BACKGROUND: Asthma is a complex disease resulting from interactions between multiple genes and environmental factors. Study of gene-gene interactions could provide insight into asthma pathophysiology. OBJECTIVE: We investigated the interaction among 12 different loci in 8 candidate genes and asthma and increased plasma total IgE concentrations in 240 Chinese asthmatic subjects and 140 control subjects. METHODS: Genotyping was performed by means of RFLP analysis. Multifactor dimensionality reduction and logistic regression were used to analyze gene-gene interactions. RESULTS: A significant interaction was found between R130Q in the IL-13 gene (IL13) and I50V in the IL-4 receptor alpha gene (IL4RA) on the risk of asthma, with a cross-validation consistency of 10 of 10 and a prediction error of 33.7% (P = .014). The odds ratio of the high-risk to low-risk group was 2.6 (95% CI, 1.4-5.0; P = .004). For increased plasma total IgE concentration, the best 2-locus model consisted of R130Q in IL13 and C-431T in the thymus and activation-regulated chemokine gene (TARC). This model showed a maximum cross-validation consistency of 10 and a minimum prediction error of 36.1% (P = .022). The odds ratio of the high-risk to low-risk group was 3.9 (95% CI, 2.0-7.7; P = .0001). Logistic regression revealed significant interactions between IL13 and IL4RA for asthma (P = .042) and IL13 and TARC for increased total IgE concentration (P = .012). CONCLUSIONS: Our data suggest significant interactions between IL13 and IL4RA for asthma and IL13 and TARC for increased plasma total IgE concentrations in Chinese children.
BACKGROUND:Asthma is a complex disease resulting from interactions between multiple genes and environmental factors. Study of gene-gene interactions could provide insight into asthma pathophysiology. OBJECTIVE: We investigated the interaction among 12 different loci in 8 candidate genes and asthma and increased plasma total IgE concentrations in 240 Chinese asthmatic subjects and 140 control subjects. METHODS: Genotyping was performed by means of RFLP analysis. Multifactor dimensionality reduction and logistic regression were used to analyze gene-gene interactions. RESULTS: A significant interaction was found between R130Q in the IL-13 gene (IL13) and I50V in the IL-4 receptor alpha gene (IL4RA) on the risk of asthma, with a cross-validation consistency of 10 of 10 and a prediction error of 33.7% (P = .014). The odds ratio of the high-risk to low-risk group was 2.6 (95% CI, 1.4-5.0; P = .004). For increased plasma total IgE concentration, the best 2-locus model consisted of R130Q in IL13 and C-431T in the thymus and activation-regulated chemokine gene (TARC). This model showed a maximum cross-validation consistency of 10 and a minimum prediction error of 36.1% (P = .022). The odds ratio of the high-risk to low-risk group was 3.9 (95% CI, 2.0-7.7; P = .0001). Logistic regression revealed significant interactions between IL13 and IL4RA for asthma (P = .042) and IL13 and TARC for increased total IgE concentration (P = .012). CONCLUSIONS: Our data suggest significant interactions between IL13 and IL4RA for asthma and IL13 and TARC for increased plasma total IgE concentrations in Chinese children.
Authors: Natalie C Battle; Shweta Choudhry; Hui-Ju Tsai; Celeste Eng; Gunjan Kumar; Kenneth B Beckman; Mariam Naqvi; Kelley Meade; H George Watson; Michael Lenoir; Esteban González Burchard Journal: Am J Respir Crit Care Med Date: 2007-02-15 Impact factor: 21.405