A Bayesian approach for analysis of whole-genome bisulfite sequencing data identifies disease-associated changes in DNA methylation
A Bayesian approach for analysis of whole-genome bisulfite sequencing data identifies disease-associated changes in DNA methylation
DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility remains to be fully understood. Here, we present a novel Bayesian smoothing approach (called ABBA) to detect differentially methylated regions (DMRs) from whole-genome bisulfite sequencing (WGBS). We also show how this approach can be leveraged to identify disease-associated changes in DNA methylation, suggesting mechanisms through which these alterations might affect disease. From a data modeling perspective, ABBA has the distinctive feature of automatically adapting to different correlation structures in CpG methylation levels across the genome while taking into account the distance between CpG sites as a covariate. Our simulation study shows that ABBA has greater power to detect DMRs than existing methods, providing an accurate identification of DMRs in the large majority of simulated cases. To empirically demonstrate the method's efficacy in generating biological hypotheses, we performed WGBS of primary macrophages derived from an experimental rat system of glomerulonephritis and used ABBA to identify >1000 disease-associated DMRs. Investigation of these DMRs revealed differential DNA methylation localized to a 600 bp region in the promoter of the Ifitm3 gene. This was confirmed by ChIP-seq and RNA-seq analyses, showing differential transcription factor binding at the Ifitm3 promoter by JunD (an established determinant of glomerulonephritis), and a consistent change in Ifitm3 expression. Our ABBA analysis allowed us to propose a new role for Ifitm3 in the pathogenesis of glomerulonephritis via a mechanism involving promoter hypermethylation that is associated with Ifitm3 repression in the rat strain susceptible to glomerulonephritis.
Animals, Bayes Theorem, DNA Methylation, Genome, Glomerulonephritis/genetics, High-Throughput Nucleotide Sequencing/methods, Membrane Proteins/genetics, Promoter Regions, Genetic, Rats, Rats, Inbred Lew, Rats, Inbred WKY, Sensitivity and Specificity, Sequence Analysis, DNA/methods
1443-1458
Rackham, Owen J L
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Langley, Sarah R
897e76ce-ff32-43dc-abe6-6b347bad7fd1
Oates, Thomas
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Vradi, Eleni
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Harmston, Nathan
017e019d-7784-4ed4-a4d9-38880f08df3f
Srivastava, Prashant K
be70bb08-c734-40bb-8b98-32c00bf42c2c
Behmoaras, Jacques
9dccab84-39e7-472f-90a2-ebad7ab0aa5e
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd
Bottolo, Leonardo
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Petretto, Enrico
a8a7d254-ea06-4ab3-ba7e-b653349a29f4
April 2017
Rackham, Owen J L
8122eb1f-6e9f-4da5-90e1-ce108ccbbcbf
Langley, Sarah R
897e76ce-ff32-43dc-abe6-6b347bad7fd1
Oates, Thomas
dd2718d5-8787-4405-bf09-49485e851fce
Vradi, Eleni
226a2aa9-9424-42c7-ace5-5f39555e66e4
Harmston, Nathan
017e019d-7784-4ed4-a4d9-38880f08df3f
Srivastava, Prashant K
be70bb08-c734-40bb-8b98-32c00bf42c2c
Behmoaras, Jacques
9dccab84-39e7-472f-90a2-ebad7ab0aa5e
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd
Bottolo, Leonardo
12a7f8db-e00a-42fd-b8f8-01763add4fb9
Petretto, Enrico
a8a7d254-ea06-4ab3-ba7e-b653349a29f4
Rackham, Owen J L, Langley, Sarah R, Oates, Thomas, Vradi, Eleni, Harmston, Nathan, Srivastava, Prashant K, Behmoaras, Jacques, Dellaportas, Petros, Bottolo, Leonardo and Petretto, Enrico
(2017)
A Bayesian approach for analysis of whole-genome bisulfite sequencing data identifies disease-associated changes in DNA methylation.
Genetics, 205 (4), .
(doi:10.1534/genetics.116.195008).
Abstract
DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility remains to be fully understood. Here, we present a novel Bayesian smoothing approach (called ABBA) to detect differentially methylated regions (DMRs) from whole-genome bisulfite sequencing (WGBS). We also show how this approach can be leveraged to identify disease-associated changes in DNA methylation, suggesting mechanisms through which these alterations might affect disease. From a data modeling perspective, ABBA has the distinctive feature of automatically adapting to different correlation structures in CpG methylation levels across the genome while taking into account the distance between CpG sites as a covariate. Our simulation study shows that ABBA has greater power to detect DMRs than existing methods, providing an accurate identification of DMRs in the large majority of simulated cases. To empirically demonstrate the method's efficacy in generating biological hypotheses, we performed WGBS of primary macrophages derived from an experimental rat system of glomerulonephritis and used ABBA to identify >1000 disease-associated DMRs. Investigation of these DMRs revealed differential DNA methylation localized to a 600 bp region in the promoter of the Ifitm3 gene. This was confirmed by ChIP-seq and RNA-seq analyses, showing differential transcription factor binding at the Ifitm3 promoter by JunD (an established determinant of glomerulonephritis), and a consistent change in Ifitm3 expression. Our ABBA analysis allowed us to propose a new role for Ifitm3 in the pathogenesis of glomerulonephritis via a mechanism involving promoter hypermethylation that is associated with Ifitm3 repression in the rat strain susceptible to glomerulonephritis.
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More information
Accepted/In Press date: 3 February 2017
e-pub ahead of print date: 1 April 2017
Published date: April 2017
Additional Information:
Copyright © 2017 Rackham et al.
Keywords:
Animals, Bayes Theorem, DNA Methylation, Genome, Glomerulonephritis/genetics, High-Throughput Nucleotide Sequencing/methods, Membrane Proteins/genetics, Promoter Regions, Genetic, Rats, Rats, Inbred Lew, Rats, Inbred WKY, Sensitivity and Specificity, Sequence Analysis, DNA/methods
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Local EPrints ID: 447554
URI: http://eprints.soton.ac.uk/id/eprint/447554
ISSN: 1943-2631
PURE UUID: 6e06a0cf-a81f-4625-8d0c-a13234e547d8
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Date deposited: 16 Mar 2021 17:30
Last modified: 17 Mar 2024 04:03
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Author:
Sarah R Langley
Author:
Thomas Oates
Author:
Eleni Vradi
Author:
Nathan Harmston
Author:
Prashant K Srivastava
Author:
Jacques Behmoaras
Author:
Petros Dellaportas
Author:
Leonardo Bottolo
Author:
Enrico Petretto
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