Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation

Abstract

Genomic selection can be implemented in pig breeding at a reduced cost using genotype imputation. Accuracy of imputation and the impact on resulting genomic breeding values (gEBV) was investigated. High-density genotype data was available for 4,763 animals from a single pig line. Three low-density genotype panels were constructed with SNP densities of 450 (L450), 3,071 (L3k) and 5,963 (L6k). Accuracy of imputation was determined using 184 test individuals with no genotyped descendants in the data but with parents and grandparents genotyped using the Illumina PorcineSNP60 Beadchip. Alternative genotyping scenarios were created in which parents, grandparents, and individuals that were not direct ancestors of test animals (Other) were genotyped at high density (S1), grandparents were not genotyped (S2), dams and granddams were not genotyped (S3), and dams and granddams were genotyped at low density (S4). Four additional scenarios were created by excluding Other animal genotypes. Test individuals were always genotyped at low density. Imputation was performed with AlphaImpute. Genomic breeding values were calculated using the single-step genomic evaluation. Test animals were evaluated for the information retained in the gEBV, calculated as the correlation between gEBV using imputed genotypes and gEBV using true genotypes. Accuracy of imputation was high for all scenarios but decreased with fewer SNP on the low-density panel (0.995 to 0.965 for S1) and with reduced genotyping of ancestors, where the largest changes were for L450 (0.965 in S1 to 0.914 in S3). Exclusion of genotypes for Other animals resulted in only small accuracy decreases. Imputation accuracy was not consistent across the genome. Information retained in the gEBV was related to genotyping scenario and thus to imputation accuracy. Reducing the number of SNP on the low-density panel reduced the information retained in the gEBV, with the largest decrease observed from L3k to L450. Excluding Other animal genotypes had little impact on imputation accuracy but caused large decreases in the information retained in the gEBV. These results indicate that accuracy of gEBV from imputed genotypes depends on the level of genotyping in close relatives and the size of the genotyped dataset. Fewer high-density genotyped individuals are needed to obtain accurate imputation than are needed to obtain accurate gEBV. Strategies to optimize development of low-density panels can improve both imputation and gEBV accuracy.