Variant-associated methylation (VAM)
Combining genetic sequence with DNA methylation data at single-base resolution on the same DNA molecule provides a powerful approach to explore the relationship between genetic variants and changes in DNA methylation.
What is variant-associated methylation?
The term variant-associated methylation (VAM) can be used to describe the direct association of genetic variation with changes in DNA methylation – either in close proximity or across large genomic distances. VAM directly links genetic sequence information with methylation data and provides powerful insight into the dynamic interactions that are occurring.
The interface between genetics and epigenetics
- DNA sequence and methylation data within the same DNA read in a single workflow
- Directly correlate to see how genetic variation influences methylation patterns
- Identify and map ASM, mQTL, and other variants including SNPs, indels, and CNVs
What do you need to identify and map VAMs?
Identifying VAM requires both genetic and DNA methylation information within a single DNA sample. The duet multiomic solution +modC allows you to identify and map individual VAMs with high accuracy and efficiency due to simultaneous sequencing of both genetic and modified cytosine bases (modC) on the same DNA fragment in a single workflow – and without the need for integration of two separate datasets.
The key advantage of combining these two analyses on the same DNA molecule to identify VAMs is direct correlation. Researchers can simultaneously observe genetic variation and methylation status at a particular genomic location and directly link specific genetic variants to changes in methylation at adjacent sites.
What are the different types of VAM?
Allele-specific methylation (ASM)
ASM occurs when the methylation level of DNA differs between the two alleles of a gene. So, if one allele is methylated whilst the other is not, or the two alleles are methylated to different extents, they are displaying ASM.
Methylation quantitative trait loci (mQTL or meQTL)
Is a region of DNA that correlates with variation in methylation – but unlike ASM, the methylation changes can be anywhere in the genome. mQTLs help researchers understand how genetic variation influences epigenetic states, and how these are linked to various traits and diseases. Identifying mQTLs can provide insights into the biological mechanisms underlying complex traits and diseases and reveal potential targets for therapeutic intervention.
Other genetic variants
DNA methylation can also be associated with other types of genetic variation and so be classified as VAM.
- Single nucleotide polymorphism (SNP)
- Insertions and deletions (Indels)
- Copy number variation (CNVs)
Why are VAMs important?
By mapping both genetic variants and methylation status on the same DNA molecule, researchers can more effectively study how genetic differences influence methylation patterns and how this affects gene expression. This is particularly important in understanding complex diseases where both genetic variation and epigenetic modifications play a role.
Cancer
Aberrant methylation patterns are a common feature of most cancers and have been shown to contribute to the initiation and progression of cancer. This makes identification of VAMs both valuable biomarkers for cancer detection and potential targets for therapeutic intervention.
Congenital and developmental disorders (imprinting)
Methylation plays a crucial role in maintaining the parent-of-origin specific expression patterns of imprinted genes, which are vital for normal development and have been implicated in various diseases when dysregulated.
Psychiatric disorders
Epigenetic modifications are significantly linked to predisposition and progression of disorders such as depression, schizophrenia, bipolar disorder, and many other neurological diseases. For example, VAMs have been indicated in predicting the development of childhood psychiatric disorders in response to trauma.
Regulation of gene expression
Genome-wide association studies are identifying methylation changes to non-coding regulatory regions, as well as linking mQTLs to phenotypic variation including BMI, arthritis, blood pressure, and white blood cell counts, and identifying multiple nuclear regulator pathways.
Further your understanding of the genome with VAMs
Identifying VAMs by integrating genetic sequencing with DNA methylation data at single-base resolution on the same DNA molecule has broad applications in various fields of healthcare and biological research, with the potential for breakthroughs in disease treatment, prevention, and understanding.
- Personalised medicine
- Diagnosis and patient stratification
- Drug development
- Understanding disease pathogenesis
- Population health studies
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