Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data.
Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data.
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DOI：
10.1093/bioinformatics/btac822
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发表时间：
2023-01-01
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--
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了解合并症对于疾病的预防、治疗和预后至关重要。特别是，深入了解哪些疾病可能或不可能同时发生可能有助于阐明复杂疾病之间的潜在关系。在这里，我们介绍使用疾病间交互网络来发现/优先考虑合并症。具体来说，我们通过考虑疾病之间共享的遗传成分的影响方向来确定疾病关联，并将这些关联分类为协同或拮抗。我们进一步开发了一种共病评分算法来预测在给定指数疾病存在的情况下疾病是否更有可能同时发生。该算法可以处理包含相反符号关系的网络。我们最终调查了英国生物银行 PheWAS 数据中 427 种表型之间的疾病间关联，并预测了共病疾病的优先级。使用英国生物银行住院患者电子健康记录验证了预测的合并症。我们的研究结果表明，考虑表型关联的相互作用可能有助于更好地预测合并症。本研究的源代码和数据可在 https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork 获取。 补充数据可在生物信息学在线获取。
Understanding comorbidity is essential for disease prevention, treatment and prognosis. In particular, insight into which pairs of diseases are likely or unlikely to co-occur may help elucidate the potential relationships between complex diseases. Here, we introduce the use of an inter-disease interactivity network to discover/prioritize comorbidities. Specifically, we determine disease associations by accounting for the direction of effects of genetic components shared between diseases, and categorize those associations as synergistic or antagonistic. We further develop a comorbidity scoring algorithm to predict whether diseases are more or less likely to co-occur in the presence of a given index disease. This algorithm can handle networks that incorporate relationships with opposite signs. We finally investigate inter-disease associations among 427 phenotypes in UK Biobank PheWAS data and predict the priority of comorbid diseases. The predicted comorbidities were verified using the UK Biobank inpatient electronic health records. Our findings demonstrate that considering the interaction of phenotype associations might be helpful in better predicting comorbidity. The source code and data of this study are available at https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork. Supplementary data are available at Bioinformatics online.
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