A kernel machine method for detecting effects of interaction between multidimensional variable sets: an imaging genetics application.

ABSTRACT: Measurements derived from neuroimaging data can serve as markers of disease and/or healthy development, are largely heritable, and have been increasingly utilized as (intermediate) phenotypes in genetic association studies. To date, imaging genetic studies have mostly focused on discovering isolated genetic effects, typically ignoring potential interactions with non-genetic variables such as disease risk factors, environmental exposures, and epigenetic markers. However, identifying significant interaction effects is critical for revealing the true relationship between genetic and phenotypic variables, and shedding light on disease mechanisms. In this paper, we present a general kernel machine based method for detecting effects of the interaction between multidimensional variable sets. This

SUBMITTER: Ge T 

PROVIDER: S-EPMC4339421 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature