Improving Cotton Agronomics with Diverse Genomic Technologies

• Main Author: Sharp, Aaron Robert
• Format: Others
• Published: BYU ScholarsArchive 2016
• Subjects: cotton; agriculture; conservation tillage; RNA-Seq; subgenome expression bias; BioNano physical mapping; genome sequence assembly; Animal Sciences; Plant Sciences
• Online Access: https://scholarsarchive.byu.edu/etd/5845; https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6844&context=etd

Agronomic outcomes are the product of a plant's genotype and its environment. Genomic technologies allow farmers and researchers new avenues to explore the genetic component of agriculture. These technologies can also enhance understanding of environmental effects. With a growing world population, a wide variety of tools will be necessary to increase the agronomic productivity. Here I use massively parallel, deep sequencing of RNA (RNA-Seq) to measure changes in cotton gene expression levels in response to a change in the plant's surroundings caused by conservation tillage. Conservation tillage is an environmentally friendly, agricultural practice characterized by little or no inversion of the soil prior to planting. In addition to changes in cotton gene expression and biological pathway activity, I assay the transcriptional activity of microbial symbiotes living in and around the cotton roots. I found a large degree of similarity between cotton individuals in different treatments. However, under conventional disk tillage I did find significantly greater activity of cotton phosphatase and sulfate transport genes, as well as greater abundance of the microbes Candidatus Burkholderia brachynathoides and Arthrobacter species L77. This study also includes the use of high-throughput physical mapping of DNA to examine the genomic structure of a wild cotton species, Gossypium raimondii, which is closely related to the economically significant crop species Gossypium hirsutum. This technology characterizes genomic regions by assembling large input DNA molecules labeled at restriction enzyme recognition sites. I created an efficient algorithm and generated 812 whole genome assemblies from two datasets. The best of these assemblies allowed us to detect 3,806 potential misassemblies in the current release of the G. raimondii genome sequence assembly.