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Allele specific expression in Alzheimer's disease.

Code ↔ Paper

13 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 13 matches · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § METHODS › ASE variant identification ↔ ASEVariantIdentification/2_ASEVariantIdentification.R, the whole file · a weak match · score 0.90 · lowMAPQDepth, rawDepth, variants located, mapping quality, reference bias, FDR
  2. [2] § METHODS › Single‐cell RNA‐seq analysis ↔ SingleCellRNASeqAnalysis/3_SingleCellRNASeqAnalysis.R, the whole file · a weak match · score 0.81 · FindMarkers, LogNormalize, Seurat, preprocessed, AsymAD, matrix
  3. [3] § RESULTS › AD‐associated ASE variants and cell‐type–specific expression of affected genes ↔ SingleCellRNASeqAnalysis/3_SingleCellRNASeqAnalysis.R, the whole file · a weak match · score 0.69 · SLC12A5, VPS13C, SYT13, AsymAD, CRTC1, TOMM7
  4. [4] § METHODS › Enrichment of ASE variant at chromosome regions ↔ ASEVariantEnrichmentAtChromosomalBand/1_RegionVsChrBandCount.R, the whole file · a weak match · score 0.61 · chromosomal band, sample variant, AsymAD, enrichment, ASE variants
  5. [5] § RESULTS › AD‐associated ASE variants and cell‐type–specific expression of affected genes ↔ ADAssociatedASEVariantIdentification/1_LinearMixedModel.R, lines 36–78 · score 0.61 · linear mixed, AD associated ASE, model, interacts, associated ASE variant, bm
  6. [6] § RESULTS › AD‐associated ASE variants and cell‐type–specific expression of affected genes ↔ SingleCellRNASeqAnalysis/2_SingleCellRNASeqAnalysis.ipynb, lines 175–202 · score 0.59 · SLC12A5, VPS13C, AsymAD, LMO7, TRIM23, cells
  7. [7] § RESULTS › AD‐associated ASE variants and cell‐type–specific expression of affected genes ↔ ADAssociatedASEVariantIdentification/1_LinearMixedModel.R, lines 36–78 · score 0.57 · linear mixed, AD associated ASE, model, AD samples, allele, disease
  8. [8] § METHODS › Single‐cell RNA‐seq analysis ↔ ASEVariantAssociationWithClinical/2_Heatmap.R, lines 81–157 · score 0.57 · fold changes, AsymAD, log2, matrix, sex, cohort
  9. [9] § RESULTS › Association of ASE variant fraction with age of death, APOE allele, and sex ↔ ASEVariantAssociationWithClinical/1_Boxplot.R, lines 1–79 · score 0.57 · APOE genotype, e3, e4, female, e2, clinical
  10. [10] § RESULTS › Association of ASE variant fraction with age of death, APOE allele, and sex ↔ ASEVariantAssociationWithClinical/2_Heatmap.R, lines 1–79 · score 0.57 · APOE genotype, e3, e4, female, e2, clinical
  11. [11] § RESULTS › Enrichment of ASE variants across chromosomal regions ↔ ASEVariantIdentification/2_ASEVariantIdentification.R, the whole file · a weak match · score 0.57 · variants located, mapping quality, FDR, chromosomal, fraction, ASE variant
  12. [12] § METHODS › Enrichment of ASE variant at chromosome regions ↔ ASEVariantEnrichmentAtChromosomalBand/2_SampleVarSignificance.R, the whole file · a weak match · score 0.53 · chromosomal band, AsymAD, permutation, enrichment, ASE variants
  13. [13] § RESULTS › Enrichment of ASE variants across chromosomal regions ↔ ASEVariantEnrichmentAtChromosomalBand/2_SampleVarSignificance.R, the whole file · a weak match · score 0.51 · chromosomal band, ASE variant enrichment, permutation, bm, mapping, AD

