TITLE: “FastGxC: a statistical framework for mapping context-specific regulatory variants using bulk and single-cell RNA-seq data.”
ABSTRACT: Recent studies suggest that context-specific eQTLs underlie genetic risk factors for complex diseases. However, methods for identifying them are still nascent, limiting their comprehensive characterization and downstream interpretation of disease-associated variants. In this talk, I will introduce FastGxC, a method to efficiently and powerfully map context-specific eQTLs by leveraging the correlation structure of bulk multi-tissue and single-cell RNA-seq studies. We applied FastGxC to simulated and real bulk and single-cell RNA-Seq data sets and show that FastGxC is orders of magnitude more powerful and computationally efficient than existing eQTL mapping approaches, making previously yearlong computations possible in minutes. In addition, FastGxC provides a three-fold increase in precision to identify relevant tissues and cell types for GWAS variants than standard eQTL mapping approaches.
