A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank.
A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank.
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DOI：
10.3390/jpm11121382
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发表时间：
2021-12-17
影响因子：
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通讯作者：
Kim D
中科院分区：
文献类型：
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作者：
Sriram V;Nam Y;Shivakumar M;Verma A;Jung SH;Lee SM;Kim D
背景：最近的研究发现，患有产科疾病的女性发生各种长期并发症的风险增加。然而，这些联系的潜在病理生理学仍未确定。基于网络的观点结合了其他疾病和遗传关联的知识，将有助于我们理解遗传学在妊娠相关疾病并发症中的作用。方法：我们使用来自全表组关联研究 (PheWAS) 的英国生物银行 (UKBB) 摘要数据构建了疾病-疾病网络 (DDN)，以详细阐述多种疾病关联。我们还构建了以自我为中心的 DDN，其中每个网络都专注于与妊娠相关的疾病及其邻近疾病。然后，我们应用基于图的半监督学习 (GSSL) 将自我中心 DDN 中的连接转化为病理知识。结果：针对 UKBB 中的每种妊娠相关表型总共构建了 26 个以自我为中心的 DDN。将 GSSL 应用于每个 DDN，我们获得了考虑到感兴趣的妊娠相关疾病的其他表型的并发症风险评分。使用来自 UKBB 电子健康记录的共现情况验证了预测。与使用完整的 DDN 相比，我们提出的方法使接收者操作特征曲线下的平均面积 (AUC) 增加了 1.35 倍，从 55.0% 增加到 74.4%。结论：以自我为中心的 DDN 有希望作为一种临床工具，用于基于网络识别各种表型的潜在疾病并发症。
Background: Recent studies have found that women with obstetric disorders are at increased risk for a variety of long-term complications. However, the underlying pathophysiology of these connections remains undetermined. A network-based view incorporating knowledge of other diseases and genetic associations will aid our understanding of the role of genetics in pregnancy-related disease complications. Methods: We built a disease–disease network (DDN) using UK Biobank (UKBB) summary data from a phenome-wide association study (PheWAS) to elaborate multiple disease associations. We also constructed egocentric DDNs, where each network focuses on a pregnancy-related disorder and its neighboring diseases. We then applied graph-based semi-supervised learning (GSSL) to translate the connections in the egocentric DDNs to pathologic knowledge. Results: A total of 26 egocentric DDNs were constructed for each pregnancy-related phenotype in the UKBB. Applying GSSL to each DDN, we obtained complication risk scores for additional phenotypes given the pregnancy-related disease of interest. Predictions were validated using co-occurrences derived from UKBB electronic health records. Our proposed method achieved an increase in average area under the receiver operating characteristic curve (AUC) by a factor of 1.35 from 55.0% to 74.4% compared to the use of the full DDN. Conclusion: Egocentric DDNs hold promise as a clinical tool for the network-based identification of potential disease complications for a variety of phenotypes.
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影响因子：
46.9
作者：
通讯作者：
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影响因子：
30.8
作者：
Loh, Po-Ru;Danecek, Petr;Palamara, Pier Francesco;Fuchsberger, Christian;Reshef, Yakir A.;Finucane, Hilary K.;Schoenherr, Sebastian;Forer, Lukas;McCarthy, Shane;Abecasis, Goncalo R.;Durbin, Richard;Price, Alkes L.
通讯作者：
Price, Alkes L.
影响因子：
3.1
作者：
Kersten, Ines;Lange, Anja Erika;Thyrian, Jochen Rene
通讯作者：
Thyrian, Jochen Rene
影响因子：
4.3
作者：
Hidalgo CA;Blumm N;Barabási AL;Christakis NA
通讯作者：
Christakis NA
DOI：
10.1038/nrg2918
发表时间：
2011-01
期刊：
Nature reviews. Genetics
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