Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss

Abstract

One of the top priorities of the aquaculture industry is the genetic improvement of economically important traits in fish, such as those related to processing and quality. However, the accuracy of genetic evaluations has been hindered by a lack of data on such traits from a sufficiently large population of animals. The objectives of this study were thus threefold: (i) to estimate genetic parameters of growth-, yield-, and quality-related traits in rainbow trout (Oncorhynchus mykiss) using three different phenotyping technologies [invasive and non-invasive: microwave-based, digital image analysis, and magnetic resonance imaging (MRI)], (ii) to detect quantitative trait loci (QTLs) associated with these traits, and (iii) to identify candidate genes present within these QTL regions. Our study collected data from 1,379 fish on growth, yield-related traits (body weight, condition coefficient, head yield, carcass yield, headless gutted carcass yield), and quality-related traits (total fat, percentage of fat in subcutaneous adipose tissue, percentage of fat in flesh, flesh colour); genotypic data were then obtained for all fish using the 57K SNP Axiom® Trout Genotyping array. Heritability estimates for most of the 14 traits examined were moderate to strong, varying from 0.12 to 0.67. Most traits were clearly polygenic, but our genome-wide association studies (GWASs) identified two genomic regions on chromosome 8 that explained up to 10% of the genetic variance (cumulative effects of two QTLs) for several traits (weight, condition coefficient, subcutaneous and total fat content, carcass and headless gutted carcass yields). For flesh colour traits, six QTLs explained 1–4% of the genetic variance. Within these regions, we identified several genes (htr1, gnpat, ephx1, bcmo1, and cyp2x) that have been implicated in adipogenesis or carotenoid metabolism, and thus represent good candidates for further functional validation. Finally, of the three techniques used for phenotyping, MRI demonstrated particular promise for measurements of fat content and distribution, while the digital image analysis-based approach was very useful in quantifying colour-related traits. This work provides new insights that may aid the development of commercial breeding programmes in rainbow trout, specifically with regard to the genetic improvement of yield and flesh-quality traits as well as the use of invasive and/or non-invasive technologies to predict such traits.

Genomic and Phenotypic Agreement Defines the Use of Microwave Dielectric Spectroscopy for Recording Muscle Lipid Content in European Seabass (Dicentrarchus labrax)

“…Previous comparisons between the dielectric spectrometer and the reference method have not calculated the CCC or reported the means and variances of both methods and their correlation needed for us to retrospectively calculate the CCC. However, we were able to calculate the CCC between magnetic resonance imaging and dielectric spectrometry in Rainbow trout ( Oncorhynchus mykiss ) from Blay et al (2021) , which was 0.59. Furthermore, the CCC values herein are lower or comparable to that obtained for fillet lipid content in Atlantic salmon ( Salmo salar ) using a commercial near infrared (NIR) scanner on whole fillet and the reference method (CCC = 0.63) or a laboratory based NIR system on ground samples (CCC = 0.68) with the reference method ( Difford et al, 2021 ).…”

“…The phenotypic correlations in the present study ranged from 0.61 to 0.71 which are slightly lower that the 0.80-0.82 reported previously for European seabass and the reference method (Saillant et al, 2009;Distell, 2011); as well as lower than the phenotypic correlation of 0.83 between low-field NMR and the dielectric spectrometer in 200 European seabass (Haffray et al, 2007). These are in range of the correlation 0.71 found when comparing the dielectric spectrometer to mid infrared spectroscopy on 359 European whitefish (Janhunen et al, 2017), as well as the correlation of 0.71 found when comparing magnetic resonance imaging and the dielectric spectrometer in 1,379 Rainbow trout (Blay et al, 2021). The reason for lower phenotypic correlations in the present study are not known, but we can speculate that changes in the diets and genetic makeup of the fish since the manufacturer created the calibration equation may be the cause of the lower reproducibility we observed for this equation.…”

Comparative Transcriptome Analysis Revealed Genes Involved in Sexual and Polyploid Growth Dimorphisms in Loach (Misgurnus anguillicaudatus)

“…Growth is one of the most important economic traits for aquaculture fish, and is of great significance to the development of the aquaculture industry [ 39 ]. Understanding the molecular mechanisms of sexual and polyploid growth dimorphisms will provide a theoretical basis for the cultivation of fast-growing populations with stable and uniform growth performance in aquaculture.…”

Genetic architecture and genomic selection of fatty acid composition predicted by Raman spectroscopy in rainbow trout

“…A piece of fin was sampled from each parent for DNA extraction and subsequent genomic analysis. Trout were reared under commercial conditions in the “Viviers de la Houtine” growing farm (Belin-Beliet, France); further details on rearing can be found in [ 46 ]. Fish were fed to satiation using extruded commercial feed: Neo start (17% lipids) and Neo CDC (23% lipids) (Le Gouessant, Lamballe, France) during the first stage, then Extra CDC AQL G25 (25% lipids) (Le Gouessant, Lamballe, France) and Viva Pro 7F NAT29 (30% lipids) (Aqualia, Arue, France) until the end of the experiment.…”

“…Furthermore, the relationships between FA composition and traits related to lipid deposition, weight, yield, or quality have not yet been characterised. Phenotype-based research has shown that total fat content (as measured with a Fatmeter) in the muscle increases with the growth and development of fish [ 45 , 46 ], but the genetic correlations between production traits and FA composition—which would play a crucial role in optimising the efficiency of breeding programmes—remain unknown.…”