疾病风险等位基因影响体内细胞的组成，但对单细胞分析揭示的细胞状态的遗传效应进行建模很困难，因为变异相关状态可能反映了难以预先定义的分析细胞特征的不同组合。我们引入了基因型邻域关联（GeNA），这是一种统计工具，用于识别高维单细胞数据集中的细胞状态丰度数量性状基因座（csaQTL）。 GeNA 不是测试与预定义细胞状态的关联，而是灵活地识别其丰度与遗传变异最相关的细胞状态。在对 969 名个体的单细胞 RNA 测序外周血分析进行的全基因组调查中，GeNA 确定了与免疫细胞状态相对丰度变化相关的 5 个独立基因座。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 与银屑病风险增加共定位，银屑病是一种对抗肿瘤坏死因子治疗有反应的自身免疫性疾病。灵活地表征颗粒细胞状态的 csaQTL 可能有助于阐明遗传背景如何改变细胞组成以赋予疾病风险。© 2024。作者获得 Springer Nature America, Inc. 的独家许可。

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 × 10-11) 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.© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.