  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. The identification
of interactions between genetic factors has been progressively pursued via
statistical or machine learning approaches. A large body of currently adopted
methods, either parametrically or nonparametrically, 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 genome-wide gene-gene
interaction detection. We treat each gene as a testing unit and derive a
model-based kernel machine method for two-dimensional genome-wide 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 two real data sets. Our method provides a conceptual
framework for the identification of gene-gene interactions which could shed
novel light on the etiology of complex diseases.
