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Early and late RNA eQTL are driven by different genetic mechanisms.

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  1. [1] § Methods › eQTL mapping ↔ 01_eQTL/02_run_PEER.R, the whole file · a weak match · score 0.73 · inverse normal, PEER factors, PC, age, death, covariates

Paper

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The authors' code

R · 77 lines · 2 KB · GPL-3.0 · 1 match

  1. library(peer)
  3. dataset = commandArgs(trailingOnly=TRUE)[1]
  4. celltype = commandArgs(trailingOnly=TRUE)[2]
  6. meta<-read.table("meta_info.n424.selected.txt",header=T,sep="\t")
  7. colnames(meta)[19]<-"RID"
  9. pcs <- read.table("pca.n424.evec")
  10. pcs <- pcs[,c(-1,-13:-23)]
  11. colnames(pcs)<-c("IID",paste0("PC",1:10))
  12. pcs <- pcs[, 1:6 ]
  14. cov<-merge(pcs,meta,by="IID")
  16. # expression data
  17. d1 <- read.csv(paste0("exp/",dataset,".",celltype,".norm_av.csv"))
  18. d1 <- d1[,-1]
  20. rownames(d1)<-d1$gene
  21. d2 <- as.matrix(d1[ ,c(-1)])
  23. cov <- cov[cov$RID %in% colnames(d2),]
  24. row.names(cov) <- as.character(cov$RID)
  26. # common sample
  27. commonsample <- intersect( cov$RID, colnames(d2) )
  29. # filtering sample and genes
  30. d2 <- d2[, commonsample] #same order of samples
  31. d2[is.na(d2)] <- 0 #missing values were set to 0
  33. exprrate <- apply(d2, 1, function(x){ length( x [ x > 0 ] ) / length(x) })
  34. d3 <- d2[ exprrate > 0.2, ]
  36. # inverse normal normalization
  37. rn<-apply(d3, 1, function(x){
  38. qnorm( (rank(x, na.last="keep") - 0.5) / sum(!is.na(x)) )
  39. })
  40. rn <- t(rn)
  42. # peer
  43. QQ <- rn
  45. model = PEER()
  46. PEER_setPhenoMean(model, as.matrix(t(QQ)) )
  47. dim(PEER_getPhenoMean(model))
  49. PEER_setAdd_mean(model, TRUE)
  51. cov2 <- cov[commonsample, ]
  52. cov2 <- cov2[,c("PC1","PC2","PC3","PC4","PC5","Study","msex","age_death","pmi")]
  53. PEER_setCovariates(model, as.matrix(cov2))
  55. K = 10
  56. PEER_setNk(model,K)
  57. PEER_getNk(model)
  58. PEER_setNmax_iterations(model,10000)
  60. # perform the inference
  61. PEER_update(model)
  63. # output
  64. residuals = PEER_getResiduals(model)
  65. colnames(residuals) <- row.names(QQ)
  66. row.names(residuals) <- colnames(QQ)
  67. out <- data.frame( ID = row.names(QQ),t(residuals))
  68. write.table(out, paste0("exp/",dataset,".",celltype,".PEER_norm.tsv"), sep = "\t", quote = F, row.names = FALSE)
  70. # when you want to get PEER factors
  71. factors = PEER_getX(model)
  72. saveRDS(factors,paste0("exp/",dataset,".",celltype,".PEER_factor.rds"))
  74. row.names(factors) <- colnames(QQ)
  76. write.table(factors, paste0("exp/",dataset,".",celltype,".PEER_factor.tsv"), sep = "\t", quote = F)

02_run_PEER.R at commit a11acf4, under GPL-3.0 · at the source

Overview

Authors: Saori Sakaue1,2,3,4, Accelerating Medicines Partnership®: RA/SLE Network, Soumya Raychaudhuri1,2,3,5
ORCID iDs: Saori Sakaue, Soumya Raychaudhuri
  1. Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School,Boston, MA USA
  2. Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School,Boston, MA USA
  3. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard,Cambridge, MA USA
  4. Department of Genome Sciences, University of Washington,Seattle, WA USA
  5. Department of Biomedical Informatics, Harvard Medical School,Boston, MA USA
Institutions: Brigham and Women's Hospital (United States); Broad Institute (United States); University of Washington (United States); Harvard University (United States)
Journal: Nature communications, volume 17, issue 1, article 5703
Dates: received 26 March 2025; accepted 3 April 2026; published online 25 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72139-8 · PMID 42034632 · PMCID PMC13319782 · OpenAlex W4408007235
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.02.24.639351
Status: code verified
Categories: genetics / omics (modality), human (organism), schizophrenia / psychosis (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, Physiology & signal measures
Keywords: Gene regulation, Genome-wide association studies, Functional genomics
MeSH: Gene Expression Regulation*, Quantitative Trait Loci*, RNA*, Animals, Brain, Humans, Kidney, Polymorphism, Single Nucleotide, Schizophrenia, Transcription, Genetic (* major topic)
Topic: RNA Research and Splicing (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIH (R01AR063759, U01HG012009, UC2AR081023)
Citations: not cited yet (Europe PMC); 87 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

immunogenomics/EarlyLate_RNA_eQTL

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a11acf4a2ec09b36d0d1188e36393a604be75bdd, 11 March 2026
Languages: R (7), Shell (2)
Size: 11 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (5 files), tidyverse (4 files), BCFtools (1 file), BEDTools (1 file), SAMtools (1 file), Seurat (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
11 files
At the source: github.com/immunogenomics/EarlyLate_RNA_eQTL

Zenodo 18962458

License: GPL-3.0
State: the link answers, verified on 30 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: data.table (5 files), tidyverse (4 files), BCFtools (1 file), BEDTools (1 file), SAMtools (1 file), Seurat (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
11 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-72139-8.

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Data

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Code and data availability statement

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

Read it in the paper: doi.org/10.1038/s41467-026-72139-8.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 3 keywords, 10 MeSH terms, 1 funder, 87 references.

Cite

This paper

Sakaue, S., Accelerating Medicines Partnership®: RA/SLE Network, & Raychaudhuri, S. (2026). Early and late RNA eQTL are driven by different genetic mechanisms. Nature communications, 17(1), 5703. https://doi.org/10.1038/s41467-026-72139-8

BibTeX

@article{sakaue2026early,
author = {Sakaue, Saori and {Accelerating Medicines Partnership®: RA/SLE Network} and Raychaudhuri, Soumya},
title = {{Early and late RNA eQTL are driven by different genetic mechanisms}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5703},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72139-8},
url = {https://doi.org/10.1038/s41467-026-72139-8},
pmid = {42034632},
pmcid = {PMC13319782}
}

RIS

TY - JOUR
AU - Sakaue, Saori
AU - Accelerating Medicines Partnership®: RA/SLE Network
AU - Raychaudhuri, Soumya
TI - Early and late RNA eQTL are driven by different genetic mechanisms
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/25
VL - 17
IS - 1
SP - 5703
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72139-8
UR - https://doi.org/10.1038/s41467-026-72139-8
LA - en
ER -

CSL-JSON

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