title: A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation.
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 theIfitm3gene. This was confirmed by ChIP-seq and RNA-seq analyses, showing differential transcription factor binding at theIfitm3promoter by JunD (an established determinant of glomerulonephritis), and a consistent change inIfitm3expression. Our ABBA analysis allowed us to propose a new role forIfitm3in the pathogenesis of glomerulonephritis via a mechanism involving promoter hypermethylation that is associated withIfitm3repression in the rat strain susceptible to glomerulonephritis.
authors.affiliation: Duke-National University of Singapore Medical School, 169857 Singapore.
authors.forename: Owen J L
authors.initials: OJ
authors.lastname: Rackham
authors.affiliation: Duke-National University of Singapore Medical School, 169857 Singapore.
authors.forename: Sarah R
authors.initials: SR
authors.lastname: Langley
authors.affiliation: Medical Research Council, London Institute of Medical Sciences, Imperial College London, W12 0NN, United Kingdom.
authors.forename: Thomas
authors.initials: T
authors.lastname: Oates
authors.affiliation: Department of Statistics, Athens University of Economics and Business, GR10434, Greece.
authors.forename: Eleni
authors.initials: E
authors.lastname: Vradi
authors.affiliation: Duke-National University of Singapore Medical School, 169857 Singapore.
authors.forename: Nathan
authors.initials: N
authors.lastname: Harmston
authors.affiliation: Division of Brain Sciences, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom.
authors.forename: Prashant K
authors.initials: PK
authors.lastname: Srivastava
authors.affiliation: Centre for Complement and Inflammation Research, Imperial College London, SW7 2AZ, United Kingdom.
authors.forename: Jacques
authors.initials: J
authors.lastname: Behmoaras
authors.affiliation: Department of Statistical Science, University College London, SW7 2AZ, United Kingdom p.dellaportas@ucl.ac.uk lb664@cam.ac.uk enrico.petretto@duke-nus.edu.sg.|The Alan Turing Institute, London, NW1 2QR, United Kingdom.
authors.forename: Petros
authors.initials: P
authors.lastname: Dellaportas
authors.affiliation: The Alan Turing Institute, London, NW1 2QR, United Kingdom p.dellaportas@ucl.ac.uk lb664@cam.ac.uk enrico.petretto@duke-nus.edu.sg.|Department of Medical Genetics, University of Cambridge, CB2 0QQ, United Kingdom.|Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, CB2 0SR, United Kingdom.
authors.forename: Leonardo
authors.initials: L
authors.lastname: Bottolo
authors.affiliation: Duke-National University of Singapore Medical School, 169857 Singapore p.dellaportas@ucl.ac.uk lb664@cam.ac.uk enrico.petretto@duke-nus.edu.sg.|Medical Research Council, London Institute of Medical Sciences, Imperial College London, W12 0NN, United Kingdom.
authors.forename: Enrico
authors.initials: E
authors.lastname: Petretto
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D019175:DNA Methylation; D016678:Genome; D005921:Glomerulonephritis / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D008565:Membrane Proteins / Q000235:genetics; D011401:Promoter Regions, Genetic; D051381:Rats; D011917:Rats, Inbred Lew; D011921:Rats, Inbred WKY; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods
chemical_list: C573129:IFITM3 protein, rat; D008565:Membrane Proteins
keywords: Bayesian statistics; DNA methylation; WGBS; glomerulonephritis