title: 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 statistically meaningful. The novelty of findings is evaluated through investigation of regional signals. A subset of findings are validated using recently updated, larger meta-analysis resources. A simulation study is presented to characterize overall performance with respect to power, control of family-wise error and computational efficiency. All analysis is performed using the GenCAT package, R version 3.2.1.
  
  
  RESULTS
  We demonstrate that class-level testing complements the common first stage minP approach that involves individual SNP-level testing followed by post-hoc ascribing of statistically significant SNPs to genes and loci. GenCAT suggests 54 protein-coding genes at 41 distinct loci for the 13 CMD traits investigated in the discovery analysis, that are beyond the discoveries of minP alone. An additional application to biological pathways demonstrates flexibility in defining genetic classes.
  
  
  CONCLUSIONS
  We conclude that it would be prudent to include class-level testing as standard practice in GWA analysis. GenCAT, for example, can be used as a simple, complementary and efficient strategy for class-level testing that leverages existing data resources, requires only summary level data in the form of test statistics, and adds significant value with respect to its potential for identifying multiple novel and clinically relevant trait associations.
authors: Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, United States of America.
authors: Jing
authors: J
authors: Qian
authors: Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA, United States of America.
authors: Sara
authors: S
authors: Nunez
authors: Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA, United States of America.
authors: Eric
authors: E
authors: Reed
authors: Department of Medicine, Columbia University, New York, NY, United States of America.
authors: Muredach P
authors: MP
authors: Reilly
authors: Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA, United States of America.
authors: Andrea S
authors: AS
authors: Foulkes
mesh_terms: D056426:Genetic Loci / Q000235:genetics*; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D015201:Meta-Analysis as Topic; D008659:Metabolic Diseases / Q000235:genetics*; D009206:Myocardium / Q000378:metabolism*; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide