This thesis involves the important crop plant soybean (Glycine max), and provides a rich information resource for breeders and geneticists working towards improving traits for pathogen resistance.Results reported here provide a systemic view at both the genetic and biochemical level, and were generated by data-mining gene expression data from soybean cultivars inoculated with plant pathogens and also recombinant inbred line (RIL) populations.The genome variability based on Single Feature
Polymorphisms (SFPs) was measured for the first time in soybean, using a genetically diverse set of cultivated G. max lines and also a G. soja line. Additionally, a genetic map spanning all 20 soybean chromosomes groups were assembled in a large RIL population.The well studied metabolic
pathways from the model plant Arabidopsis thaliana, were reconstructed in G. max based on sequence similarity comparison between the genomes of the two species. We performed algorithmic analysis of pathways in our set of soybean lines and RILs using the gene expression data, and acquired a systemic view of the metabolic response to pathogen infection in different genetic backgrounds.Significant differences in the patterns of pathway perturbation was observed in the different lines, and also between four different chromosomal regions that have been known to contain genetic elements contributing to pathogen resistance.
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