Dataset Information

Impact of SNPs on methylation readouts by Illumina Infinium HumanMethylation450 BeadChip Array: implications for comparative population studies.

ABSTRACT: Infinium HumanMethylation 450 BeadChip Arrays by Illumina (Illumina HM450K) are among the most popular CpG microarray platforms widely used in biological and medical research. Several recent studies highlighted the potentially confounding impact of the genomic variation on the results of methylation studies performed using Illumina's Infinium methylation probes. However, the complexity of SNPs impact on the methylation level measurements (? values) has not been comprehensively described.In our comparative study of European and Asian populations performed using Illumina HM450K, we found that the majority of Infinium probes, which differentiated two examined groups, had SNPs in their target sequence. Characteristic tri-modal or bi-modal patterns of ? values distribution among individual samples were observed for CpGs with SNPs in the first and second position, respectively. To better understand how SNPs affect methylation readouts, we investigated their impact in the context of SNP position and type, and of the Illumina probe type (Infinium I or II).Our study clearly demonstrates that SNP variation existing in the genome, if not accounted for, may lead to false interpretation of the methylation signal differences suggested by some of the Illumina Infinium probes. In addition, it provides important practical clues for discriminating between differences due to the methylation status and to the genomic polymorphisms, based on the inspection of methylation readouts in individual samples. This approach is of special importance when Illumina Infinium assay is used for any comparative population studies, whether related to cancer, disease, ethnicity where SNP frequencies differentiate the studied groups.

SUBMITTER: Daca-Roszak P

PROVIDER: S-EPMC4659175 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Impact of SNPs on methylation readouts by Illumina Infinium HumanMethylation450 BeadChip Array: implications for comparative population studies.

Daca-Roszak Patrycja P Pfeifer Aleksandra A Żebracka-Gala Jadwiga J Rusinek Dagmara D Szybińska Aleksandra A Jarząb Barbara B Witt Michał M Ziętkiewicz Ewa E

BMC genomics 20151125

<h4>Background</h4>Infinium HumanMethylation 450 BeadChip Arrays by Illumina (Illumina HM450K) are among the most popular CpG microarray platforms widely used in biological and medical research. Several recent studies highlighted the potentially confounding impact of the genomic variation on the results of methylation studies performed using Illumina's Infinium methylation probes. However, the complexity of SNPs impact on the methylation level measurements (β values) has not been comprehensively ...[more]

PMID: 26607064

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Project description:BACKGROUND: Measurement of genome-wide DNA methylation (DNAm) has become an important avenue for investigating potential physiologically-relevant epigenetic changes. Illumina Infinium (Illumina, San Diego, CA, USA) is a commercially available microarray suite used to measure DNAm at many sites throughout the genome. However, it has been suggested that a subset of array probes may give misleading results due to issues related to probe design. To facilitate biologically significant data interpretation, we set out to enhance probe annotation of the newest Infinium array, the HumanMethylation450 BeadChip (450 k), with >485,000 probes covering 99% of Reference Sequence (RefSeq) genes (National Center for Biotechnology Information (NCBI), Bethesda, MD, USA). Annotation that was added or expanded on includes: 1) documented SNPs in the probe target, 2) probe binding specificity, 3) CpG classification of target sites and 4) gene feature classification of target sites. RESULTS: Probes with documented SNPs at the target CpG (4.3% of probes) were associated with increased within-tissue variation in DNAm. An example of a probe with a SNP at the target CpG demonstrated how sample genotype can confound the measurement of DNAm. Additionally, 8.6% of probes mapped to multiple locations in silico. Measurements from these non-specific probes likely represent a combination of DNAm from multiple genomic sites. The expanded biological annotation demonstrated that based on DNAm, grouping probes by an alternative high-density and intermediate-density CpG island classification provided a distinctive pattern of DNAm. Finally, variable enrichment for differentially methylated probes was noted across CpG classes and gene feature groups, dependant on the tissues that were compared. CONCLUSION: DNAm arrays offer a high-throughput approach for which careful consideration of probe content should be utilized to better understand the biological processes affected. Probes containing SNPs and non-specific probes may affect the assessment of DNAm using the 450 k array. Additionally, probe classification by CpG enrichment classes and to a lesser extent gene feature groups resulted in distinct patterns of DNAm. Thus, we recommend that compromised probes be removed from analyses and that the genomic context of DNAm is considered in studies deciphering the biological meaning of Illumina 450 k array data.

Project description:BackgroundCurrent array-based methods for the measurement of DNA methylation rely on the process of sodium bisulfite conversion to differentiate between methylated and unmethylated cytosine bases in DNA. In the absence of genotype data this process can lead to ambiguity in data interpretation when a sample has polymorphisms at a methylation probe site. A common way to minimize this problem is to exclude such potentially problematic sites, with some methods removing as much as 60% of array probes from consideration before data analysis.ResultsHere, we present an algorithm implemented in an R Bioconductor package, MethylToSNP, which detects a characteristic data pattern to infer sites likely to be confounded by polymorphisms. Additionally, the tool provides a stringent reliability score to allow thresholding on SNP predictions. We calibrated parameters and thresholds used by the algorithm on simulated and real methylation data sets. We illustrate findings using methylation data from YRI (Yoruba in Ibadan, Nigeria), CEPH (European descent) and KhoeSan (southern African) populations. Our polymorphism predictions made using MethylToSNP have been validated through SNP databases and bisulfite and genomic sequencing.ConclusionsThe benefits of this method are threefold. First, it prevents extensive data loss by considering only SNPs specific to the individuals in the study. Second, it offers the possibility to identify new polymorphisms in samples for which there is little known about the genetic landscape. Third, it identifies variants as they exist in functional regions of a genome, such as in CTCF (transcriptional repressor) sites and enhancers, that may be common alleles or personal mutations with potential to deleteriously affect genomic regulatory activities. We demonstrate that MethylToSNP is applicable to the Illumina 450K and Illumina 850K EPIC array data and is also backwards compatible to the 27K methylation arrays. Going forward, this kind of nuanced approach can increase the amount of information derived from precious data sets by considering samples of the project individually to enable more informed decisions about data cleaning.

Dataset Information

Impact of SNPs on methylation readouts by Illumina Infinium HumanMethylation450 BeadChip Array: implications for comparative population studies.

Publications

Impact of SNPs on methylation readouts by Illumina Infinium HumanMethylation450 BeadChip Array: implications for comparative population studies.

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