Sriram Vivek, Nam Yonghyun, Shivakumar Manu, Verma Anurag, Jung Sang-Hyuk, Lee Seung Mi, Kim Dokyoon
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
J Pers Med. 2021 Dec 17;11(12):1382. doi: 10.3390/jpm11121382.
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.
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.
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.
Egocentric DDNs hold promise as a clinical tool for the network-based identification of potential disease complications for a variety of phenotypes.
近期研究发现，患有产科疾病的女性出现各种长期并发症的风险增加。然而，这些关联背后的病理生理学仍未明确。基于网络的观点，结合其他疾病和基因关联的知识，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。
我们使用来自全表型关联研究（PheWAS）的英国生物银行（UKBB）汇总数据构建了一个疾病-疾病网络（DDN），以阐述多种疾病关联。我们还构建了以自我为中心的DDN，每个网络聚焦于一种妊娠相关疾病及其邻近疾病。然后，我们应用基于图的半监督学习（GSSL）将以自我为中心的DDN中的关联转化为病理知识。
针对UKBB中每种妊娠相关表型构建了总共26个以自我为中心的DDN。将GSSL应用于每个DDN，我们在给定感兴趣的妊娠相关疾病的情况下，获得了其他表型的并发症风险评分。使用从UKBB电子健康记录中得出的共现情况对预测进行了验证。与使用完整的DDN相比，我们提出的方法使受试者工作特征曲线（AUC）下的平均面积提高了1.35倍，从55.0%提高到74.4%。
以自我为中心的DDN有望成为一种临床工具，用于基于网络识别各种表型的潜在疾病并发症。
