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Nucleic Acids Res. 2019 Aug 8. pii: gkz674. doi: 10.1093/nar/gkz674. [Epub ahead of print]

Analyzing whole genome bisulfite sequencing data from highly divergent genotypes.

Author information

1
Center for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe St, Baltimore, MD 21205, USA.
2
Department of Computer Science, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, USA.
3
Department of Medicine, Johns Hopkins School of Medicine, 855 N. Wolfe St, Baltimore, MD 21205, USA.
4
Department of Biomedical Engineering, Whiting School of Engineering, 3400 N. Charles St, Baltimore, MD 21218, USA.
5
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, MD 21205, USA.
6
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, MD 21205, USA.
7
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, 733 N. Broadway, Baltimore, MD 21205, USA.

Abstract

In the study of DNA methylation, genetic variation between species, strains or individuals can result in CpG sites that are exclusive to a subset of samples, and insertions and deletions can rearrange the spatial distribution of CpGs. How to account for this variation in an analysis of the interplay between sequence variation and DNA methylation is not well understood, especially when the number of CpG differences between samples is large. Here, we use whole-genome bisulfite sequencing data on two highly divergent mouse strains to study this problem. We show that alignment to personal genomes is necessary for valid methylation quantification. We introduce a method for including strain-specific CpGs in differential analysis, and show that this increases power. We apply our method to a human normal-cancer dataset, and show this improves accuracy and power, illustrating the broad applicability of our approach. Our method uses smoothing to impute methylation levels at strain-specific sites, thereby allowing strain-specific CpGs to contribute to the analysis, while accounting for differences in the spatial occurrences of CpGs. Our results have implications for joint analysis of genetic variation and DNA methylation using bisulfite-converted DNA, and unlocks the use of personal genomes for addressing this question.

PMID:
31392989
DOI:
10.1093/nar/gkz674

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