Verification of SNPs Associated with Growth Traits in Two Populations of Farmed Atlantic Salmon
AbstractUnderstanding the relationship between genetic variants and traits of economic importance in aquaculture species is pertinent to selective breeding programmes. High-throughput sequencing technologies have enabled the discovery of large numbers of SNPs in Atlantic salmon, and high density SNP arrays now exist. A previous genome-wide association study (GWAS) using a high density SNP array (132K SNPs) has revealed the polygenic nature of early growth traits in salmon, but has also identified candidate SNPs showing suggestive associations with these traits. The aim of this study was to test the association of the candidate growth-associated SNPs in a separate population of farmed Atlantic salmon to verify their effects. Identifying SNP-trait associations in two populations provides evidence that the associations are true and robust. Using a large cohort (N = 1152), we successfully genotyped eight candidate SNPs from the previous GWAS, two of which were significantly associated with several growth and fillet traits measured at harvest. The genes proximal to these SNPs were identified by alignment to the salmon reference genome and are discussed in the context of their potential role in underpinning genetic variation in salmon growth. View Full-Text
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Tsai, H.Y.; Hamilton, A.; Guy, D.R.; Tinch, A.E.; Bishop, S.C.; Houston, R.D. Verification of SNPs Associated with Growth Traits in Two Populations of Farmed Atlantic Salmon. Int. J. Mol. Sci. 2016, 17, 5.
Tsai HY, Hamilton A, Guy DR, Tinch AE, Bishop SC, Houston RD. Verification of SNPs Associated with Growth Traits in Two Populations of Farmed Atlantic Salmon. International Journal of Molecular Sciences. 2016; 17(1):5.Chicago/Turabian Style
Tsai, Hsin Y.; Hamilton, Alastair; Guy, Derrick R.; Tinch, Alan E.; Bishop, Steve C.; Houston, Ross D. 2016. "Verification of SNPs Associated with Growth Traits in Two Populations of Farmed Atlantic Salmon." Int. J. Mol. Sci. 17, no. 1: 5.
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