Estimation of prevalence of white spot syndrome virus (WSSV) by polymerase chain reaction in Penaeus monodon postlarvae at time of stocking in shrimp farms of Karnataka, India: A population-based study
Citation Thakur PC, Corsin F, Turnbull J, Shankar KM, Hao NV, Padiyar PA, Madhusudhan M, Morgan KL & Mohan CV (2002) Estimation of prevalence of white spot syndrome virus (WSSV) by polymerase chain reaction in Penaeus monodon postlarvae at time of stocking in shrimp farms of Karnataka, India: A population-based study. Diseases of Aquatic Organisms, 49 (3), pp. 235-243. https://doi.org/10.3354/dao049235
Abstract White spot disease (WSD) is at present the most serious viral disease affecting cultivated shrimp species globally. The causative agent, white spot syndrome virus (WSSV), is extremely virulent, has a wide host range and can also be transmitted from broodstock to their offspring. The shrimp postlarvae (PL) act as asymptomatic, latent carriers of the virus, and stocking of WSSV-infected PL has been reported as a risk factor for WSD outbreaks in culture ponds. However, there is no population- based study on WSSV prevalence in PL of shrimp. The present manuscript documents the approaches and the results in the estimation of prevalence of WSSV in PL populations of Penaeus monodon at the time of stocking. A maximum of 300 PL from each of the 73 batches of PL stocked at various farms in the west coast of India during September 1999 to January 2000 were tested for the presence of WSSV by 2-step nested PCR. Thirty-six (49%) of the 73 batches tested positive for WSSV either by 1-step alone (3 batches) or after 2-step nested PCR (33 batches). Sub-samples of 5 PL each or 1 PL each tested to quantify the proportion of infected PL within batches showed that WSSV prevalence was very high in 1-step PCR-positive batches and low in 2-step PCR-positive batches. The study also showed that appropriate sampling and sample size were major factors in determining the prevalence of WSSV in PL populations, underlining the need for testing large samples of PL to reduce errors from falsely negative results.