A genome-by-environment interaction classifier for precision medicine: Personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations

Vincent Gardeux, Joanne Berghout, Ikbel Achour, A. Grant Schissler, Qike Li, Colleen Kenost, Jianrong Li, Yuan Shang, Anthony Bosco, Donald Saner, Marilyn J. Halonen, Daniel J. Jackson, Haiquan Li, Fernando D. Martinez, Yves A. Lussier

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Objective: To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation. Materials and Methods: Within an illustrative case study to predict asthma exacerbation, we designed a stimulation assay that reveals individualized transcriptomic response to human rhinovirus. Gene expression from peripheral blood mononuclear cells was quantified from 23 pediatric asthmatic patients and stimulated in vitro with human rhinovirus. Responses were obtained via the single-subject gene set testing methodology "N-of-1- pathways." The classifier was trained on a related independent training dataset (n=19). Novel visualizations of personal transcriptomic responses are provided. Results: Of the 23 pediatric asthmatic patients, 12 experienced recurrent exacerbations. Our classifier, using individualized responses and trained on an independent dataset, obtained 74% accuracy (area under the receiver operating curve of 71%; 2-sided P=.039). Conventional classifiers using messenger RNA (mRNA) expression within the viralexposed samples were unsuccessful (all patients predicted to have recurrent exacerbations; accuracy of 52%). Discussion: Prognosis based on single time point, static mRNA expression alone neglects the importance of dynamic genome-by-environment interplay in phenotypic presentation. Individualized transcriptomic response quantified at the pathway (gene sets) level reveals interpretable signals related to clinical outcomes. Conclusion: The proposed framework provides an innovative approach to precision medicine. We show that quantifying personal pathway-level transcriptomic response to a disease-relevant environmental challenge predicts disease progression. This genome-by-environment interaction assay offers a noninvasive opportunity to translate omics data to clinical practice by improving the ability to predict disease exacerbation and increasing the potential to produce more effective treatment decisions.

Original languageEnglish (US)
Pages (from-to)1116-1126
Number of pages11
JournalJournal of the American Medical Informatics Association
Issue number6
StatePublished - Nov 1 2017


  • Asthma
  • Dynamic expression
  • Gene-by-environment
  • Genomic classifier
  • HRV stimulation
  • PBMC stimulated
  • Pathways
  • Personal transcriptome
  • Precision medicine
  • Prognostic
  • Virogram

ASJC Scopus subject areas

  • Health Informatics


Dive into the research topics of 'A genome-by-environment interaction classifier for precision medicine: Personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations'. Together they form a unique fingerprint.

Cite this