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Detection and accurate False Discovery Rate control of differentially methylated regions from Whole Genome Bisulfite Sequencing

View ORCID ProfileKeegan D. Korthauer, Sutirtha Chakraborty, Yuval Benjamini, Rafael A. Irizarry
doi: https://doi.org/10.1101/183210
Keegan D. Korthauer
1Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute
2Department of Biostatistics, Harvard T.H. Chan School of Public Health
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  • ORCID record for Keegan D. Korthauer
Sutirtha Chakraborty
3Novartis
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Yuval Benjamini
4Department of Statistics, Hebrew University
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Rafael A. Irizarry
1Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute
2Department of Biostatistics, Harvard T.H. Chan School of Public Health
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Summary

With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the entire genome. In most applications, biologists are interested in detecting differentially methylated regions, composed of multiple sites with differing methylation levels among populations. However, current computational approaches for detecting such regions do not provide accurate statistical inference. A major challenge in reporting uncertainty is that a genome-wide scan is involved in detecting these regions, which needs to be accounted for. A further challenge is that sample sizes are limited due to the costs associated with the technology. We have developed a new approach that overcomes these challenges and assesses uncertainty for differentially methylated regions in a rigorous manner. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. We develop an inferential approach, based on a pooled null distribution, that can be implemented even when as few as two samples per population are available. Here we demonstrate the advantages of our method using both experimental data and Monte Carlo simulation. We find that the new method improves the specificity and sensitivity of list of regions and accurately controls the False Discovery Rate (FDR).

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 31, 2017.
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Detection and accurate False Discovery Rate control of differentially methylated regions from Whole Genome Bisulfite Sequencing
Keegan D. Korthauer, Sutirtha Chakraborty, Yuval Benjamini, Rafael A. Irizarry
bioRxiv 183210; doi: https://doi.org/10.1101/183210
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Detection and accurate False Discovery Rate control of differentially methylated regions from Whole Genome Bisulfite Sequencing
Keegan D. Korthauer, Sutirtha Chakraborty, Yuval Benjamini, Rafael A. Irizarry
bioRxiv 183210; doi: https://doi.org/10.1101/183210

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