Abstract Background: Comorbidities greatly increase global health burdens, but the landscapes of their genetic factors have not been systematically investigated. Methods: We used the hospital inpatient data of 385,335 patients in UK Biobank to investigate the comorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify comorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for interpretable comorbidity networks to detect the hub diseases and the involved molecules in comorbidity modules. Results: 11,285 comorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture level. The comorbidities affecting the same and different physiological systems showed different patterns at the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by comorbidities mainly converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable comorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the comorbidity modules could help provide useful insights into the genetic contributors for comorbiditities. Conclusions: Our results provide a systematic resource for understanding the genetic predispositions of comorbidity, and indicate that hub diseases and converged molecules and functions may be the key for treating comorbidity. We have created an online database to facilitate researchers and physicians to browse, search or download these comorbidities (https://comorbidity.comp-sysbio.org).

中文翻译：

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英国生物库中常见疾病的遗传可解释合并症全球概述

摘要背景：合并症极大地增加了全球健康负担，但尚未对其遗传因素的概况进行系统的研究。方法：我们使用英国生物库中385,335例患者的住院数据，调查了439种常见疾病之间的合并症关系。进行了GWAS后分析，以鉴定在基因组位点，网络以及总体遗传结构水平上合并症的遗传风险。我们对可解释的合并症网络进行了网络分解，以检测中枢疾病和合并症模块中涉及的分子。结果：在439种常见疾病中鉴定出11285种合并症，其中46％可以在基因座，网络或整体遗传结构水平上进行遗传解释。影响相同和不同生理系统的合并症在共享的遗传成分上表现出不同的模式，前者更可能共享基因座水平的遗传成分，而后者更可能共享网络级的遗传成分。此外，合并症共有的基因位点和网络级遗传成分都集中在细胞免疫，蛋白质代谢和基因沉默上。此外，我们发现遗传上可解释的合并症倾向于形成网络模块，由中枢疾病介导并具有生理类别。最后，我们展示了介导合并症模块的中枢疾病如何帮助提供有用的见解，以了解合并症的遗传因素。结论：我们的结果为了解合并症的遗传易感性提供了系统的资源，并表明轮毂疾病和聚合分子和功能可能是治疗合并症的关键。我们创建了一个在线数据库，以方便研究人员和医生浏览，搜索或下载这些合并症（https://comorbidity.comp-sysbio.org）。