Modern Bioinformatics As A Tool To Understand Genomic And Transcriptopmic Variation In Legumes

Abstract

The research presented here focuses on the deployment of modern bioinformatics to gain a greater understanding of legume genomes and gene functions. While improvement of legume crops still relies on conventional breeding approaches, transgenesis, the introduction of a foreign piece of DNA in a host genome, is becoming increasingly common. Using a transgenic approach, the integration of foreign DNA into the host genome using Agrobacterium-mediated transformation is almost always random and is known to induce mutations at the insertion site, but questions have been raised about the potential for mutagenesis at other loci. While genetic engineering has been widely used for crop improvement, few studies have addressed the genome-wide effects of transgenesis. Chapters two and three of this thesis address this question in the context of Glycine max, a major agricultural crop (soybean). Specifically, chapter two features a reanalysis of data from a previous study that reported a large number of mutations in soybean transgenic plants and describes several factors that led to an overestimation. Chapter three addresses the effects on the genome in a series of soybean plants transformed with CRISPR/Cas9, the most recently developed platform for genome editing. The findings of this work have implications on the frequency and transmission of novel variation resulting from soybean biotechnology. Chapter four focuses on applying transcriptome network analysis for predicting the genes that underlie nodule development variation in the Medicago-Ensifer symbiosis. Co-expression networks were constructed for Medicago truncatula and were integrated with data from genome-wide association analysis to prioritize candidate genes with a high likelihood of causal association with nodule development phenotypes. This approach sheds light on potential new genetic factors underlying an important phenotype, and more broadly, could be applied to understand genomic and phenotypic variation for a wide range of plant species and traits.