Whole-Genome Sequence Approach and Phylogenomic Stratification Improve the Association Analysis of Mutations With Patient Data in Influenza Surveillance - Van Poelvoorde, Laura; Vanneste, Kevin; De Keersmaecker, Sigrid C. J.; Thomas, Isabelle; Van Goethem, Nina; Van Gucht, Steven; Saelens, Xavier; Roosens, Nancy H. C. (Frontiers in Microbiology, 2022)
Each year, seasonal influenza results in high mortality and morbidity. The current classification of circulating influenza viruses is mainly focused on the hemagglutinin gene. Whole-genome sequencing enables tracking mutations across all influenza segments allowing a better understanding of the epidemiological effects of intra- and inter-seasonal evolutionary dynamics, and exploring potential associations between mutations across the viral genome and patient’s clinical data. In this study, mutations were identified in 253 Influenza A(H3N2) clinical isolates from the 2016-2017 influenza season in Belgium. As a proof of concept, available patient data were integrated with this genomic data, resulting in statistically significant associations that could be relevant to improve the vaccine and clinical management of infected patients. Several mutations were significantly associated with the sampling period. A new approach was proposed for exploring mutational effects in highly diverse Influenza A(H3N2) strains through considering the viral genetic background by using phylogenetic classification to stratify the samples. This resulted in several mutations that were significantly associated with patients suffering from renal insufficiency. We investigated the GISAID database to verify whether observed associations related to the sampling period identified in the Belgium A(H3N2) samples, could be extrapolated to a global level. This analysis at the international level confirmed associations with the sampling period observed at Belgian level. This study demonstrates the usefulness of tracking mutations across the complete genome and linking these to patient data, and illustrates the importance of accounting for the viral genetic background in association studies. This work highlights the need to construct databases with both information of viral genome sequences and patient data.