Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine, Pavillon H-413, Montréal, Québec, H3T 1E2, Canada.
McGill University, Montreal, QC, Canada.
Hum Genet. 2023 Oct;142(10):1461-1476. doi: 10.1007/s00439-023-02590-w. Epub 2023 Aug 28.
Identifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize-which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ. We conducted MR of cis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7. Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.
识别 GWAS 位点的因果基因有助于确定治疗干预的靶点。表达研究可以阐明这些位点，但表达数量性状基因座 (eQTL) 的信号往往无法共定位，这意味着测量表达的遗传控制与疾病风险的遗传控制不共享。这可能是因为基因表达是在错误的细胞类型、生理状态或器官中测量的。我们测试了孟德尔随机化 (MR) 是否可以识别影响 COVID-19 结果的基因，以及基因表达的遗传控制与 COVID-19 结果的共定位是否受到细胞类型、细胞刺激和器官的影响。我们进行了顺式-eQTL 的 MR 分析，来自单细胞 (scRNA-seq) 和批量 RNA 测序。然后，我们测试了可能影响共定位的变量，包括细胞类型、细胞刺激、RNA 测序模式、器官、COVID-19 症状和有 COVID-19 症状个体中的 SARS-CoV-2 状态。用于测试共定位的结果是宿主遗传学倡议第 7 版评估的 COVID-19 严重程度和易感性。使用 MR 识别的大多数转录本在跨细胞类型、细胞状态和不同器官进行测试时没有共定位。由于连锁不平衡，大多数共定位的转录本可能代表假阳性。一般来说，共定位在跨细胞类型、细胞刺激和器官时高度可变，有时对于同一转录本不一致。虽然我们确定了一些因素会影响特定转录本的共定位，但确定了 33 个介导 COVID-19 结果的转录本，我们的研究表明，即使在经过质量控制和敏感性测试后，基因表达与 COVID-19 结果的共定位部分是由于噪声信号。这些发现说明了将表达转录本与疾病结果联系起来的当前困难，并且即使考虑到细胞类型、刺激状态和不同器官，在观察 eQTL MR 结果时也需要持怀疑态度。
