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Cell-type-aware transcriptome-wide association studies identify 91 independent risk genes for Alzheimer's disease dementia.

Code ↔ Paper

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The 2 matches
  1. [1] § Methods › Fine-map xWAS-O risk genes by GIFT ↔ Example scripts/5th_step_GIFT_running.R, lines 1–47 · score 0.59 · cis SNP, gene expression, vector, matrix, LD, GIFT
  2. [2] § Methods › Fine-map xWAS-O risk genes by GIFT ↔ Example scripts/5th_step_GIFT_running.R, lines 1–47 · score 0.53 · cis SNPs, gene expression, GIFT, LD, GWAS, TWAS

Paper

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The authors' code

R · 94 lines · 3.4 KB · no license · 2 matches

  1. # Install GIFT package
  2. install.packages('devtools')
  3. devtools::install_github('yuanzhongshang/GIFT')
  5. library(GIFT)
  6. #### load the directory containing files of summary statistics from eQTL data only
  7. eQTLfilelocation <- $path_to_eQTL
  8. #### load the directory of summary statistics from GWAS data
  9. GWASfile <- $path_to_gwas
  10. #### load the directory of LD matrix from eQTL data and GWAS data
  11. eQTLLDfile <- $path_to_eQTLLD
  12. GWASLDfile <- $path_to_GWASLD
  13. #### load the SNP list and cis-SNP number for each gene in a region (pindex)
  14. snplist <- $path_to_snplist
  15. pindex <- $path_to_pindex
  16. #### pre-process the file to be a list including gene names vector, z-score matrix and LD matrix of eQTL data and GWAS data
  17. convert <- pre_process_summary(eQTLfilelocation, eQTLLDfile, GWASfile, GWASLDfile, snplist, pindex)
  18. gene <- convert$gene
  19. Zscore1 <- convert$Zscore1
  20. Zscore2 <- convert$Zscore2
  21. LDmatrix1 <- convert$LDmatrix1
  22. LDmatrix2 <- convert$LDmatrix2
  24. ### input the sample sizes of eQTL data and GWAS data
  25. n1 <- $sample_size_eqtl
  26. n2 <- $sample_size_gwas
  28. R <- $path_to_gene_expressions
  30. result <- GIFT_summary(Zscore1, Zscore2, LDmatrix1, LDmatrix2, n1, n2, gene, pindex, R=R, maxiter=1000, tol=1e-4, pleio=0, ncores=1, in_sample_LD=T)
  32. # Visualization
  33. #### GWAS result
  34. GWASresult=$path_to_gwas
  35. GWASresult=GWASresult[,c(2,3,11)]
  36. GWASresult$index="GWAS"
  37. colnames(GWASresult)=c("X","BP","P","index")
  39. #### TWAS result
  40. regions <- $path_to_twas
  41. index <- gsub("region", "", region)
  42. regions <- regions[which(regions[,"region"]==index),]
  43. TWASresult=regions[,c(5,1,2,3,10)]
  44. TWASresult$BP=apply(TWASresult[,c(3,4)],1,mean)
  45. TWASresult=TWASresult[,c(1,6,5)]
  46. TWASresult$index="TWAS"
  47. colnames(TWASresult)=c("X","BP","P","index")
  49. #### GIFT result
  50. GIFTresult=result
  51. GIFTresult$BP=TWASresult$BP
  52. GIFTresult=GIFTresult[,c(1,4,3)]
  53. GIFTresult$index="GIFT"
  54. colnames(GIFTresult)=c("X","BP","P","index")
  56. #### visualize the result by Manhattan plot
  57. data=rbind(GWASresult,TWASresult)
  58. data=rbind(data,GIFTresult)
  59. data$BP=data$BP/1000000
  60. data$index=factor(data$index,levels=c("GWAS","TWAS","GIFT"))
  62. library(ggrepel)
  63. library(ggplot2)
  65. p1 <- ggplot(data) +
  66. labs(x = "region name", y = expression(paste(-log[10], " (p-value)"))) +
  67. geom_point(aes(x = BP, y = -log10(P), color = index, shape = index, size = index)) +
  68. theme_bw() +
  69. theme(
  70. legend.title = element_blank(),
  71. legend.position = "bottom",
  72. panel.border = element_blank(),
  73. panel.grid.major.x = element_blank(),
  74. panel.grid.minor.x = element_blank(),
  75. axis.line.y = element_line(color = "black", linetype = "solid"),
  76. axis.line.x = element_line(color = "black", linetype = "solid"),
  77. axis.title.x = element_text(size = 14),
  78. axis.title.y = element_text(size = 14),
  79. plot.title = element_text(hjust = 0.5, size = 16, face = "bold")
  80. ) +
  81. geom_hline(yintercept = -log10(0.05), lty = "dashed") +
  82. scale_discrete_manual(values = c("grey", "#377EB8", "#F23557"), aesthetics = 'colour') +
  83. scale_shape_manual(values = c(19, 15, 18)) +
  84. scale_size_manual(values = c(1, 1.5, 2)) +
  85. theme(panel.grid = element_blank()) +
  86. geom_text_repel(data = subset(data, index == "GIFT" & P < 0.05),
  87. aes(x = BP, y = -log10(P), label = X),
  88. size = 4,
  89. fontface="bold",
  90. box.padding = 1,
  91. point.padding = 0.8,
  92. segment.color = "black",
  93. segment.size = 0.5,
  94. nudge_y = 0.3)

