SNP-guided identification of monoallelic DNA-methylation events from enrichment-based sequencing data

Abstract

Monoallelic gene expression is typically initiated early in the development of an organism. Dysregulation of monoallelic gene expression has already been linked to several non-Mendelian inherited genetic disorders. In humans, DNA-methylation is deemed to be an important regulator of monoallelic gene expression, but only few examples are known. One important reason is that current, cost-affordable truly genome-wide methods to assess DNA-methylation are based on sequencing post-enrichment. Here, we present a new methodology based on classical population genetic theory, i.e. the Hardy–Weinberg theorem, that combines methylomic data from MethylCap-seq with associated SNP profiles to identify monoallelically methylated loci. Applied on 334 MethylCap-seq samples of very diverse origin, this resulted in the identification of 80 genomic regions featured by monoallelic DNA-methylation. Of these 80 loci, 49 are located in genic regions of which 25 have already been linked to imprinting. Further analysis revealed statistically significant enrichment of these loci in promoter regions, further establishing the relevance and usefulness of the method. Additional validation was done using both 14 whole-genome bisulfite sequencing data sets and 16 mRNA-seq data sets. Importantly, the developed approach can be easily applied to other enrichment-based sequencing technologies, like the ChIP-seq-based identification of monoallelic histone modifications.

Trisomy 21 Alters DNA Methylation in Parent-of-Origin-Dependent and -Independent Manners

“…We replicated this observation in twelve biosamples (brain, blood, fallopian tube, fetal large and small intestine, hESCs, large airway epithelial cells, thyroid, muscle, epidermal keratinocytes, ovary, skin, and testis). These results are in agreement with recently reported findings from three genome-wide scale analyses of public RNA-Seq data [ 26 , 59 , 68 ]. These studies reported mean allele fractions showing either non-significant deviation (mean value = 0.504) [ 26 , 59 ] or significant deviation (average value ranging from 0.553 to 0.803) [ 26 , 68 ] for the WRB 3´-UTR rs1060180 and rs13230 SNPs.…”

“…These results are in agreement with recently reported findings from three genome-wide scale analyses of public RNA-Seq data [ 26 , 59 , 68 ]. These studies reported mean allele fractions showing either non-significant deviation (mean value = 0.504) [ 26 , 59 ] or significant deviation (average value ranging from 0.553 to 0.803) [ 26 , 68 ] for the WRB 3´-UTR rs1060180 and rs13230 SNPs. Furthermore, we provide evidence from 15 tissue repositories of allele rates that are consistent with the biallelic transcript expression of 10 genes that map to within 2-Mb around the WRB gene.…”

“…Steyaert and collaborators [ 26 ] used a SNP-guided analytical framework to identify monoallelic DNA methylation events from enrichment-based sequencing data. In the WRB gene, they functionally annotated the locus comprising the SNP rs2299739 with significant monoallelic DNA methylation.…”

“…We performed single-nucleotide polymorphism primer extension (SNuPE) at informative 3´-UTR SNP variants in DNA from blood cells, and in human embryonic stem cell lines (hESCs). Furthermore, we conducted an integrative and comparative epigenomic computational analysis using transcriptome RNA sequencing (RNA-Seq) public repositories, essentially adopting the frameworks recently described to explore enrichment-based sequencing data [ 26 , 27 ].…”

A comprehensive overview of genomic imprinting in breast and its deregulation in cancer

“…Contrasting previous genome-wide methods, no DNA genotyping data is required, as we solely rely on genotyping of RNA-seq data. The basic rationale is that in case of 100% imprinting, no heterozygous samples can be found in RNA-seq data, as they perfectly resemble homozygous samples (only a single allele is expressed) (see Methods section and Steyaert et al 30 ). The novel imprinting model describes the data for each SNP as a mixture of homozygous and heterozygous samples, more specifically as a mixture of genotype-dependent binomial distributions, with weights derived from Hardy–Weinberg equilibrium.…”