Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort
- Christopher M. Vockley1,2,8,
- Cong Guo2,3,8,
- William H. Majoros2,4,8,
- Michael Nodzenski5,
- Denise M. Scholtens5,
- M. Geoffrey Hayes6,
- William L. Lowe Jr.6 and
- Timothy E. Reddy4,7
- 1Department of Cell Biology, Duke University Medical School, Durham, North Carolina 27710, USA;
- 2Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27710, USA;
- 3University Program in Genetics and Genomics, Duke University, Durham, North Carolina 27710, USA;
- 4Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina 27710, USA;
- 5Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA;
- 6Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA;
- 7Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, North Carolina 27710, USA
- Corresponding author: tim.reddy{at}duke.edu
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↵8 These authors contributed equally to this work.
Abstract
We report a novel high-throughput method to empirically quantify individual-specific regulatory element activity at the population scale. The approach combines targeted DNA capture with a high-throughput reporter gene expression assay. As demonstration, we measured the activity of more than 100 putative regulatory elements from 95 individuals in a single experiment. In agreement with previous reports, we found that most genetic variants have weak effects on distal regulatory element activity. Because haplotypes are typically maintained within but not between assayed regulatory elements, the approach can be used to identify causal regulatory haplotypes that likely contribute to human phenotypes. Finally, we demonstrate the utility of the method to functionally fine map causal regulatory variants in regions of high linkage disequilibrium identified by expression quantitative trait loci (eQTL) analyses.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.190090.115.
- Received January 26, 2015.
- Accepted June 15, 2015.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
