BACKGROUND: Product quality and production efficiency of Atlantic salmon are, to a large extent, influenced by the deposition and depletion of lipid reserves. Fillet lipid content is a heritable trait and is unfavourably correlated with growth, thus genetic management of fillet lipid content is needed for sustained genetic progress in these two traits. The laboratory-based reference method for recording fillet lipid content is highly accurate and precise but, at the same time, expensive, time-consuming, and destructive. Here, we test the use of rapid and cheaper vibrational spectroscopy methods, namely near-infrared (NIR) and Raman spectroscopy both as individual phenotypes and phenotypic predictors of lipid content in Atlantic salmon. RESULTS: Remarkably, 827 of the 1500 individual Raman variables (i.e. Raman shifts) of the Raman spectrum were significantly heritable (heritability (h2) ranging from 0.15 to 0.65). Similarly, 407 of the 2696 NIR spectral landscape variables (i.e. wavelengths) were significantly heritable (h2 = 0.27-0.40). Both Raman and NIR spectral landscapes had significantly heritable regions, which are also informative in spectroscopic predictions of lipid content. Partial least square predicted lipid content using Raman and NIR spectra were highly concordant and highly genetically correlated with the lipid content values ([Formula: see text] = 0.91-0.98) obtained with the reference method using Lin's concordance correlation coefficient (CCC = 0.63-0.90), and were significantly heritable ([Formula: see text] = 0.52-0.67). CONCLUSIONS: Both NIR and Raman spectral landscapes show substantial additive genetic variation and are highly genetically correlated with the reference method. These findings lay down the foundation for rapid spectroscopic measurement of lipid content in salmonid breeding programmes.
BACKGROUND: Product quality and production efficiency of Atlantic salmon are, to a large extent, influenced by the deposition and depletion of lipid reserves. Fillet lipid content is a heritable trait and is unfavourably correlated with growth, thus genetic management of fillet lipid content is needed for sustained genetic progress in these two traits. The laboratory-based reference method for recording fillet lipid content is highly accurate and precise but, at the same time, expensive, time-consuming, and destructive. Here, we test the use of rapid and cheaper vibrational spectroscopy methods, namely near-infrared (NIR) and Raman spectroscopy both as individual phenotypes and phenotypic predictors of lipid content in Atlantic salmon. RESULTS: Remarkably, 827 of the 1500 individual Raman variables (i.e. Raman shifts) of the Raman spectrum were significantly heritable (heritability (h2) ranging from 0.15 to 0.65). Similarly, 407 of the 2696 NIR spectral landscape variables (i.e. wavelengths) were significantly heritable (h2 = 0.27-0.40). Both Raman and NIR spectral landscapes had significantly heritable regions, which are also informative in spectroscopic predictions of lipid content. Partial least square predicted lipid content using Raman and NIR spectra were highly concordant and highly genetically correlated with the lipid content values ([Formula: see text] = 0.91-0.98) obtained with the reference method using Lin's concordance correlation coefficient (CCC = 0.63-0.90), and were significantly heritable ([Formula: see text] = 0.52-0.67). CONCLUSIONS: Both NIR and Raman spectral landscapes show substantial additive genetic variation and are highly genetically correlated with the reference method. These findings lay down the foundation for rapid spectroscopic measurement of lipid content in salmonid breeding programmes.
Authors: G Rovere; G de Los Campos; R J Tempelman; A I Vazquez; F Miglior; F Schenkel; A Cecchinato; G Bittante; H Toledo-Alvarado; A Fleming Journal: J Dairy Sci Date: 2018-12-20 Impact factor: 4.034
Authors: Hsin Yuan Tsai; Alastair Hamilton; Derrick R Guy; Alan E Tinch; Stephen C Bishop; Ross D Houston Journal: BMC Genet Date: 2015-05-19 Impact factor: 2.797
Authors: Siri S Horn; Anna K Sonesson; Aleksei Krasnov; Hooman Moghadam; Borghild Hillestad; Theo H E Meuwissen; Bente Ruyter Journal: Sci Rep Date: 2019-03-07 Impact factor: 4.379
Authors: Siri S Horn; Bente Ruyter; Theo H E Meuwissen; Borghild Hillestad; Anna K Sonesson Journal: Genet Sel Evol Date: 2018-05-02 Impact factor: 4.297
Authors: Nils Kristian Afseth; Katinka Dankel; Petter Vejle Andersen; Gareth Frank Difford; Siri Storteig Horn; Anna Sonesson; Borghild Hillestad; Jens Petter Wold; Erik Tengstrand Journal: Foods Date: 2022-03-26