Rewrite the introduction for paper:

Introduction

Variant in the non-coding regions (Welter et al., 2014; Farh et al., 2015). While variants in coding regions may confer disease risk through altered protein sequences and, therefore, altered protein functions, variants in non-coding regions (usually enhancers) contribute to disease susceptibility through changing gene transcription and non-coding RNAs (Hrdlickova et al., 2014). These genetic variants might engage in crosstalk with sequence-specific transcription factors and epigenetic patterns (including DNA methylation) (McVicker et al., 2013), thereby impacting the transcription of genes locally or remotely.


Development of direct long-read DNA sequencing opens new possibilities for simultaneous studies of the individual genetic and epigenetic variation. Sequencing of large genomic fragments allows performing the highly accurate allele discrimination and identifying variants specific for each haplotype in a diploid human genome. Direct DNA sequencing with Oxford Nanopore opens possibilities also for detection of the epigenetic modifications. Deciphering the role of interindividual epigenetics variation allowed to associate methylation profile changes with development of diseases and propose viable biomarkers for risk stratification, diagnostics, and serve for prediction of disease prognosis. Precise characterization of the epigenetic differences between alleles is crucially important for understanding of the complex cis-regulatory interfaces that govern precise control of the gene regulation to dynamically respond to environmental exposure. DNA methylation (5-methyl cytosine modification of CpG sites) represents one of the most common epigenetic modifications that is usually associated with inhibition of the gene transcription.






Differences of the DNA methylation between alleles represent allele-specific methylation (ASM). These regions are usually recognised as imprinted loci and arise in germline cells. Moreover, the primary DNA sequence in the ASM sites can be absolutely the same. However, “non-imprinted” ASM can be not only heritable but also dynamic with context specific influence on the non-coding genomic elements and gene expression [2, 3]. Thus, ASM provides a mechanism for adaptation to environmental factors and may change regulatory networks involved in diseases emergence [].



Moreover, analysis of the methylation differences between alleles discovered their role not only as imprinted loci but also   

ASM is heritable, dynamic and remarkably, enriched in regulatory regions associated with histone modifications representative for promoter and enhancer elements (Benton et al., 2019; Gagliano et al., 2016; Hannon et al., 2015). 

Genomic variations in the human genome may influence the methylation levels of neighboring CpG sites, contribute to the shifts in the epigenetic marks that ultimately can lead to the phenotypic alterations, and even diseases. Thus, determination of SNPs associated ASM events is highly important for better understanding of the relationship between genetic and epigenetic variations. 


Allele specific methylation can be imprinted or sequence-dependent (Kerkel et al. 2008). The imprinted ASM can be gametic that originates during gametogenesis or somatic occurring in post-implantation embryos  2], [3]. The number of P-ASM regions is very limited but each P-ASM region may span over mega-bases in the genome [2]. In contrast, sequence-dependent ASM (S-ASM) at non-imprinted loci has been found to be prevalent but highly localized in genomes and frequently restricted to a very small number of CpG sites [4], [5]. Some of these S-ASM CpG sites may co-localize in a narrow genomic region and result in clustered-S-ASM (cS-ASM). Since cS-ASM may affect a significant number of CpG sites within functional elements, there is a growing interest to determine cS-ASM regions in genome.
ASM loci may be identified experimentally with the comparisons of methylation levels between two SNP alleles. To quantitatively assess ASM on a genome-wide scale, the high-density SNP microarrays have been exploited intensively [1], [6], [7]. In such a procedure, genomic DNA was first digested with methylation sensitive enzymes. Differently methylated alleles showed distinct enzymatic digestion patterns, which result in allele-skewed hybridization signals on SNP arrays. Apparently, such an approach heavily depends on the availability of methylation sensitive enzymes and their recognition sites in the genome. In combination with bisulfite treatment, high-throughput sequencing technologies offer alternative ways to identify ASM loci. Bisulfite sequencing data provides the methylation information for CpG sites on the two alleles in addition to sequence variations. The CpG sites adjacent to SNPs may be grouped by alleles and their methylation statuses may be subjected to standard statistical tests such as the Fisher's exact test [4], [5]. However, identifying SNPs from bisulfite converted sequence reads is a challenging task. It was estimated that an average 30Xs sequence read depth would be required to call 96% SNP accurately from bisulfite sequencing data [8]. Unfortunately, most of the current high-throughput methylation datasets are with the average sequence coverage around 10Xs.
 ASM is dynamic, heritable, and has been associated with non-coding genomic elements and gene expression [2, 3]. This type of ASM may allow rapid adaptation to environmental exposures and potentially play an integral role in regulatory networks involved in complex disease traits [4, 5].

