Please use this identifier to cite or link to this item: https://doi.org/10.1534/genetics.116.195008
Title: A bayesian approach for analysis of whole-genome bisulfite sequencing data identifies disease-associated changes in DNA methylation
Authors: Rackham, O.J.L 
Langley, S.R 
Oates, T
Vradi, E
Harmston, N 
Srivastava, P.K
Behmoaras, J
Dellaportas, P
Bottolo, L
Petretto, E 
Keywords: DNA
transcription factor JunD
IFITM3 protein, rat
membrane protein
animal cell
animal experiment
Article
Bayesian learning
controlled study
CpG island
DNA methylation
gene
gene expression
gene function
gene identification
gene repression
glomerulonephritis
lfitm3 gene
macrophage
nonhuman
pathogenesis
priority journal
promoter region
protein binding
rat
RNA sequence
whole genome bisulfite sequencing
whole genome sequencing
animal
Bayes theorem
DNA sequence
genetics
genome
glomerulonephritis
high throughput sequencing
Lewis rat
procedures
sensitivity and specificity
Wistar Kyoto rat
Animals
Bayes Theorem
DNA Methylation
Genome
Glomerulonephritis
High-Throughput Nucleotide Sequencing
Membrane Proteins
Promoter Regions, Genetic
Rats
Rats, Inbred Lew
Rats, Inbred WKY
Sensitivity and Specificity
Sequence Analysis, DNA
Issue Date: 2017
Publisher: Genetics
Citation: Rackham, O.J.L, Langley, S.R, Oates, T, Vradi, E, Harmston, N, Srivastava, P.K, Behmoaras, J, Dellaportas, P, Bottolo, L, Petretto, E (2017). A bayesian approach for analysis of whole-genome bisulfite sequencing data identifies disease-associated changes in DNA methylation. Genetics 205 (4) : 1443-1458. ScholarBank@NUS Repository. https://doi.org/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. © 2017 Rackham et al.
Source Title: Genetics
URI: https://scholarbank.nus.edu.sg/handle/10635/175229
ISSN: 0016-6731
DOI: 10.1534/genetics.116.195008
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