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Introduction

Variations in non-coding regions of the genome have emerged as significant contributors to disease susceptibility, playing a crucial role in gene regulation and expression (Welter et al., 2014; Farh et al., 2015). Unlike variants in coding regions that typically influence disease by altering protein sequences or functions, non-coding variants—often located within enhancer regions—affect disease risk primarily through modulation of gene transcription and the regulation of non-coding RNAs (Hrdlickova et al., 2014). These non-coding variants interact with transcription factors and are influenced by epigenetic modifications, such as DNA methylation, thereby impacting gene expression both locally and at a distance (McVicker et al., 2013).

The advent of direct long-read DNA sequencing has opened new avenues for investigating genetic and epigenetic variations simultaneously. By sequencing large genomic fragments, researchers can accurately discriminate between alleles and identify haplotype-specific variants within the diploid human genome. Technologies like Oxford Nanopore sequencing further facilitate the detection of epigenetic modifications, allowing for an in-depth understanding of interindividual variations in DNA methylation. These variations have been linked to changes in disease susceptibility, and underscore the potential for using methylation profiles as biomarkers for risk assessment, diagnosis, and prognostic predictions. A precise characterization of epigenetic differences among alleles is vital for unraveling the intricate cis-regulatory mechanisms that govern gene regulation in response to environmental factors.

Allele-specific methylation (ASM) represents a key aspect of this complex regulatory landscape. ASM can occur in both imprinted loci and non-imprinted contexts, with the latter demonstrating heritability and dynamism influenced by various environmental conditions. Interestingly, while sequence-dependent ASM often arises in regions exhibiting identical primary DNA sequences, it can still have a profound effect on neighboring regulatory elements and gene expression (Benton et al., 2019; Hannon et al., 2015).

Understanding the interplay between genetic variations, particularly single nucleotide polymorphisms (SNPs), and allele-specific methylation is crucial for shedding light on the molecular mechanisms underpinning disease susceptibility. Recent large-scale studies have indicated that SNPs may influence the methylation levels of 34% to 45% of CpG sites, thereby affecting the functional landscape of the genome (Min et al., 2021; Villicaña et al., 2023). Moreover, mediation analyses have demonstrated that methylation can serve as a critical mechanism through which genetic risk manifests in diseases such as rheumatoid arthritis and inflammatory bowel disease (Ventham et al., 2016; Yun Liu et al., 2013).

This study focuses on elucidating the multifaceted relationships between genetic variants, epigenetic modifications, and disease susceptibility. By employing advanced computational tools, we aim to address the following pivotal questions:

Which specific genetic variants influence methylation levels?
What regulatory mechanisms govern the interaction between genetic variants and methylation levels?
What are the functional consequences of sequence-dependent ASM for gene expression?
By unraveling these complex interactions, our research seeks not only to enhance the understanding of genetic predisposition to complex diseases but also to pave the way for novel therapeutic strategies and personalized medicine approaches (Do et al., 2020; Martos et al., 2017). The integration of genetic and epigenetic datasets holds the potential to revolutionize our understanding of disease mechanisms and inform future research endeavours (Wang, Lou, and Wang, 2018).