Saonli Basu; Division of Biostatistics, University of Minnesota
The development of a complex trait is an intricate dynamic process controlled by a network of genes as well as by environmental factors. In recent years, the availability of high throughput genomic data has generated ample interests in investigating the complex interplay or interaction between these genes and environmental factors (G-E interaction). One way to increase power for detection of G-E interaction is to improve the effect size(s) by aggregating the DNA polymorphisms (e.g., single-nucleotide polymorphisms, SNPs) in what we call SNP-sets, which also reduces the multiple-testing problem. We propose here a test for detection of interaction between a SNP-set and a group of correlated environmental factors in families by using a likelihood-based dimension reduction approach within a random-effect model framework. The proposed approach employs a parsimonious model to capture the effect of a group of interacting SNPs and environmental exposures on the disease. We have also extended several score-based approaches to study G-E interaction in families. We illustrate our model and compare the performance of different methods to detect G-E interaction through simulation studies. We demonstrate that the performance of these methods vary widely based on the directionality and sparsity of the interaction effects and our dimension reduction approach performs very well in presence of interaction effects in the opposite direction.
This is joint work with Brandon Coombes, Matt McGue at the University of Minnesota.