Poon, Zhi Weng Josiah, Vu, Nguyen Thanh, Shen, Xueyan, Gibson-Kueh, Susan, Loh, Jiun-Yan, Carrai, Maura, Priyanka Nelson, Sarah, Terence, Celestine, Chew, Jian Howe, Awate, Sunita, Dong, Ha Thanh, Senapin, Saengchan, Vij, Shubha, Jerry, Dean R., and Domingos, Jose A. (2026) Influence of infection route and population origin on survival and genomic prediction of scale drop disease resistance in barramundi (Lates calcarifer). Aquaculture, 627 (1). 744530.

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Abstract

Scale drop disease virus (SDDV) is a major threat to barramundi (Lates calcarifer) aquaculture, causing mass mortalities and severe economic losses. While laboratory challenge models based on intraperitoneal (IP) injection based on Singaporean stocks have been explored for genomic selection, the extent to which these results generalise across alternative infection routes and different population backgrounds remains poorly understood. This study evaluated the influence of infection route (IP injection versus cohabitation) and population origin (Singapore versus Malaysia) on survival, genetic parameter estimation, and genomic prediction performance for SDDV resistance using 1709 individuals genotyped using the ThermoFisher MyDesign Axiom barramundi 70 k SNP array in a within-tank mixed -route and -origin challenge design. Malaysian fish exhibited significantly higher survivability than Singaporean fish under IP injection (49.1% vs 25.6%), whereas no significant population differences were detected under cohabitation. Heritability estimates were moderate, ranging from 0.12 to 0.44, and were consistently higher for survival time (continuous trait) than for survival status (binary trait). Censoring-aware Cox-GBLUP analysis confirmed that the ranking of breeding values for survival time was robust to right censoring (r = 0.97 with linear GBLUP). High genetic correlations (rg = 0.74 to 0.92) and consistent clinical signs indicate that both infection routes are biologically valid models with substantial shared genetic control for laboratory SDDV resistance. Within-dataset genomic prediction accuracies were comparable (accuracy = 0.19 to 0.54) between survival time and survival status; however predictive ability was consistently higher for survival time and under IP infection (r = 0.21 to 0.33), likely reflecting a stronger genetic signal and more synchronous disease progression than survival status and cohabitation exposure. Across-dataset analyses showed positive cross-scenario prediction between infection routes (accuracy = 0.18 to 0.41), but poor transferability between origins (accuracy = 0.10 to 0.15). These results indicate that survival time under IP injection provides the most reliable signal for genetic evaluation and is the preferred trait for genomic selection of SDDV resistance under laboratory conditions, with population origin representing a key factor influencing resistance expression and implementation.

Item ID:	94038
Item Type:	Article (Research - C1)
ISSN:	1873-5622
Copyright Information:	© 2026 Published by Elsevier B.V. CC BY Attribution 4.0 International.
Date Deposited:	17 Sep 2026 06:49
FoR Codes:	30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3005 Fisheries sciences > 300501 Aquaculture @ 100%
SEO Codes:	10 ANIMAL PRODUCTION AND ANIMAL PRIMARY PRODUCTS > 1002 Fisheries - aquaculture > 100202 Aquaculture fin fish (excl. tuna) @ 100%
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