Identification of candidate susceptibility genes in a murine model of respiratory syncytial virus (RSV)‐induced bronchiolitis

Abstract

RSV is the most common cause of serious lower respiratory illnesses in infants and children. While genetic association studies have been reported for candidate susceptibility genes for RSV disease, no biomarkers exist for predicting severe versus mild disease. Here we use a combined genetics and genomics approach to understand the genetic basis of RSV disease susceptibility. We screened 32 inbred mouse strains for response to RSV by measuring disease phenotypes, including bronchoalveolar lavage inflammatory cells. Quantitative trait loci (QTL) were found for all disease phenotypes using SNPster (Novartis), and expression QTLs were identified with the FastMap haplotype algorithm. To identify transcripts differentially expressed at baseline that predict response to RSV, baseline mouse lung gene transcript expression for 24 strains of mice (Novartis) was correlated to phenotype data from the RSV strain screen using linear regression. These studies identified a battery of baseline gene transcripts significantly correlated with RSV disease severity. Disease and gene correlations in the final model were validated using cross validation. Together, these approaches have identified genetic markers of susceptibility to RSV disease to predict severe responders and potentially provide more effective therapeutic targets.Supported by the NIEHS Intramural Program.