| Until recently, few data were available to approach the question of how gene function, developmental processes, and natural selection interact on a genome-wide scale. Now, an understanding of the complex relationship between the organization of the genome, development, and organismal fitness is possible. Here, I explore the relative impact of natural selection and developmental constraint on rates of protein, intron, and cis-regulatory evolution in duplicated and unduplicated genes utilizing genomic data from Caenorhabditis elegans, Caenorhabditis briggsae and Homo sapiens. In chapter 1, I test the hypothesis that evolutionary changes should be more frequent in later ontogeny than early ontogeny due to developmental constraint and find that genes expressed post-embryogenesis have a significantly greater number of duplicates in both the C. elegans and C. briggsae genomes versus early expressed and non-modulated genes. This apparent constraint at the level of gene duplication may have important implications for macroevolutionary change. In chapter 2, I test the hypothesis that natural selection favors short introns in highly expressed genes to minimize the cost of transcription and find that introns in highly expressed genes are substantially shorter than introns in genes that are expressed at low levels. In chapter 3, I test the hypothesis that protein and regulatory evolution is coupled in both orthologs (species homologs) and paralogs (duplicate genes). After developing and validating a functional measure of cis-regulatory sequence evolution I find that protein and regulatory evolution is weakly coupled in both orthologs and paralogs. This pattern suggests that selective pressure on gene expression and protein evolution is quite similar and persists over millions of years following divergence either by speciation or gene duplication. At the same time, duplicate genes exhibit a dramatic acceleration of both regulatory and protein evolution in comparison with orthologs suggesting increased directional selection and/or relaxed selection on both gene expression patterns and protein function in duplicate genes. Lastly, in the Appendix, a software framework called GeneMerge that facilitates data mining and hypothesis testing using genomic and proteomic data is presented. |
