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A Simple Test Of Class-Level Genetics Association Can Reveal Novel Cardiometabolic Trait Loci, Jing Qian, Sara Nunez, Eric Reed, Muredach P. Reilly, Andrea S. Foulkes
A Simple Test Of Class-Level Genetics Association Can Reveal Novel Cardiometabolic Trait Loci, Jing Qian, Sara Nunez, Eric Reed, Muredach P. Reilly, Andrea S. Foulkes
Biostatistics and Epidemiology Faculty Publications Series
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 …