Abstract
Genomic information can contribute significantly to the increase in accuracy of genetic predictions compared to using pedigree relationships alone. The main objective of this study was to compare the prediction ability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic BLUP (ssGBLUP) models. Turkey records of feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking ability were provided by Hybrid Turkeys, Kitchener, Canada. This data was analyzed using pedigree-based and single-step genomic models. The genomic relationship matrix was calculated either using observed allele frequencies, all allele frequencies equal to 0.5 or with a different scaling. To avoid potential problems with inversion, three different weighting factors were applied to combine the genomic and pedigree matrices. Across the studied traits, ssGBLUP had higher heritability estimates and significantly outperformed PBLUP in terms of accuracy. Walking ability was genetically negatively correlated to body weight and breast meat yield; however, it was not correlated to feed conversion ratio (FCR) or residual feed intake (RFI). Body weight showed a moderate positive genetic correlation to feed conversion ratio, residual feed intake and breast meat yield. Feed conversion ratio was strongly correlated to residual feed intake (0.68 ± 0.06). There was almost no genetic correlation between breast meat yield and feed efficiency traits. Larger differences in accuracy between PBLUP and ssGBLUP were observed for traits with lower heritability. Results of the three weighting factors showed only slight differences and an increase in accuracy of prediction compared to PBLUP. Slightly different levels of bias were observed across the models, but were higher among the traits; BMY was the only trait that had a regression coefficient higher than 1 (1.38 to 1.41). We show that incorporating genomic information increases the prediction accuracy for preselection of young candidate turkeys for the five traits investigated. Single-step genomic prediction showed substantially higher accuracy estimates than the pedigree-based model, and only slight differences in bias were observed across the alternate models.
Highlights
The traditional mixed model equations (Henderson, 1975) are a versatile approach in animal breeding for fitting a variety of models and have been successful for many decades
Adjusted phenotypes corrected for fixed effects were obtained for all animals based on the pedigree-based best linear unbiased prediction (PBLUP) model (PBLUP full model)
The accuracy of the PBLUP model was calculated as the Pearson correlation coefficient between the adjusted phenotypes of the animals in the validation group from the PBLUP full model and their corresponding estimated breeding values (EBV) obtained from the PBLUP reduced model
Summary
The traditional mixed model equations (Henderson, 1975) are a versatile approach in animal breeding for fitting a variety of models and have been successful for many decades In such models, genetic effects are estimated based on the covariance structure proportional to the relationship matrix based on pedigree information. Incorporating genomic information in genetic evaluations was initially based on the two-step technique (e.g., VanRaden et al, 2009; Hayes et al, 2009a) This technique includes defining a response variable (e.g., de-regressed estimated breeding values; Garrick et al, 2009) for the genotyped animals, training the markers effects and applying a genomic prediction procedure based on the estimated marker effects for genotyped animals, usually the selection candidates. The omission of information from non-genotyped animals and selection bias based on availability of genomic information may affect the analyses (Misztal et al, 2009)
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