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Genome Res. 2015 Jun;25(6):907-17. doi: 10.1101/gr.183749.114. Epub 2015 Apr 24.

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

Author information

1
Department of Computer Science, Stanford University, Stanford, California 94305, USA; Department of Biology, Stanford University, Stanford, California 94305, USA;
2
Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, Vancouver, British Columbia V5Z 4H4, Canada;
3
Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, Vancouver, British Columbia V5Z 4H4, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia V5Z 4H4, Canada.
4
Department of Biology, Stanford University, Stanford, California 94305, USA;

Abstract

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.

PMID:
25910490
PMCID:
PMC4448686
DOI:
10.1101/gr.183749.114
[Indexed for MEDLINE]
Free PMC Article

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