Pandey, GauravPandey, Om P.Rogers, Angela J.Ahsen, Mehmet E.Hoffman, Gabriel E.Raby, BenjaminWeiss, ScottSchadt, Eric E.Bunyavanich, Supinda2018-07-252018Pandey, Gaurav, Om P. Pandey, Angela J. Rogers, Mehmet E. Ahsen, Gabriel E. Hoffman, Benjamin A. Raby, Scott T. Weiss, Eric E. Schadt, and Supinda Bunyavanich. 2018. “A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.” Scientific Reports 8 (1): 8826. doi:10.1038/s41598-018-27189-4. http://dx.doi.org/10.1038/s41598-018-27189-4.http://nrs.harvard.edu/urn-3:HUL.InstRepos:37298458Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.en-USJournal Article2018-07-2510.1038/s41598-018-27189-4