Abstract The potential of genomic selection to improve production traits has been widely demonstrated in many aquaculture species. Atlantic salmon breeding programmes typically consist of sibling testing schemes, where traits that cannot be measured on the selection candidates are measured on the candidates’ siblings (such as disease resistance traits). While annual testing on close relatives is effective, it is expensive due to high genotyping and phenotyping costs. Therefore, accurate prediction of breeding values in distant relatives could significantly reduce the cost of genomic selection. The aims of this study were (i) to evaluate the impact of decreasing the genomic relationship between the training and validation populations on the accuracy of genomic prediction for two key target traits; body weight and resistance to sea lice; and (ii) to assess the interaction of genetic relationship with SNP density, a major determinant of genotyping costs. Phenotype and genotype data from two year classes of a commercial breeding population of Atlantic salmon were used. The accuracy of genomic predictions obtained within a year class was similar to that obtained combining the data from the two year classes for sea lice count (0.49 - 0.48) and body weight (0.63 - 0.61), but prediction accuracy was close to zero when the prediction was performed across year groups. Systematically reducing the relatedness between the training and validation populations within a year class resulted in decreasing accuracy of genomic prediction; when the training and validation populations were set up to contain no relatives with genomic relationships &gt;0.3, the accuracies fell from 0.48 to 0.27 for sea lice count and from 0.63 to 0.29 for body weight. Lower relatedness between training and validation populations also tended to result in highly biased predictions. No clear interaction between decreasing SNP density and relatedness between training and validation population was found. These results confirm the importance of genetic relationships between training and selection candidate populations in salmon breeding programmes, and suggests that prediction across generations using existing approaches would severely compromise the efficacy of genomic selection.

The impact of genetic relationship between training and validation populations on genomic prediction accuracy in Atlantic salmon