Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, P. R. China.
Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, P. R. China.
Nat Commun. 2024 Jun 7;15(1):4890. doi: 10.1038/s41467-024-49263-4.
The human brain has been implicated in the pathogenesis of several complex diseases. Taking advantage of single-cell techniques, genome-wide association studies (GWAS) have taken it a step further and revealed brain cell-type-specific functions for disease loci. However, genetic causal associations inferred by Mendelian randomization (MR) studies usually include all instrumental variables from GWAS, which hampers the understanding of cell-specific causality. Here, we developed an analytical framework, Cell-Stratified MR (csMR), to investigate cell-stratified causality through colocalizing GWAS signals with single-cell eQTL from different brain cells. By applying to obesity-related traits, our results demonstrate the cell-type-specific effects of GWAS variants on gene expression, and indicate the benefits of csMR to identify cell-type-specific causal effect that is often hidden from bulk analyses. We also found csMR valuable to reveal distinct causal pathways between different obesity indicators. These findings suggest the value of our approach to prioritize target cells for extending genetic causation studies.
人类大脑与多种复杂疾病的发病机制有关。利用单细胞技术，全基因组关联研究（GWAS）更进一步，揭示了疾病基因座在脑细胞类型中的特异性功能。然而，孟德尔随机化（MR）研究推断的遗传因果关联通常包括 GWAS 的所有工具变量，这阻碍了对细胞特异性因果关系的理解。在这里，我们开发了一个分析框架，细胞分层 MR（csMR），通过将 GWAS 信号与来自不同脑细胞的单细胞 eQTL 进行共定位，来研究细胞分层的因果关系。通过应用于肥胖相关特征，我们的结果表明 GWAS 变体对基因表达的细胞类型特异性影响，并表明 csMR 识别细胞类型特异性因果效应的优势，而这些效应通常在批量分析中被隐藏。我们还发现 csMR 有助于揭示不同肥胖指标之间的不同因果途径。这些发现表明，我们的方法在确定目标细胞以扩展遗传因果研究方面具有价值。
