Methodology for genetic evaluation of disease resistance in aquaculture species: challenges and future prospects

TLDR

Disease resistance in aquaculture species shows moderate to high heritability, yet genetic evaluation is hampered by challenges such as inadequate survival metrics, differing resistance aspects, and the exclusion of challenged individuals from selection, which reduces accuracy and intensity. The study aims to develop advanced statistical models and supplementary indicators, including within‑family selection strategies, to improve disease resistance breeding and leverage genomic information for scalable, cost‑effective selection. The authors propose indirect selection on correlated traits, QTL‑based selection, and genomic selection as methods to enable within‑family selection and harness genomic data for disease resistance improvement.

Abstract

Resistance against specific diseases affecting aquaculture species often show moderate to high heritabilities, and there is thus a large potential for genetic improvement. However, genetic evaluation of disease resistance based on survival under challenge testing still face some challenges that may complicate both recording and statistical analysis of disease resistance, e.g., susceptibility and time until death may be different aspects of resistance, or survival may be an inadequate measure of resistance. Hence, for some diseases, more advanced statistical modelling and/or supplementing indicators besides survival would be an advantage. Furthermore, tested individuals are usually excluded as selection candidates as they might be potential disease carriers, and thus pose a health risk. Under classical selection, this restriction reduces both accuracy and intensity of selection, as little or nothing is known about how sib selection candidates deviate from the family means. Thus, selection methods allowing within-family selection are of particular importance in selection for improved disease resistance based on disease challenge testing. Examples of such methods are indirect selection on correlated traits measurable on selection candidates, selection on identified quantitative trait loci and genomic selection. In the future, genomic information has the potential to substantially improve selective breeding for disease resistance traits, given that this information can be acquired on a massive scale and at an affordable cost.