Poon, Zhi Weng Josiah, Vu, Nguyen Thanh, Shen, Xueyan, Gibson-Kueh, Susan, Carrai, Maura, Nelson, Sarah Priyanka, Terence, Celestine, Oh, Joseph, Seah, Theophilus, Tan, Ying Quan, Awate, Sunita, Dong, Ha Thanh, Senapin, Saengchan, Tan, Marie R., Vij, Shubha, Jones, David B., Jerry, Dean R., and Domingos, Jose A. (2026) Cross-environment genomic prediction of scale drop disease resistance in barramundi (Lates calcarifer) using laboratory challenge and farm survival data. Aquaculture, 621. 744030.

Preview

PDF (Published Version) - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

 

1

Abstract

Scale drop disease virus (SDDV) is a major cause of mortality and economic loss in barramundi (Lates calcarifer) aquaculture across Southeast Asia, with outbreaks resulting in up to 90% mortality. While laboratory challenge models have been developed and enable accurate, standardised measurement of resistance, the extent to which these traits predict survival under natural farm outbreaks remains poorly understood, representing a key knowledge gap for implementing genomic selection. This study evaluated the genetic relationship between laboratory-derived resistance traits and survival from a commercial SDDV outbreak using two independent laboratory challenges and one farm outbreak, involving a total of 3144 fish genotyped at ∼49 k genome-wide SNPs. Resistance traits measured in the laboratory included survival time after injection, survival status, and survival 50%. Variance components were estimated using GBLUP models, and cross-environment prediction accuracies for farm survival were assessed under four training-validation scenarios. Heritability estimates for SDDV resistance ranged from h² = 0.17 to 0.44 under laboratory conditions, while under farm conditions they were substantially higher (h² = 0.73 to 0.81). Genetic correlations between laboratory and farm SDDV resistance were high within spawning batches (r<inf>g</inf> = 0.73 to 0.86), indicating low genotype-by-environment (GxE) interactions and stable genetic ranking of related individuals across environments; across batches they were moderate (r<inf>g</inf> = 0.62 to 0.75), highlighting GxE effects and the importance of relatedness. Prediction accuracy for farm survival was highest when using farm data (accuracy = 0.54), moderate when laboratory data from related populations were used (accuracy = 0.25 to 0.38), and lowest when training on unrelated batches (accuracy = 0.17 to 0.19). These results demonstrate that SDDV resistance measured in laboratory challenges captures key genetic components relevant to farm survival, while also emphasising the importance of genetic relatedness for cross-environment genomic prediction. Laboratory challenge data therefore provide a biosecure and informative source of phenotypes, enabling genomic selection to enhance disease resilience and support sustainable aquaculture breeding programmes.

Actions (Repository Staff Only)

Item Control Page