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Analyzing whole genome bisulfite sequencing data from highly divergent genotypes

Phillip Wulfridge, View ORCID ProfileBen Langmead, Andrew P. Feinberg, View ORCID ProfileKasper D. Hansen
doi: https://doi.org/10.1101/076844
Phillip Wulfridge
1Center for Epigenetics, Johns Hopkins School of Medicine
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Ben Langmead
2Center for Computational Biology, Johns Hopkins University
3Department of Computer Science, Johns Hopkins University
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  • ORCID record for Ben Langmead
Andrew P. Feinberg
1Center for Epigenetics, Johns Hopkins School of Medicine
4Department of Medicine, Johns Hopkins School of Medicine
5Department of Biomedical Engineering, Whiting School of Engineering
6Department of Mental Health, Johns Hopkins Bloomberg School of Public Health
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Kasper D. Hansen
1Center for Epigenetics, Johns Hopkins School of Medicine
2Center for Computational Biology, Johns Hopkins University
7Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
8McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
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  • For correspondence: khansen@jhsph.edu
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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 inbred mouse strains to study this problem. We find that while the large number of strain-specific CpGs necessitates considerations regarding the reference genomes used during alignment, properties such as CpG density are surprisingly conserved across the genome. We introduce a method for including strain-specific CpGs in differential analysis, and show that accounting for strain-specific CpGs increases the power to find differentially methylated regions between the strains. Our method uses smoothing to impute methylation levels at strain-specific sites, thereby allowing strain-specific CpGs to contribute to the analysis, and also allowing us to account for differences in the spatial occurrences of CpGs. Our results have implications for analysis of genetic variation and DNA methylation using bisulfite-converted DNA.

  • Abbreviations

    WGBS
    whole-genome bisulfite sequencing
    DMR
    differentially methylated region
  • Copyright 
    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 4.0 International license.
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    Posted June 06, 2018.
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    Analyzing whole genome bisulfite sequencing data from highly divergent genotypes
    Phillip Wulfridge, Ben Langmead, Andrew P. Feinberg, Kasper D. Hansen
    bioRxiv 076844; doi: https://doi.org/10.1101/076844
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    Analyzing whole genome bisulfite sequencing data from highly divergent genotypes
    Phillip Wulfridge, Ben Langmead, Andrew P. Feinberg, Kasper D. Hansen
    bioRxiv 076844; doi: https://doi.org/10.1101/076844

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