Practical application of daughter yield deviations in dairy cattle breeding

Accuracy of genomic breeding values revisited: Assessment of two established approaches and a novel one to determine the accuracy in two-step genomic prediction

Selection decisions in genomic selection schemes are made based on genomic breeding values (GBV) of candidates. Thus, the accuracy of GBV is a relevant parameter, as it reflects the stability of prediction and the possibility that the GBV might change when more information becomes available. Accuracy of genomic prediction defined as the correlation between GBV and true breeding values (TBV), however, is difficult to assess, considering TBV of the candidates are not available in reality. In previous studies, several methods were proposed to assess the accuracy of GBV including methods using population parameters or parameters inferred from mixed-model equations. In practice, most approaches tended to overestimate the accuracy of genomic prediction. We thus tested approaches used in previous studies in order to assess the magnitude of bias. Analyses were performed based on simulated data under a variety of scenarios mimicking different livestock breeding programmes. Furthermore, we proposed a novel method and tested it both with simulated data and in a real Holstein data set. The new method provided a better prediction for the accuracy of GBV in the simulated scenarios.

Contribution of domestic production records, Interbull estimated breeding values, and single nucleotide polymorphism genetic markers to the single-step genomic evaluation of milk production

Estimated breeding values (EBV) for first-lactation milk production of Holstein cattle in the Czech Republic were calculated using a conventional animal model and by single-step prediction of the genomic enhanced breeding value. Two overlapping data sets of milk production data were evaluated: (1) calving years 1991 to 2006, with 861,429 lactations and 1,918,901 animals in the pedigree and (2) calving years 1991 to 2010, with 1,097,319 lactations and 1,906,576 animals in the pedigree. Global Interbull (Uppsala, Sweden) deregressed proofs of 114,189 bulls were used in the analyses. Reliabilities of Interbull values were equivalent to an average of 8.53 effective records, which were used in a weighted analysis. A total of 1,341 bulls were genotyped using the Illumina BovineSNP50 BeadChip V2 (Illumina Inc., San Diego, CA). Among the genotyped bulls were 332 young bulls with no daughters in the first data set but more than 50 daughters (88.41, on average) with performance records in the second data set. For young bulls, correlations of EBV and genomic enhanced breeding value before and after progeny testing, corresponding average expected reliabilities, and effective daughter contributions (EDC) were calculated. The reliability of prediction pedigree EBV of young bulls was 0.41, corresponding to EDC=10.6. Including Interbull deregressed proofs improved the reliability of prediction by EDC=13.4 and including genotyping improved prediction reliability by EDC=6.2. Total average expected reliability of prediction reached 0.67, corresponding to EDC=30.2. The combination of domestic and Interbull sources for both genotyped and nongenotyped animals is valuable for improving the accuracy of genetic prediction in small populations of dairy cattle.

Genetic evaluation of dairy cattle using a simple heritable genetic ground

The evaluation of an animal is based on production records, adjusted for environmental effects, which gives a reliable estimation of its breeding value. Highly reliable daughter yield deviations are used as inputs for genetic marker evaluation. Genetic variability is explained by particular loci and background polygenes, both of which are described by the genomic breeding value selection index. Automated genotyping enables the determination of many single-nucleotide polymorphisms (SNPs) and can increase the reliability of evaluation of young animals (from 0.30 if only the pedigree value is used to 0.60 when the genomic breeding value is applied). However, the introduction of SNPs requires a mixed model with a large number of regressors, in turn requiring new algorithms for the best linear unbiased prediction and BayesB. Here, we discuss a method that uses a genomic relationship matrix to estimate the genomic breeding value of animals directly, without regressors. A one-step procedure evaluates both genotyped and ungenotyped animals at the same time, and produces one common ranking of all animals in a whole population. An augmented pedigree-genomic relationship matrix and the removal of prerequisites produce more accurate evaluations of all connected animals.

Estimation of genetic parameters for monthly egg production in laying hens based on random regression models

Heritability and genetic correlations of monthly egg production under random regression models were estimated. Three layer lines (A22, A88, K66) in six consecutive generations were analysed. A22 (13,770 recorded hens) and A88 (13,950 recorded hens) are maternal lines of Rhode Island White birds selected on egg production and shell colour; K66 (9,351 recorded birds) is a paternal line of Rhode Island Red birds selected on egg weight. Eight models with different orders of Legendre polynomials were applied. Adequacy of the models was checked by the Akaike Information Criterion. According to the most adequate model including second order Legendre polynomials for fixed effects and third order for additive genetic and permanent environmental effects, relatively high heritabilities were estimated in the first (h2=0.3) and final (h2 above 0.3) periods of production with a substantial decrease in heritability during the egg production peak. Methodology based on random regression animal models can be recommended for genetic evaluation of laying hens.