Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data

Motivation: 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. Results: 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. Availability and implementation: The source code and data of this study are available at https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork. Contact: wonhh@skku.edu or dokyoon.kim@pennmedicine.upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Automated summary

Understanding comorbidity is crucial for disease prevention and treatment. In this study, we introduce the use of an inter-disease interactivity network to discover and prioritize comorbidities. By considering phenotype associations, we develop a comorbidity scoring algorithm and predict the priority of comorbid diseases. The findings highlight the importance of considering interaction when predicting comorbidity.