GENETIC MECHANISMS OF OZONE TOLERANCE IN SOYBEAN AND METHODS OF EVOLUTIONARY DISTANCE

1 online resource (167 pages) : PDF

2015

University of North Carolina at Charlotte

At ground level, ozone is a pollutant that poses serious risk to both human health and crop yields. In addition to being a vital economic crop in the US and abroad, conventional soybean lines are susceptible to damage caused by ozone exposure. In order to better understand the genetic differences between existing soybean lines which show extreme differences in sensitivity to ozone, RNA-seq and qRT-PCR based transcriptome analyses were performed. In utilizing these techniques, this study has shown that despite being the same plant, each cultivar exhibits a unique response in both amount and timing of gene expression. These unique responses, in addition to identifying the genes responsibility for ozone tolerance, hint at a secondary, physiological reason for ozone tolerance. Evolutionary distances are measures based on the divergence of two sequences and are used to characterize the time since divergence and selective pressures acting on a pair of duplicated genes. Despite being a useful method of characterizing duplicate genes, the applications for applying these methods have largely gone unchanged in recent years and the community which uses them is underserved. To this end, this study has created two additional analysis pipelines, one which recreates existing methodologies and another which applies a new method to better characterize localized selective forces within subsets of a gene pair. Additionally, a complex and biologically relevant sequence simulation program has been created that, in addition to parameter tuning, has identified overestimates in the measures of the evolutionary distance. This provides a framework for future additions to the pipeline which will automate parameter tuning for more accurate estimates.

doctoral dissertations

Bioinformatics
Genetics

Ph.D.

Gene Mutation
Gene Selection
Genomics
Soybean
Transcriptomics

Bioinformatics

Schlueter, Jessica

Janies, Daniel
Brouwer, Cory
Shi, Xinghua
Diemer, John

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2015.

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Whaley_uncc_0694D_10949

http://hdl.handle.net/20.500.13093/etd:760