Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine

The variation in antibody response to vaccination likely involves small contributions of numerous genetic variants, such as single-nucleotide polymorphisms (SNPs), which interact in gene networks and pathways. To accumulate the bits of genetic information relevant to the phenotype that are distributed throughout the interaction network, we develop a network eigenvector centrality algorithm (SNPrank) that is sensitive to the weak main effects, gene–gene interactions and small higher-order interactions through hub effects. Analogous to Google PageRank, we interpret the algorithm as the simulation of a random SNP surfer (RSS) that accumulates bits of information in the network through a dynamic probabilistic Markov chain. The transition matrix for the RSS is based on a data-driven genetic association interaction network (GAIN), the nodes of which are SNPs weighted by the main-effect strength and edges weighted by the gene–gene interaction strength. We apply SNPrank to a GAIN analysis of a candidate-gene association study on human immune response to smallpox vaccine. SNPrank implicates a SNP in the retinoid X receptor α (RXRA) gene through a network interaction effect on antibody response. This vitamin A- and D-signaling mediator has been previously implicated in human immune responses, although it would be neglected in a standard analysis because its significance is unremarkable outside the context of its network centrality. This work suggests SNPrank to be a powerful method for identifying network effects in genetic association data and reveals a potential vitamin regulation network association with antibody response

NIOX VERO: Individualized Asthma Management in Clinical Practice

As we move toward an era of precision medicine, novel biomarkers of disease will enable the identification and personalized treatment of new endotypes. In asthma, fractional exhaled nitric oxide (FeNO) serves as a surrogate marker of airway inflammation that often correlates with the presence of sputum eosinophils. The increase in FeNO is driven by an upregulation of inducible nitric oxide synthase (iNOS) by cytokines, which are released as a result of type-2 airway inflammation. Scientific evidence supports using FeNO in routine clinical practice. In steroid-naive patients and in patients with mild asthma, FeNO levels decrease within days after corticosteroid treatment in a dose-dependent fashion and increase after steroid withdrawal. In difficult asthma, FeNO testing correlates with anti-inflammatory therapy compliance. Assessing adherence by FeNO testing can remove the confrontational aspect of questioning a patient about compliance and change the conversation to one of goal setting and ways to improve disease management. However, the most important aspect of incorporating FeNO in asthma management is the reduction in the risk of exacerbations. In a recent primary care study, reduction of exacerbation rates and improved symptom control without increasing overall inhaled corticosteroid (ICS) use were demonstrated when a FeNO-guided anti-inflammatory treatment algorithm was assessed and compared to the standard care. A truly personalized asthma management approach—showing reduction of exacerbation rates, overall use of ICS and neonatal hospitalizations—was demonstrated when FeNO testing was applied as part of the treatment algorithm that managed asthma during pregnancy. The aim of this article is to describe how FeNO and the NIOX VERO® analyzer can help to optimize diagnosis and treatment choices and to aid in the monitoring and improvement of clinical asthma outcomes in children and adults