Table_1_Genetic Analyses and Genomic Predictions of Root Rot Resistance in Common Bean Across Trials and Populations.xlsx (37.53 kB)
Download file

Table_1_Genetic Analyses and Genomic Predictions of Root Rot Resistance in Common Bean Across Trials and Populations.xlsx

Download (37.53 kB)
dataset
posted on 12.03.2021, 05:27 by Lucy Milena Diaz, Victoria Arredondo, Daniel Ariza-Suarez, Johan Aparicio, Hector Fabio Buendia, Cesar Cajiao, Gloria Mosquera, Stephen E. Beebe, Clare Mugisha Mukankusi, Bodo Raatz

Root rot in common bean is a disease that causes serious damage to grain production, particularly in the upland areas of Eastern and Central Africa where significant losses occur in susceptible bean varieties. Pythium spp. and Fusarium spp. are among the soil pathogens causing the disease. In this study, a panel of 228 lines, named RR for root rot disease, was developed and evaluated in the greenhouse for Pythium myriotylum and in a root rot naturally infected field trial for plant vigor, number of plants germinated, and seed weight. The results showed positive and significant correlations between greenhouse and field evaluations, as well as high heritability (0.71–0.94) of evaluated traits. In GWAS analysis no consistent significant marker trait associations for root rot disease traits were observed, indicating the absence of major resistance genes. However, genomic prediction accuracy was found to be high for Pythium, plant vigor and related traits. In addition, good predictions of field phenotypes were obtained using the greenhouse derived data as a training population and vice versa. Genomic predictions were evaluated across and within further published data sets on root rots in other panels. Pythium and Fusarium evaluations carried out in Uganda on the Andean Diversity Panel showed good predictive ability for the root rot response in the RR panel. Genomic prediction is shown to be a promising method to estimate tolerance to Pythium, Fusarium and root rot related traits, indicating a quantitative resistance mechanism. Quantitative analyses could be applied to other disease-related traits to capture more genetic diversity with genetic models.

History

References