Genome-wide environmental interaction analysis using multidimensional data reduction principles to identify asthma pharmacogenetic loci in relation to corticosteroid therapy

Genome-wide gene-environment (GxE) and gene-gene (GxG) interaction studies share a lot of challenges via the common genetic component they involve. GWEI studies may therefore benefit from the abundance of methodologies that are available in the context of genome-wide epistasis detection methods. One of these is Model-Based Multifactor Dimensionality Reduction (MB-MDR), which does not make any assumption about the genetic inheritance model. MB-MDR involves reducing a high-dimensional GxE space to GxE factor levels that either exhibit high or low or no evidence for their association to disease outcome. In contrast to logistic regression and random forests, MB-MDR can be used to detect GxE interactions in the absence of any main effects or when sample sizes are too small to be able to model all main and GxE interaction effects. In this ongoing study, we demonstrate the opportunities and challenges of MB-MDR for genome-wide GxE interaction analysis and analyzed the difference in prebronchodilator FEV1 following 8 weeks of inhaled corticosteroid therapy, for 565 pediatric Caucasian CAMP (ages 5-12) from the SHARE project.