A paper written by Jordi Abante – a fifth-year PhD candidate in the Department of Electrical and Computer Engineering who is a member of ECE professor John Goutsias’ lab – has been published in Nature Communications today.
The paper, which is titled “Detection of haplotype-dependent allele-specific DNA methylation in WGBS data,” introduces a novel method to study allelic imbalances in DNA methylation, an epigenetic mark that plays an essential role in defining a cells’ phenotype.
“It’s incredibly exciting to be featured in such a great journal, especially during these trying times,” Abante said. “Dr. Goutsias’ guidance and support throughout this project has been essential to our success.”
The presented method extends previously developed ideas in the Goutsias lab to provide a comprehensive picture of the circumstances under which DNA methylation exhibits allelic imbalances in diploid organisms. The method draws ideas from statistical physics and information theory and addresses the main pitfalls of the state of the art.
“Allelic imbalances in DNA methylation are quite wide-spread in the human genome and, in several instances, are known to play a functional role. We have developed the first method that comprehensively detects allelic imbalances at the haplotype level by leveraging concepts from information theory and statistical physics,” Abante said. “Our work can potentially lead to biological discoveries with important implications for the genetics of complex human diseases, by for example detecting new candidate imprinted genes, and will allow to better characterize the relationship between genetic sequence and DNA methylation.”
See below for the full abstract from Abante’s paper.
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.