Paper

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

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The authors' code

R · 48 lines · 3.1 KB · MIT · 2 matches

  1. rm(list = ls()); setwd('/sc/arion/projects/DiseaseGeneCell/Huang_lab_project/ADASE/ASE_individual')
  3. ### Load in related information
  4. # clinical information
  5. clinicals <- as.data.frame(readxl::read_xlsx('../data/SampleClassification/SampleClassification.xlsx'),stringsAsFactors=F)
  6. colnames(clinicals)[match('Sample',colnames(clinicals))] <- 'Sampleid'
  7. clinicals <- clinicals[clinicals$Classification%in%c('AD','AsymAD','Control'),]
  8. #clinicals <- clinicals[clinicals$Sampleid%in%c('B18C014.hB_RNA_8825','B18C014.hB_RNA_8835','B18C014.hB_RNA_8845','B18C014.hB_RNA_8865'),]
  9. # Variant list sample frequency
  10. infile <- gzfile('/sc/arion/projects/DiseaseGeneCell/Huang_lab_project/ADASE/ASE_individual_analysis/ReferenceBias/ReferenceFraction/ReferenceFraction_VariantListSta_10_0.01.txt.gz','r')
  11. VarSta <- read.table(infile,header = T,sep = '\t',stringsAsFactors = F); close(infile)
  12. tmp <- unlist(gregexpr(':',VarSta$HGVSg)); VarSta$Chr <- as.character(substr(VarSta$HGVSg,1,tmp-1)); VarSta <- VarSta[VarSta$Chr%in%as.character(1:22),]
  13. VarSta <- VarSta$HGVSg[VarSta$All_Freq>0.2]
  15. m <- as.data.frame(matrix(nrow = nrow(clinicals),ncol = 8, dimnames = list(NULL,c('Sampleid','All','Auto','AutoHighMAPQ','AutoHighMAPQReadsSuf','AutoHighMAPQReadsSufSamFreq','AutoHighMAPQReadsSufSamFreqSig','AutoHighMAPQReadsSufSamFreqNotSig'))),stringsAsFactors = F)
  16. for (i in 1:nrow(clinicals)) {
  17. tmpall <- read.table(file.path('ASE_basic/',paste(clinicals$Sampleid[i],'.ASE.WES_WGS_het_clean.counts.tsv',sep = '')),header = T,sep = '\t',stringsAsFactors = F)
  18. tmpall <- tmpall[,setdiff(colnames(tmpall),c('fdr','fdr1'))]
  19. tmpall$HGVSg <- paste(substr(tmpall$contig,4,nchar(tmpall$contig)),':g.',tmpall$position,tmpall$refAllele,'>',tmpall$altAllele,sep = '')
  20. m[i,'Sampleid'] <- clinicals$Sampleid[i]; m[i,'All'] <- nrow(tmpall)
  22. # remove variant located at XY chromosome
  23. tmpall <- tmpall[!(tmpall$contig%in%c('chrX','chrY')),]
  24. m[i,'Auto'] <- nrow(tmpall)
  26. # remove variant with low maping quality
  27. tmpall <- tmpall[(tmpall$lowMAPQDepth/tmpall$rawDepth)<=0.01,]
  28. m[i,'AutoHighMAPQ'] <- nrow(tmpall)
  30. # remove variant without enough read count
  31. tmpall <- tmpall[(tmpall$refCount+tmpall$altCount)>=10,]
  32. m[i,'AutoHighMAPQReadsSuf'] <- nrow(tmpall)
  34. # remove variant with lower frequency
  35. tmpall <- tmpall[tmpall$HGVSg%in%VarSta,]
  36. m[i,'AutoHighMAPQReadsSufSamFreq'] <- nrow(tmpall)
  38. # significant varaint
  39. tmpall$fdr <- p.adjust(tmpall$pval,method = 'BH')
  40. tmpsig <- tmpall[tmpall$fdr<0.05,]
  41. tmpnotsig <- tmpall[tmpall$fdr>=0.05,]
  42. m[i,'AutoHighMAPQReadsSufSamFreqSig'] <- nrow(tmpsig)
  43. m[i,'AutoHighMAPQReadsSufSamFreqNotSig'] <- nrow(tmpnotsig)
  45. write.table(tmpsig,file.path('ASE_basic_Sig',paste('TenReads_',paste(clinicals$Sampleid[i],'.ASE.WES_WGS_het_clean.counts.tsv',sep = ''),sep = '')),row.names = F,col.names = T,quote = F,sep = '\t')
  46. write.table(tmpnotsig,file.path('ASE_basic_NotSig',paste('TenReads_',paste(clinicals$Sampleid[i],'.ASE.WES_WGS_het_clean.counts.tsv',sep = ''),sep = '')),row.names = F,col.names = T,quote = F,sep = '\t')
  47. }# for i
  48. writexl::write_xlsx(m,'ASE_basic_SigSta_TenReads.xlsx',format_headers = F)

2_ASEVariantIdentification.R at commit 89765ed, under MIT · at the source

Overview

Authors: Zishan Wang1, Delowar Hossain2, Judy Jiaru Wang3, Varun R. Subramaniam1, Bin Zhang1, Minghui Wang1, Kuan‐lin Huang1
ORCID iDs: Zishan Wang
  1. Department of Genetics and Genomic Sciences, Center for Transformative Disease Modeling, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York New York USA
  2. Division of Experimental Medicine McGill University Montréal Québec Canada
  3. College of Arts and Sciences Cornell University Ithaca New York USA
Institutions: Tisch Cancer Institute; Icahn School of Medicine at Mount Sinai (United States); McGill University (Canada); Cornell University (United States)
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association, volume 22, issue 6, article e71558
Dates: received 30 May 2025; accepted 30 April 2026; published online 11 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/alz.71558 · PMID 42273872 · PMCID PMC13254825 · OpenAlex W7164313882
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics
Keywords: allele specific expression, Alzheimer's disease, multi‐omics
MeSH: Alleles*, Alzheimer Disease*, Brain*, Aged, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male (* major topic)
Topic: Genetic Syndromes and Imprinting (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Clinical and Translational Science Awards (CTSA) (UL1TR004419); Office of Research Infrastructure of the National Institutes of Health (S10OD026880, S10OD030463); National Institute of General Medical Sciences (R35GM138113, 2R35GM138113); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (R21AG077168, RF1AG077828); Alzheimer's Association (AARG‐22‐928419)
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

INTRODUCTION: Allele‐specific expression (ASE), preferential expression of one allele at a heterozygous locus, is implicated in various brain diseases but remains largely uncharacterized in Alzheimer's disease (AD).

METHODS: We performed a genome‐wide characterization of ASE variants across seven brain regions of 2,231 AD and Control patients from Mount Sinai Brain Bank (MSBB) and Religious Orders Study/Memory and Aging Project (ROSMAP) cohorts and investigated cell‐type–specific activity via single‐cell analysis.

RESULTS: We identified 56,136 unique ASE variants that were enriched in imprinted chromosomal regions, e.g., chr6, chr14q32, and chr15q11. ASE variants were also found in exons of known AD‐associated genes, including apolipoprotein E (APOE), CLU, CTSB, and HLA‐DRB1. Forty variants exhibited AD‐associated ASE, and the affected genes, including SLC12A5, SYT13, and TOMM7, were predominantly downregulated in multiple cell types, including astrocytes, excitatory neurons, and oligodendrocytes.

DISCUSSION: We provided a detailed landscape of ASE in AD, uncovering novel functional variants and highlighting their potential cell‐type–specific contributions to disease pathogenesis.

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 13 matches between paragraphs and lines of code.

WangZishan/ADASE

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 89765ed4f817d2f890e890439678b85e4c9908c5, 13 March 2026
Languages: R (13), Jupyter (2)
Size: 17 files, 15 scripts
Software Heritage: not archived
Found in: “SOFTWARE AVAILABILITY”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (6 files), reshape2 (6 files), anndata (2 files), Matplotlib (2 files), NumPy (2 files), pandas (2 files), Scanpy (2 files), seaborn (2 files), lme4 (1 file), Seurat (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
17 files
At the source: github.com/WangZishan/ADASE

Software availability

Code scripts to reproduce the analyses are deposited at Github: https://github.com/WangZishan/ADASE

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;
  • 15 scripts, each with its path and the digest of its content;
  • 13 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 statement

The multi‐omics datasets of MSBB and ROSMAP are available via the AD Knowledge Portal (https://adknowledgeportal.org). The AD Knowledge Portal is a platform for accessing data, analyses, and tools generated by the Accelerating Medicines Partnership (AMP‐AD) Target Discovery Program and other National Institute on Aging (NIA)‐supported programs to enable open‐science practices and accelerate translational learning. The data, analyses and tools are shared early in the research cycle without a publication embargo on secondary use. Data is available for general research use according to the following requirements for data access and data attribution (https://adknowledgeportal.synapse.org/Data%20Access (https://adknowledgeportal.synapse.org/Data Access)). The MSBB bulk tissue data is available at https://www.synapse.org/Synapse:syn3159438 and the ROSMAP bulk tissue data is available at https://www.synapse.org/Synapse:syn3219045. The significant ASE variants identified by binomial test is available at figshare platform https://doi.org/10.6084/m9.figshare.31549300. The ROSMAP snRNA‐seq data is available at https://www.synapse.org/Synapse:syn52293417.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 9 MeSH terms, 5 funders, 50 references.

Cite

This paper

Wang, Z., Hossain, D., Wang, J. J., Subramaniam, V. R., Zhang, B., Wang, M., & Huang, K. (2026). Allele specific expression in Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(6), e71558. https://doi.org/10.1002/alz.71558

BibTeX

@article{wang2026allele,
author = {Wang, Zishan and Hossain, Delowar and Wang, Judy Jiaru and Subramaniam, Varun R. and Zhang, Bin and Wang, Minghui and Huang, Kuan‐lin},
title = {{Allele specific expression in Alzheimer's disease}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
month = jun,
volume = {22},
number = {6},
pages = {e71558},
publisher = {Wiley},
issn = {1552-5260},
doi = {10.1002/alz.71558},
url = {https://doi.org/10.1002/alz.71558},
pmid = {42273872},
pmcid = {PMC13254825}
}

RIS

TY - JOUR
AU - Wang, Zishan
AU - Hossain, Delowar
AU - Wang, Judy Jiaru
AU - Subramaniam, Varun R.
AU - Zhang, Bin
AU - Wang, Minghui
AU - Huang, Kuan‐lin
TI - Allele specific expression in Alzheimer's disease
T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association
J2 - Alzheimers Dement
PY - 2026
DA - 2026/06/01
VL - 22
IS - 6
SP - e71558
SN - 1552-5260
PB - Wiley
DO - 10.1002/alz.71558
UR - https://doi.org/10.1002/alz.71558
LA - en
ER -

CSL-JSON

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