Dataset Information

Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data.

ABSTRACT:

Background

It is well known that DNA methylation, as an epigenetic factor, has an important effect on gene expression and disease development. Detecting differentially methylated loci under different conditions, such as cancer types or treatments, is of great interest in current research as it is important in cancer diagnosis and classification. However, inappropriate testing approaches can result in large false positives and/or false negatives. Appropriate and powerful statistical methods are desirable but very limited in the literature.

Results

In this paper, we propose a nonparametric method to detect differentially methylated loci under multiple conditions for Illumina Array Methylation data. We compare the new method with other methods using simulated and real data. Our study shows that the proposed one outperforms other methods considered in this paper.

Conclusions

Due to the unique feature of the Illumina Array Methylation data, commonly used statistical tests will lose power or give misleading results. Therefore, appropriate statistical methods are crucial for this type of data. Powerful statistical approaches remain to be developed.

Availability

R codes are available upon request.

SUBMITTER: Chen Z

PROVIDER: S-EPMC3552689 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data.

Chen Zhongxue Z Huang Hanwen H Liu Jianzhong J Tony Ng Hon Keung HK Nadarajah Saralees S Huang Xudong X Deng Youping Y

BMC medical genomics 20130123

<h4>Background</h4>It is well known that DNA methylation, as an epigenetic factor, has an important effect on gene expression and disease development. Detecting differentially methylated loci under different conditions, such as cancer types or treatments, is of great interest in current research as it is important in cancer diagnosis and classification. However, inappropriate testing approaches can result in large false positives and/or false negatives. Appropriate and powerful statistical metho ...[more]

PMID: 23369576

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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.

Project description:BackgroundAs the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterized. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods for DNA methylation analysis but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets.ResultsThe standard index of DNA methylation at any specific CpG site is ? = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities, respectively. Betas (?s) calculated from raw signal intensities (the default GenomeStudio behavior) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive.ConclusionsCareful selection of preprocessing steps can minimize variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created a user-friendly R software package called wateRmelon, downloadable from bioConductor, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilize the same normalization methods and data quality tests on 450K data.

Dataset Information

Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data.

Background

Results

Conclusions

Availability

Publications

Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data.

Similar Datasets

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