Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations

• Published in: Genetics Selection Evolution
• Main Authors: Tsai, Hsin-Yuan, Hamilton, Alastair, Tinch, Alan E., Guy, Derrick R., Bron, James E., Taggart, John B., Gharbi, Karim, Stear, Michael, Matika, Oswald, Pong-Wong, Ricardo, Bishop, Steve C., Houston, Ross D.
• Format: Article in Journal/Newspaper
• Language: English
• Published: BioMed Central 2016
• Subjects: Atlantic salmon
• Online Access: https://eprints.gla.ac.uk/121328/; https://eprints.gla.ac.uk/121328/1/121328.pdf

Background Sea lice have significant negative economic and welfare impacts on marine Atlantic salmon farming. Since host resistance to sea lice has a substantial genetic component, selective breeding can contribute to control of lice. Genomic selection uses genome-wide marker information to predict breeding values, and can achieve markedly higher accuracy than pedigree-based methods. Our aim was to assess the genetic architecture of host resistance to sea lice, and test the utility of genomic prediction of breeding values. Individual lice counts were measured in challenge experiments using two large Atlantic salmon post-smolt populations from a commercial breeding programme, which had genotypes for ~33 K single nucleotide polymorphisms (SNPs). The specific objectives were to: (i) estimate the heritability of host resistance; (ii) assess its genetic architecture by performing a genome-wide association study (GWAS); (iii) assess the accuracy of predicted breeding values using varying SNP densities (0.5 to 33 K) and compare it to that of pedigree-based prediction; and (iv) evaluate the accuracy of prediction in closely and distantly related animals. Results Heritability of host resistance was significant (0.22 to 0.33) in both populations using either pedigree or genomic relationship matrices. The GWAS suggested that lice resistance is a polygenic trait, and no genome-wide significant quantitative trait loci were identified. Based on cross-validation analysis, genomic predictions were more accurate than pedigree-based predictions for both populations. Although prediction accuracies were highest when closely-related animals were used in the training and validation sets, the benefit of having genomic-versus pedigree-based predictions within a population increased as the relationships between training and validation sets decreased. Prediction accuracy reached an asymptote with a SNP density of ~5 K within populations, although higher SNP density was advantageous for cross-population prediction. Conclusions Host ...