Related Experiment Video
Updated: Mar 27, 2026
Automating Citrus Budwood Processing for Downstream Pathogen Detection Through Instrument Engineering
Published on: April 21, 2023
Modeling the Accuracy of Three Detection Methods of Grapevine leafroll-associated virus 3 During the Dormant Period
Antonio Olmos1, Edson Bertolini1, Ana B Ruiz-García1
1First, third, fourth, and sixth authors: Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 4.5, 46113 Moncada, Valencia, Spain; second author: Departamento de Fitossanidade, Faculdade de Agronomia, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves 7712, 91450-000 Porto Alegre, RS, Brazil; and fifth author: Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de Valencia (COMAV-UPV), 46022 Valencia, Spain.
Abstract:
Grapevine leafroll-associated virus 3 (GLRaV-3) has a worldwide distribution and is the most economically important virus that causes grapevine leafroll disease. Reliable, sensitive, and specific methods are required for the detection of the pathogen in order to assure the production of healthy plant material and control of the disease. Although different serological and nucleic acid-based methods have been developed for the detection of GLRaV-3, diagnostic parameters have not been established, and there is no gold standard method. Therefore, the main aim of this work was to determine the sensitivity, specificity, and likelihood ratios of three commonly used methods, including one serological test (double-antibody sandwich enzyme-linked immunosorbent assay [DAS-ELISA]) and two nucleic acid-based techniques (spot and conventional real-time reverse transcription-polymerase chain reaction [RT-PCR]). Latent class models using a Bayesian approach have been applied to determine diagnostic test parameters and to facilitate decision-making regarding diagnostic test selection. Statistical analysis has been based on the results of a total of 281 samples, which were collected during the dormant period from three different populations. The best-fit model out of the 49 implemented models revealed that DAS-ELISA was the most specific method (value = 0.99) and provided the highest degree of confidence in positive results. Conversely, conventional real-time RT-PCR was the most sensitive method (value = 0.98) and produced the highest degree of confidence in negative results. Furthermore, the estimation of likelihood ratios showed that in populations with low GLRaV-3 prevalence the most appropriate method could be DAS-ELISA, while conventional real-time RT-PCR could be the most appropriate method in medium or high prevalence populations. Combining both techniques significantly increases detection accuracy. The flexibility and power of Bayesian latent class models open new possibilities for the evaluation of diagnostic tests for plant viruses.
