Detection of haplotype-dependent allele-specific DNA methylation in WGBS data

• Published in: Nature communications 2020-10, Vol.11 (1), p.5238-5238, Article 5238
• Main Authors: Abante, J., Fang, Y., Feinberg, A. P., Goutsias, J.
• Format: Article
• Language: English
• Subjects: 631/114/2397; 631/1647/2210; 631/208/176/1988; 631/337/176/1988; 631/61/212/177; Alleles; CpG Islands; Disease - genetics; DNA Methylation; Haplotypes; Humanities and Social Sciences; Humans; multidisciplinary; Polymorphism, Single Nucleotide; Science; Science (multidisciplinary); Species Specificity; Whole Genome Sequencing
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-19077-1

In heterozygous genomes, allele-specific measurements can reveal biologically significant differences in DNA methylation between homologous alleles associated with local changes in genetic sequence. Current approaches for detecting such events from whole-genome bisulfite sequencing (WGBS) data perform statistically independent marginal analysis at individual cytosine-phosphate-guanine (CpG) sites, thus ignoring correlations in the methylation state, or carry-out a joint statistical analysis of methylation patterns at four CpG sites producing unreliable statistical evidence. Here, we employ the one-dimensional Ising model of statistical physics and develop a method for detecting allele-specific methylation (ASM) events within segments of DNA containing clusters of linked single-nucleotide polymorphisms (SNPs), called haplotypes. Comparisons with existing approaches using simulated and real WGBS data show that our method provides an improved fit to data, especially when considering large haplotypes. Importantly, the method employs robust hypothesis testing for detecting statistically significant imbalances in mean methylation level and methylation entropy, as well as for identifying haplotypes for which the genetic variant carries significant information about the methylation state. As such, our ASM analysis approach can potentially lead to biological discoveries with important implications for the genetics of complex human diseases. Allele-specific measurements can reveal differences in DNA methylation between homologous alleles associated with changes in genetic sequence. Here, the authors develop a method for detecting allele specific methylation events within haplotypes of linked SNPs, compare it with existing methods, and show it identifies haplotypes for which the genetic variant carries significant information about the methylation state of the allele of origin.