A Statistical Method for Expression Quantitative Trait Loci Mapping with Single-cell RNA-seq Data

This project proposes a novel statistical and computational framework for integrating tissue­ level and single-cell RNA sequencing data to identify cell-type-specific expression quantitative trait loci (ct-eQTLs). Despite the success of genome-wide association studies (GWAS) in identifying genetic variants associated with complex traits, the functional interpretation of these variants remains challenging, particularly for those in non-coding regions. Current eQTL studies, predominantly based on bulk RNA sequencing, lack the resolution to pinpoint cell­ type-specific regulatory mechanisms. While single-cell RNA sequencing offers higher resolution, its reliability is limited by high costs and data sparsity. We aim to bridge these gaps by developing a unified model that leverages the strengths of both data types, enhancing the statistical power to detect ct-eQTLs. The proposed method will utilize summary statistics from large-scale tissue-level eQTL studies and smaller single-cell datasets, employing a generalized method of moments (GMM) framework for efficient computation. We will validate the proposed method using real-world datasets, including GTEx and single-cell eQTL studies, and explore downstream applications such as transcriptome-wide association studies (TWAS) to uncover disease mechanisms at a cellular resolution. This research has the potential to significantly advance our understanding of genetic regulation in complex diseases and contribute to the development of targeted therapies.