Extended Data: Fast and powerful statistical method for context-specific QTL mapping in multi-context genomic studies

Recent studies suggest that context-specific eQTLs mediate genetic risk for complex diseases. However, limitations in current methods for identifying these eQTLs have hindered their comprehensive characterization and downstream interpretation of disease-associated variants. Here, we introduce FastGxC, a method to efficiently and powerfully map context-specific eQTLs by leveraging the correlation structure in genomic studies with repeated sampling, e.g., single-cell RNA-sequencing. We first show via simulations that FastGxC is orders of magnitude more powerful and computationally efficient than existing approaches, reducing computational time from years to minutes. We next apply FastGxC to bulk multi-tissue and single-cell PBMC RNA-seq data sets to produce comprehensive tissue- and PBMC cell-type-specific eQTL maps and validate these maps by establishing that context-specific eQTLs are enriched in genomic elements with context-specific regulatory functions. Finally, we examine the relationship between context-specific eQTLs and human disease and show that FastGxC context-specific eQTLs provide a three-fold increase in precision to identify relevant contexts. Additionally, they lead to a 6\% and 26\% increase in the number of candidate causal genes for GWAS variants across tissues and cell types, respectively, compared to standard eQTLs. In summary, FastGxC enables the construction of context-specific eQTL maps that can be used to understand the context-specific gene regulatory mechanisms underlying complex human diseases. For the related source code, see: Github For the preprint, see: biorxiv