Genomic Selection and Identification of Causal Regions Associated with Resistance to Stenocarpella Maydis in Maize Lines Using Dartseq Markers.

The identification of lines resistant to 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 due to significant decrease of grain yield. Ear rot may be caused by the fungus Stenocarpella maydi; however, little information about genetic resistance to this pathogen is available in maize, mainly related to causal regions in genome. In order to exploit this genome information we used 23.154 Dart-seq markers in 238 lines and apply genome wide selection to select resistance genotypes. We divide the lines in clusters in order to indentify groups related to resistance to Stenocarpella maydi and use Bayesian stochastic search variable approach to indentify causal regions related to resistance to ear rot. 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 where 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 regions. 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. Our work showed that is possible to select maize lines presenting high resistance to Stenocarpella maydis. This claim is based on the high level of predictive accuracy obtained by GWAS using different models. Furthermore, the lines related to background Suwan present higher level of resistance than lines related to other groups. Three sequences related to significant markers presented a fine association with mechanism of resistance in genes linked to Cytokinin dehydrogenase, Peroxidase activity and G Proteins.