Disease & heat resistance

Cross-environment genomic prediction of scale drop disease resistance using laboratory challenge and field survival data in barramundi Lates calcarifer

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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 selective breeding offers a sustainable solution to enhance disease resilience, genomic prediction models for SDDV resistance have been developed only under controlled laboratory conditions. However, genotype-by-environment (GxE) interactions can cause re-ranking of genotypes across environments, and the extent to which laboratory-derived resistance traits predict survival under natural outbreaks remains unclear. This study aimed to determine whether resistance phenotypes measured in laboratory challenges can predict survival outcomes during natural SDDV outbreaks in farm environments. Two independent laboratory challenges and one natural farm outbreak were evaluated using 3,144 fish genotyped with 48,829 SNPs from a 70K barramundi SNP array. The laboratory challenges involved 654 and 705 juveniles from two separate spawning batches exposed to purified SDDV via intraperitoneal injection, while the farm dataset comprised 2,011 fish (1,031 diseased, 754 healthy survivors) from a commercial sea-based natural outbreak. Resistance was defined as survival time, survival status (alive/dead), and survival 50% (alive/dead at 50% survival). Variance components and genetic correlations were estimated using GBLUP models, with challenge and batch fitted as fixed effects and the genomic relationship matrix as the random effect. Genomic estimated breeding value (gEBV) prediction accuracy for farm survival was evaluated using fivefold cross-validation (10 replicates) stratified by dataset under four scenarios including within-farm prediction and cross-environment predictions from laboratory challenges within the same batch, across mixed batches, and from a different batch. Heritability estimates (h² ± SE) ranged from 0.17 ± 0.05 to 0.44 ± 0.06 for laboratory challenges and 0.73 ± 0.03 to 0.81 ± 0.02 for farm survival. Genetic correlations (r­g ± SE) between laboratory and field resistance were high within spawning batch (0.85 ± 0.09 to 0.86 ± 0.05) but moderate across batches (0.62 ± 0.15 to 0.75 ± 0.11), indicating low genotype-by-environment interaction among related fish but moderate re-ranking across unrelated groups. Prediction accuracy for field survival was highest when using farm data (0.54 ± 0.07), moderate when trained on laboratory data from related populations (0.25-0.38), and lowest when trained on unrelated batches (0.17-0.19). These results demonstrate that SDDV resistance measured under controlled laboratory infection reflects survival under farm conditions when training and validation populations are genetically connected. Laboratory challenges therefore provide an effective and biosecure means of generating phenotypic data for genomic selection, enabling estimation of breeding values for unchallenged broodstock and reducing reliance on unpredictable farm outbreaks. This study provides the first cross-environment validation of genomic prediction for disease resistance in barramundi, supporting the integration of genomic selection into tropical aquaculture breeding programs to enhance disease resilience, productivity, and long-term farm sustainability.

Keywords: 2026

How to Cite:

Domingos, J., Jerry, D., nguyen, v., Poon, Z. & shen, x., (2026) “Cross-environment genomic prediction of scale drop disease resistance using laboratory challenge and field survival data in barramundi Lates calcarifer”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2285315. doi: https://doi.org/10.31274/wcgalp.23647

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