Identification of Aggravation-Predicting Gene Polymorphisms in Coronavirus Disease 2019 Patients Using a Candidate Gene Approach Associated With Multiple Phase Pathogenesis: A Study in a Japanese City of 1 Million People

The pathology caused by the coronavirus disease 2019 is mediated by host-mediated lung inflammation, driving severity, and mortality. Polymorphisms in genes encoding host inflammation and immune-related molecules may be associated with the development of serious pathologies, and identifying such gene polymorphisms may lead to the identification of therapeutic targets. OBJECTIVES: We attempted to identify aggravation-predicting gene polymorphisms. DESIGN: We use a candidate gene approach associated with multiple phase pathogenesis in coronavirus disease 2019 patients among a cohort in Hiroshima, a city with a population of 1 million, in Japan. DNA samples from the study populations were genotyped for 34 functional polymorphisms from 14 distinct candidate genes, which encode proteins related to viral cell entry, regulation of viral replication, innate immune modulators, regulatory cytokines, and effector cytokines. SETTING AND PARTICIPANTS: Three core hospitals providing different services for patients with coronavirus disease 2019 under administrative control. A total of 230 patients with coronavirus disease 2019 were recruited from March 1, 2020, to March 31, 2021. MAIN RESULTS AND MEASUREMENTS: Among the 14 genes, we found rs1131454 in OAS1 and rs1143627 in IL1B genes as independent genetic factors associated with disease severity (adjusted odds ratio = 7.1 and 4.6 in the dominant model, respectively). Furthermore, we investigated the effect of multiple phase pathogenesis of coronavirus disease 2019 with unbiased multifactor dimensionality reduction analysis and identified a four-gene model with rs1131454 (OAS1), rs1143627 (IL1B), rs2074192 (ACE2), and rs11003125 (MBL). By combining these polygenetic factors with polyclinical factors, including age, sex, higher body mass index, and the presence of diabetes and hypertension, we proposed a composite risk model with a high area under the curve, sensitivity, and probability (0.917, 96.4%, and 74.3%, respectively) in the receiver operating characteristic curve analysis. CONCLUSIONS AND RELEVANCE: We successfully identified significant genetic factors in OAS1 and IL1B genes using a candidate gene approach study as valuable information for further mechanistic investigation and predictive model building.