Genomic Selection and Identification of Genes Associated with Resistance to Stenocarpella Maydis.

ABSTRACT The identification of lines resistant to corn ear diseases is of great importance in maize breeding because such diseases directly interfere with kernel quality and yield. Among these diseases, ear rot disease is widely relevant and may be caused by the fungus Stenocarpella maydis. The objectives of this study were to characterize the germplasm bank of the Federal University of Lavras, evaluate the use of predictive models for the identification of markers associated with pathogen resistance, and identify the best method of evaluating the disease's incidence. Through a principal component analysis (PCA) and hierarchical clustering, it was observed that the three main genetic groups (stiff stalk synthetic, non-stiff stalk synthetic and tropical) were clustered in a consistent manner, and information on the resistance sources could be obtained according to the line of origin, with populations derived from genetic subgroup Suwan presenting higher levels of resistance. The ridge regression best linear unbiased prediction (rr-BLUP) and Bayesian stochastic search variable (BSSV) models presented equivalent abilities regarding predictive processes; however, the BSSV method was superior in the identification of resistance genes. Three markers highly associated with resistance to Stenocarpella maydis were identified, and they were all located within or very close to genes that act directly or indirectly on mechanisms of resistance to maize diseases. Keywords: ear rot, ridge regression best linear unbiased prediction, Bayesian stochastic search variable.