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BACKGROUND: Respiratory syncytial virus (RSV) is a significant cause of infant morbidity and mortality worldwide; however, understanding the genetic risk factors of severe RSV is incomplete. Neutrophils and monocytes have been previously identified as major cell subsets involved in airway inflammation, however the pathophysiology of these events is also not fully characterised. Given that the majority of children experience at least one RSV infection by the age of two years, but not all develop severe disease, we used genomic and transcriptomic data to explore potential mechanistic biomarkers of disease severity. METHODS: We conducted a genome-wide association study (GWAS) to investigate the genetic factors underlying RSV severity, assessed by the ReSVinet scale, in a cohort of 251 infants aged from 1 week old to 1 year of age. Genotyping data was collected from multiple European study sites as part of the RESCEU Consortium. Data and were analysed following quality control and genotype imputation using the TOPMed server. Generalised linear regression models were employed to assess the impact of genotype on RSV severity. Matrix eQTL in R was used to model the impact of candidate SNPs genotype on gene expression as measured by microarray. RESULTS: While no SNPs reached the genome-wide statistical significance threshold (p 

Original publication




Journal article


J Infect Dis

Publication Date



GWAS, RSV, eQTL, infection, inflammation