Allele-specific DNA methylation is increased in cancers and its dense mapping in normal plus neoplastic cells increases the yield of disease-associated regulatory SNPs

Moura A, Filho ODMM, Pessoa C, Miranda Furtado CL. Hypomethylation at H19DMR in penile squamous cell carcinoma is not related to HPV infection

Penile squamous cell carcinoma (SCC) is a rare and aggressive tumour mainly related to lifestyle behaviour and human papillomavirus (HPV) infection. Environmentally induced loss of imprinting (LOI) at the H19 differentially methylated region (H19DMR) is associated with many cancers in the early events of tumorigenesis and may be involved in the pathogenesis of penile SCC. We sought to evaluate the DNA methylation pattern at H19DMR and its association with HPV infection in men with penile SCC by bisulfite sequencing (bis-seq). We observed an average methylation of 32.2% ± 11.6% at the H19DMR of penile SCC and did not observe an association between the p16INK4a+ (p = 0.59) and high-risk HPV+ (p = 0.338) markers with methylation level. The average methylation did not change according to HPV positive for p16INK4a+ or hrHPV+ (35.4% ± 10%) and negative for both markers (32.4% ± 10.1%) groups. As the region analysed has a binding site for the CTCF protein, the hypomethylation at the surrounding CpG sites might alter its insulator function. In addition, there was a positive correlation between intense polymorphonuclear cell infiltration and hypomethylation at H19DMR (p = 0.035). Here, we report that hypomethylation at H19DMR in penile SCC might contribute to tumour progression and aggressiveness regardless of HPV infection.

ChIP-Atlas 3.0: a data-mining suite to explore chromosome architecture together with large-scale regulome data

ChIP-Atlas (https://chip-atlas.org/) presents a suite of data-mining tools for analyzing epigenomic landscapes, powered by the comprehensive integration of over 376 000 public ChIP-seq, ATAC-seq, DNase-seq and Bisulfite-seq experiments from six representative model organisms. To unravel the intricacies of chromatin architecture that mediates the regulome-initiated generation of transcriptional and phenotypic diversity within cells, we report ChIP-Atlas 3.0 that enhances clarity by incorporating additional tracks for genomic and epigenomic features within a newly consolidated 'annotation track' section. The tracks include chromosomal conformation (Hi-C and eQTL datasets), transcriptional regulatory elements (ChromHMM and FANTOM5 enhancers), and genomic variants associated with diseases and phenotypes (GWAS SNPs and ClinVar variants). These annotation tracks are easily accessible alongside other experimental tracks, facilitating better elucidation of chromatin architecture underlying the diversification of transcriptional and phenotypic traits. Furthermore, 'Diff Analysis,' a new online tool, compares the query epigenome data to identify differentially bound, accessible, and methylated regions using ChIP-seq, ATAC-seq and DNase-seq, and Bisulfite-seq datasets, respectively. The integration of annotation tracks and the Diff Analysis tool, coupled with continuous data expansion, renders ChIP-Atlas 3.0 a robust resource for mining the landscape of transcriptional regulatory mechanisms, thereby offering valuable perspectives, particularly for genetic disease research and drug discovery.

Rockfish: A transformer-based model for accurate 5-methylcytosine prediction from nanopore sequencing

DNA methylation plays an important role in various biological processes, including cell differentiation, ageing, and cancer development. The most important methylation in mammals is 5-methylcytosine mostly occurring in the context of CpG dinucleotides. Sequencing methods such as whole-genome bisulfite sequencing successfully detect 5-methylcytosine DNA modifications. However, they suffer from the serious drawbacks of short read lengths and might introduce an amplification bias. Here we present Rockfish, a deep learning algorithm that significantly improves read-level 5-methylcytosine detection by using Nanopore sequencing. Rockfish is compared with other methods based on Nanopore sequencing on R9.4.1 and R10.4.1 datasets. There is an increase in the single-base accuracy and the F1 measure of up to 5 percentage points on R.9.4.1 datasets, and up to 0.82 percentage points on R10.4.1 datasets. Moreover, Rockfish shows a high correlation with whole-genome bisulfite sequencing, requires lower read depth, and achieves higher confidence in biologically important regions such as CpG-rich promoters while being computationally efficient. Its superior performance in human and mouse samples highlights its versatility for studying 5-methylcytosine methylation across varied organisms and diseases. Finally, its adaptable architecture ensures compatibility with new versions of pores and chemistry as well as modification types.

