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

Irene M. Kaplow, Julia L. MacIsaac, Sarah M. Mah, Lisa M. McEwen, Michael S. Kobor, Hunter B. Fraser
doi: https://doi.org/10.1101/013649
Irene M. Kaplow
1Department of Computer Science
2Department of Biology, Stanford University, Stanford CA;
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Julia L. MacIsaac
3Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute,
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Sarah M. Mah
3Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute,
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Lisa M. McEwen
3Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute,
4Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
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Michael S. Kobor
3Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute,
4Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
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Hunter B. Fraser
2Department of Biology, Stanford University, Stanford CA;
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  • For correspondence: hbfraser@stanford.edu
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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 less than 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 over 2,000 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted April 15, 2015.
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A pooling-based approach to mapping genetic variants associated with DNA methylation
Irene M. Kaplow, Julia L. MacIsaac, Sarah M. Mah, Lisa M. McEwen, Michael S. Kobor, Hunter B. Fraser
bioRxiv 013649; doi: https://doi.org/10.1101/013649
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A pooling-based approach to mapping genetic variants associated with DNA methylation
Irene M. Kaplow, Julia L. MacIsaac, Sarah M. Mah, Lisa M. McEwen, Michael S. Kobor, Hunter B. Fraser
bioRxiv 013649; doi: https://doi.org/10.1101/013649

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