While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility. Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb); http://cpag.oit.duke.edu) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs with severe COVID-19 demonstrated colocalization of the GWAS signal of the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN), pointing to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity. Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches.

中文翻译：

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连接人类疾病的共享遗传结构和分子表型的图谱提供了对 COVID-19 易感性的洞察

虽然全基因组关联研究 (GWAS) 已成功阐明复杂人类特征和疾病的遗传结构，但理解从遗传变异到病理生理学的机制仍然是一个重要的挑战。需要方法来系统地弥合这一关键差距，以促进假设的实验测试和转化为临床效用。在这里，我们利用跨表型关联来识别具有共享遗传结构的性状，使用连锁不平衡 (LD) 信息通过代理准确捕获共享 SNP，并计算富集的重要性。通过整合来自临床、细胞和分子 GWAS 目录的数据，这种共享的遗传结构在不同的生物尺度上进行了检查。我们创建了一个交互式网络数据库（GWAS 数据库的交互式交叉表型分析 (iCPAGdb)；http://cpag.oit.duke.edu）以促进探索并允许对用户上传的 GWAS 汇总统计数据进行快速分析。该数据库揭示了表型之间众所周知的关系，以及解释常见疾病病理生理学的新假设的产生。将 iCPAGdb 应用于最近的严重 COVID-19 GWAS 表明 COVID-19 与人类疾病之间 GWAS 信号意外重叠，包括由 DPP9 基因座驱动的特发性肺纤维化。来自 COVID-19 患者外周血的转录组学表明，与健康对照或细菌感染者相比，SARS-CoV-2 中诱导了 DPP9。对具有严重 COVID-19 的交叉表型 SNP 的进一步研究表明，ABO 基因座的 GWAS 信号与报道的 SARS-CoV-2 受体 CD209 (DC-SIGN) 的血浆蛋白水平共定位，指出了一种可能的机制ABO 对 CD209 的糖基化可能调节 COVID-19 疾病的严重程度。因此，跨表型尺度连接遗传相关性状将人类疾病与分子和细胞测量联系起来，这些测量可以揭示机制并导致新的生物标志物和治疗方法。