Neural-net-based cell deconvolution from DNA methylation reveals tumor microenvironment associated with cancer prognosis

DNA methylation is a pivotal epigenetic modification that defines cellular identity. While cell deconvolution utilizing this information is considered useful for clinical practice, current methods for deconvolution are limited in their accuracy and resolution. In this study, we collected DNA methylation data from 945 human samples derived from various tissues and tumor-infiltrating immune cells and trained a neural network model with them. The model, termed MEnet, predicted abundance of cell population together with the detailed immune cell status from bulk DNA methylation data, and showed consistency to those of flow cytometry and histochemistry. MEnet was superior to the existing methods in the accuracy, speed, and detectable cell diversity, and could be applicable for peripheral blood, tumors, cell-free DNA, and formalin-fixed paraffin-embedded sections. Furthermore, by applying MEnet to 72 intrahepatic cholangiocarcinoma samples, we identified immune cell profiles associated with cancer prognosis. We believe that cell deconvolution by MEnet has the potential for use in clinical settings.

Genome-wide screening and functional validation of methylation barriers near promoters

CpG islands near promoters are normally unmethylated despite being surrounded by densely methylated regions. Aberrant hypermethylation of these CpG islands has been associated with the development of various human diseases. Although local genetic elements have been speculated to play a role in protecting promoters from methylation, only a limited number of methylation barriers have been identified. In this study, we conducted an integrated computational and experimental investigation of colorectal cancer methylomes. Our study revealed 610 genes with disrupted methylation barriers. Genomic sequences of these barriers shared a common 41-bp sequence motif (MB-41) that displayed homology to the chicken HS4 methylation barrier. Using the CDKN2A (P16) tumor suppressor gene promoter, we validated the protective function of MB-41 and showed that loss of such protection led to aberrant hypermethylation. Our findings highlight a novel sequence signature of cis-acting methylation barriers in the human genome that safeguard promoters from silencing.

Investigating the potential of single-cell DNA methylation data to detect allele-specific methylation and imprinting

Allele-specific methylation (ASM) is an epigenetic modification whereby one parental allele becomes methylated and the other unmethylated at a specific locus. ASM is most often driven by the presence of nearby heterozygous variants that influence methylation, but also occurs somatically in the context of genomic imprinting. In this study, we investigate ASM using publicly available single-cell reduced representation bisulfite sequencing (scRRBS) data on 608 B cells sampled from six healthy B cell samples and 1,230 cells from 11 chronic lymphocytic leukemia (CLL) samples. We developed a likelihood-based criterion to test whether a CpG exhibited ASM, based on the distributions of methylated and unmethylated reads both within and across cells. Applying our likelihood ratio test, 65,998 CpG sites exhibited ASM in healthy B cell samples according to a Bonferroni criterion (p < 8.4 × 10-9), and 32,862 CpG sites exhibited ASM in CLL samples (p < 8.5 × 10-9). We also called ASM at the sample level. To evaluate the accuracy of our method, we called heterozygous variants from the scRRBS data, which enabled variant-based calls of ASM within each cell. Comparing sample-level ASM calls to the variant-based measures of ASM, we observed a positive predictive value of 76%-100% across samples. We observed high concordance of ASM across samples and an overrepresentation of ASM in previously reported imprinted genes and genes with imprinting binding motifs. Our study demonstrates that single-cell bisulfite sequencing is a potentially powerful tool to investigate ASM, especially as studies expand to increase the number of samples and cells sequenced.

