Background

Conventional genomic studies have primarily relied on bulk tissue analysis to elucidate the genetic causes of complex diseases. Bulk tissue data, which consists of a mixture of various cell types, often has limitations in detecting subtle gene expression changes occurring in specific cells. This has hindered the identification of precise molecular mechanisms by which genetic variations contribute to disease, leading to stagnation in research. Recently, the field of genetics has shown increasing interest in the hypothesis that gene expression regulation varies across cell types. Bulk tissue data, which fails to capture cellular diversity in detail, has proven insufficient for fully explaining the genetic factors behind complex diseases. As a result, the need for analytical techniques capable of precisely tracking gene expression regulation at the single-cell level has steadily grown. In particular, the existence of expression quantitative trait loci (eQTLs), which act as regulatory switches for gene expression in specific cell types, has been identified. However, quantifying their activity at the level of specific cell types has been extremely challenging. Researchers are now intensively working on developing models to separate cell-type-specific contributions to gene expression regulation in order to enhance the precision of disease heritability analysis.

Key Findings

A U.S. research team developed a statistical model called CIGMA (Component Integration for Genetic Mapping and Analysis) to unbiasedly estimate the contribution of shared and cell-type-specific eQTLs using single-cell RNA-sequencing (scRNA-seq) data. This analytical tool was rigorously validated using large peripheral blood mononuclear cell (PBMC) cohorts, including OneK1K and CLUES/ImmVar datasets. The analysis revealed that the heritability of complex traits, including autoimmune diseases, is densely concentrated in cell-type-specific eQTLs, drawing significant attention. In contrast, the heritability contribution of eQTLs shared across multiple cell types remained minimal. Genes regulated by cell-type-specific eQTLs were found to be strongly conserved through evolution and often contained multiple enhancer regions that facilitate gene expression. Additionally, there were clear differences based on the physical distance of regulation within the genome. While only about 30% of cis-eQTLs, which regulate nearby gene expression, showed cell-type specificity, over 60% of trans-eQTLs, which regulate distant genes, exhibited activation only in specific cell types. These trans-eQTLs are estimated to explain about 25% of overall expression heritability and are considered a core axis in regulatory networks that determine disease susceptibility.

Implications and Outlook

This discovery clarifies the principle that the progression of disease from genetic variation originates from malfunctioning regulatory switches specific to cell types. With the ability to clearly identify key cells and gene regulatory pathways that trigger disease, the pace of precision medicine research is expected to accelerate significantly. Establishing drug design based on genetic information at the cell-type level provides a foundation for improving drug target efficiency and ensuring long-term safety. However, future research still faces considerable practical challenges. Most analyses have focused on blood cell data, leaving the regulatory mechanisms of complex organs such as the brain and heart as ongoing challenges. The process of reducing the cost of single-cell sequencing and acquiring diverse global population cohorts also presents many constraints. In response, the research team aims to maximize the computational efficiency of statistical algorithms to rapidly complete expression maps of diverse tissue cell types.