2024年9月26日，美国哈佛医学院Soumya Raychaudhuri研究组在《自然—遗传学》杂志在线发表论文，利用新方法识别影响单细胞数据中细胞状态丰度的遗传变异。

研究人员引入了基因型-邻域关联（GeNA），这是一种统计工具，用于在高维单细胞数据集中识别细胞状态丰度定量性状基因座（csaQTL）。GeNA不再测试与预定义细胞状态的关联，而是灵活地识别与遗传变异关联最强的细胞状态丰度。

在对969名个体的单细胞RNA测序外周血轮廓的全基因组调查中，GeNA识别出五个独立基因座与免疫细胞状态相对丰度的变化相关。例如，rs3003-T（P=1.96×10⁻¹¹）与表达肿瘤坏死因子反应程序的自然杀伤细胞丰度增加相关。该csaQTL与银屑病（对抗肿瘤坏死因子治疗有反应的自身免疫疾病）风险增加共定位。

因此，灵活地表征细胞状态的csaQTL可能有助于揭示遗传背景如何改变细胞组成，从而赋予疾病风险。

据悉，疾病风险等位基因会影响体内细胞组成，但由于与变异相关的细胞状态可能反映多种难以预定义的细胞特征组合，因此建模基因对单细胞谱系揭示的细胞状态的影响变得困难。

附：英文原文

Title: Identifying genetic variants that influence the abundance of cell states in single-cell data

Author: Rumker, Laurie, Sakaue, Saori, Reshef, Yakir, Kang, Joyce B., Yazar, Seyhan, Alquicira-Hernandez, Jose, Valencia, Cristian, Lagattuta, Kaitlyn A., Mah-Som, Annelise, Nathan, Aparna, Powell, Joseph E., Loh, Po-Ru, Raychaudhuri, Soumya

Issue&Volume: 2024-09-26

Abstract: Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype–Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P=1.96×1011) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

DOI: 10.1038/s41588-024-01909-1

Source: https://www.nature.com/articles/s41588-024-01909-1