Off-campus South Dakota State University users: To download campus access theses, please use the following link to log into our proxy server with your South Dakota State University ID and password.
Non-South Dakota State University users: Please talk to your librarian about requesting this thesis through interlibrary loan.
Dissertation - University Access Only
Doctor of Philosophy (PhD)
Department / School
Mathematics and Statistics
During the course of evolution, phenotypic adaptations can arise from changes in both gene function and gene expression. Evolution of gene function is well-documented in comparative genomics through the study of protein and DNA sequences. But the evolution of gene expression is not well-understood. The first objective of the thesis is to evaluate the methods for quantifying divergence in gene expression and examine conservation of gene expression based on a set of DNA microarray data. The second objective is to explore the correlations between divergence of gene expression and various genetic factors. Pairwise comparison of gene expression across species has been often used in study of evolution of gene expression. We showed that the existing methods for quantifying divergence of gene expression may give unreliable results. We proposed to modify the Pearson’s distance method by adding a stabilizing factor to avoid overestimation of expression differences due to small variation of gene expression. We showed that the modification improves the estimation of expression divergence. We applied the proposed method to quantify gene expression divergence across 9 corresponding tissues of human, mouse, and rat, based on gene expression measured by species-specific whole-genome DNA microarrays. We demonstrated that gene expression diverges rapidly but conservation can be observed in more than 30% of human-rodent orthologous genes, and 70% of the mouse-rat orthologs. Moreover, we showed that expression of a significant portion of genes do not evolve strictly according to the neutral model, suggesting strong influence of stabilizing selection. We used a linear regression to systematically investigate effects of multiple factors on expression divergence, and found that level of tissue specificity is the most important predictor for expression divergence. Expression of tissue-specific genes is more conserved as compared to genes selectively-expressed in multiple tissues. Ninetyeight percent of orthologs whose expression found in only one tissue are expressed in the same tissue between any two species. In summary, we demonstrated that conservation of mammalian gene expression can be detected based on comparison of genome-wide gene expression data. By studying the relationship between divergence in gene expression and genomic data, we gained more insights into evolution of gene expression.
Library of Congress Subject Headings
Includes bibliographical references (pages 82-89)
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Yuguang, Ban, "Understanding Evolution of Gene Expression by Comparative Analysis" (2013). Electronic Theses and Dissertations. 1370.