ZFIN ID: ZDB-PUB-160511-4
Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures
Wang, X., Tucker, N.R., Rizki, G., Mills, R., Krijger, P.H., de Wit, E., Subramanian, V., Bartell, E., Nguyen, X.X., Ye, J., Leyton-Mange, J., Dolmatova, E.V., van der Harst, P., de Laat, W., Ellinor, P.T., Newton-Cheh, C., Milan, D.J., Kellis, M., Boyer, L.A.
Date: 2016
Source: eLIFE   5: (Journal)
Registered Authors: Ellinor, Patrick, Milan, David J.
Keywords: complex traits, enhancers, epigenomics, evolutionary biology, genome-wide association study, genomics, heritability, human, human biology, medicine, mouse, zebrafish
MeSH Terms:
  • Animals
  • Epigenomics*
  • Genetic Loci*
  • Genome-Wide Association Study*
  • Heart Conduction System/physiology*
  • Humans
  • Mice
PubMed: 27162171 Full text @ Elife
Genetic variants identified by genome-wide association studies explain only a modest proportion of heritability, suggesting that meaningful associations lie 'hidden' below current thresholds. Here, we integrate information from association studies with epigenomic maps to demonstrate that enhancers significantly overlap known loci associated with the cardiac QT interval and QRS duration. We apply functional criteria to identify loci associated with QT interval that do not meet genome-wide significance and are missed by existing studies. We demonstrate that these 'sub-threshold' signals represent novel loci, and that epigenomic maps are effective at discriminating true biological signals from noise. We experimentally validate the molecular, gene-regulatory, cellular and organismal phenotypes of these sub-threshold loci, demonstrating that most sub-threshold loci have regulatory consequences and that genetic perturbation of nearby genes causes cardiac phenotypes in mouse. Our work provides a general approach for improving the detection of novel loci associated with complex human traits.