Illumina Mouse Methylation arrays. Differentially methylated positions and regions were identified, and their functional relevance was examined through gene and biological annotation. We integrated these findings with transcriptomic and proteomics datasets, and assessed mitochondrial complex I activity to explore potential downstream functional effects.

Study Results: Setd1a haploinsufficiency resulted in widespread hypomethylation of genes related to ribosomal function and RNA processing that persisted across all developmental stages. Setd1a-targeted promoter regions and noncoding small nucleolar RNAs were also enriched for differentially methylated sites. Despite the downregulation of mitochondrial gene expression, the same genes were not differentially methylated, and complex I activity in Setd1a+/− mice did not differ significantly from controls. Genes overlapping hypomethylated regions were enriched for common genetic associations with schizophrenia.

Conclusions: Our findings suggest that SETD1A haploinsufficiency disrupts the epigenetic regulation of ribosomal pathways. These results provide insight into an alternative mechanism through which genetic variation in SETD1A influences developmental and synaptic plasticity, contributing to schizophrenia pathophysiology.

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 4 matches between paragraphs and lines of code.

ew367/mouseArray

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 6365ee38c5176073f15f5b23cbdd0ad4cbe3ec6e, 31 January 2025
Languages: R (28), Shell (4)
Size: 37 files, 32 scripts
Software Heritage: not archived
Found in: the text, “DNA Methylation Profiling”
Holds: README, 5 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (16 files), data.table (9 files), ggplot2 (5 files), lme4 (3 files), lmerTest (3 files), pheatmap (3 files), reshape2 (2 files), car (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
33 files
At the source: github.com/ew367/mouseArray

ejh243/CETYGO

License: Artistic-2.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 78a050cf8baf1347ad263e27d4d87d7598b8f6f4, 17 October 2024
Languages: R (10)
Size: 29 files, 10 scripts
Software Heritage: not archived
Found in: the text, “Cell Type Deconvolution”
Holds: README, license file, environment (DESCRIPTION), documentation, 1 notebook
Not found: CITATION.cff, tests, continuous integration
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
12 files
At the source: github.com/ejh243/CETYGO

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 42 scripts, each with its path and the digest of its content;
  • 4 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

Raw and processed data are available at the Gene Expression Omnibus (accession GSE295008 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE295008)).

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 8 keywords, 10 MeSH terms, 1 funder, 55 references.

Cite

This paper

Clifton, N. E., Policicchio, S., Walker, E. M., Castanho, I., Bosworth, M. L., Saravanaraj, K. S., Burrage, J., Hall, J., Dempster, E. L., Hannon, E., Isles, A. R., & Mill, J. (2026). Setd1a Loss-of-function Disrupts Epigenetic Regulation of Ribosomal Genes via Altered DNA Methylation. Schizophrenia bulletin, 52(2), sbaf091. https://doi.org/10.1093/schbul/sbaf091

BibTeX

@article{clifton2026setd1a,
author = {Clifton, Nicholas E and Policicchio, Stefania and Walker, Emma M and Castanho, Isabel and Bosworth, Matthew L and Saravanaraj, Kirtikesav S and Burrage, Joe and Hall, Jeremy and Dempster, Emma L and Hannon, Eilis and Isles, Anthony R and Mill, Jonathan},
title = {{Setd1a Loss-of-function Disrupts Epigenetic Regulation of Ribosomal Genes via Altered DNA Methylation}},
journal = {Schizophrenia bulletin},
year = {2026},
month = mar,
volume = {52},
number = {2},
pages = {sbaf091},
publisher = {Oxford University Press},
issn = {1787-9965},
doi = {10.1093/schbul/sbaf091},
url = {https://doi.org/10.1093/schbul/sbaf091},
pmid = {40500874},
pmcid = {PMC12996923}
}

RIS

TY - JOUR
AU - Clifton, Nicholas E
AU - Policicchio, Stefania
AU - Walker, Emma M
AU - Castanho, Isabel
AU - Bosworth, Matthew L
AU - Saravanaraj, Kirtikesav S
AU - Burrage, Joe
AU - Hall, Jeremy
AU - Dempster, Emma L
AU - Hannon, Eilis
AU - Isles, Anthony R
AU - Mill, Jonathan
TI - Setd1a Loss-of-function Disrupts Epigenetic Regulation of Ribosomal Genes via Altered DNA Methylation
T2 - Schizophrenia bulletin
J2 - Schizophr Bull
PY - 2026
DA - 2026/03/01
VL - 52
IS - 2
SP - sbaf091
SN - 1787-9965
PB - Oxford University Press
DO - 10.1093/schbul/sbaf091
UR - https://doi.org/10.1093/schbul/sbaf091
LA - en
ER -

CSL-JSON

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