Title

A Statistical Analysis of DNA Methylation In a Shift Work Study

Presenter Information

Jason Viehman

Department

Mathematics and Statistics

Major

Applied Mathematics

Research Advisor

Olbricht, Gayla R.

Advisor's Department

Mathematics and Statistics

Funding Source

OURE

Abstract

DNA methylation occurs when methyl groups attach to cytosine nucleotides on DNA segments. Previous studies have established links between specific methylation patterns and certain lifestyles. In this research, statistical methods are employed to test for significant differences in methylation levels between females who work the night shift and females who work the day shift. DNA methylation levels are measured at cytosines across the genome with Illumina 450K methylation microarrays. After initial pre-processing to eliminate low-quality data, testing was performed at each cytosine site using t-tests and empirical Bayes tests to identify any statistically significant site level methylation differences between the different shifts. A region level statistical method known as Bumphunter was also applied to identify statistically significant regions of interest in the genome. Significant sites or regions that overlap with genes, CpG Islands, or other genomic annotations can help researchers better understand the molecular impact of DNA methylation and its connection to shift work.

Biography

Jason Viehman is a senior in applied mathematics. He is also getting a second bachelors in economics, and an emphasis area in actuarial science. He has been the historian, community service chair, and president of S&T's chapter of Kappa Mu Epsilon.

Research Category

Sciences

Presentation Type

Poster Presentation

Document Type

Poster

Location

Upper Atrium

Presentation Date

17 Apr 2018, 9:00 am - 12:00 pm

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Apr 17th, 9:00 AM Apr 17th, 12:00 PM

A Statistical Analysis of DNA Methylation In a Shift Work Study

Upper Atrium

DNA methylation occurs when methyl groups attach to cytosine nucleotides on DNA segments. Previous studies have established links between specific methylation patterns and certain lifestyles. In this research, statistical methods are employed to test for significant differences in methylation levels between females who work the night shift and females who work the day shift. DNA methylation levels are measured at cytosines across the genome with Illumina 450K methylation microarrays. After initial pre-processing to eliminate low-quality data, testing was performed at each cytosine site using t-tests and empirical Bayes tests to identify any statistically significant site level methylation differences between the different shifts. A region level statistical method known as Bumphunter was also applied to identify statistically significant regions of interest in the genome. Significant sites or regions that overlap with genes, CpG Islands, or other genomic annotations can help researchers better understand the molecular impact of DNA methylation and its connection to shift work.