MOD-scTWAS: Leveraging gene co-expression for single-cell transcriptome-wide association studies
- 1Tsinghua University
Abstract
Transcriptome-wide association studies (TWAS) provide an effective framework for identifying genes associated with complex traits. Population-scale single-cell transcriptomic data enable genetically regulated expression (GReX) prediction and TWAS analyses at cell-type resolution, but the predictive performance of existing single-cell TWAS methods remains limited. Here, we develop MOD-scTWAS, a module-based method that jointly models GReX for genes within co-expression modules to borrow information across genes. Starting from a generative model for single-cell gene expression, MOD-scTWAS accounts for the heteroscedasticity and cross-gene correlation of individual-level pseudobulk expression in joint GReX prediction. In cross-validation analyses of the OneK1K dataset, MOD-scTWAS achieved higher mean GReX prediction accuracy than scTWAS across all 14 cell types and increased the number of imputable genes. When applied to TWAS analyses of UK Biobank quantitative hematological traits, MOD-scTWAS identified more significant cell type-gene-trait associations than scTWAS. These results demonstrate the potential of leveraging gene co-expression through joint modeling to improve cell-type-specific GReX prediction and TWAS discovery.
Related
- Multi-ancestry genome- and transcriptome-wide association studies expand the susceptibility landscape for breast cancer overall and across subtypes
- High false sign rates in transcriptome-wide association studies
- Transcriptome- and methylome-wide association studies prioritized lung cancer genes in East Asian populations
- FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies
- Skin transcriptome comparison of Changthangi Pashmina and Sirohi goats reveals fibre-barrier and skin remodeling expression signatures
BibTeX
@misc{guo2026modsctwas,
title = {{MOD-scTWAS: Leveraging gene co-expression for single-cell transcriptome-wide association studies}},
author = {H. Guo and Y. Lu and L. Hou},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.24.754034},
url = {https://www.biorxiv.org/content/10.64898/2026.09.24.754034v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - MOD-scTWAS: Leveraging gene co-expression for single-cell transcriptome-wide association studies
AU - H. Guo
AU - Y. Lu
AU - L. Hou
PY - 2026
DA - 2026/09/24
PB - bioRxiv
DO - 10.64898/2026.09.24.754034
UR - https://www.biorxiv.org/content/10.64898/2026.09.24.754034v1
AB - Transcriptome-wide association studies (TWAS) provide an effective framework for identifying genes associated with complex traits. Population-scale single-cell transcriptomic data enable genetically regulated expression (GReX) prediction and TWAS analyses at cell-type resolution, but the predictive performance of existing single-cell TWAS methods remains limited. Here, we develop MOD-scTWAS, a module-based method that jointly models GReX for genes within co-expression modules to borrow information across genes. Starting from a generative model for single-cell gene expression, MOD-scTWAS accounts for the heteroscedasticity and cross-gene correlation of individual-level pseudobulk expression in joint GReX prediction. In cross-validation analyses of the OneK1K dataset, MOD-scTWAS achieved higher mean GReX prediction accuracy than scTWAS across all 14 cell types and increased the number of imputable genes. When applied to TWAS analyses of UK Biobank quantitative hematological traits, MOD-scTWAS identified more significant cell type-gene-trait associations than scTWAS. These results demonstrate the potential of leveraging gene co-expression through joint modeling to improve cell-type-specific GReX prediction and TWAS discovery.
ER -