近日，韩国首尔国立大学Murim Choi及其小组的最新研究揭示了单细胞eQTL分析确定代谢功能障碍相关脂肪性肝炎的遗传变异。该研究于2025年6月25日发表于国际一流学术期刊《自然—遗传学》杂志上。

为了满足个性化治疗的迫切需求，该研究团队展示了对25名MASLD患者和23名对照组的肝脏活检进行单细胞表达定量性状定位（sc-eQTL）分析的结果。该方法鉴定了3500多个主要肝细胞类型的sc-eqtl和细胞状态相互作用的eQTLs (ieQTL)，这些eqtl具有显著的疾病遗传性富集（对于MASLD性状，ieQTL富集优势比为10.27）。课题组整合了转录因子作为ieQTL的上游调控因子，揭示了由转录因子、细胞状态、ieQTL的SNP组分（ieSNPs）和ieQTL的基因组分（ieGenes）组成的601个功能单元（“四重奏”）。根据这些结果，该课题组人员确定了EFHD1中eQTL的缺失与FOXO1基因型特异性调节相关的肝细胞适应不良，进一步增加了MASLD的风险。他们的方法强调了eQTL分析在捕捉影响复杂疾病的基因表达和临床结果的关键遗传变异方面的作用。

据了解，代谢功能障碍相关的脂肪变性肝病（MASLD）因其医学和社会经济影响而日益得到认可，受到多种遗传和环境因素的驱动。

附：英文原文

Title: Single-cell eQTL analysis identifies genetic variation underlying metabolic dysfunction-associated steatohepatitis

Author: Hong, Sung Eun, Mun, Seon Ju, Lee, Young Joo, Yoo, Taekyeong, Suh, Kyung-Suk, Kang, Keon Wook, Son, Myung Jin, Kim, Won, Choi, Murim

Issue&Volume: 2025-06-25

Abstract: Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly recognized for its medical and socioeconomic impacts, driven by diverse genetic and environmental factors. Here, to address the urgent need for individually tailored therapies, we show results from single-cell expression quantitative trait locus (sc-eQTL) analysis on liver biopsies from 25 patients with MASLD and 23 controls. This approach identified over 3,500 sc-eQTLs across major liver cell types and cell-state-interacting eQTLs (ieQTLs) with significant enrichment for disease heritability (for MASLD trait, ieQTL enrichment odds ratio10.27). We integrated transcription factors as upstream regulators of ieQTLs, revealing 601 functional units (‘quartets’) composed of transcription factors, cell states, SNP components of ieQTL (ieSNPs) and Gene component of ieQTL (ieGenes). From these results, we pinpoint the loss of an eQTL in EFHD1 during hepatocyte maladaptation associated with genotype-specific regulation by FOXO1, further contributing to the risk of MASLD. Our approach underscores the role of eQTL analysis in capturing crucial genetic variations that influence gene expression and clinical outcomes in complex diseases.

DOI: 10.1038/s41588-025-02237-8

Source: https://www.nature.com/articles/s41588-025-02237-8