Prospects of Genomic Prediction for Goss's Bacterial Wilt and Leaf Blight Resistance Using Bi-Parental Populations and a Diversity Panel of Maize.

Goss’s wilt and leaf blight is a bacterial disease of maize (Zea mays L.) caused by gram positive bacterium Clavibacter michiganensis subsp. nebraskensis (Cmn). Goss’s wilt has re-emerged as an important disease in the western United States and is spreading to other areas of North America. One possible reason for re-appearance of Goss’s wilt could be the increase in susceptibility of germplasm used in commercial maize breeding. Therefore, it is important to identify the sources of resistance to Goss’s wilt that can be used to deploy resistance to Goss’s wilt into commercial maize hybrids. Genomic prediction could be used to predict resistance to Goss’s wilt. The objectives of study were to (i) assess the ability of genomic prediction to predict the resistance of maize inbred lines in a diverse germplasm pool that can potentially be used as sources of resistance, and (ii) assess the prospects of genomic prediction in maize bi-parental populations. Two types of datasets consisting of a diverse panel of 555 maize inbred lines, and three bi-parental populations B73 x Oh43 (189 lines), B73 x HP301 (140 lines), and B73 x P39 (121 lines), phenotyped for Goss’s wilt and genotyped with high-density genotyping-by-sequencing (GBS) SNP data were used. Genomic best linear unbiased prediction (GBLUP) model was used in each dataset to assess the genomic prediction accuracy of prediction of Goss’s wilt resistance through a ten-fold cross validation procedure. Prediction accuracy of 0.54 was achieved in the diversity panel. Among bi-parental populations, a maximum prediction accuracy of 0.68 was obtained in B73 x Oh43 population. Results from this study indicate that genomic prediction may be helpful to predict the resistance to Goss’s wilt in maize.