Preprint Identification of genetic and non-genetic modifiers of genomic imprinting through whole genome screening in humans 2025 Riccio et al

Andy

Retired committee member
Abstract

Genomic imprinting is required for normal development, and abnormal methylation of differentially methylated regions (iDMRs) controlling the parent of origin-dependent expression of the imprinted genes has been found in constitutional disorders affecting growth, metabolism, neurobehavior, and in cancer. In most of these cases the cause of the imprinting abnormalities is unknown. Also, these studies have generally been performed on a limited number of CpGs, and a systematic investigation of iDMR methylation in the general population is lacking.

By analysing the iDMRs in a large number of peripheral blood DNA methylation array datasets of unaffected individuals and patients with rare disorders, we determined the most common iDMR methylation profiles and identified many genetic and non-genetic factors contributing to their variability.

We found that methylation variability is not homogeneous within the iDMRs and that the CpGs closer to the ZFP57 binding sites are less susceptible to methylation changes. We demonstrated the methylation polymorphism of three iDMRs and the atypical behaviour of several others, and reported the association of 25 disease- and 47 non-disease-complex traits, including blood cell type composition, as well as 15 mendelian or chromosomal disorders, with iDMR methylation changes in blood DNA.

These findings identify several genetic and non-genetic factors associated with genomic imprinting maintenance in humans, which may have a role in the aetiology of the diseases associated with imprinting abnormalities and have clear implications in molecular diagnostics.

Preprint
 
"Furthermore, we observed a negative correlation between myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and 20 NNAT:TSS CpGs (Fig. 2f)"

"Similarly, BCTC changes may underly at least in part the NNAT:TSS CpG hypomethylation detected in individuals affected by ME/CFS."

"Fig. 2 Association of complex traits with iDMR methylation. a) Associated traits with iDMR CpGs: Left: Venn diagram displaying the CpGs associated with Disease and non-Disease traits. Right: Barplot showing the number of unique disease (red) and non-disease (cyan) traits associated with iDMR methylation. b) Barplot showing the number of affected CpGs for each iDMR associated with disease (red) or non-disease (cyan) traits. c) Barplot representing the percentage of CpGs of each iDMR associated with non-disease traits. d) Barplot displaying the number of CpGs associated with specific non-disease traits. Negative correlations are in purple, positive correlations in forestgreen. e) Barplot representing the percentage of CpGs of each iDMR associated with Disease traits. f) Barplot displaying the number of CpGs associated with specific disease traits displayed as in d. g) Stacked Barplot showing the percentage of disease (red) and non-disease (cyan) trait associations with LOWvar, Mvar and SDvar CpGs."


Image is poor quality in original.
 
We compared levels of methylation of affected individuals with DDs and unaffected controls by employing our Episign Knowledge Database (EKD) DNA methylation data derived from peripheral blood (https://episign.com/). The EKD consists of cohorts for specific conditions as well as unaffected and healthy controls with varying age and gender, and type of array (Illumina 450k and EPIC).
Does this mean that they already had samples of ME/CFS patients? Do we know how they collected them and how diagnosis was confirmed?
 
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