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Analyzing whole genome bisulfite sequencing data from highly divergent genotypes
Phillip Wulfridge1, Ben Langmead2, Andrew P Feinberg1,3,4,5
1Center for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe St, Baltimore, MD 21205, USA.
Nucleic Acids Research
|August 9, 2019
Summary
This study introduces a new method to analyze DNA methylation differences by accounting for genetic variations like CpG sites unique to specific samples. This approach improves the accuracy and power of methylation studies, especially when comparing divergent genomes.
Area of Science:
- Genomics
- Epigenetics
- Bioinformatics
Background:
- Genetic variation, including unique CpG sites and structural rearrangements, complicates DNA methylation analysis.
- Accurately quantifying DNA methylation in the presence of significant genetic differences between samples is challenging.
Purpose of the Study:
- To develop a method for analyzing DNA methylation that accounts for genetic variation, specifically strain-specific CpG sites.
- To improve the power and accuracy of differential DNA methylation analysis in the context of sequence variation.
Main Methods:
- Utilized whole-genome bisulfite sequencing data from two highly divergent mouse strains.
- Developed a method incorporating alignment to personal genomes and imputation of methylation levels at strain-specific CpG sites using smoothing.
- Applied the method to a human normal-cancer dataset.
Main Results:
- Alignment to personal genomes is essential for accurate DNA methylation quantification.
- The novel method increases statistical power in differential methylation analysis by including strain-specific CpGs.
- The approach demonstrated improved accuracy and power when applied to human cancer data.
Conclusions:
- The developed method enables the inclusion of strain-specific CpG sites in DNA methylation analyses, improving accuracy and power.
- This approach is broadly applicable, facilitating joint analysis of genetic variation and DNA methylation using bisulfite sequencing.
- The findings unlock the potential of personal genomes for understanding DNA methylation patterns in relation to genetic diversity.
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