Transcriptome-wide association studies at cell-state level using single-cell eQTL data

Transcriptome-wide association studies (TWASs) are widely used to prioritize genes for diseases. Current methods test gene-disease associations at the bulk tissue or cell-type-specific pseudobulk level, which do not account for the heterogeneity within cell types. We present TWiST, a statistical method for TWAS at cell-state resolution using single-cell expression quantitative trait locus (eQTL) data. Our method uses pseudotime to represent cell states and models the effect of gene expression on the trait as a continuous pseudotemporal curve. Therefore, it allows flexible hypothesis testing of global, dynamic, and nonlinear associations. Through simulation studies and real data analysis, we demonstrated that TWiST leads to significantly improved power compared to pseudobulk methods. Application to the OneK1K study identified hundreds of genes with dynamic effects on autoimmune diseases along the trajectory of immune cell differentiation. TWiST presents great promise to understand disease genetics using single-cell studies.