| Title: | A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank |
| Journal: | Journal of Personalized Medicine |
| Published: | 17 Dec 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34945853/ |
| DOI: | https://doi.org/10.3390/jpm11121382 |
| URL: | https://www.mdpi.com/2075-4426/11/12/1382/pdf?version=1640186973 |
Publication 7229
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Abstract
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.
7 Authors
- Vivek Sriram
- Yonghyun Nam
- Manu Shivakumar
- Anurag Verma
- Sang-Hyuk Jung
- Seung Mi Lee
- Dokyoon Kim
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