Two approaches reveal a new paradigm of 'switchable or genetics-influenced allele-specific DNA methylation' with potential in human disease.

Imprinted genes are vulnerable to environmental influences during early embryonic development, thereby contributing to the onset of disease in adulthood. Monoallelic methylation at several germline imprints has been reported as DNMT1-dependent. However, which of these two epigenetic attributes, DNMT1-dependence or allelic methylation, renders imprinted genes susceptible to environmental stressors has not been determined. Herein, we developed a new approach, referred to as NORED, to identify 2468 DNMT1-dependent DNA methylation patterns in the mouse genome. We further developed an algorithm based on a genetic variation-independent approach (referred to as MethylMosaic) to detect 2487 regions with bimodal methylation patterns. Two approaches identified 207 regions, including known imprinted germline allele-specific methylation patterns (ASMs), that were both NORED and MethylMosaic regions. Examination of methylation in four independent mouse embryonic stem cell lines shows that two regions identified by both NORED and MethylMosaic ( and ) did not display parent-of-origin-dependent allelic methylation. In these four F1 hybrid cell lines, genetic variation in Cast allele at locus introduces a transcription factor binding site for MTF-1 that may predispose Cast allelic hypomethylation in a reciprocal cross with either C57 or 129 strains. In contrast, each allele of ASM in J1 inbred cell line and ASM in four F1 hybrid cell lines seems to exhibit similar propensity to be either hypo- or hypermethylated, suggesting a 'random, switchable' ASM. Together with published results, our data on ASMs prompted us to propose a hypothesis of regional 'autosomal chromosome inactivation (ACI)' that may control a subset of autosomal genes. Therefore, our results open a new avenue to understand monoallelic methylation and provide a rich resource of candidate genes to examine in environmental and nutritional exposure models.