# scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies

## TL;DR

This paper introduces scTWAS, a new method for analyzing gene expression in specific cell types using single-cell RNA data, improving the discovery of gene-disease associations.

## Contribution

The novel contribution is a statistical framework, scTWAS, that enables cell-type-specific transcriptome-wide association studies using single-cell RNA sequencing data.

## Key findings

- scTWAS improves prediction of genetically regulated gene expression across cell types in blood and brain tissues.
- scTWAS identified more gene-trait associations in immune cell types compared to existing methods.
- Application to Alzheimer’s disease revealed cell-subtype-specific associations, including MS4A6A and PPP1R37.

## Abstract

Transcriptome-wide association studies (TWAS) have successfully identified genes associated with complex traits and diseases, but most rely on bulk transcriptome data, overlooking cell-type-specific contexts. Population-scale single-cell RNA sequencing data now enable such analyses, but present unique challenges due to strong noises, technical variations, and high sparsity. Here, we propose scTWAS, a statistical method to conduct cell-type-specific TWAS using single-cell data. Leveraging a latent-variable model and moment-based estimation to address the challenges of single-cell data, scTWAS consistently improves the prediction of genetically regulated gene expression across cell types in both blood and brain tissues. Compared to existing methods, scTWAS identified substantially more gene-trait associations across 29 hematological traits and three immune-related diseases in immune cell types. An application to Alzheimer’s disease also revealed cell-subtype-specific associations, including MS4A6A in disease-associated microglia and PPP1R37 in both inflammatory microglial and astrocyte subtypes.