Zhao Gefei, Lai Binbin
Institute of Medical Technology, Peking University Health Science Center, 38 Xueyuan Rd, Hai Dian Qu, Beijing 100191, China.
Biomedical Engineering Department, Institute of Advanced Clinical Medicine, Peking University, 5 Yiheyuan Rd, Haidian District, Beijing 100191, China.
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf123.
One major challenge of interpreting variants from genome-wide association studies (GWAS) of complex traits or diseases is how to efficiently annotate noncoding variants. These variants influence gene expression by disrupting cis-regulatory elements (CREs), whose spatial and cell-type specificity are not adequately captured by conventional tools like multi-marker analysis of genomic annotation. Current methods either rely on linear proximity to genes or quantitative trait locus (QTL) data yet fail to integrate single-cell epigenomic information for a comprehensive annotation.
We present SC-VAR, a novel computational tool designed to enhance the interpretation of disease-associated risks from GWAS using single-cell epigenomic data. SC-VAR leverages single-cell epigenomic data to predict functional outcomes including risk genes, pathways, and cell types for both coding and noncoding disease-associated variants. We demonstrate that SC-VAR outperforms state-of-the-art methods by predicting more validated disease-related genes and pathways for multiple diseases. Additionally, SC-VAR identifies cell types that are susceptible to disease, along with their specific CREs and target genes linked to risk. By capturing a broad range of disease risks across human tissues at distinct developmental stages, SC-VAR could enhance our understanding of disease mechanisms in complex tissues across different life stages.
解释复杂性状或疾病的全基因组关联研究（GWAS）中的变异的一个主要挑战是如何有效地注释非编码变异。这些变异通过破坏顺式调控元件（CRE）来影响基因表达，而传统工具如基因组注释的多标记分析无法充分捕捉其空间和细胞类型特异性。当前的方法要么依赖于与基因的线性接近度，要么依赖于数量性状基因座（QTL）数据，但未能整合单细胞表观基因组信息进行全面注释。
我们提出了SC-VAR，这是一种新颖的计算工具，旨在利用单细胞表观基因组数据增强对GWAS中疾病相关风险的解释。SC-VAR利用单细胞表观基因组数据来预测功能结果，包括编码和非编码疾病相关变异的风险基因、通路和细胞类型。我们证明，SC-VAR通过为多种疾病预测更多经过验证的疾病相关基因和通路，优于现有方法。此外，SC-VAR识别出易患疾病的细胞类型，以及它们与风险相关的特定CRE和靶基因。通过在不同发育阶段捕捉人类组织中的广泛疾病风险，SC-VAR可以增强我们对不同生命阶段复杂组织中疾病机制的理解。
