破译细胞类型特异性遗传因子与脑成像衍生表型和疾病之间的因果关系

Anyi Yang, Xingzhong Zhao, Yucheng T. Yang, Xing-Ming Zhao
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引用次数: 0

摘要

整合表达定量性状位点(eQTLs)和全基因组关联研究(GWAS)结果以确定因果基因有助于阐明复杂性状的生物学机制和发现潜在的药物靶点。这可以通过孟德尔随机化(MR)来实现,但迄今为止,大多数调查基因对大脑表型贡献的 MR 研究都是在异质脑组织而非特定细胞类型上进行的,因此限制了我们对细胞水平的了解。在本研究中,我们利用八种细胞类型的 eQTL 数据以及 123 种成像衍生表型(IDPs)和 26 种脑部疾病和行为(DBs)的大规模 GWAS,采用 MR 框架来推断基因表达与脑相关复杂性状之间的细胞类型特异性因果关系。我们的分析为 IDPs 和 DBs 构建了细胞类型特异性因果基因图谱,其中分别包括 IDPs 和 DBs 的 254 个和 217 个潜在的细胞类型特异性 eQTL 靶基因(eGenes)。鉴定结果显示出高度的细胞类型特异性,超过 90% 的基因-IDP 关联和 80% 的基因-DB 关联为单一细胞类型所独有。我们强调了IDPs和DBs之间共享的细胞类型特异性模式,描述了细胞类型特异性因果电子基因、DBs和IDPs之间的假定因果途径,并揭示了这些细胞类型特异性因果电子基因的时空表达模式。我们还证明,细胞类型特异性因果关系 eGenes 可以描述 IDPs 和 DBs 之间的关联。总之,我们的研究提供了关于影响大脑结构、失调和行为的细胞水平遗传基础的新见解,揭示了治疗目标和大脑健康管理的重要意义。
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Deciphering causal relationships between cell type-specific genetic factors and brain imaging-derived phenotypes and disorders
The integration of expression quantitative trait loci (eQTLs) and genome-wide association study (GWAS) findings to identify causal genes aids in elucidating the biological mechanisms and the discovery of potential drug targets underlying complex traits. This can be achieved by Mendelian randomization (MR), but to date, most MR studies investigating the contribution of genes to brain phenotypes have been conducted on heterogeneous brain tissues and not on specific cell types, thus limiting our knowledge at the cellular level. In this study, we employ a MR framework to infer cell type-specific causal relationships between gene expression and brain-associated complex traits, using eQTL data from eight cell types and large-scale GWASs of 123 imaging-derived phenotypes (IDPs) and 26 brain disorders and behaviors (DBs). Our analysis constructs a cell type-specific causal gene atlas for IDPs and DBs, which include 254 and 217 potential causal cell type-specific eQTL target genes (eGenes) for IDPs and DBs, respectively. The identified results exhibit high cell type specificity, with over 90% of gene-IDP and 80% of gene-DB associations being unique to a single cell type. We highlight shared cell type-specific patterns between IDPs and DBs, characterize the putative causal pathways among cell type-specific causal eGenes, DBs and IDPs, and reveal the spatiotemporal expression patterns of these cell type-specific causal eGenes. We also demonstrate that cell type-specific causal eGenes can characterize the associations between IDPs and DBs. In summary, our study provides novel insights into the genetic foundations at the cellular level that influence brain structures, disorders and behaviors, which reveals important implications for therapeutic targets and brain health management.
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