美国哈佛医学院Rahul C. Deo等研究人员合作发现，人类血细胞的扰动表型揭示与常见疾病子集相关的基因决定潜伏特征。2023年12月4日，国际知名学术期刊《自然—遗传学》在线发表了这一成果。

研究人员建立了一个框架，其利用人类外周血细胞、物理、化学和药理学扰动以及基于流式细胞仪的功能读数来揭示潜在的细胞过程，并根据这些诱发特征对多达2600人进行了全基因组关联研究（GWAS）。研究人员确定了119个基因组位点，涉及96个与这些细胞反应相关的基因，并发现了诱发的血液表型与常见疾病子集之间的关联。

研究人员发现，在患有特定心脏代谢疾病的人群中，促炎症抗凋亡的中性粒细胞群体十分普遍。基于这一特征的多基因模型可预测2型糖尿病患者罹患慢性肾病的风险。通过扩大人类基因研究的表型空间，研究人员可以确定与巨大效应反应差异相关的变异，对患者进行分层，并有效地描述潜在的生物学特征。

据悉，尽管GWAS已成功地将遗传风险位点与各种疾病联系起来，但由于常见疾病的复杂性，确定潜在的细胞生物学机制仍具有挑战性。

附：英文原文

Title: Perturbational phenotyping of human blood cells reveals genetically determined latent traits associated with subsets of common diseases

Author: Homilius, Max, Zhu, Wandi, Eddy, Samuel S., Thompson, Patrick C., Zheng, Huahua, Warren, Caleb N., Evans, Chiara G., Kim, David D., Xuan, Lucius L., Nsubuga, Cissy, Strecker, Zachary, Pettit, Christopher J., Cho, Jungwoo, Howie, Mikayla N., Thaler, Alexandra S., Wilson, Evan, Wollison, Bruce, Smith, Courtney, Nascimben, Julia B., Nascimben, Diana N., Lunati, Gabriella M., Folks, Hassan C., Cupelo, Matthew, Sridaran, Suriya, Rheinstein, Carolyn, McClennen, Taylor, Goto, Shinichi, Truslow, James G., Vandenwijngaert, Sara, MacRae, Calum A., Deo, Rahul C.

Issue&Volume: 2023-12-04

Abstract: Although genome-wide association studies (GWAS) have successfully linked genetic risk loci to various disorders, identifying underlying cellular biological mechanisms remains challenging due to the complex nature of common diseases. We established a framework using human peripheral blood cells, physical, chemical and pharmacological perturbations, and flow cytometry-based functional readouts to reveal latent cellular processes and performed GWAS based on these evoked traits in up to 2,600 individuals. We identified 119 genomic loci implicating 96 genes associated with these cellular responses and discovered associations between evoked blood phenotypes and subsets of common diseases. We found a population of pro-inflammatory anti-apoptotic neutrophils prevalent in individuals with specific subsets of cardiometabolic disease. Multigenic models based on this trait predicted the risk of developing chronic kidney disease in type 2 diabetes patients. By expanding the phenotypic space for human genetic studies, we could identify variants associated with large effect response differences, stratify patients and efficiently characterize the underlying biology.

DOI: 10.1038/s41588-023-01600-x

Source: https://www.nature.com/articles/s41588-023-01600-x