School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4067, Australia.
Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia.
J Biomed Inform. 2023 May;141:104345. doi: 10.1016/j.jbi.2023.104345. Epub 2023 Mar 21.
Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; P > 0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR < 0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including "cell aging", "metal ion binding" and "oxidative damage". Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.
中风是全球第二大致死原因。全基因组关联研究（GWAS）已经确定了一些与中风风险相关的遗传变异，但它们潜在的功能因果基因尚不清楚。因此，我们旨在确定中风风险的潜在功能因果基因生物标志物。我们使用基于汇总的孟德尔随机化（SMR）方法来识别遗传调控特征（即基因表达和 DNA 甲基化）与中风风险的多效关联。使用 SMR 方法，我们整合了 cis 表达数量性状基因座（cis-eQTL）和 cis-DNA 甲基化数量性状基因座（cis-mQTL）数据与中风的 GWAS 汇总统计数据。我们还利用异质性依赖工具（HEIDI）测试来区分通过 SMR 分析观察到的关联中的多效性和连锁。我们的综合 SMR 分析和 HEIDI 测试揭示了 45 个候选生物标志物基因（FDR<0.05；P>0.01），这些基因与中风风险存在多效性或潜在因果关系。在这些候选生物标志物基因中，有 10 个基因（HTRA1、PMF1、FBN2、C9orf84、COL4A1、BAG4、NEK6、SH2B3、SH3PXD2A、ACAD10）在中风和健康个体的全基因组血液转录组学数据中差异表达（FDR<0.05）。鉴定的候选生物标志物基因的功能富集分析揭示了与中风相关的基因本体论和途径，包括“细胞衰老”、“金属离子结合”和“氧化损伤”。基于 SMR 中基因表达的遗传调控和血液中基因的直接测量表达的证据，我们的综合分析表明十个基因是中风风险的血液生物标志物。此外，我们的研究提供了对 DNA 甲基化对与中风风险相关的基因表达影响的更好理解。