Metastable epialleles in humans

First identified in isogenic mice, metastable epialleles (MEs) are loci where the extent of DNA methylation (DNAm) is variable between individuals but correlates across tissues derived from different germ layers within a given individual. This property, termed systemic interindividual variation (SIV), is attributed to stochastic methylation establishment before germ layer differentiation. Evidence suggests that some putative human MEs are sensitive to environmental exposures in early development. In this review we introduce key concepts pertaining to human MEs, describe methods used to identify MEs in humans, and review their genomic features. We also highlight studies linking DNAm at putative human MEs to early environmental exposures and postnatal (including disease) phenotypes.

Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection

Clostridioides difficile (C. diff.) infection (CDI) is a leading cause of hospital acquired diarrhea in North America and Europe and a major cause of morbidity and mortality. Known risk factors do not fully explain CDI susceptibility, and genetic susceptibility is suggested by the fact that some patients with colons that are colonized with C. diff. do not develop any infection while others develop severe or recurrent infections. To identify common genetic variants associated with CDI, we performed a genome-wide association analysis in 19,861 participants (1349 cases; 18,512 controls) from the Electronic Medical Records and Genomics (eMERGE) Network. Using logistic regression, we found strong evidence for genetic variation in the DRB locus of the MHC (HLA) II region that predisposes individuals to CDI (P > 1.0 × 10-14; OR 1.56). Altered transcriptional regulation in the HLA region may play a role in conferring susceptibility to this opportunistic enteric pathogen.

Methylomics and cancer: the current state of methylation profiling and marker development for clinical care

Epigenetic modifications have long been recognized as an essential level in transcriptional regulation linking behavior and environmental conditions or stimuli with biological processes and disease development. Among them, methylation is the most abundant of these reversible epigenetic marks, predominantly occurring on DNA, RNA, and histones. Methylation modification is intimately involved in regulating gene transcription and cell differentiation, while aberrant methylation status has been linked with cancer development in several malignancies. Early detection and precise restoration of dysregulated methylation form the basis for several epigenetics-based therapeutic strategies. In this review, we summarize the current basic understanding of the regulation and mechanisms responsible for methylation modification and cover several cutting-edge research techniques for detecting methylation across the genome and transcriptome. We then explore recent advances in clinical diagnostic applications of methylation markers of various cancers and address the current state and future prospects of methylation modifications in therapies for different diseases, especially comparing pharmacological methylase/demethylase inhibitors with the CRISPRoff/on methylation editing systems. This review thus provides a resource for understanding the emerging role of epigenetic methylation in cancer, the use of methylation-based biomarkers in cancer detection, and novel methylation-targeted drugs.

Placental ischemia disrupts DNA methylation patterns in distal regulatory regions in rats

Preeclampsia (PE) is a leading cause of maternal and fetal morbidity and mortality worldwide. However, the impact of PE on the organization of the functional architecture of the placental methylome remains largely unknown. We performed whole-genome bisulfite sequencing of placental DNA and applied a Hidden Markov Model to investigate epigenome-wide alterations in functional structures, including partially methylated domains (PMDs), low-methylated regions (LMRs), and unmethylated regions (UMRs), in a reduced uterine perfusion pressure (RUPP) rat model of PE. The remarkable similarity we observed between the rat and human placental DNA methylomes suggests that the RUPP rat model is appropriate to elucidate the epigenetic mechanisms underlying human PE. The notable changes in PMDs indicate RUPP-induced perturbation of the stressed placental methylome. This was probably regulated via modulation of the epigenetic modifier expression, including significant downregulation of Dnmt1 and Dnmt3a and upregulation of Tet2. More importantly, changes in RUPP-induced DNA methylation occurred predominately in LMRs (80 %), which represent active enhancers, rather than in canonical UMRs (3 %), which represent promoters, suggesting that placental ischemia disrupts enhancer DNA methylation. Our findings emphasize the role of enhancer methylation in response to PE, corroborating discoveries in human PE studies. We suggest paying more attention to enhancer regions in future studies on PE.