5th_step_GIFT_running.R at commit 7c28a08, no license · at the source

Overview

Authors: Qiang Liu1,2, Randy L Parrish1,2, Shizhen Tang1,2, Shinya Tasaki3, David A Bennett3, Nicholas T Seyfried4, Philip L De Jager5, Vilas Menon5, Aron S Buchman3, Jingjing Yang1
ORCID iDs: Qiang Liu, Randy L Parrish, Shinya Tasaki, Nicholas T Seyfried, Philip L De Jager, Vilas Menon, Aron S Buchman, Jingjing Yang
  1. Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA USA
  2. Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA USA
  3. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL USA
  4. Department of Biochemistry, Emory University School of Medicine, Atlanta, GA USA
  5. Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY USA
Institutions: Emory University (United States); Rush University Medical Center (United States); Columbia University Irving Medical Center (United States); Columbia University (United States)
Journal: Communications biology, volume 9, issue 1, article 855
Dates: received 9 August 2025; accepted 30 March 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10030-4 · PMID 42020711 · PMCID PMC13287724 · OpenAlex W7155069180
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing
Keywords: Genetic association study, Gene expression, Alzheimer's disease
MeSH: Alzheimer Disease*, Gene Expression Profiling*, Prefrontal Cortex*, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Models, Genetic, Protein Interaction Maps, Proteomics (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) (R35GM138313); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (R01AG089703); U.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical Sciences (R35GM138313); U.S. Department of Health &amp; Human Services | NIH | National Institute on Aging (R01AG089703)
Citations: cited by 4 papers (Europe PMC); 86 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 2 matches between paragraphs and lines of code.

Leo-LiuQiang/CTA-TWAS

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 7c28a08f8be79915903c623ebe3930c8d93dab53, 12 March 2026
Languages: R (5), Shell (3)
Size: 13 files, 8 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), patchwork (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
9 files
At the source: github.com/Leo-LiuQiang/CTA-TWAS

Zenodo 18994273

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (5), Shell (3)
Size: 8 files, 8 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), patchwork (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
8 files
At the source:

Code availability statement

The paper has a code 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/s42003-026-10030-4.

Tracing map

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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;
  • 16 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.

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Data

Datasets cited

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:

Read it in the paper: doi.org/10.1038/s42003-026-10030-4.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 9 MeSH terms, 4 funders, 81 references.

Cite

This paper

Liu, Q., Parrish, R. L., Tang, S., Tasaki, S., Bennett, D. A., Seyfried, N. T., De Jager, P. L., Menon, V., Buchman, A. S., & Yang, J. (2026). Cell-type-aware transcriptome-wide association studies identify 91 independent risk genes for Alzheimer's disease dementia. Communications biology, 9(1), 855. https://doi.org/10.1038/s42003-026-10030-4

BibTeX

@article{liu2026cell,
author = {Liu, Qiang and Parrish, Randy L and Tang, Shizhen and Tasaki, Shinya and Bennett, David A and Seyfried, Nicholas T and De Jager, Philip L and Menon, Vilas and Buchman, Aron S and Yang, Jingjing},
title = {{Cell-type-aware transcriptome-wide association studies identify 91 independent risk genes for Alzheimer's disease dementia}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {855},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10030-4},
url = {https://doi.org/10.1038/s42003-026-10030-4},
pmid = {42020711},
pmcid = {PMC13287724}
}

RIS

TY - JOUR
AU - Liu, Qiang
AU - Parrish, Randy L
AU - Tang, Shizhen
AU - Tasaki, Shinya
AU - Bennett, David A
AU - Seyfried, Nicholas T
AU - De Jager, Philip L
AU - Menon, Vilas
AU - Buchman, Aron S
AU - Yang, Jingjing
TI - Cell-type-aware transcriptome-wide association studies identify 91 independent risk genes for Alzheimer's disease dementia
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/22
VL - 9
IS - 1
SP - 855
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10030-4
UR - https://doi.org/10.1038/s42003-026-10030-4
LA - en
ER -

CSL-JSON

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