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


Other Contributors

Yonghyun Nam 1, Sang-Hyuk Jung 1 2, Jae-Seung Yun 1 3, Vivek Sriram 1, Pankhuri Singhal 4, Marta Byrska-Bishop 5, Anurag Verma 6, Hyunjung Shin 7, Woong-Yang Park 8, Hong-Hee Won 2 8, Dokyoon Kim 1 9

1Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
2Department of Digital Health, SAIHST, Sungkyunkwan University, Samsung Medical Center, Seoul 06355, Republic of Korea.
3Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
4Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
5New York Genome Center, New York, NY 10013, USA.
6Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
7Department of Artificial Intelligence, Ajou University, Suwon 16499, Republic of Korea.
8Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea.
9Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.