Development of Statistical Methods for Analyzing Whole Genome Bisulfite Sequencing Experiment Data to Identify Differentially Methylated Regions

University Of Missouri-Columbia, Columbia MO

Investigators

Abstract

DNA methylation is a chemical modification to DNA that imparts information on how and when genes should be turned on or off. As such DNA methylation plays a vital role in many biological processes. There are many instances in which DNA methylation between samples is different. Examples are: 1) different organs (liver vs brain), 2) health vs disease (cancer exhibits methylation patterns that are different from those of healthy tissue), and 3) environmental responses (plants that endure heat and drought stress). These differences are known as differential methylation. Recently, technology has been developed to simultaneously query the millions of methylation sites in DNA.  This technology, however, creates computational and statistical challenges. A pressing question in the field of biology is how to analyze the massive amount of data generated in this way so as to draw biologically relevant, and statistically sound conclusions, without requiring expensive computing equipment. The output of this project will provide statistical methods for detecting differential methylation. This information can then be used by specialists to understand cell function, develop therapeutic interventions, or tackle questions associated with environment such as climate change. Freely available statistical tools that can be used by all scientists interested in analyzing differential methylation will be developed. This interdisciplinary research project between the mathematical and biological sciences also supports the training of students in these fields. DNA methylation is an epigenetic modification that directs gene expression and chromatin conformation. The most common form of DNA methylation in vertebrates is an addition of a methyl group to a cytosine base that is directly followed by a guanine, which is referred to as a CpG site. There are approximately 30 million CpG sites in the human genome with an even larger combination of states of methylation. A differentially methylated region is a region in the genome where mean methylation levels of CpGs are different between two sample groups, such as disease versus normal. Bisulfite sequencing methods are typically used for measuring DNA methylation at CpG sites. Expansion of this technology to permit simultaneous query of all CpG sites, known as whole genome bisulfite sequencing, has created computational and statistical challenges. These include improving the capability to distinguish signals from noise in very large datasets, a current focus of much modern statistics. The project team will develop statistical methods to detect differential methylation between sample groups. Specifically, the methods in this research project improve existing approaches by recognizing and accounting for correlations among methylation sites based on their genomic locations. Such model assumptions ensure that statistical results are biologically meaningful and interpretable. In addition, the methods will borrow information across the whole genome to improve estimation and statistical testing reliability. Bayesian models with theoretical justifications will be developed and implemented using efficient and scalable algorithms to ensure their applicability to a wide variety of high-throughput methylation datasets. Methods will be implemented into software tools and will be freely available for biology and statistics researchers.