Singh, Amritpal2018-07-262018-07-262017-07https://hdl.handle.net/11299/198362University of Minnesota Ph.D. dissertation. 2017. Major: Applied Plant Sciences. Advisor: Aaron Lorenz. 1 computer file (PDF); 164 pages.Goss’s wilt is a bacterial disease of maize caused by the Gram-positive bacterium Clavibacter michiganensis subsp. nebraskensis (Cmn). Goss’s wilt was discovered for the first time in South Central Nebraska in 1969. Following its discovery, the disease spread to the neighboring states over the next decade. Maize germplasm was screened for resistance to Goss’s wilt, and possibly due to the deployment of partially resistant hybrids, Goss’s wilt did not cause any significant damage during the 1980s and 1990s. However, Goss’s wilt re-emerged around 2006 and has been spreading to major maize growing areas in the United States and Canada. It is important to understand the genetic basis of resistance to Goss’s wilt to devise strategies for breeding resistance into maize hybrids. The main objectives of this dissertation were to (i) map quantitative trait loci (QTL) for resistance to Goss’s wilt using linkage mapping, joint linkage mapping, and genome-wide association mapping; (ii) identify differentially expressed genes in resistant and susceptible inbred lines in response to Cmn using RNA-seq; and (iii) to explore the prospects of genomic prediction of resistance to Goss’s wilt. Three bi-parental linkage mapping families including B73 x Oh43, B73 x HP301, and B73 x P39 that were evaluated for Goss’s wilt were used for joint linkage and linkage mapping. Eleven QTL were detected for resistance to Goss’s wilt on chromosomes 1, 2, 3, 4, 5, and 10 through joint linkage mapping. Linkage mapping in each of the three families identified nine, six, and four QTL in the families B73 × Oh43, B73 × HP301, and B73 × P39, respectively. Genome-wide association analysis conducted using a diversity panel of 555 maize inbred lines and 450 recombinant inbred lines (RILs) from three bi-parental mapping populations found three SNPs in the diversity panel and 10 SNPs in the combined dataset of diversity panel and RILs that were associated with Goss’s wilt resistance. Two modules of correlated genes were discovered that showed differential regulation in response to Cmn between resistant (N551) and susceptible (B14A) inbred lines using a weighted gene co-expression network analysis. Gene ontology analysis revealed that the genes inside one of the modules were enriched in defense related functions. Genomic prediction of Goss’s wilt resistance was conducted on the data obtained from bi-parental families and the diversity panel. Highest predictive ability of 0.56 and 0.64 was achieved in the diversity panel and B73 x Oh43 population respectively. Effect of training population size, composition, and adding diverse lines to training population on predictive ability was also assessed. Results indicated that predictive ability is not highly benefited when training population is designed by adding equal number of lines from each of the three families. Adding diverse lines to the training population lead to minor changes in predictive ability. Overall, the results improved our understanding of the genetic architecture of Goss’s wilt resistance and showed that the resistance to Goss’s wilt is a complex trait, controlled by small effect QTL.enDisease resistanceGoss's wiltGWASQTL mappingThesis or Dissertation