Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures
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Author(s)
Wang, Xinchen
Rizki, Gizem
Mills, Robert
de Wit, Elzo
Subramanian, Vidya
Nguyen, Xinh-Xinh
Ye, Jiangchuan
Leyton-Mange, Jordan
van der Harst, Pim
de Laat, Wouter
Date Issued
May 2016
Journal
eLife
Publisher
eLife Sciences Publications, Ltd.
Citation
Wang, Xinchen, Nathan R Tucker, Gizem Rizki, Robert Mills, Peter HL Krijger, Elzo de Wit, Vidya Subramanian, et al. “Discovery and Validation of Sub-Threshold Genome-Wide Association Study Loci Using Epigenomic Signatures.” eLife 5 (May 10, 2016).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.7554/eLife.10557
