The ability to predict the risk for having allergic disease in later childhood is enormously helpful in terms of offering an accurate prognosis to parents and identifying children for investigation of preventative strategies. We have previously reported the development of a predictive index for risk of having persistent asthma up to age 10 years using information available from history and SPT responses.23
The development of allergic disease, including rhinitis, depends on both genetic and environmental factors and their interactions. It has been suggested that heritable factors predominate over shared environmental influences in asthma,24
and the same might be true for rhinitis because both diseases are closely linked.25
Using the genotype information available, we estimated the risk of an individual having rhinitis at age 10 years. As expected, the majority of children fall into the category in which the risk is similar to that of the whole group (around 20%). However, a significant number of children were identified at both ends of the spectrum, having very low or high risk ().
Because of the critical role of GATA3
2 cell development and its role in regulating expression of IL4
, and IL13
, it is important to examine GATA3
along with IL13
. Considering the biologic relation between GATA3
, we sought to study the SNP patterns and interactions between these genes in the context of rhinitis and atopy. GATA3
has been associated with human asthma6
and is located within a quantitative trait locus for allergic asthma in a murine model,7
is one of the genes most consistently associated with asthma and IgE-related phenotypes in association studies.8–14
Gene-gene interactions, in particular 2 loci interactions, in the TH
2 pathway have been examined for IL13
for asthmatic subjects in Dutch, Chinese, and African American populations.26–28
SNPs for this analysis were chosen to provide information on the entire gene, and we found that a model with 4 loci to be a better predictor than models with 2 loci for rhinitis. To our knowledge, this is the first association analysis for GATA3
interactions for rhinitis and atopy.
GATA3 is expressed at high levels in TH2 lineage cells. From a pathogenetic viewpoint, the proteins these genes encode are important in the IgE-mediated immunologic pathway. It can therefore be hypothesized that polymorphisms in GATA3 and IL13 and their interactions are associated with allergic rhinitis. This was indeed the case because a number of SNPs and SNP-SNP interactions had statistically significant associations with allergic rhinitis.
In nonallergic rhinitis symptoms are perennial, and no evidence of IgE-mediated hypersensitivity can be detected. However, in many of these patients, inflammation and cellular infiltration is similar to that seen in allergic rhinitis. The underlying pathogenesis might be T cell–mediated inflammation, in addition to an inherent instability of vascular tone, which responds to various immunologic and nonimmunologic triggers. In allergic rhinitis activation of TH
2 cells results in the production of specific IgE to relevant allergens, as evidenced by a positive SPT response. There is the possibility that some subjects with nonallergic rhinitis might be sensitized to an allergen that has not been tested (covert allergy), and in others IgE can be produced locally and SPT responses remain negative.29
Thus in a subgroup of patients with nonallergic rhinitis, TH
2 immune responses can be equally important. We did observe an association of GATA3
SNPs in those with nonallergic rhinitis, although, as expected, this association was not as strong as that seen for allergic rhinitis.
The SNPs found to have significant associations in this study were located in the flanking regions of the genes with rs1800925 in the 5′ promoter of IL13
, rs1058240 and rs379568 in the 3′ UTR of GATA3
, and rs4143094 in the 5′ promoter of GATA3
. Although no functional consequence has been reported for any of these SNPs, their locations in areas of potential importance for gene regulation make them of interest. Nevertheless, a single SNP that is not a missense mutation might have only a small effect and might not efficiently discriminate between cases and control subjects in a genetic association allergy study. Thus first examining main effects and eliminating some before considering 2-way interactions might miss important epistatic effects. However, patterns of SNPs could contribute to the risk of a complex disease. SNP-SNP interactions provide insight into the relationship between genes and their combined effect. Several algorithms are available to examine SNP combinations for complex diseases.30–33
These methods include dimension reduction, which examines 2-way interactions for a given subset size of the SNPs and chooses the combination that minimizes the classification error of cases and control subjects.32,33
Some analyses use classification scoring functions to identify subsets of SNPs likely associated with disease risk.30
Multivariate logistic regression and bootstrap analyses can be used to select SNP-SNP interactions through stepwise regression.31
There has been great interest in the random forest classification procedure in studies with a large number of SNPs.34
In unbalanced association studies the misclassification rate can be high, and therefore we use the random forest ranking only in combination with other criteria. Use of a 2-step procedure, with model selection based on AIC and a ranking function for the SNPs for validation, reduces the effect of selecting a model that by chance produces the largest difference between cases and control subjects. This approach examines main effects and all possible 2-way interactions. A large number of SNPs would take much computational time.
In summary, using the 2-step method, we have identified combinations of SNPs and their interactions associated with rhinitis and atopy in the Isle of Wight birth cohort. We found that GATA3 polymorphisms and their interactions with IL13 SNPs are associated with rhinitis and atopy. Future studies that include additional genes and environmental factors in a systematic assessment will likely improve the understanding of the interactions of genes in the TH2 pathway in rhinitis and associated phenotypes.
The ability to predict risk based on set genetic patterns and loci interactions might allow for early intervention and individualized therapy.