A Score-Based Test for Detection of Gene-Environment Interaction in Families

• Activity Number: 565 - Data Science in Statistical Genomics: Challenges and Solutions
• Type: Topic Contributed
• Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Saonli Basu* and Brandon Coombes and Matt McGue
• Companies: University of Minnesota and University of Minnesota and University of Minnesota
• Keywords: score test ; dimension reduction ; gene-environment interaction ; family study

Abstract

In recent years, the availability of high throughput genomic data has generated ample interests in investigating the complex interplay or interaction between genes and environmental factors (G-E interaction). One way to increase the power for detection of G-E interaction is to improve the effect size(s) by aggregating the DNA polymorphisms (e.g. SNPs) in what we call SNP-sets, which also reduces the multiple-testing problem. We propose here a test 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. 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 and a real data analysis with substance abuse disorders in Minnesota Twin 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 opposite direction.