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Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort
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 puta...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4510004/ https://www.ncbi.nlm.nih.gov/pubmed/26084464 http://dx.doi.org/10.1101/gr.190090.115 |
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author | Vockley, Christopher M. Guo, Cong Majoros, William H. Nodzenski, Michael Scholtens, Denise M. Hayes, M. Geoffrey Lowe, William L. Reddy, Timothy E. |
author_facet | Vockley, Christopher M. Guo, Cong Majoros, William H. Nodzenski, Michael Scholtens, Denise M. Hayes, M. Geoffrey Lowe, William L. Reddy, Timothy E. |
author_sort | Vockley, Christopher M. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4510004 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-45100042016-01-31 Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort Vockley, Christopher M. Guo, Cong Majoros, William H. Nodzenski, Michael Scholtens, Denise M. Hayes, M. Geoffrey Lowe, William L. Reddy, Timothy E. Genome Res Method 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. Cold Spring Harbor Laboratory Press 2015-08 /pmc/articles/PMC4510004/ /pubmed/26084464 http://dx.doi.org/10.1101/gr.190090.115 Text en © 2015 Vockley et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/4.0/ 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/. |
spellingShingle | Method Vockley, Christopher M. Guo, Cong Majoros, William H. Nodzenski, Michael Scholtens, Denise M. Hayes, M. Geoffrey Lowe, William L. Reddy, Timothy E. Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort |
title | Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort |
title_full | Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort |
title_fullStr | Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort |
title_full_unstemmed | Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort |
title_short | Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort |
title_sort | massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4510004/ https://www.ncbi.nlm.nih.gov/pubmed/26084464 http://dx.doi.org/10.1101/gr.190090.115 |
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