Decision Strategies for Absorbance Readings from an Enzyme-Linked Immunosorbent Assay—A Case Study about Testing Genotypes of Sugar Beet (Beta vulgaris L.) for Resistance against Beet Necrotic Yellow Vein Virus (BNYVV)

Abstract

The Beet necrotic yellow vein virus (BNYVV) causes rhizomania in sugar beet (Beta vulgaris L.), which is one of the most destructive diseases in sugar beet worldwide. In breeding projects towards resistance against BNYVV, the enzyme-linked immunosorbent assay (ELISA) is used to determine the virus concentration in plant roots and, thus, the resistance levels of genotypes. Here, we present a simulation study to generate 10,000 small samples from the estimated density functions of ELISA values from susceptible and resistant sugar beet genotypes. We apply receiver operating characteristic (ROC) analysis to these samples to optimise the cutoff values for sample sizes from two to eight and determine the false positive rates (FPR), true positive rates (TPR), and area under the curve (AUC). We present, furthermore, an alternative approach based upon Bayes factors to improve the decision procedure. The Bayesian approach has proven to be superior to the simple cutoff approach. The presented results could help evaluate or improve existing breeding programs and help design future selection procedures based upon ELISA. An R-script for the classification of sample data based upon Bayes factors is provided.