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A UNIFIED QUANTILE FRAMEWORK FOR NONLINEAR HETEROGENEOUS TRANSCRIPTOME-WIDE ASSOCIATIONS

Wang, Tianying ; Ionita-Laza, Iuliana LU and Wei, Ying (2025) In Annals of Applied Statistics 19(2). p.967-985
Abstract

Transcriptome-wide association studies (TWAS) are powerful tools for identifying gene-level associations by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that may be present in biological systems. To address this limitation, we propose a novel framework, QTWAS, which integrates a quantile-based gene expression model into the TWAS model, allowing for the discovery of nonlinear and heterogeneous gene-trait associations. Via compre-hensive simulations and applications to both continuous and binary traits, we demonstrate that the proposed model is more powerful than conventional TWAS in... (More)

Transcriptome-wide association studies (TWAS) are powerful tools for identifying gene-level associations by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that may be present in biological systems. To address this limitation, we propose a novel framework, QTWAS, which integrates a quantile-based gene expression model into the TWAS model, allowing for the discovery of nonlinear and heterogeneous gene-trait associations. Via compre-hensive simulations and applications to both continuous and binary traits, we demonstrate that the proposed model is more powerful than conventional TWAS in identifying gene-trait associations.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
nonlinear association test, Regression quantile process, transcriptome-wide association studies
in
Annals of Applied Statistics
volume
19
issue
2
pages
19 pages
publisher
Institute of Mathematical Statistics
external identifiers
  • scopus:105008007410
ISSN
1932-6157
DOI
10.1214/24-AOAS1999
language
English
LU publication?
yes
additional info
Publisher Copyright: © Institute of Mathematical Statistics, 2025.
id
8c693c2f-755d-47ee-a78e-d9bfed16da78
date added to LUP
2025-12-17 15:24:01
date last changed
2025-12-17 15:24:54
@article{8c693c2f-755d-47ee-a78e-d9bfed16da78,
  abstract     = {{<p>Transcriptome-wide association studies (TWAS) are powerful tools for identifying gene-level associations by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that may be present in biological systems. To address this limitation, we propose a novel framework, QTWAS, which integrates a quantile-based gene expression model into the TWAS model, allowing for the discovery of nonlinear and heterogeneous gene-trait associations. Via compre-hensive simulations and applications to both continuous and binary traits, we demonstrate that the proposed model is more powerful than conventional TWAS in identifying gene-trait associations.</p>}},
  author       = {{Wang, Tianying and Ionita-Laza, Iuliana and Wei, Ying}},
  issn         = {{1932-6157}},
  keywords     = {{nonlinear association test; Regression quantile process; transcriptome-wide association studies}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{967--985}},
  publisher    = {{Institute of Mathematical Statistics}},
  series       = {{Annals of Applied Statistics}},
  title        = {{A UNIFIED QUANTILE FRAMEWORK FOR NONLINEAR HETEROGENEOUS TRANSCRIPTOME-WIDE ASSOCIATIONS}},
  url          = {{http://dx.doi.org/10.1214/24-AOAS1999}},
  doi          = {{10.1214/24-AOAS1999}},
  volume       = {{19}},
  year         = {{2025}},
}