MPCI: A novel metric for quantifying DNA methylation patterns in NGS data

Epigenetic processes, particularly disruptions in DNA methylation profiles, are associated with many disease states. Traditional approaches for DNA methylation biomarker discovery focusing on individual CpG sites do not account for fragment-level methylation states. Methylation haplotype analysis offers a more comprehensive approach leading to increased distinction capability between reads originating from tissues with diverse methylation profiles. This can be particularly valuable in liquid biopsy where detecting small amounts of disease-specific cell-free DNA (cfDNA) amidst a bulk of healthy cfDNA is challenging. To address limitations of existing metrics for quantifying methylation patterns in a region from sequencing data, we propose the Methylation Pattern Consistency Index (MPCI), a novel metric that captures consistent methylation patterns across sequencing reads, accounting for both methylated and unmethylated blocks of CpGs. Using whole-genome bisulfite sequencing data, we demonstrate that MPCI outperforms MHL and its symmetric counterpart, dMHL (MHL - uMHL), across several benchmarks: distinguishing closely related cell types (CD4 vs. CD8; AUC 0.915), multi-tissue classification (0.92 accuracy), and detection of in-silico cfDNA spike-ins at abundances as low as 1%. Notably, in a clinical liquid-biopsy cohort of liver transplant patients, MPCI achieved significantly higher classification performance than dMHL (Accuracy: MPCI: 0.868 ± 0.023 vs. dMHL: 0.768 ± 0.027, p = 0.014) in discriminating pre- from post-transplant cfDNA profiles. These findings position MPCI as a reliable quantification approach for biomarker selection or diagnostic testing in epigenetic studies. We have made MPCI available as an R function for usage convenience.