A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci.

ABSTRACT:

Background

Characterizing the genetic determinants of complex diseases can be further augmented by incorporating knowledge of underlying structure or classifications of the genome, such as newly developed mappings of protein-coding genes, epigenetic marks, enhancer elements and non-coding RNAs.

Methods

We apply a simple class-level testing framework, termed Genetic Class Association Testing (GenCAT), to identify protein-coding gene association with 14 cardiometabolic (CMD) related traits across 6 publicly available genome wide association (GWA) meta-analysis data resources. GenCAT uses SNP-level meta-analysis test statistics across all SNPs within a class of elements, as well as the size of the class and its unique correlation structure, to determine if the class is statisti

SUBMITTER: Qian J 

PROVIDER: S-EPMC4747495 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature