Imputation of parent-offspring trios and their effect on accuracy of genomic prediction using Bayesian method

The objective of this study was to evaluate the imputation accuracy of parent-offspring trios under different scenarios. By using simulated datasets, the performance Bayesian LASSO in genomic prediction was also examined. The genome consisted of 5 chromosomes and each chromosome was set as 1 Morgan length. The number of SNPs per chromosome was 10000. One hundred QTLs were randomly distributed across chromosomes. Three low density SNP panels, containing 0.5k, 1k and 5k SNPs, were generated from the 10k panel. Six scenarios were evaluated, each containing two trios (dam, sire and offspring) and sire of each dam for parent-offspring pair data. These scenarios were compared from completely genotyped offspring to low-density genotyped and dams that were completely genotyped, low density genotyped and non-genotyped. It was assumed that the genotypes of the offspring’s sires were available. The Beagle 3.3.2 program was used for imputation of parent-offspring trios. The Bayesian LASSO were used to estimate the marker effects using the R package of “BLR”. The results showed that accuracy of both imputation and genomic evaluation was influenced by imputation errors. Imputation accuracy ranged from 0.67 to 0.96 for genotyped individuals. Genotype imputation accuracy increased with increasing marker density of low-density genotyping platform and with dams having high-density genotypes. Results showed that imputation accuracies decreased significantly (P < 0.05) when dam was non-genotyped and both of offspring were low-density genotyped. In case of factors affecting imputation accuracy, the imputation accuracy of SNPs with low MAF increased considerably when a dam was completely genotyped. Imputation of non-genotyped individuals can help to include valuable phenotypes for genome-wide association studies or for genomic prediction, especially when the non-genotyped individuals have genotyped offspring.