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bioRxivGenomicsPreprint

Linking genomic prediction for muscle fat content in Atlantic salmon to underlying changes in lipid metabolism regulation

Thomas Nelson HarveyiD, Hanne DvergedaliD, Lars GrønvoldiD, Yang Oh JiniD, Jørgen ØdegårdiD, Sven Arild Korsvoll, Tim Martin KnutseniD, Torgeir Rhoden HvidsteniD, Simen R. SandveiD

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Abstract

Muscle fat content is an important production trait in Atlantic salmon (Salmo salar) because it influences the flavor, texture, and nutritional properties of the fillet. Genomic selection can be applied to alter muscle fat content, however how such selection changes the underlying molecular physiology of these animals is unknown. Here, we examine the link between genomic prediction and underlying molecular physiology by correlating genomic breeding values for fat content to liver gene expression in 184 fish. We found that Salmon with higher genomic breeding values had higher expression of genes in lipid metabolism pathways. This included key lipid metabolism genes hmgcrab, fasn-b, fads2d5, and fads2d6, and lipid transporters fatp2f, fabp7b, and apobc. We also found several regulators of lipid metabolism with negative correlation to genomic breeding vales, including pparg-b, fxr-a, and fxr-b. A quantitative trait loci analysis for variation in gene expression levels (eQTLs) for 167 trait associated genes found that 71 genes had at least one eQTL, and that most were trans eQTLs. Closer examination revealed distinct eQTL clustering on chromosomes 3 and 6, indicating the presence of putative common regulator in these regions. Taken together, these results suggest that increased fat content in high genomic breeding value salmon is associated with elevated lipid synthesis, elevated lipid transport, and reduced glycerolipid breakdown; and that this is at least partly achieved by selection on genetic variants that impact the function of top-level transcription factors involved in liver metabolism. Our study sheds light on how genomic selection alters lipid content in Atlantic salmon, and the results could be used to prioritize SNPs to improve the efficiency of genomic selection in the future.

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