Keywords and Phrases
DNA; DNA sequence; Epigenetics; fields of biomedicine and agriculture.; Fuzzy logistic regression; next generation sequence (NGS) data
“Epigenetics is the study of changes in gene activity or function that are not related to a change in the DNA sequence. DNA methylation is one of the main types of epigenetic modifications, that occur when a methyl chemical group attaches to a cytosine on the DNA sequence. Although the sequence does not change, the addition of a methyl group can change the way genes are expressed and produce different phenotypes. DNA methylation is involved in many biological processes and has important implications in the fields of biomedicine and agriculture.
Statistical methods have been developed to compare DNA methylation at cytosine nucleotides between populations of interest (e.g., healthy and diseased) across the entire genome from next generation sequence (NGS) data. Testing for the differences between populations in DNA methylation at specific sites is often followed by an assessment of regional difference using post hoc aggregation procedures to group neighboring sites that are differentially methylated. Although site-level analysis can yield some useful information, there are advantages to testing for differential methylation across entire genomic regions. Examining genomic regions produces less noise, reduces the numbers of statistical tests, and has the potential to provide more informative results to biologists.
In this research, several different types of logistic regression models are investigated to test for differentially methylated regions (DMRs). The focus of this work is on developing a fuzzy logistic regression model for DMR detection. Two other logistic regression methods (weighted average logistic regression and ordinal logistic regression) are also introduced as alternative approaches. The performance of these novel approaches are then compared with an existing logistic regression method (MAGIg) for region-level testing, using data simulated based on two (one plant, one human) real NGS methylation data sets”--Abstract, page iii.
Olbricht, Gayla R.
Samaranayake, V. A.
Wen, Xuerong Meggie
Frank, Ronald L.
Mathematics and Statistics
Ph. D. in Applied Mathematics
Missouri University of Science and Technology
x, 72 pages
© 2020 Tarek M. Bubaker Bennaser, All rights reserved.
Dissertation - Open Access
Bennaser, Tarek M. Bubaker, "Fuzzy logistic regression for detecting differential DNA methylation regions" (2020). Doctoral Dissertations. 2938.