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Dissecting the epigenomic footprint Genome-wide epigenetic marks regulate gene expression, but the amount and function of variability in these marks are poorly understood. Working with human-derived samples, Onuchic et al. examined disease-associated genetic variation and sequence-dependent allele-specific methylation at gene regulatory loci. Regulatory sequences within individual chromosomal DNA molecules showed full or no methylation at specific sites corresponding to “on” and “off” switches. Interestingly, methylation did not occur on each DNA molecule, resulting in a variable fraction of methylated chromosomes. This stochastic type of gene regulation was more common for rare genetic variants, which may suggest a role in human disease. Science, this issue p. eaar3146 Genome-wide analyses of epigenetic markers in human cells identify allele-specific functions that affect gene expression in health and disease. INTRODUCTION A majority of imbalances in DNA methylation between homologous chromosomes in humans are sequence-dependent; the DNA sequence differences between the two chromosomes cause differences in the methylation state of neighboring cytosines on the same chromosome. The analyses of this sequence-dependent allele-specific methylation (SD-ASM) traditionally involved measurement of average methylation levels across many cells. Detailed understanding of SD-ASM at the single-cell and single-chromosome levels is lacking. This gap in understanding may hide the connection between SD-ASM, ubiquitous stochastic cell-to-cell and chromosome-to-chromosome variation in DNA methylation, and the puzzling and evolutionarily conserved patterns of intermediate methylation at gene regulatory loci. RATIONALE Whole-genome bisulfite sequencing (WGBS) provides the ultimate single-chromosome level of resolution and comprehensive whole-genome coverage required to explore SD-ASM. However, the exploration of the link between SD-ASM, stochastic variation in DNA methylation, and gene regulation requires deep coverage by WGBS across tissues and individuals and the context of other epigenomic marks and gene transcription. RESULTS We constructed maps of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep (1691-fold) combined WGBS read coverage across 49 methylomes revealed CpG methylation imbalances exceeding 30% differences at 5% of the loci, which is more conservative than previous estimates in the 8 to 10% range; a similar value (8%) is observed in our dataset when we lowered our threshold for detecting allelic imbalance to 20% methylation difference between the two alleles. Extensive sequence-dependent CpG methylation imbalances were observed at thousands of heterozygous regulatory loci. Stochastic switching, defined as random transitions between fully methylated and unmethylated states of DNA, occurred at thousands of regulatory loci bound by transcription factors (TFs). Our results explain the conservation of intermediate methylation states at regulatory loci by showing that the intermediate methylation reflects the relative frequencies of fully methylated and fully unmethylated epialleles. SD-ASM is explainable by different relative frequencies of methylated and unmethylated epialleles for the two alleles. The differences in epiallele frequency spectra of the alleles at thousands of TF-bound regulatory loci correlated with the differences in alleles’ affinities for TF binding, which suggests a mechanistic explanation for SD-ASM. We observed an excess of rare variants among those showing SD-ASM, which suggests that an average human genome harbors at least ~200 detrimental rare variants that also show SD-ASM. The methylome’s sensitivity to genetic variation is unevenly distributed across the genome, which is consistent with buffering of housekeeping genes against the effects of random mutations. By contrast, less essential genes with tissue-specific expression patterns show sensitivity, thus providing opportunity for evolutionary innovation through changes in gene regulation. CONCLUSION Analysis of allelic epigenome maps provides a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, selective buffering of the regulatory circuitry against the effects of random mutations, and disease-associated genetic variation. SD-ASM is explainable by different frequencies of epialleles. Genetic variants affect the methylation state of neighboring cytosines on the same chromosome. Every WGBS read provides a readout of both a genetic variant (G or T) at a heterozygous locus and the methylation state of neighboring cytosines (“epiallele”). Epiallele frequencies correlate with the affinity of TF binding at regulatory sites. To assess the impact of genetic variation in regulatory loci on human health, we constructed a high-resolution map of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep whole-genome bisulfite sequencing of 49 methylomes revealed sequence-dependent CpG methylation imbalances at thousands of heterozygous regulatory loci. Such loci are enriched for stochastic switching, which is defined as random transitions between fully methylated and unmethylated states of DNA. The methylation imbalances at thousands of loci are explainable by different relative frequencies of the methylated and unmethylated states for the two alleles. Further analyses provided a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, and disease-associated genetic variation.