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A pooling-based approach to mapping genetic variants associated with DNA methylation

DNA methylation is an epigenetic modification that plays a key role in gene regulation. Previous studies have investigated its genetic basis by mapping genetic variants that are associated with DNA methylation at specific sites, but these have been limited to microarrays that cover <2% of the gen...

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Autores principales: Kaplow, Irene M., MacIsaac, Julia L., Mah, Sarah M., McEwen, Lisa M., Kobor, Michael S., Fraser, Hunter B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448686/
https://www.ncbi.nlm.nih.gov/pubmed/25910490
http://dx.doi.org/10.1101/gr.183749.114
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author Kaplow, Irene M.
MacIsaac, Julia L.
Mah, Sarah M.
McEwen, Lisa M.
Kobor, Michael S.
Fraser, Hunter B.
author_facet Kaplow, Irene M.
MacIsaac, Julia L.
Mah, Sarah M.
McEwen, Lisa M.
Kobor, Michael S.
Fraser, Hunter B.
author_sort Kaplow, Irene M.
collection PubMed
description DNA methylation is an epigenetic modification that plays a key role in gene regulation. Previous studies have investigated its genetic basis by mapping genetic variants that are associated with DNA methylation at specific sites, but these have been limited to microarrays that cover <2% of the genome and cannot account for allele-specific methylation (ASM). Other studies have performed whole-genome bisulfite sequencing on a few individuals, but these lack statistical power to identify variants associated with DNA methylation. We present a novel approach in which bisulfite-treated DNA from many individuals is sequenced together in a single pool, resulting in a truly genome-wide map of DNA methylation. Compared to methods that do not account for ASM, our approach increases statistical power to detect associations while sharply reducing cost, effort, and experimental variability. As a proof of concept, we generated deep sequencing data from a pool of 60 human cell lines; we evaluated almost twice as many CpGs as the largest microarray studies and identified more than 2000 genetic variants associated with DNA methylation. We found that these variants are highly enriched for associations with chromatin accessibility and CTCF binding but are less likely to be associated with traits indirectly linked to DNA, such as gene expression and disease phenotypes. In summary, our approach allows genome-wide mapping of genetic variants associated with DNA methylation in any tissue of any species, without the need for individual-level genotype or methylation data.
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spelling pubmed-44486862015-12-01 A pooling-based approach to mapping genetic variants associated with DNA methylation Kaplow, Irene M. MacIsaac, Julia L. Mah, Sarah M. McEwen, Lisa M. Kobor, Michael S. Fraser, Hunter B. Genome Res Method DNA methylation is an epigenetic modification that plays a key role in gene regulation. Previous studies have investigated its genetic basis by mapping genetic variants that are associated with DNA methylation at specific sites, but these have been limited to microarrays that cover <2% of the genome and cannot account for allele-specific methylation (ASM). Other studies have performed whole-genome bisulfite sequencing on a few individuals, but these lack statistical power to identify variants associated with DNA methylation. We present a novel approach in which bisulfite-treated DNA from many individuals is sequenced together in a single pool, resulting in a truly genome-wide map of DNA methylation. Compared to methods that do not account for ASM, our approach increases statistical power to detect associations while sharply reducing cost, effort, and experimental variability. As a proof of concept, we generated deep sequencing data from a pool of 60 human cell lines; we evaluated almost twice as many CpGs as the largest microarray studies and identified more than 2000 genetic variants associated with DNA methylation. We found that these variants are highly enriched for associations with chromatin accessibility and CTCF binding but are less likely to be associated with traits indirectly linked to DNA, such as gene expression and disease phenotypes. In summary, our approach allows genome-wide mapping of genetic variants associated with DNA methylation in any tissue of any species, without the need for individual-level genotype or methylation data. Cold Spring Harbor Laboratory Press 2015-06 /pmc/articles/PMC4448686/ /pubmed/25910490 http://dx.doi.org/10.1101/gr.183749.114 Text en © 2015 Kaplow 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
Kaplow, Irene M.
MacIsaac, Julia L.
Mah, Sarah M.
McEwen, Lisa M.
Kobor, Michael S.
Fraser, Hunter B.
A pooling-based approach to mapping genetic variants associated with DNA methylation
title A pooling-based approach to mapping genetic variants associated with DNA methylation
title_full A pooling-based approach to mapping genetic variants associated with DNA methylation
title_fullStr A pooling-based approach to mapping genetic variants associated with DNA methylation
title_full_unstemmed A pooling-based approach to mapping genetic variants associated with DNA methylation
title_short A pooling-based approach to mapping genetic variants associated with DNA methylation
title_sort pooling-based approach to mapping genetic variants associated with dna methylation
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448686/
https://www.ncbi.nlm.nih.gov/pubmed/25910490
http://dx.doi.org/10.1101/gr.183749.114
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