The role of common interindividual variation in DNAm on disease mechanisms has not yet been well characterized. It has, however, been hypothesized that DNAm serves as a viable biomarker for risk stratification, early disease detection, and the prediction of disease prognosis and progression . As genetic influences on DNAm in blood are widespread2–4 , a powerful avenue for studying the functional consequences of differences in DNAm levels is to map genetic differences associated with population-level variation, identifying mQTLs that include both local (cis-mQTL) and distal (trans-mQTL) effects. We can harness mQTLs as natural experiments, allowing the observation of randomly perturbed DNAm levels in a manner that is not confounded with environmental factors5,6 . In this regard, mapping even very small genetic effects on DNAm is valuable for gaining power to evaluate whether its variation has a substantial causal role in disease and other biological processes. To date, only a small fraction of the total genetic variation estimated to influence DNAm across the genome has been identified7 , and the proportion of trans heritability explained by trans-mQTLs (defined as variants >1Mb from the DNAm site) is much smaller than the proportion of cis heritability explained by cis-mQTLs. Therefore, most genetic effects are likely to act in trans, with small effect sizes5,7–9 , while being potentially biologically informative8,10. Much larger sample sizes are required to map associations involving small genetic effects to permit greater understanding of the genetic architecture and the biological processes underlying DNAm . To this end, we established the Genetics of DNA Methylation Consortium (GoDMC), an international collaboration of human epidemiological studies that comprises >30,000 study participants with genetic and DNAm data. We use this resource to develop a comprehensive catalog of cis- and trans-mQTLs, which enables us to examine the genetic architecture of DNAm. By constructing networks of multiple cis- and trans-mQTLs, we learn about their collective impact on pathways and complex traits. Finally, we interrogate the potential role of DNAm in disease mechanisms by mapping the causal relationships between variable DNAm and 116 complex traits and diseases in a bidirectional manner. A database of our results is available as a resource to the community at http://mqtldb. godmc.org.uk.



One limitation of such approaches is that many imprinted genes may only show allele-specific expressions in particular tissues at appropriate developmental stages (13). 

DNA methylation, as a crucial epigenetic mechanism, showcases the variability of different alleles in an organism's genome. This phenomenon, known as allele-specific methylation (ASM), was initially identified in imprinted genetic loci but has since been observed in a broader range of genomic regions (Benton et al. 2019; Tycko 2010). Non-imprinted ASM, characterized by its dynamic nature, heritability, and correlation with gene expression patterns, has become a significant area of research in the field of epigenetics (Hutchinson et al. 2014; Do et al. 2020). 

Currently, compelling evidence has been obtained regarding the influence of sequence on allele-specific methylation: recent large-scale studies indicate that methylation levels of 34% to 45% of CpG sites are dependent on SNPs (Min et al. 2021; Villicaña et al. 2023). Moreover, mediation analyses conducted in epidemiological studies have elucidated how methylation acts as a mediator of genetic risk in conditions like rheumatoid arthritis and inflammatory bowel disease (Ventham et al. 2016; Yun Liu et al. 2013).  These findings underscore the importance of understanding the molecular mechanisms underlying ASM and its role in mediating the genetic predisposition to complex diseases.

By unraveling the complex interactions between genetic variations, epigenetic modifications, and disease susceptibility, researchers can potentially identify novel therapeutic targets and develop personalized treatment strategies (Do et al. 2020; Martos et al. 2017). This growing body of evidence highlights the significance of integrating genetic and epigenetic data to gain a comprehensive understanding of the underlying mechanisms driving disease pathogenesis (Wang, Lou, and Wang 2018).

ASM for gene expression.
In our study, we delved into the intricate interplay between genomic context and regulatory mechanisms that impact allele-specific methylation. Our work aims to provide a comprehensive understanding of the differentiation of allele-specific methylation sites and identify their regulatory roles. We employ various computational tools to address the following questions:
1. Which specific genetic variants influence methylation levels?
2. What regulatory mechanisms determine the interaction between genetic variants and methylation levels?
3. What are the consequences of sequence-dependent ASM for gene expression?