Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits.

ABSTRACT: Many genetic association studies collect a wide range of complex traits. As these traits may be correlated and share a common genetic mechanism, joint analysis can be statistically more powerful and biologically more meaningful. However, most existing tests for multiple traits cannot be used for high-dimensional and possibly structured traits, such as network-structured transcriptomic pathway expressions. To overcome potential limitations, in this article we propose the dual kernel-based association test (DKAT) for testing the association between multiple traits and multiple genetic variants, both common and rare. In DKAT, two individual kernels are used to describe the phenotypic and genotypic similarity, respectively, between pairwise subjects. Using kernels allows for capturing structur

SUBMITTER: Zhan X 

PROVIDER: S-EPMC5560787 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature