The addition of a methyl group to the fifth carbon of cytidine is the most prevalent and stable epigenetic modification of human DNA (Laurent et al., 2010). DNA methylation is involved in gene regulation and influences a vast array of biological mechanisms, including embryonic development and cell fate, genome imprinting, X-chromosome inactivation, and transposon silencing (Moore et al., 2013; Smith and Meissner, 2013). In mammals, there are two copies or alleles of a gene, one inherited from each parent. Most gene transcripts are expressed from both alleles. However, a subset of genes are only expressed from a single allele; this allele can be selected either randomly, as seen in X-chromosome inactivation in females, or based upon the parent-of-origin (PofO), referred to as imprinting (Chess, 2013; Khamlichi and Feil, 2018).

In imprinting, mono-allelic expression of a gene or cluster of genes is controlled by a cis-acting imprinting control region (ICR) (Bartolomei and Ferguson-Smith, 2011). The main mechanism by which this occurs is PofO-defined differential methylation at ICRs, also known as imprinted differentially methylated regions (DMRs) (Bartolomei and Ferguson-Smith, 2011; Maupetit-Méhouas et al., 2016). Imprinted DMRs are classified as germline (primary) or somatic (secondary), hereinafter referred to as gDMR and sDMR. gDMRs are established during the first wave of methylation reprogramming in germ cell development and escape the second methylation reprogramming after fertilization (Zink et al., 2018). sDMRs are established de novo after fertilization during somatic development, usually under the control of a nearby gDMR (Zink et al., 2018). Imprinted clusters of genes may span up to ~4 Mb, by acting through a CCCTC-binding factor (CTCF)-binding site or by allelic expression of a long non-coding RNA (Bartolomei and Ferguson-Smith, 2011; da Rocha and Gendrel, 2019). By contrast, individually imprinted genes are typically regulated by PofO-derived differential methylation at the gene promoter (Bartolomei and Ferguson-Smith, 2011).

Imprinting is implicated in various genetic disorders, either from aberrations in imprinted methylation or from deleterious variants affecting the ICR and imprinted genes. Aberrant imprinted methylation is also detected in several human cancers (Goovaerts et al., 2018; Jelinic and Shaw, 2007; Tomizawa and Sasaki, 2012). Thus, accurate mapping and characterization of imprinting in humans is key to the treatment and actionability of genetic disorders, and to personalized oncogenomonics.

To detect imprinted methylation, accurate assignment of methylation data to paternal and maternal alleles is required. Achieving this with traditional bisulfite sequencing or arrays is challenging. Several studies have used samples with large karyotypic abnormalities, such as uniparental disomies, teratomas, and hydatidiform moles, to infer regions of imprinting (Court et al., 2014; Hernandez Mora et al., 2018; Joshi et al., 2016). This approach relies not only on rare structural variants, but also on the assumption that both normal methylation and the imprinted state remain intact in spite of substantial genomic aberrations. A study by Zink et al., 2018, leveraged a genotyped, multi-generation pedigree spanning nearly half the population of Iceland (n=150,000), in combination with whole-genome oxidative bisulfite sequencing, to phase methylation and infer PofO (Zink et al., 2018). However, despite being able to phase nearly every SNP in that cohort, they were only able to phase 84% of the human autosomal methylome in over 200 samples due to the short length of reads. Furthermore, the study was based on a single, genetically isolated population, which may not be representative of the wider human population. A comprehensive mapping of imprinted methylation using a technology more suited to phasing reads, based on individuals more representative of the human population, could greatly advance our understanding of imprinting, with direct benefits for human health.

Previously, we have shown that nanopore sequencing can detect allelic methylation in a single sample and accurately determine PofO using only trio data. We also previously developed the software NanoMethPhase for this purpose (Akbari et al., 2021). Here, we applied NanoMethPhase to public nanopore data from a diverse set of 12 lymphoblastoid cell lines (LCLs) from the 1000 Genomes Project (1KGP) and Genome in a Bottle (GIAB) to investigate genome-wide allele-specific methylation (ASM) and detect novel imprinted DMRs (Figure 1A; Auton et al., 2015; De Coster et al., 2019; Jain et al., 2018; Shafin et al., 2020; Zook et al., 2016; Zook et al., 2019). Using trio data from GIAB and 1KGP for these cell lines, we phased nanopore long reads to their PofO and inferred allelic methylation (Akbari et al., 2021; Auton et al., 2015; Zook et al., 2019). We were able to detect haplotype and methylation status for 26.5 million autosomal CpGs comprising 95% of the human autosomal methylome (GRCh38 main chromosomes). We further used public whole-genome bisulfite sequencing (WGBS) data to confirm the presence of the detected DMRs in other tissues and to classify the novel DMRs as germline or somatic. We captured 94% of the well-characterized DMRs and detected 42 novel DMRs (16 gDMRs and 26 sDMRs). Of these novel DMRs, 40.5% show evidence of conservation. We also detected seven blocks of PofO methylation bias at seven imprinted clusters with mutual exclusive allelic H3K36me3 and H3K27me3 histone marks. Collectively, our results extend the set of known imprinted intervals in humans and demonstrate a major contribution in our ability to characterize imprinting by ASM, brought about by the capabilities of long-read nanopore sequencing.

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Here, we describe the first genome-wide map of human ASM intended to detect novel imprinted intervals using nanopore sequencing. Leveraging long reads and parental SNVs allowed us to phase methylation for ~26.5 million autosomal CpGs representing 95% of the CpGs in the human autosomal genome (GRCh38) across 12 LCLs. This effort achieves a much higher resolution than previous studies aimed at capturing allelic methylation using bisulfite sequencing or methylation arrays (Court et al., 2014; Hernandez Mora et al., 2018; Joshi et al., 2016; Zink et al., 2018). Fourteen of our novel DMRs did not have any phased CpG from previous WGBS or array studies (Supplementary file 4), illustrating the utility of longer reads for imprinted methylation calling. DMRs that are detected in only a single study displayed higher variations across individuals compared to those detected by at least two studies. Therefore, lack of phasing at some novel DMRs in previous studies and higher variation in imprinted methylation at novel DMRs could explain the reason they were not detected previously. We also demonstrated that germline DMRs with a greater number of ZFP57 motif tend to be more consistently imprinted across individuals suggesting motifs redundancy increases ZFP57 recruitment and tolerance to any DNA sequence variation. However, due to the availability of DNA sequence in a limited number of samples, we were not able to examine sequence variation at the DMRs and the ZFP57-binding motifs for any possible association with polymorphic imprinted methylation which will require further study.

Even though we detected methylation for all the CpGs in the human genome (GRCh38), we were not able to phase 5% of the human methylome (Kent et al., 2002). We used SNVs detected from short-reads data in the 1KGP and GIAB databases for phasing (Auton et al., 2015; Zook et al., 2019). Seventy-five percent of the unphased CpGs mapped to the ENCODE blacklist, regions with low mappability, indicative of lack of SNVs to phase reads (Amemiya et al., 2019). Improvement in base calling and variant calling from nanopore reads could enable the phasing of a complete genome-wide methylome using nanopore-detected SNVs.

We detected 16 novel gDMRs and 26 novel sDMRs. These novel DMRs were supported by several lines of evidence in our analyses. (1) They displayed significant PofO methylation bias in nanopore-sequenced cell line samples. (2) They were partially methylated in WGBS data. (3) gDMRs demonstrated establishment of methylation in sperm or oocyte and escape from the second de-methylation step. (4) Eighty-four percent of those for which H3K4me3 ChIP-seq data could be phased and examined showed significant allelic H3K4me3. (5) Forty percent showed evidence of conservation. (6) Eighty-three percent mapped to at least one regulatory region including CGI, CTCF-binding site, and enhancer (Supplementary file 4). These novel DMRs represent a substantial and well-confirmed expansion of known regions of imprinting, which may aid future research and diagnosis in the fields of genetic medicine and oncology.

We detected seven blocks of allelic methylation bias (Figure 8; Figure 8—figure supplements 1–6). All of the blocks represented several common features. (1) They were detected in imprinted genes that appeared in a cluster. (2) There was at least one well-characterized and conserved gDMR in each block (except ZNF597/NAA60 block with a conserved sDMR). (3) The well-characterized DMRs in these blocks displayed significant allelic H3K4me3 (except the DMR in the L3MBTL1 block, which could not be examined due to the lack of an SNV). (4) The well-characterized DMRs in these blocks overlapped with the promoters of genes with subtle PofO methylation bias at the gene body and DMR itself displayed opposite PofO methylation (except for GPR1-AS/ZDBF2 block where DMR did not map to the promoter and had the same PofO with the gene body). (5) All the blocks were accompanied by a strong allelic expression and H3K36me3 histone mark on the subtle hypermethylated allele and H3K27me3 on the hypomethylated allele. This represents a novel facet of imprinting biology and suggests a link between allelic expression and histone modifications with biased PofO methylation at these blocks. However, the mechanism regulating such blocks and the rule of these PofO-biased methylation remain to be determined. One possible explanation could be that the subtle parental methylation bias is used by cells to express important genes (genes that can regulate other genes in the cluster or have regulatory roles) in an imprinted cluster with higher fidelity through its gene body methylation on the active allele. For example, at the KCNQ1OT1 and GNAS/GNAS-AS1 clusters, the methylation blocks overlap with KCNQ1OT1 and GNAS-AS1 gene bodies, both of which encode antisense RNA transcripts that regulate other genes in the imprinted cluster (Chiesa et al., 2012; Turan and Bastepe, 2013).

Orthologous regions of ~40% of the detected DMRs demonstrated partial methylation in one or more of the three mammals including chimpanzee, rhesus macaque, and mouse, suggesting their conservation. There were a considerably higher number of orthologous sites and partially methylated orthologous DMRs in chimpanzee and rhesus macaque, in agreement with more similarities and less distance to these primates compared to the mouse in human evolution. Previously, Court et al., 2014, detected 14 novel DMRs, and did not detect any imprinted orthologs of their novel DMRs in mice (Court et al., 2014). All 14 DMRs also overlapped with our detected DMRs and six of them had orthologous regions in mm10 using the UCSC liftover file (Kent et al., 2002). Two of the orthologs displayed partial methylation in mouse; the first is MEG8 human DMR with its orthologous Rian gene in the mouse, which was not examined by Court et al., 2014, and the other is found in the Htr5a gene, which was previously reported as not conserved in mouse (Court et al., 2014). After reviewing their analysis, Court et al., 2014, seem to have examined different orthologous region (Appendix 2—figure 1). For Htr5a, they examined the CGI (CpG:_102) ~50 kb away from the gene, while we examined the region spanning the first or second exon (two transcripts) of Htr5a which was partially methylated while CpG:_102 was also unmethylated in our study.

Using reported imprinted genes, 36% of the novel DMRs mapped close to known imprinted genes (Babak et al., 2015; Baran et al., 2015; Geneimprint, 2021; Jadhav et al., 2019; Morison et al., 2001; Zink et al., 2018). Five of our novel DMRs could be potential ICRs for reported imprinted genes. Specifically, imprinted DMRs overlapping the promoters of ZNF714, PAX8-AS1, ACTL10, and SYCE1 genes (Figures 6 and 7). ZNF714 is a member of the zinc finger family of proteins which have several imprinted genes with developmental roles (Babak et al., 2015; Baran et al., 2015; Camargo et al., 2012; Jadhav et al., 2019; Zink et al., 2018). ZNF714 has been reported to be associated with non-syndromic cleft lip (Camargo et al., 2012). Aberrant methylation of multiple CpGs overlapping with the novel DMR at PAX8-AS1 has been implicated in thyroid disorders (Candler et al., 2021). SYCE1 and ACTL10 are also implicated in human diseases (Bak et al., 2016; Maor-Sagie et al., 2015). Thus, these imprinted DMRs could be of potential clinical value.

In addition to the aforementioned novel DMRs, two of the reported DMRs in PTCHD3 and FANCC are also interesting. Paternal expression of PTCHD3 and maternal expression for FANCC were previously detected by Zink et al., 2018, though they could not detect any associated DMR due to the lack of phased CpG (Zink et al., 2018). Hernandez Mora et al., 2018, detected three maternally methylated probes at the promoter of PTCHD3 and one maternally methylated probe in intron 1 of FANCC, but were unable to examine the parental expression (Hernandez Mora et al., 2018). We also detected two maternally methylated gDMRs overlapping with the promoter of PCTHD3 and intron 1 of FANCC (Appendix 3—figure 1; Appendix 3—figure 2). Therefore, these gDMRs could be the ICRs for these genes. The maternal gDMR at the PTCHD3 promoter can directly suppress the maternal allele and results in paternal expression. FANCC gDMR overlaps with a CGI and CTCF-binding site. CTCF is a methylation-sensitive DNA-binding protein and CpG methylation can inhibit CTCF binding (Hashimoto et al., 2017; Renda et al., 2007). Moreover, CTCF binding to the first intron of major immediate early (MIE) gene of the human cytomegalovirus (HCMV) in HCMV-infected cells resulted in repression of this gene (Puerta et al., 2014). Therefore, the maternally methylated DMR in intron 1 of maternally expressed FANCC suggests a mechanism through which the paternal allele is suppressed by CTCF binding at DMR while DNA methylation inhibits CTCF binding at the maternal allele.

Overall, our study demonstrates a near-complete genome-wide map of human ASM by leveraging long-read nanopore technology. The use of nanopore technology allowed us to expand the set of known imprinted DMRs using 12 LCLs with parental SNPs. Moreover, we detected seven large PofO bias methylation blocks with enriched allelic expression and histone modifications. We showed that nanopore sequencing has the ability to achieve a higher resolution of phased CpGs using a small sample size and allows for the calling of imprinted methylation in a single sample, potentially reducing the cost by reducing the sample size.

The current manuscript is a computational study, so no new datasets have been generated for this manuscript. The source of each dataset is provided under the ‘“Materials and methods’” section under the appropriate subsection. Genomic tracks generated in this study including DNA methylation and histone modification tracks are deposited in the Mendeley data repository (https://doi.org/10.17632/f4k2gytbh5.1). Codes are uploaded to GitHub https://github.com/vahidAK/NanoMethPhase/tree/master/scripts (copy archived at swh:1:rev:1657f7aed60604aa7c7f3e77d992d76bee6bf6d3): PartialMethylation_AtDMR.sh, CountReadsAtSNV.py and Binomial_test.py.

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Author details

1. Vahid Akbari

   1. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada
   2. Department of Medical Genetics, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8005-7776

2. Jean-Michel Garant

   Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada

   Contribution

   Conceptualization, Data curation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

3. Kieran O'Neill

   Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7609-5905

4. Pawan Pandoh

   Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada

   Contribution

   Conceptualization, Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Richard Moore

   Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada

   Contribution

   Conceptualization, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

6. Marco A Marra

   1. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada
   2. Department of Medical Genetics, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Formal analysis, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7146-7175

7. Martin Hirst

   1. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada
   2. Department of Microbiology and Immunology, Michael Smith Laboratories, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Formal analysis, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

8. Steven JM Jones

   1. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada
   2. Department of Medical Genetics, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review and editing

   For correspondence

   sjones@bcgsc.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3394-2208

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