STAR Protoc. 2026 Feb 13;7(1):104306. doi: 10.1016/j.xpro.2025.104306. Online ahead of print.
ABSTRACT
The scPrediXcan framework enables cell-type-specific transcriptome-wide association studies (TWASs) by integrating deep learning-based prediction of gene expression from DNA sequence and epigenetic features. We present a protocol for scPrediXcan: training cell-type-specific models for expression prediction, predicting personalized expression, and testing associations with genome-wide association study (GWAS) summary statistics. This framework produces scalable TWAS models for different cellular contexts with minimal computational burden. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.
PMID:41689808 | DOI:10.1016/j.xpro.2025.104306
