Background

The glutathione S-transferase M1 (GSTM1) null variant is a common copy number variant associated with adverse pulmonary outcomes, including asthma and airflow obstruction, with evidence of important gene-by-environment interactions with exposures to oxidative stress.

Objective

To explore the joint interactive effects of GSTM1 copy number and tobacco smoke exposure on the development of asthma and asthma-related phenotypes in a family-based cohort of childhood asthmatics.

Methods

We performed quantitative PCR-based genotyping for GSTM1 copy number in children of self-reported white ancestry with mild to moderate asthma in the Childhood Asthma Management Program. Questionnaire data regarding intrauterine (IUS) and postnatal, longitudinal environmental tobacco smoke exposure were available. We performed both family-based and population-based tests of association for the interaction between GSTM1 copy number and tobacco smoke exposure with asthma and asthma-related phenotypes.

Results

Associations of GSTM1 null variants with asthma (p= .03), younger age of asthma symptom onset (p=.03), and greater airflow obstruction (reduced FEV1/FVC, p=.01) were observed among the 50 children (10% of the cohort) with exposure to IUS. In contrast, no associations were observed between GSTM1 null variants and asthma-related phenotypes among children without IUS exposure. Presence of at least one copy of GSTM1 conferred protection.

Conclusion

These findings support an important gene-by-environment interaction between two common factors: increased risk of asthma and asthma-related phenotypes conferred by GSTM1-null homozygosity in children is restricted to those with a history of IUS exposure.

Background

The etiology of multifactorial human diseases involves complex interactions between numerous environmental factors and alleles of many genes. Efficient statistical tools are demanded in identifying the genetic and environmental variants that affect the risk of disease development. This paper introduces a retrospective polytomous logistic regression model to measure both the main and interaction effects in genetic association studies of human discrete and continuous complex traits. In this model, combinations of genotypes at two interacting loci or of environmental exposure and genotypes at one locus are treated as nominal outcomes of which the proportions are modeled as a function of the disease trait assigning both main and interaction effects and with no assumption of normality in the trait distribution. Performance of our method in detecting interaction effect is compared with that of the case-only model.

Results

Results from our simulation study indicate that our retrospective model exhibits high power in capturing even relatively small effect with reasonable sample sizes. Application of our method to data from an association study on the catalase -262C/T promoter polymorphism and aging phenotypes detected significant main and interaction effects for age-group and allele T on individual's cognitive functioning and produced consistent results in estimating the interaction effect as compared with the popular case-only model.

Conclusion

The retrospective polytomous logistic regression model can be used as a convenient tool for assessing both main and interaction effects in genetic association studies of human multifactorial diseases involving genetic and non-genetic factors as well as categorical or continuous traits.