title: A Flexible, Efficient Binomial Mixed Model for Identifying Differential DNA Methylation in Bisulfite Sequencing Data.
abstract: Identifying sources of variation in DNA methylation levels is important for understanding gene regulation. Recently, bisulfite sequencing has become a popular tool for investigating DNA methylation levels. However, modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples, and because of the computational challenges of controlling for genetic covariance in count data. To address these challenges, we present a binomial mixed model and an efficient, sampling-based algorithm (MACAU: Mixed model association for count data via data augmentation) for approximate parameter estimation and p-value computation. This framework allows us to simultaneously account for both the over-dispersed, count-based nature of bisulfite sequencing data, as well as genetic relatedness among individuals. Using simulations and two real data sets (whole genome bisulfite sequencing (WGBS) data from Arabidopsis thaliana and reduced representation bisulfite sequencing (RRBS) data from baboons), we show that our method provides well-calibrated test statistics in the presence of population structure. Further, it improves power to detect differentially methylated sites: in the RRBS data set, MACAU detected 1.6-fold more age-associated CpG sites than a beta-binomial model (the next best approach). Changes in these sites are consistent with known age-related shifts in DNA methylation levels, and are enriched near genes that are differentially expressed with age in the same population. Taken together, our results indicate that MACAU is an efficient, effective tool for analyzing bisulfite sequencing data, with particular salience to analyses of structured populations. MACAU is freely available at www.xzlab.org/software.html.
authors.affiliation: Department of Biology, Duke University, Durham, North Carolina, United States of America.
authors.forename: Amanda J
authors.initials: AJ
authors.lastname: Lea
authors.affiliation: Department of Biology, Duke University, Durham, North Carolina, United States of America.|Institute of Primate Research, National Museums of Kenya, Karen, Nairobi, Kenya.|Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, United States of America.|Duke University Population Research Institute, Duke University, Durham, North Carolina, United States of America.
authors.forename: Jenny
authors.initials: J
authors.lastname: Tung
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.|Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D018899:CpG Islands / Q000235:genetics*; D019175:DNA Methylation / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D017422:Sequence Analysis, DNA; D012984:Software