Long-read sequencing of diagnosis and post-therapy medulloblastoma reveals complex rearrangement patterns and epigenetic signatures

Cancer genomes harbor a broad spectrum of structural variants (SVs) driving tumorigenesis, a relevant subset of which escape discovery using short-read sequencing. We employed Oxford Nanopore Technologies (ONT) long-read sequencing in a paired diagnostic and post-therapy medulloblastoma to unravel the haplotype-resolved somatic genetic and epigenetic landscape. We assembled complex rearrangements, including a 1.55-Mbp chromothripsis event, and we uncover a complex SV pattern termed templated insertion (TI) thread, characterized by short (mostly <1 kb) insertions showing prevalent self-concatenation into highly amplified structures of up to 50 kbp in size. TI threads occur in 3% of cancers, with a prevalence up to 74% in liposarcoma, and frequent colocalization with chromothripsis. We also perform long-read-based methylome profiling and discover allele-specific methylation (ASM) effects, complex rearrangements exhibiting differential methylation, and differential promoter methylation in cancer-driver genes. Our study shows the advantage of long-read sequencing in the discovery and characterization of complex somatic rearrangements.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Malignancies in Patients with Celiac Disease: Diagnostic Challenges and Molecular Advances

Celiac disease (CD) is a multiorgan autoimmune disorder of the chronic intestinal disease group characterized by duodenal inflammation in genetically predisposed individuals, precipitated by gluten ingestion. The pathogenesis of celiac disease is now widely studied, overcoming the limits of the purely autoimmune concept and explaining its hereditability. The genomic profiling of this condition has led to the discovery of numerous genes involved in interleukin signaling and immune-related pathways. The spectrum of disease manifestations is not limited to the gastrointestinal tract, and a significant number of studies have considered the possible association between CD and neoplasms. Patients with CD are found to be at increased risk of developing malignancies, with a particular predisposition of certain types of intestinal cancer, lymphomas, and oropharyngeal cancers. This can be partially explained by common cancer hallmarks present in these patients. The study of gut microbiota, microRNAs, and DNA methylation is evolving to find the any possible missing links between CD and cancer incidence in these patients. However, the literature is extremely mixed and, therefore, our understanding of the biological interplay between CD and cancer remains limited, with significant implications in terms of clinical management and screening protocols. In this review article, we seek to provide a comprehensive overview of the genomics, epigenomics, and transcriptomics data on CD and its relation to the most frequent types of neoplasms that may occur in these patients.

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods—both reference-based and reference-free—generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.

Fast and memory-efficient mapping of short bisulfite sequencing reads using a two-letter alphabet

DNA cytosine methylation is an important epigenomic mark with a wide range of functions in many organisms. Whole genome bisulfite sequencing is the gold standard to interrogate cytosine methylation genome-wide. Algorithms used to map bisulfite-converted reads often encode the four-base DNA alphabet with three letters by reducing two bases to a common letter. This encoding substantially reduces the entropy of nucleotide frequencies in the resulting reference genome. Within the paradigm of read mapping by first filtering possible candidate alignments, reduced entropy in the sequence space can increase the required computing effort. We introduce another bisulfite mapping algorithm (abismal), based on the idea of encoding a four-letter DNA sequence as only two letters, one for purines and one for pyrimidines. We show that this encoding can lead to greater specificity compared to existing encodings used to map bisulfite sequencing reads. Through the two-letter encoding, the abismal software tool maps reads in less time and using less memory than most bisulfite sequencing read mapping software tools, while attaining similar accuracy. This allows in silico methylation analysis to be performed in a wider range of computing machines with limited hardware settings.

Investigation of measurable residual disease in acute myeloid leukemia by DNA methylation patterns

Assessment of measurable residual disease (MRD) upon treatment of acute myeloid leukemia (AML) remains challenging. It is usually addressed by highly sensitive PCR- or sequencing-based screening of specific mutations, or by multiparametric flow cytometry. However, not all patients have suitable mutations and heterogeneity of surface markers hampers standardization in clinical routine. In this study, we propose an alternative approach to estimate MRD based on AML-associated DNA methylation (DNAm) patterns. We identified four CG dinucleotides (CpGs) that commonly reveal aberrant DNAm in AML and their combination could reliably discern healthy and AML samples. Interestingly, bisulfite amplicon sequencing demonstrated that aberrant DNAm patterns were symmetric on both alleles, indicating that there is epigenetic crosstalk between homologous chromosomes. We trained shallow-learning and deep-learning algorithms to identify anomalous DNAm patterns. The method was then tested on follow-up samples with and without MRD. Notably, even samples that were classified as MRD negative often revealed higher anomaly ratios than healthy controls, which may reflect clonal hematopoiesis. Our results demonstrate that targeted DNAm analysis facilitates reliable discrimination of malignant and healthy samples. However, since healthy samples also comprise few abnormal-classified DNAm reads the approach does not yet reliably discriminate MRD positive and negative samples.

