美国哈佛大学公共卫生学院Alkes L. Price和Benjamin J. Strober研究组取得新进展。他们的最新研究绘制了疾病相关基因座的致病组织和基因图谱。这一研究成果发表在2025年1月2日出版的国际学术期刊《自然—遗传学》上。

研究人员绘制了组织-基因精细图谱（TGFM），它通过分析汇总统计和表达定量性状位点（eQTL）数据，推断出每个基因-组织配对介导疾病位点的后纳入概率（PIP）；TGFM还为非介导变体分配PIP。TGFM考虑了跨基因和组织的共调控，并对顺式预测表达模型的不确定性进行了建模，从而实现了正确的校准。

研究利用来自38个基因型-组织表达 (GTEx) 组织的eQTL数据，运用TGFM分析了英国生物库中的45种疾病或性状。TGFM对每种疾病或性状平均鉴定出147个PIP > 0.5的因果遗传因子，其中11%是基因-组织对。TGFM发现的因果基因-组织对既反映了已知的生物学特性（如甲状腺功能减退症的TPO-甲状腺），也反映了生物学上合理的发现（如舒张压的SLC20A2-大动脉）。

运用TGFM分析外周血单核细胞（PBMCs）中与九种细胞类型有关的单细胞eQTL数据，并与GTEx组织联合分析，研究人员发现了另外30对与PBMC细胞类型有关的因果基因。

附：英文原文

Title: Fine-mapping causal tissues and genes at disease-associated loci

Author: Strober, Benjamin J., Zhang, Martin Jinye, Amariuta, Tiffany, Rossen, Jordan, Price, Alkes L.

Issue&Volume: 2025-01-02

Abstract: Complex diseases often have distinct mechanisms spanning multiple tissues. We propose tissue–gene fine-mapping (TGFM), which infers the posterior inclusion probability (PIP) for each gene–tissue pair to mediate a disease locus by analyzing summary statistics and expression quantitative trait loci (eQTL) data; TGFM also assigns PIPs to non-mediated variants. TGFM accounts for co-regulation across genes and tissues and models uncertainty in cis-predicted expression models, enabling correct calibration. We applied TGFM to 45 UK Biobank diseases or traits using eQTL data from 38 Genotype–Tissue Expression (GTEx) tissues. TGFM identified an average of 147 PIP>0.5 causal genetic elements per disease or trait, of which 11% were gene–tissue pairs. Causal gene–tissue pairs identified by TGFM reflected both known biology (for example, TPO–thyroid for hypothyroidism) and biologically plausible findings (for example, SLC20A2–artery aorta for diastolic blood pressure). Application of TGFM to single-cell eQTL data from nine cell types in peripheral blood mononuclear cells (PBMCs), analyzed jointly with GTEx tissues, identified 30 additional causal gene–PBMC cell type pairs.

DOI: 10.1038/s41588-024-01994-2

Source: https://www.nature.com/articles/s41588-024-01994-2