Academic Journal
A pseudotime-dependent TWAS framework identifies disease genes along cell developmental paths.
| Τίτλος: | A pseudotime-dependent TWAS framework identifies disease genes along cell developmental paths. |
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| Συγγραφείς: | Cao R; Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. Electronic address: cao00128@umn.edu., Li C; Department of Statistics, College and Graduate School of Arts and Sciences, University of Virginia, Charlottesville, VA 22904, USA; Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, IA 50011, USA., Cui E; Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA., Spector L; Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55454, USA., Raduski A; Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55454, USA., Anderson N; Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55454, USA., Guan W; Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA., Gordon P; Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55454, USA., Im C; Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55454, USA., Yang T; Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. Electronic address: yang3704@umn.edu. |
| Πηγή: | HGG advances [HGG Adv] 2026 Oct 08; Vol. 7 (4), pp. 100634. Date of Electronic Publication: 2026 Jun 23. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Inc Country of Publication: United States NLM ID: 101772885 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2666-2477 (Electronic) Linking ISSN: 26662477 NLM ISO Abbreviation: HGG Adv Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Elsevier Inc., [2020]- |
| Ιατρικοί όροι (MeSH): | Genome-Wide Association Study*/methods , Transcriptome*/genetics , Precursor Cell Lymphoblastic Leukemia-Lymphoma*/genetics , Genetic Predisposition to Disease*, Gene Expression Profiling/methods ; Humans ; Single-Cell Gene Expression Analysis ; Single-Cell Analysis |
| Περίληψη: | Transcriptome-wide association studies (TWASs) link genes to disease risk by integrating gene expression with genome-wide association study (GWAS) data. The growing availability of single-cell expression data offers the opportunity to dissect these associations at finer cellular resolution and uncover effects masked in bulk TWAS analyses. Existing single-cell TWAS methods often map associations to discrete cell types, potentially overlooking the continuous nature of cellular processes and misidentifying the causal cell stages where genes exert their effects. To address this limitation, we developed the pseudotime-dependent TWAS (pt-TWAS), a framework that models gene expression as a continuous function of pseudotime to capture dynamic gene effects along developmental trajectories. By flexibly modeling and utilizing shared genetic effects across cell stages, this approach achieved higher statistical power than existing single-cell TWAS methods in our extensive simulations. pt-TWAS further enables identification of causal cell stages underlying disease risk by constructing confidence bands for gene effect curves. Applied to a GWAS of B cell acute lymphoblastic leukemia using single-cell data from OneK1K, pt-TWASs replicated known risk genes and pinpointed their relevant cell stages, demonstrating its utility for revealing fine-grained, cell-stage-specific genetic mechanisms. An R package implementing pt-TWASs is available on GitHub. (Copyright © 2026 The Authors. Published by Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of interests The authors declare no competing interests. |
| Σχόλια: | Update of: medRxiv. 2025 Oct 29:2025.10.28.25338929. doi: 10.1101/2025.10.28.25338929.. (PMID: 41282726) |
| Grant Information: | R01 AG074858 United States AG NIA NIH HHS |
| Contributed Indexing: | Keywords: OneK1K; acute lymphoblastic leukemia; causal cell stages; pseudotime; single-cell data analysis; transcriptome-wide association study |
| Entry Date(s): | Date Created: 20260624 Date Completed: 20261008 Latest Revision: 20261008 |
| Update Code: | 20261009 |
| PubMed Central ID: | PMC13400862 |
| DOI: | 10.1016/j.xhgg.2026.100634 |
| PMID: | 42337910 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2666-2477 |
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| DOI: | 10.1016/j.xhgg.2026.100634 |