Allele-specific DNA methylation maps in monozygotic twins discordant for psychiatric disorders reveal that disease-associated switching at the EIPR1 regulatory loci modulates neural function

The non-Mendelian features of phenotypic variations within monozygotic twins are likely complicated by environmental modifiers of genetic effects that have yet to be elucidated. Here, we performed methylome and genome analyses of blood DNA from psychiatric disorder-discordant monozygotic twins to study how allele-specific methylation (ASM) mediates phenotypic variations. We identified that thousands of genetic variants with ASM imbalances exhibit phenotypic variation-associated switching at regulatory loci. These ASMs have plausible causal associations with psychiatric disorders through effects on interactions between transcription factors, DNA methylations, and other epigenomic markers and then contribute to dysregulated gene expression, which eventually increases disease susceptibility. Moreover, we also experimentally validated the model that the rs4854158 alternative C allele at an ASM switching regulatory locus of EIPR1 encoding endosome-associated recycling protein-interacting protein 1, is associated with demethylation and higher RNA expression and shows lower TF binding affinities in unaffected controls. An epigenetic ASM switching induces C allele hypermethylation and then recruits repressive Polycomb repressive complex 2 (PRC2), reinforces trimethylation of lysine 27 on histone 3 and inhibits its transcriptional activity, thus leading to downregulation of EIPR1 in schizophrenia. Moreover, disruption of rs4854158 induces gain of EIPR1 function and promotes neural development and vesicle trafficking. Our study provides a powerful framework for identifying regulatory risk variants and contributes to our understanding of the interplay between genetic and epigenetic variants in mediating psychiatric disorder susceptibility.

ASMdb: a comprehensive database for allele-specific DNA methylation in diverse organisms

DNA methylation is known to be the most stable epigenetic modification and has been extensively studied in relation to cell differentiation, development, X chromosome inactivation and disease. Allele-specific DNA methylation (ASM) is a well-established mechanism for genomic imprinting and regulates imprinted gene expression. Previous studies have confirmed that certain special regions with ASM are susceptible and closely related to human carcinogenesis and plant development. In addition, recent studies have proven ASM to be an effective tumour marker. However, research on the functions of ASM in diseases and development is still extremely scarce. Here, we collected 4400 BS-Seq datasets and 1598 corresponding RNA-Seq datasets from 47 species, including human and mouse, to establish a comprehensive ASM database. We obtained the data on DNA methylation level, ASM and allele-specific expressed genes (ASEGs) and further analysed the ASM/ASEG distribution patterns of these species. In-depth ASM distribution analysis and differential methylation analysis conducted in nine cancer types showed results consistent with the reported changes in ASM in key tumour genes and revealed several potential ASM tumour-related genes. Finally, integrating these results, we constructed the first well-resourced and comprehensive ASM database for 47 species (ASMdb, www.dna-asmdb.com).

Decoding DNA methylation in epigenetics of multiple myeloma

Dysregulation of DNA methylation in B cells has been observed during their neoplastic transformation and therefore closely associated with various B-cell malignancies including multiple myeloma (MM), a malignancy of terminally differentiated plasma cells. Emerging evidence has unveiled pronounced alterations in DNA methylation in MM, including both global and gene-specific changes that can affect genome stability and gene transcription. Moreover, dysregulated expression of DNA methylation-modifying enzymes has been related with myelomagenesis, disease progression, and poor prognosis. However, the functional roles of the epigenetic abnormalities involving DNA methylation in MM remain elusive. In this article, we review current understanding of the alterations in DNA methylome and DNA methylation modifiers in MM, particularly focusing on DNA methyltransferases (DNMTs) and tet methylcytosine dioxygenases (TETs). We also discuss how these DNA methylation modifiers may be regulated and function in MM cells, therefore providing a rationale for developing novel epigenetic therapies targeting DNA methylation in MM.

