Background and objective. A variety of cytokines are involved in the incidence and development of bronchial asthma. This study was designed to reveal the relationship among genetic polymorphism of cytokine genes, expression levels of cytokines and incidence of bronchial asthma. Methods. We analyzed 14 single nucleotide polymorphism loci in the 10 major cytokine genes plus plasma protein levels of 7 proteins in the bronchial asthma patients (n = 108) and the healthy population (n = 88) of the Han people in northern China. The polymorphism-based genotypes were identified by the sequence-specific primer-polymerase chain reaction. The plasma protein levels were determined by enzyme-linked immunoassay. Bioinformatics analysis was applied to further data processing.Results. Data presented here showed that the 6 polymorphism loci were significantly correlated with the incidence of asthma (p < 0.05). Two of them, IL-2 (−330) and IL-2 (+166), constituted a linkage disequilibrium block. The GG haplotype of this block gave a relatively higher correlation with asthma (p = 0.0767). The plasma protein levels of IgE, IL-6 and IL-1 β correlated with a number of polymorphism loci tested (p < 0.05), of which IgE gave the most significant correlation. The plasma IL-10 and IL-12 levels of the patients in the asthma group were significantly lower than those of the healthy adults in the control group (p < 0.05), while IgE gave the opposite result (p < 0.0001). We constructed a prototype of the metabolic and regulatory network composed of bronchial asthma-related proteins. In this network, IL-6 and TNF-α, were found with a high degree (D = 343 and 235, respectively) and IL-1β with a moderate degree of connection (D = 155). Conclusions.We have found that various degrees of correlation with cytokine genes and protein expression of single nucleotide polymorphism in bronchial asthma. IL-6 and IL-1β appear to play important biological roles in the pathogenesis of asthma. During the analysis of correlation between genetic polymorphism and a complex disease, the effects of environmental factors should be taken into account. The information at the protein level should be fully developed and the bioinformatics techniques can be used for the comprehensive analysis, to have a deep understanding of molecular mechanisms of incidence and development of diseases.