Masters Theses

Abstract

”Finding possible connections and solutions to help fight progression of diseases is a major area of research. Genomics is a primary path of research in disease research. Through the DNA sequence, possible connections to diseases have been found. However, most methods for fixing issues within a DNA sequence are still out of reach. One potential path is to investigate epigenetic modifications, such as DNA methylation. DNA methylation occurs when a methyl group attached to cytosines on the DNA sequence. Statistical methods can be used to identify sites or regions of significant differences in methylation levels between groups ( e. g. disease vs. healthy). If these particular sites or regions can be linked to diseases they could aid in better understanding the disease pathway and could potentially be used to diagnose or treat the disease. With recent advancement in technology, tools to measure and analyze methylation levels have become easier and are more accessible. Thus, more and more researchers are investigating methylation to help identify possible connections to diseases.

The statistical tools to analyze and find significantly different methylated sites or regions have advanced. Since methylation levels have been shown to have correlation among neighboring sites, meaningful differences in methylation levels commonly occur over regions rather than individual sites. There are several statistical tools to test for these regions, including three that are the focus of this thesis: Bumphunter, Probe Lasso, and DMRcate. Each of these methods identifies regions that have significantly different methylation levels in different ways. In this thesis, a thorough examination of each of these methods is presented and all three methods are used to identify differentially methylated regions (DMRs) between HIV positive women with and without squamons cell carcinoma cervical cancer. The methods are compared to help better understand their impact of identifying DMRs”--Abstract, page iii.

Advisor(s)

Olbricht, Gayla R.

Committee Member(s)

Samaranayake, V. A.
Wen, Xuerong Meggie

Department(s)

Mathematics and Statistics

Degree Name

M.S. in Applied Mathematics

Comments

Master of Science in Applied Mathematics with Statistics Emphasis

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2017

Pagination

viii, 56 pages

Note about bibliography

Includes bibliographic references (pages 53-55).

Rights

© 2017 Arnold Albert Harder, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12017

Electronic OCLC #

1313117324

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