Exploring epigenetic biomarkers of universal specificities and commonalities among pan-cancer cohorts in The Cancer Genome Atlas

Aim: To explore the mechanism of cancer by employing a comprehensive analysis of DNA methylation patterns and variations among pan-cancer cohorts. Materials & methods: This research focused on the discovery of universally specific or common biomarkers by mathematical statistics and machine learning methods in The Cancer Genome Atlas. Results: We found 138 differently methylated CpGs (DMCs) with a common methylation trend and eight common differently methylated regions in different cancer cohorts. Additionally, we found 99 DMCs to distinguish 32 different cancer cohorts in random forest analysis because of the specificity mechanism, but each DMC still had high instability. Conclusion: Our results could facilitate the development of biomarkers that are universally specific and common features across pan-cancer cohorts.

Celiac disease susceptibility: The genome and beyond

Celiac Disease (CeD) is an immune-mediated complex disease that is triggered by the ingestion of gluten and develops in genetically susceptible individuals. It has been known for a long time that the Human Leucocyte Antigen (HLA) molecules DQ2 and DQ8 are necessary, although not sufficient, for the disease development, and therefore other susceptibility genes and (epi)genetic events must participate in CeD pathogenesis. The advances in Genomics during the last 15 years have made CeD one of the immune-related disorders with the best-characterized genetic component. In the present work, we will first review the main Genome-Wide Association Studies (GWAS) carried out in the disorder, and emphasize post-GWAS discoveries, including diverse integrative strategies, SNP prioritization approaches, and insights into the Microbiome through the host Genomics. Second, we will explore CeD-related Epigenetics and Epigenomics, mostly focusing on the emerging knowledge of the celiac methylome, and the vast but yet under-explored non-coding RNA (ncRNA) landscape. We conclude that much has been done in the field although there are still completely unvisited areas in the post-Genomics of CeD. Chromatin conformation and accessibility, and Epitranscriptomics are promising domains that need to be unveiled to complete the big picture of the celiac Genome.

Allele-specific DNA methylation is increased in cancers and its dense mapping in normal plus neoplastic cells increases the yield of disease-associated regulatory SNPs

BACKGROUND

Mapping of allele-specific DNA methylation (ASM) can be a post-GWAS strategy for localizing regulatory sequence polymorphisms (rSNPs). The advantages of this approach, and the mechanisms underlying ASM in normal and neoplastic cells, remain to be clarified.

RESULTS

We perform whole genome methyl-seq on diverse normal cells and tissues and three cancer types. After excluding imprinting, the data pinpoint 15,112 high-confidence ASM differentially methylated regions, of which 1838 contain SNPs in strong linkage disequilibrium or coinciding with GWAS peaks. ASM frequencies are increased in cancers versus matched normal tissues, due to widespread allele-specific hypomethylation and focal allele-specific hypermethylation in poised chromatin. Cancer cells show increased allele switching at ASM loci, but disruptive SNPs in specific classes of CTCF and transcription factor binding motifs are similarly correlated with ASM in cancer and non-cancer. Rare somatic mutations affecting these same motif classes track with de novo ASM. Allele-specific transcription factor binding from ChIP-seq is enriched among ASM loci, but most ASM differentially methylated regions lack such annotations, and some are found in otherwise uninformative "chromatin deserts."

CONCLUSIONS

ASM is increased in cancers but occurs by a shared mechanism involving disruptive SNPs in CTCF and transcription factor binding sites in both normal and neoplastic cells. Dense ASM mapping in normal plus cancer samples reveals candidate rSNPs that are difficult to find by other approaches. Together with GWAS data, these rSNPs can nominate specific transcriptional pathways in susceptibility to autoimmune, cardiometabolic, neuropsychiatric, and neoplastic diseases.