Liang Niu1, Zongli Xu2, Jack A Taylor3. 1. Division of Biostatistics and Bioinformatics, Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH, USA. 2. Epidemiology Branch. 3. Epidemiology Branch Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA.
Abstract
MOTIVATION: The Illumina HumanMethylation450 BeadChip has been extensively utilized in epigenome-wide association studies. This array and its successor, the MethylationEPIC array, use two types of probes-Infinium I (type I) and Infinium II (type II)-in order to increase genome coverage but differences in probe chemistries result in different type I and II distributions of methylation values. Ignoring the difference in distributions between the two probe types may bias downstream analysis. RESULTS: Here, we developed a novel method, called Regression on Correlated Probes (RCP), which uses the existing correlation between pairs of nearby type I and II probes to adjust the beta values of all type II probes. We evaluate the effect of this adjustment on reducing probe design type bias, reducing technical variation in duplicate samples, improving accuracy of measurements against known standards, and retention of biological signal. We find that RCP is statistically significantly better than unadjusted data or adjustment with alternative methods including SWAN and BMIQ. AVAILABILITY: We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website (https://www.bioconductor.org/packages/release/bioc/html/ENmix.html). CONTACT: niulg@ucmail.uc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.
MOTIVATION: The Illumina HumanMethylation450 BeadChip has been extensively utilized in epigenome-wide association studies. This array and its successor, the MethylationEPIC array, use two types of probes-Infinium I (type I) and Infinium II (type II)-in order to increase genome coverage but differences in probe chemistries result in different type I and II distributions of methylation values. Ignoring the difference in distributions between the two probe types may bias downstream analysis. RESULTS: Here, we developed a novel method, called Regression on Correlated Probes (RCP), which uses the existing correlation between pairs of nearby type I and II probes to adjust the beta values of all type II probes. We evaluate the effect of this adjustment on reducing probe design type bias, reducing technical variation in duplicate samples, improving accuracy of measurements against known standards, and retention of biological signal. We find that RCP is statistically significantly better than unadjusted data or adjustment with alternative methods including SWAN and BMIQ. AVAILABILITY: We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website (https://www.bioconductor.org/packages/release/bioc/html/ENmix.html). CONTACT: niulg@ucmail.uc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.
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