Substantial progress has been made in identifying single genetic variants
predisposing to common complex diseases. Nonetheless, the genetic etiology of
human diseases remains largely unknown. Human complex diseases are likely
influenced by the joint effect of a large number of genetic variants instead of
a single variant. The joint analysis of multiple genetic variants considering
linkage disequilibrium (LD) and potential interactions can further enhance the
discovery process, leading to the identification of new disease-susceptibility
genetic variants. Motivated by the recent development in spatial statistics, we
propose a new statistical model based on the random field theory, referred to
as a genetic random field model (GenRF), for joint association analysis with
the consideration of possible gene-gene interactions and LD. Using a
pseudo-likelihood approach, a GenRF test for the joint association of multiple
genetic variants is developed, which has the following advantages: 1.
considering complex interactions for improved performance; 2. natural dimension
reduction; 3. boosting power in the presence of LD; 4. computationally
efficient. Simulation studies are conducted under various scenarios. Compared
with a commonly adopted kernel machine approach, SKAT, GenRF shows overall
comparable performance and better performance in the presence of complex
interactions. The method is further illustrated by an application to the Dallas
Heart Study.Comment: 17 pages, 4 tables, the paper has been published on Biometric
