COVID-19 phenomics

ABSTRACT

Phenomic studies of coronavirus disease 2019 (COVID-19) attempt to comprehensively describe the range of phenotypes associated with disease-related outcomes, by either breadth or depth of characterization. The primary aims of such studies are the unbiased generation of hypotheses concerning COVID-19 pathophysiology and the empirical determination of effective prognostic indicators. Of particular relevance to COVID-19 are phenome-wide association studies—large-scale, data-driven studies evaluating associations between a multitude of phenotypic traits and COVID-19 severity or other outcomes of interest, often employing bioinformatic and statistical approaches for the analysis of databases of electronic health records. This type of extensive phenotyping, in combination with intensive interrogation of particular aspects of the pathophysiological response, also allows investigators to reconstruct the network of phenomena that underpin disease, of particular significance because of the systemic nature of COVID-19. Because of their ability to detect novel associations, another great utility of extensive phenomic analyses applied to COVID-19 is in the development of prognostic tools and biomarkers that improve the efficacy of patient care. Finally, when applied to those in the convalescent phase, phenomics has helped to elucidate both the nature of postacute sequalae of COVID-19 and the characteristics that predispose an individual toward them. Hence, phenomics provides an additional and unique perspective which is crucial to our understanding of COVID-19 to better equip us against unforeseen adverse outcomes of this pandemic and potential infectious outbreaks in the future. © 2023 Elsevier Inc. All rights reserved.