Abstract
Accurate and repeated whole-body fat (WBF) measurements across production phases are important for optimizing feed utilization, reducing production waste, safeguarding fish health, ensuring product quality and improving overall salmon production. However, the chemical extraction (reference) methods for WBF recording, although precise and accurate, are costly, destructive and have limited applicability for repeated measurements on live fish. This study validates two digital phenotyping technologies: Dielectric spectroscopy (DS) and Near-infrared spectroscopy (NIR) against the reference Soxhlet chemical extraction method. DS is a fast and non-destructive commercial fat meter, whereas the specially designed NIR interactance allows deep penetration (up to 10 mm) through the fish skin into the body. Approximately 2800 fish belonging to 35 full sibs fish families were recorded for WBF at mean body weight (BW) of ∼110, 300 and 750 g in parr, pre-smolt and post-smolt phases, respectively. The WBF percentage changed across the studied production phases: with parr having a mean of 11.3 % and coefficient of variation (CV = 11 %), pre-smolt decreasing slightly in both mean 10.9 – 11.1 % and CV (5.5 – 8.8 %) and post-smolt with an increase to 14.9 % and CV (8.5 %). Both methods performed well relative to the reference, with NIR (R2 = 0.77 – 0.91) slightly outperforming the DS (R2 = 0.61 – 0.71). Significantly high genetic estimates for NIR (h2 = 0.57 ± 0.04 – 0.62 ± 0.06) and DS (h2 = 0.38 ± 0.07 – 0.56 ± 0.10) signify the potential use of these digital technologies for improving WBF in future selective breeding. The low genetic correlations (rg = 0.22 ± 0.13 – 0.33 ± 0.14) across fresh and seawater phases raise questions about the generalizability of metabolic, lipid and feed efficiency research conducted in parr and pre-smolt phases in freshwater, to the post-smolt stage in seawater. The study highlighted the unexplored detail of the genetic regulation of WBF as fish undergoes production phases. Overall, these results will add to our understanding of genetic architecture for WBF and pave the way for digital phenotyping technologies to record complex but economically important traits and refine breeding strategies.
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