Abstract
Recent advances in biotechnology have led to an explosion in the amount of biological data available to researchers. Since the introduction of high-throughput technologies, a massive number of genetic markers can now be investigated for large numbers of study participants. This has led to the discovery of thousands of genetic markers that are associated with various human traits and diseases. Technological advances have made it possible to investigate not only diseases that are caused by alteration of a single gene, but also to explore the whole genome simultaneously. Most diseases are not only caused by a single genetic mutation, but by many genetic variants contributing to the disease risk, either on their own or in interaction with other variants or other environmental factors. In complex diseases, both genetic variants and environmental factors contribute to disease susceptibility, and identifying the underlying genetic risk variants for these diseases has been a major challenge in genomics. Statistics is a tool for data analysis that has played an important role in the breakthroughs in genetic studies. Statistics have shaped experimental design by addressing issues such as false positive control, sample sizes requirements, and population heterogeneity. It has also played a prominent role in the development of quality-control protocols and different normalizations methods. New types of genetic data require development of both new methodologies as well as adaptations of existing methods, and this has led to the integration of statistical methodology into almost all aspects of genetic analyses. The title of this thesis, DNA Methylation and Exome Chip Analysis in Complex Disease, is a broad title encompassing many aspects of both genetic and epigenetic epidemiology. Common themes in this thesis are methods for the identification of genetic biomarkers in complex diseases and the aggregation of genetic information across several genetic sites. Two papers involve DNA methylation data and one paper assesses the constraints in genetic studies involving low-frequency and rare variants using the Exome Chip. The structure of the thesis is as follows. An introduction and background to the study of genetics and epigenetics of complex diseases is presented in Chapter II. Chapter III lists the aims of this thesis, and Chapter IV presents the materials and methods that were applied and developed. Chapter V outlines the results from the three papers. In Chapter VI, the methods and results are discussed, and the thesis finishes with suggestions for future extensions and a concluding remark.
List of papers
Paper I: Bos SD, Page CM, Andreassen BK, Elboudwarej E, Gustavsen MW, Briggs F, Quach H, Leikfoss IS, Bjølgerud A, Berge T, Harbo HF, Barcellos LF (2015). Genome-Wide DNA Methylation Profiles Indicate CD8+ T Cell Hypermethylation in Multiple Sclerosis. PLoS ONE 10(3): e0117403. The paper is available in DUO: http://urn.nb.no/URN:NBN:no-56455 |
Paper II: Page CM, Baranzini SE, Mevik BH, Bos SD, Harbo HF, Andreassen BK (2015). Assessing the Power of Exome Chips. PLoS ONE 10(10): e0139642. The paper is available in DUO: http://urn.nb.no/URN:NBN:no-52655 |
Paper III: Page CM, Vos L, Rounge TB, Harbo HF, Andreassen BK, Assessing genome-wide significance for the detection of differentially methylated regions. Statistical Applications in Genetics and Molecular Biology (2018). DOI: 10.1515/sagmb-2017-0050. The paper is not available in DUO due to publisher restrictions. The published version is available at: https://doi.org/10.1515/sagmb-2017-0050 |