Abstract

IntroductionScab is a fungal disease of common beans caused by the pathogen Elsinoë phaseoli. The disease results in major economic losses on common beans, and there are efforts to develop integrated pest management strategies to control the disease. Modern computational biology and bioinformatics tools were utilized to identify scab disease resistance genes in the common bean by identification of genomic regions and genes associated with resistance to scab disease during natural infection in the field.MethodsA diverse set of common bean accessions were analyzed for genetic association with scab disease resistance using a Genome-Wide Association Study design of infected plants and non-infected plants (controls). A fixed and random model circulating probability unification model of these two covariates that considers a minor allele frequency threshold value of 0.03 were deployed during the analysis. Annotation of genes proteins with significant association values was conducted using a machine learning algorithm of support vector machine on prPred using python3 on Linux Ubuntu 18.04 computing platform with an accuracy of 0.935.ResultsCommon bean accessions tested showed varying phenotypes of susceptibility to scab disease. Out of 179 accessions, 16 and 163 accessions were observed to be resistant and susceptible to scab disease, respectively. Genomic analysis revealed a significant association on chromosome one SNP S1_6571566 where the protein-coding sequence had a resistant possibility of 55% and annotated to the Enhancer of Poly-comb like protein.ConclusionThe significant differences in the phenotypic variability for scab disease indicate wide genetic variability among the common bean accessions. The resistant gene associated with scab disease was successfully identified by GWAS analysis. The identified common bean accessions resistant to scab disease can be adopted into breeding programs as sources of resistance.

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