Modeling the Accuracy of Three Detection Methods of Grapevine leafroll-associated virus 3 During the Dormant Period Using a Bayesian Approach
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 plan...
| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
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The American Phytopathological Society (APS)
2020
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.11939/6795 https://apsjournals.apsnet.org/doi/10.1094/PHYTO-10-15-0246-R |
| _version_ | 1855032491416485888 |
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| author | Olmos, Antonio Bertolini, Edson Ruiz-García, Ana B. Martínez, M. Carmen Peiró, Rosa Vidal, Eduardo |
| author_browse | Bertolini, Edson Martínez, M. Carmen Olmos, Antonio Peiró, Rosa Ruiz-García, Ana B. Vidal, Eduardo |
| author_facet | Olmos, Antonio Bertolini, Edson Ruiz-García, Ana B. Martínez, M. Carmen Peiró, Rosa Vidal, Eduardo |
| author_sort | Olmos, Antonio |
| collection | ReDivia |
| description | 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. |
| format | article |
| id | ReDivia6795 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | The American Phytopathological Society (APS) |
| publisherStr | The American Phytopathological Society (APS) |
| record_format | dspace |
| spelling | ReDivia67952025-04-25T14:47:50Z Modeling the Accuracy of Three Detection Methods of Grapevine leafroll-associated virus 3 During the Dormant Period Using a Bayesian Approach Olmos, Antonio Bertolini, Edson Ruiz-García, Ana B. Martínez, M. Carmen Peiró, Rosa Vidal, Eduardo openBUGS Post-test probability H20 Plant diseases U10 Mathematical and statistical methods 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. 2020-11-23T07:56:54Z 2020-11-23T07:56:54Z 2016 article publishedVersion Olmos, A., Bertolini, E., Ruiz-García, A. B., Martínez, C., Peiró, R., & Vidal, E. (2016). Modeling the accuracy of three detection methods of Grapevine leafroll-associated virus 3 during the dormant period using a Bayesian approach. Phytopathology, 106(5), 510-518. 0031-949X 1943-7684 (e-ISSN) http://hdl.handle.net/20.500.11939/6795 10.1094/PHYTO-10-15-0246-R https://apsjournals.apsnet.org/doi/10.1094/PHYTO-10-15-0246-R en Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ closedAccess The American Phytopathological Society (APS) electronico |
| spellingShingle | openBUGS Post-test probability H20 Plant diseases U10 Mathematical and statistical methods Olmos, Antonio Bertolini, Edson Ruiz-García, Ana B. Martínez, M. Carmen Peiró, Rosa Vidal, Eduardo Modeling the Accuracy of Three Detection Methods of Grapevine leafroll-associated virus 3 During the Dormant Period Using a Bayesian Approach |
| title | Modeling the Accuracy of Three Detection Methods of Grapevine leafroll-associated virus 3 During the Dormant Period Using a Bayesian Approach |
| title_full | Modeling the Accuracy of Three Detection Methods of Grapevine leafroll-associated virus 3 During the Dormant Period Using a Bayesian Approach |
| title_fullStr | Modeling the Accuracy of Three Detection Methods of Grapevine leafroll-associated virus 3 During the Dormant Period Using a Bayesian Approach |
| title_full_unstemmed | Modeling the Accuracy of Three Detection Methods of Grapevine leafroll-associated virus 3 During the Dormant Period Using a Bayesian Approach |
| title_short | Modeling the Accuracy of Three Detection Methods of Grapevine leafroll-associated virus 3 During the Dormant Period Using a Bayesian Approach |
| title_sort | modeling the accuracy of three detection methods of grapevine leafroll associated virus 3 during the dormant period using a bayesian approach |
| topic | openBUGS Post-test probability H20 Plant diseases U10 Mathematical and statistical methods |
| url | http://hdl.handle.net/20.500.11939/6795 https://apsjournals.apsnet.org/doi/10.1094/PHYTO-10-15-0246-R |
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