W674 A Kernel Machine Method for the Detection of Gene-Gene Interactions: A Gene Centric View

Date: Sunday, January 15, 2012
Time: 10:20 AM
Room: Pacific Salon 3
Shaoyu Li , St. Jude Children's Research Hospital
Yuehua Cui , Michigan State University, East Lansing, MI
Much of the natural variation for a complex trait can be explained by variation in DNA sequence levels. As part of sequence variation, gene-gene interaction has been ubiquitously observed in nature, where its role in shaping the development of an organism has been broadly recognized. A large body of currently adopted methods for gene-gene interaction detection, either parametrically or non-parametrically, predominantly focus on pairwise single marker interaction analysis. As genes are the functional units in living organisms, analysis by focusing on a gene as a system could potentially yield more biologically meaningful results. In this work, we conceptually propose a gene-centric framework for gene-gene interaction detection. We treat each gene as a testing unit and derive a model-based kernel machine method for two-dimensional scanning of gene-gene interactions. In addition to the biological advantage, our method is statistically appealing because it reduces the number of hypotheses tested in a genome-wide scan. Extensive simulation studies are conducted to evaluate the performance of the method. The utility of the method is further demonstrated with applications to a real data set. Our method provides a conceptual framework for the identification of gene-gene interactions, which could lead to novel biological insights into the genetic architecture of complex traits.