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Elife. 2016 May 10;5. pii: e10557. doi: 10.7554/eLife.10557.

Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures.

Author information

1
Department of Biology, Massachusetts Institute of Technology, Cambridge, United States.
2
Broad Institute of MIT and Harvard, Cambridge, United States.
3
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, United States.
4
Cardiovascular Research Center, Massachusetts General Hospital, Boston, United States.
5
Hubrecht Institute-KNAW, University Medical Center Utrecht, Utrecht, Netherlands.
6
University Medical Center Utrecht, Utrecht, Netherlands.
7
Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
8
Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
9
Center for Human Genetic Research, Massachusetts General Hospital, Boston, United States.

Abstract

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.

KEYWORDS:

complex traits; enhancers; epigenomics; evolutionary biology; genome-wide association study; genomics; heritability; human; human biology; medicine; mouse; zebrafish

PMID:
27162171
PMCID:
PMC4862755
DOI:
10.7554/eLife.10557
[Indexed for MEDLINE]
Free PMC Article

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