Surreal illustration of a DNA strand with highlighted SNP markers

Decoding DNA Methylation: How SNPs Can Skew Your Health Data

Beau Callahan in Science & Nature June 2025 • 4 min read.

"Unraveling the complex relationship between genetic variations and methylation readouts in Illumina arrays for more accurate health insights."

In the realm of biological and medical research, understanding DNA methylation has become increasingly vital. Techniques like the Illumina Infinium HumanMethylation450 BeadChip Array (HM450K array) have surged in popularity, offering a genome-wide perspective on CpG methylation. However, the intricacies of interpreting data from these arrays are now under scrutiny, particularly concerning the influence of single nucleotide polymorphisms (SNPs).

SNPs, representing the most common type of genetic variation among people, can act as silent saboteurs, confounding methylation readouts. Recent studies have highlighted the potential for genomic variation to skew results obtained from Illumina's Infinium methylation probes. Understanding the nature of SNP interference is essential for ensuring the reliability of methylation studies, especially in varied populations.

Imagine trying to understand a city’s traffic patterns with faulty speedometers. Similarly, if left unaddressed, SNPs can lead to the flawed interpretation of methylation signals, suggesting differences where none truly exist. It is important to develop strategies to differentiate true methylation variations from SNP-driven artifacts.

The SNP Effect: A Closer Look

Surreal illustration of a DNA strand with highlighted SNP markers

The primary concern arises when methylation levels are compared across different populations. A study that examined European and Asian populations using the Illumina HM450K array brought this issue to light. Researchers found that a significant portion of Infinium probes differentiating the two groups had SNPs within their target sequences. This genetic variation resulted in peculiar patterns, such as tri-modal or bi-modal distributions of β values among individual samples.

These distinctive patterns emerged when SNPs were located in the first and second positions of the CpG sites, respectively. To fully grasp how SNPs influence methylation readouts, researchers investigated their impact relative to the SNP position and type and the Illumina probe type (Infinium I or II). The study revealed a concerning trend: SNP variation, if unaccounted for, could easily lead to misinterpretations of methylation signal differences suggested by certain Illumina Infinium probes.

SNPs can cause skewed methylation readouts.
Tri-modal or bi-modal distribution is a tell tale sign.
SNP location and probe type affect results.
Comparative population studies are most at risk.

Specifically, when analyzing the data, researchers identified that a high proportion of probes that seemed to show population differences in methylation were, in fact, influenced by common genetic variations (SNPs). These SNPs caused unusual patterns in the data, making it appear as though there were significant methylation differences between the groups when, in reality, these differences were attributable to genetic polymorphisms.

Practical Implications for Researchers

This study provides vital practical guidance for distinguishing between genuine methylation differences and those driven by genomic polymorphisms. The key lies in scrutinizing methylation readouts within individual samples. By carefully inspecting the data, researchers can discern patterns indicative of SNP influence, especially when using the Illumina Infinium assay in comparative population studies. Whether related to cancer, disease, or ethnicity, this approach is critical in ensuring the accuracy and reliability of results. Ultimately, being vigilant about SNP interference ensures that conclusions drawn from methylation studies are grounded in true biological phenomena, paving the way for more accurate insights into human health.

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This article is based on research published under:

DOI-LINK: 10.1186/s12864-015-2202-0, Alternate LINK

Title: Impact Of Snps On Methylation Readouts By Illumina Infinium Humanmethylation450 Beadchip Array: Implications For Comparative Population Studies

Subject: Genetics

Journal: BMC Genomics

Publisher: Springer Science and Business Media LLC

Authors: Patrycja Daca-Roszak, Aleksandra Pfeifer, Jadwiga Żebracka-Gala, Dagmara Rusinek, Aleksandra Szybińska, Barbara Jarząb, Michał Witt, Ewa Ziętkiewicz

Published: 2015-11-25

Everything You Need To Know

How do Single Nucleotide Polymorphisms (SNPs) confound methylation data obtained from Illumina arrays?

Single Nucleotide Polymorphisms, or SNPs, can interfere with methylation readings, leading to potentially inaccurate conclusions about how methylation differs across populations. This interference can manifest as unusual patterns in the data, such as tri-modal or bi-modal distributions of β values, particularly when using Illumina Infinium probes. Addressing SNP interference is crucial for the reliability of methylation studies, especially in diverse populations.

How do SNPs located within Illumina Infinium probes affect methylation readings when comparing different populations using HM450K arrays?

When SNPs are located within the target sequences of Illumina Infinium probes used in HM450K arrays, they can skew methylation readouts. This is particularly evident when comparing methylation levels across different populations. The location of the SNP relative to the CpG site and the type of Illumina probe (Infinium I or II) also influence the degree of interference, leading to potential misinterpretations of methylation signal differences.

What practical steps can researchers take to differentiate between genuine methylation differences and those driven by genomic polymorphisms when using the Illumina Infinium assay?

Researchers should carefully scrutinize methylation readouts within individual samples for patterns indicative of SNP influence, especially when using the Illumina Infinium assay in comparative population studies. By recognizing patterns like tri-modal or bi-modal distributions, researchers can differentiate between true methylation variations and SNP-driven artifacts. Vigilance about SNP interference ensures that conclusions drawn from methylation studies are grounded in true biological phenomena.

What is the relationship between Illumina Infinium HumanMethylation450 BeadChip Array (HM450K array) and single nucleotide polymorphisms (SNPs)?

The Illumina Infinium HumanMethylation450 BeadChip Array (HM450K array) is a tool to examine CpG methylation. However, single nucleotide polymorphisms (SNPs) can skew methylation readouts. When analyzing data look for tri-modal or bi-modal distribution which is a telltale sign. SNP location and probe type affect results, so comparative population studies are most at risk.

What are the implications of being vigilant about Single Nucleotide Polymorphisms (SNPs) for ensuring accuracy in methylation studies, and how does it impact our understanding of human health?

By being vigilant about SNP interference, researchers ensure that the conclusions drawn from methylation studies are based on actual biological phenomena, which results in more precise insights into human health. Failing to account for SNPs can lead to flawed interpretations of methylation signals and false conclusions about the role of methylation in various biological processes. The correct intrepretation improves our understanding in diseases, cancer, or ethnicity.