Department of Bioinformatics and Life Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, South Korea.
Department of Bioinformatics and Life Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, South Korea.
Comput Biol Chem. 2024 Jun;110:108086. doi: 10.1016/j.compbiolchem.2024.108086. Epub 2024 Apr 27.
A colocalization analysis of genome-wide association study (GWAS) signals and expression quantitative trait loci (eQTL) was conducted to pinpoint target genes and their regulatory nucleotide variants for subtypes of ischemic stroke. We utilized GWAS data from prominent meta-analysis consortia (MEGASTROKE and GIGASTROKE) and single-cell eQTL data in brain and blood tissues to enhance accuracy and minimize noise inherent in bulk RNA-seq. Employing Bayesian colocalization methods, we identified ten shared loci between GWAS and eQTL signals, targeting five eGenes. Specifically, RAPH1 and ICA1L were discovered for small vessel stroke (SVS), whereas SCYL3, CAV1, and CAV2 were for cardioembolic stroke (CS). However, no findings have been made for large artery stroke. The exploration and subsequent functional analysis of causal variants within the colocalized regions revealed their regulatory roles, particularly as enhancer variants (e.g., rs144505847 and rs72932755 targeting ICA1L; rs629234 targeting SCYL3; rs3807989 targeting CAV1 and CAV2). Notably, our study unveiled that all eQTL for CS were identified in oligodendrocytes, while those for SVS were across excitatory neurons, astrocytes, and oligodendrocyte precursor cells. This underscores the heterogeneous tissue-specific genetic factors by subtypes of ischemic stroke. The study emphasizes the need for intensive research efforts to discover causative genes and variants, unravelling the cell type-specific genetic architecture of ischemic stroke subtypes. This knowledge is crucial for advancing our understanding of the underlying pathophysiology and paving the way for precision neurology applications.
我们进行了全基因组关联研究 (GWAS) 信号和表达数量性状基因座 (eQTL) 的共定位分析，以确定缺血性卒中亚型的靶基因及其调节核苷酸变异。我们利用来自主要荟萃分析联盟 (MEGASTROKE 和 GIGASTROKE) 的 GWAS 数据和大脑和血液组织中的单细胞 eQTL 数据，以提高准确性并最大限度地减少批量 RNA-seq 固有的噪声。我们采用贝叶斯共定位方法，在 GWAS 和 eQTL 信号之间确定了十个共享位点，针对五个 eGenes。具体来说，RAPH1 和 ICA1L 被发现与小血管卒中 (SVS) 有关，而 SCYL3、CAV1 和 CAV2 则与心源性栓塞性卒中 (CS) 有关。然而，在大动脉卒中方面没有发现任何结果。在共定位区域内对因果变异的探索和随后的功能分析揭示了它们的调节作用，特别是作为增强子变异 (例如，针对 ICA1L 的 rs144505847 和 rs72932755；针对 SCYL3 的 rs629234；针对 CAV1 和 CAV2 的 rs3807989)。值得注意的是，我们的研究表明，CS 的所有 eQTL 都是在少突胶质细胞中鉴定出来的，而 SVS 的 eQTL 则跨越兴奋性神经元、星形胶质细胞和少突胶质细胞前体细胞。这突显出缺血性卒中亚型的组织特异性遗传因素的异质性。该研究强调需要进行深入的研究努力，以发现致病基因和变异，揭示缺血性卒中亚型的细胞类型特异性遗传结构。这一知识对于推进我们对潜在病理生理学的理解并为精准神经学应用铺平道路至关重要。
