Use of DNA pools of a reference population for genomic selection of a binary trait in Atlantic salmon - Dagnachew, Binyam; Aslam, Muhammad Luqman; Hillestad, Borghild; Meuwissen, Theo; Sonesson, Anna (Frontiers in Genetics, 2022)
Genomic selection has a great potential in aquaculture breeding since most traits are not directly measured on the candidates themselves. However, its implementation in these species has been hindered by staggering genotyping cost due to many individuals to genotype. In this study, we explored the potentials of DNA pooling for creating reference population as a tool for genomic selection for a binary trait. Two Datasets from SalmoBreed population challenged with salmonid alphavirus (SAV), which causes pancreases disease (PD), were used. Dataset-1 contains 855 individuals (478 survivors and 377 dead) which were used to develop four DNA pool samples (i.e., two pools each for dead and survival). Dataset-2 contains 914 individuals (435 survivors and 479 dead) belong to 65 full-sibling families and was used to develop in-silico DNA pools. SNP effects from the pool data were calculated based on allele frequencies estimated form the pools, and used to calculate genomic breeding values (GEBVs). The correlation between SNP effects estimated based on individual genotypes and pooled data increased from 0.3 to 0.912 when the number of pools increased from 1 to 200. Similar trend was also observed for the correlation between GEBVs, it increased from 0.84 to 0.976, as the number of pools per phenotype increased from 1 to 200. For dataset-1, the accuracy of prediction was 0.71 and 0.70 when the DNA pools were sequenced in 40X and 20X respectively, compared to accuracy of 0.73 for the SNPchip genotypes. For dataset-2, the accuracy of prediction increased from 0.574 to 0.691 when the number of in-silico DNA pools increased from 1 to 200. For this dataset, the accuracy of prediction for this dataset using individual genotypes was 0.712. Limited effect of sequencing depth on correlation of GEBVs and prediction accuracy was observed. Results showed that large number of pools are required to achieve as good prediction as individual genotypes, however, alternative effective pooling strategies should be studied to reduce the number of pools without reducing prediction power. Nevertheless, it is demonstrated that pooling of reference population can be used as a tool to optimize between cost and accuracy of selection.