Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels

Abstract

The objectives of this study were: (i) to assess genotype imputation accuracy in different scenarios using genomewide single nucleotide polymorphisms (SNP) data from a population comprising two generations of farmed Atlantic salmon and (ii) to assess the accuracy of genomic predictions for a quantitative trait (body weight) using the imputed genotypes. The pedigree consisted of 53 parents and 1069 offspring genotyped using a high-density SNP panel (50 K). Two groups were created: Group A: 90% of the offspring were included into training and 10% into validation sets; Group B: 10% of the offspring were included into training and 90% into validation sets. Different scenarios of available genotypic information from relatives were tested for the two groups previously described. Imputation was performed using three in silico low-density panels (0.5, 3 and 6 K) with all markers except the markers present on the low-density panel masked in the validation sets. The accuracy of genomic selection was tested using the scenarios that resulted in the best and the worst imputation accuracy for the three low density panels and were compared to accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions using the 50 K SNP panel. In general, imputation accuracy ranged from 0.74 to 0.98 depending on scenario. For the best scenario with the highest number of animals in reference population (Group A), the accuracy of imputation ranged from 0.95 to 0.98 depending on the low-density panel used. For the best scenario with the lowest number of animals in reference population (Group B), the accuracy of imputation ranged from 0.94 to 0.98 depending on the low-density panel used. In general, the number of SNPs in the low-density panels had a greater influence on the accuracy of imputation than the size of the reference set. The accuracies of genomic predictions using imputed genotypes, ranging from 0.71 to 0.73, outperformed PBLUP (0.66) and were identical or very similar to the use of all true genotype data (0.73). The high imputation and genomic prediction accuracy suggest that the imputation of genotypes from low density (0.5 to 3 K) to high density (50 K) could be a cost-effective strategy for the feasibility of the practical implementation of genomic selection in Atlantic salmon.

Atlantic salmon (Salmo salar L.) genetics in the 21st century: taking leaps forward in aquaculture and biological understanding

“…; Yoshida et al . ). GS has been shown to be effective in salmon for which the training and test populations are closely related (such as in a typical sib‐testing scheme), but the ability to predict breeding values in animals more distantly related to the training population is rather limited (Tsai et al .…”

Advances in genetic improvement for salmon and trout aquaculture: the Chilean situation and prospects

“…; Yoshida et al . 2018b). The increase in accuracy of selection provided by GS for resistance to P. salmonis , sea lice and other important diseases, will be of major commercial benefit to the Chilean salmon farming industry.…”

“…; Yoshida et al . 2018b). Moreover, the identification of functional causative variants, also called quantitative trait nucleotides (QTN), will maximize genomic prediction accuracies (Pérez‐Enciso et al .…”

“…For instance, the requirement of a large training population, consisting of thousands of individuals with phenotypes and genotypes, and the cost of genotyping a large number of candidates to achieve an appropriate selection intensity are most likely prohibitively expensive. However, the use of genotyping strategies exploiting a combination of low-and medium-density SNP panels and genotype imputation have proven effective in increasing selection accuracy compared to PBLUP while minimizing the costs of genotyping, thus representing a cost-effective alternative for the implementation of GS in aquaculture species (Tsai et al 2017;Yoshida et al 2018b). The increase in accuracy of selection provided by GS for resistance to P. salmonis, sea lice and other important diseases, will be of major commercial benefit to the Chilean salmon farming industry.…”

“…For instance, GS increased selection accuracy compared to traditional pedigree-based selection in salmon and trout. Although there is null published information about objective economical appraisal of the implementation of GS in aquaculture breeding programmes, the development and implementation of effective genotype imputation strategies will promote the establishment of cost-effective GS for traits that are difficult to measure in selection candidates (Tsai et al 2017;Yoshida et al 2018b). Moreover, the identification of functional causative variants, also called quantitative trait nucleotides (QTN), will maximize genomic prediction accuracies (P erez-Enciso et al 2015) within and across populations, decreasing the need to obtain phenotypes for each generation and population (i.e.…”