image: (A) Input data preparation. (B) Linear regression for eQTL identification. (C) Results of eQTLs. (D) Different types of eQTLs. (E) Subsequent analysis of eQTLs
Credit: Zhe Jia, Jing Xu, Yingnan Ma, Siyu Wei, Chen Sun, Xingyu Chen, Jingxuan Kang, Haiyan Chen, Chen Zhang, Yu Dong, Junxian Tao, Xuying Guo, Hongchao Lv, Guoping Tang, Yongshuai Jiang, Mingming Zhang
In a recent Genes & Diseases review, researchers from Harbin Medical University and Zhejiang University provide an overview of eQTLs, their identification and analysis, development during different physiological periods, existing data resources, and their application in four typical diseases.
eQTLs modulate gene expression through several molecular mechanisms, including altering transcription factor binding affinity or chromatin accessibility when located within transcription initiation regions, or by directly influencing the activity of the encoded protein when located in coding regions, and via intermediary regulatory molecules, such as microRNAs.
scRNA-seq has facilitated the identification of crucial tissue-specific and cell-type-specific eQTLs, highlighting the critical role of cellular context in genetic regulation and providing deeper insights into disease etiology, such as autoimmune diseases.
The review describes the data types used in eQTL analysis—including raw genotype data, gene expression data, covariate data, and location files—while discussing the tools—MatrixeQTL, FastQTL, and QTLtools. Apart from these scRNA-seq-based tools—eQTLsingle method, SURGE, and Cell Regulatory Map—cell-type-specific eQTL (ct-eQTL) and single-nucleotide variant eQTLs are also used for eQTL identification.
eQTLs are crucial for elucidating the functional relationship between GWAS-identified genetic variants in non-coding regions and the associated complex traits, enabling causal inference methods, such as TWAS and Mendelian randomization. eCAVIAR, SMR, XGR, and OmicKriging are some of the tools used for integrating eQTL and GWAS data.
The review then summarizes the development of eQTL research over the microarray method, high-throughput sequencing period, post-genome period, and single-cell sequencing period. It also lists the common data sources—GTEx (V8), eQTLGen, eQTL Catalogue, OneK1K, scQTLbase, SingleQ, PancanQTL, Blood eQTL Browser, EyeGEx, and ImmuNexUT.
eQTL analysis has provided molecular insights into the pathogenesis of diseases such as rheumatoid arthritis (RA), type 2 diabetes (T2D), breast cancer, and schizophrenia (SZ), by elucidating the precise molecular regulatory mechanisms of risk variants. For RA, eQTLs identified novel genes (e.g., JAZF1, PTPN22) and mechanisms, such as rs142845557, regulating chromatin accessibility in synovial tissue. In T2D, eQTLs were crucial for understanding the impact of genetic variants on islet cell function and insulin secretion (e.g., PFKM, KCNA6), providing insights beyond standard GWAS risk loci. For breast cancer, eQTL analysis revealed risk loci regulating key genes (ESR1, MYC) and affecting promoter activity of cancer-related genes. Similarly, in SZ, eQTLs, often combined with single-cell RNA sequencing, facilitated the identification of novel risk genes (ALMS1, SNX19) and highlighted the role of specific brain regions and cell types (e.g., inhibitory neurons) in pathogenesis, consistently underscoring the power of eQTLs as functional connectors in understanding complex disease etiology.
In conclusion, this review provides a comprehensive understanding of eQTLs, tools used for their identification and analysis, and their potential in understanding the molecular pathogenesis of diseases, such as RA, T2D, breast cancer, and schizophrenia.
Journal
Genes & Diseases