Association between candidate gene polymorphisms and asthma severity in adults. A Bayesian analysis

Abstract

Aim: The present analysis is aimed at identifying genetic variants associated with disease severity in adult subjects with ever asthma.

Methods: We evaluated 326 subjects (aged 20-64) with ever asthma, who were identified from the general population between 2008 and 2010 in Verona (Italy), in the Gene Environment Interactions in Respiratory Diseases study. A panel of 236 tag-SNPs, which are representative of 51 candidate gene regions with a previous indication of a possible association with asthma/COPD/rhinitis, was genotyped by a custom GoldenGate Genotyping Assay. Measures of asthma severity were: a Symptom frequency and anti-asthmatic Treatment intensity Score (STS), which ranges from 0 to 10 (high values represent severe conditions), and pre-bronchodilator FEV1%predicted. The association of SNPs with STS and FEV1%predicted was evaluated by using Bayesian least absolute shrinkage and selection operator zero-inflated Poisson and linear regression models, respectively. These models allows the simultaneous selection of genetic variants and the estimation of their effect, adjusting for the effect of gender, BMI and smoking habits.

Results: SNP rs2427829 in FCER1A is significantly associated with FEV1%predicted (mean = -0.323, 95% credible interval=[-4.533, -0.2677]). SNP rs676750 in CHML (mean= -0.2482, 95% credible interval=[-0.434, -0.072]), rs4334089 in VDR (mean= -0.255, 95% credible interval=[-0.254, -0.059]) and rs11080344 in NOS2 (mean= -0.2239, 95% credible interval=[-0.390, -0.064]) are independently associated with STS.

Conclusion: These preliminary results suggest that FCER1A, CHML, VDR and NOS2 could play a role in disease severity among adult subjects with ever asthma.