Single-step genomic BLUP (ssGBLUP) has been widely used in genomic evaluation due to relatively higher prediction accuracy and simplicity of use. The prediction accuracy from ssGBLUP depends on the amount of information available concerning both genotype and phenotype. This study investigated how information on genotype and phenotype that had been acquired from previous generations influences the prediction accuracy of ssGBLUP, and thus we sought an optimal balance about genotypic and phenotypic information to achieve a cost-effective and computationally efficient genomic evaluation. We generated two genetically correlated traits (h2 = 0.35 for trait A, h2 = 0.10 for trait B and genetic correlation 0.20) as well as two distinct populations mimicking purebred swine. Phenotypic and genotypic information in different numbers of previous generations and different genotyping rates for each litter were set to generate different datasets. Prediction accuracy was evaluated by correlating genomic estimated breeding values with true breeding values for genotyped animals in the last generation. The results revealed a negligible impact of previous generations that lacked genotyped animals on the prediction accuracy. Phenotypic and genotypic data, including the most recent three to four generations with a genotyping rate of 40% or 50% for each litter, could lead to asymptotic maximum prediction accuracy for genotyped animals in the last generation. Single-step genomic best linear unbiased prediction yielded an optimal balance about genotypic and phenotypic information to ensure a cost-effective and computationally efficient genomic evaluation of populations of polytocous animals such as purebred pigs.

Most dairy cattle populations found in different countries around the world are small to medium sized and use many artificial insemination bulls imported from different foreign countries. The Walloon population in the southern part of Belgium is a good example for such a small-scale population. Wallonia has also a very active community of Holstein breeders requesting high level genetic evaluation services. Single-step Genomic BLUP (ssGBLUP) methods allow the simultaneous use of genomic, pedigree and phenotypic information and could reduce potential biases in the estimation of genomically enhanced breeding values (GEBV). Therefore, in the context of implementing a Walloon genomic evaluation system for Holsteins, it was considered as the best option. However, in contrast to multi-step genomic predictions, natively ssGBLUP will only use local phenotypic information and is unable to use directly important other sources of information coming from abroad, for example Multiple Across Country Evaluation (MACE) results as provided by the Interbull Center (Uppsala, Sweden). Therefore, we developed and implemented single-step Genomic Bayesian Prediction (ssGBayes), as an alternative method for the Walloon genomic evaluations. The ssGBayes method approximated the correct system of equations directly using estimated breeding values (EBV) and associated reliabilities (REL) without any explicit deregression step. In the Walloon genomic evaluation, local information refers to Walloon EBV and REL and foreign information refers to MACE EBV and associated REL. Combining simultaneously all available genotypes, pedigree, local and foreign information in an evaluation can be achieved but adding contributions to left-hand and right-hand sides subtracting double-counted contributions. Correct propagation of external information avoiding double counting of contributions due to relationships and due to records can be achieved. This ssGBayes method computed more accurate predictions for all types of animals. For example, for genotyped animals with low Walloon REL (<0.25) without MACE results but sired by genotyped bulls with MACE results, the average increase of REL for the studied traits was 0.38 points of which 0.08 points could be traced to the inclusion of MACE information. For other categories of genotyped animals, the contribution by MACE information was also high. The Walloon genomic evaluation system passed for the first time the Interbull GEBV tests for several traits in July 2013. Recent experiences reported here refer to its use in April 2016 for the routine genomic evaluations of milk production, udder health and type traits. Results showed that the proposed methodology should also be of interest for other, similar, populations.

We studied the effect of including genomic data for cows in the reference population of single-step evaluations. Deregressed individual cow genetic evaluations (DRP) from milk production evaluations of Nordic Red Dairy cattle were used to estimate the single-step breeding values. Validation reliability and bias of the evaluations were calculated with four data sets including different amount of DRP record information from genotyped cows in the reference population. The gain in reliability was from 2% to 4% units for the production traits, depending on the used DRP data and the amount of genomic data. Moreover, inclusion of genotyped bull dams and their genotyped daughters seemed to create some bias in the single-step evaluation. Still, genotyping cows and their inclusion in the reference population is advantageous and should be encouraged.

An a posteriori granddaughter design was applied to estimate quantitative trait loci genotypes of sires with many sons in the US Holstein population. The results of this analysis can be used to determine concordance between specific polymorphisms and segregating quantitative trait loci. Determination of the actual polymorphisms responsible for observed genetic variation should increase the accuracy of genomic evaluations and rates of genetic gain. A total of 52 grandsire families, each with ⩾100 genotyped sons with genetic evaluations based on progeny tests, were analyzed for 33 traits (milk, fat and protein yields; fat and protein percentages; somatic cell score (SCS); productive life; daughter pregnancy rate; heifer and cow conception rates; service-sire and daughter calving ease; service-sire and daughter stillbirth rates; 18 conformation traits; and net merit). Of 617 haplotype segments spanning the entire bovine genome and each including ~5×106 bp, 5 cM and 50 genes, 608 autosomal segments were analyzed. A total of 19 335 unique haplotypes were found among the 52 grandsires. There were a total of 133 chromosomal segment-by-trait combinations, for which the nominal probability of significance for the haplotype effect was <10−8, which corresponds to genome-wide significance of <10−4. The number of chromosomal regions that met this criterion by trait ranged from one for rear legs (rear view) to seven for net merit. For each of the putative quantitative trait loci, at least one grandsire family had a within-family contrast with a t-value of >3. Confidence intervals (CIs) were estimated by the nonparametric bootstrap for the largest effect for each of nine traits. The bootstrap distribution generated by 100 samples was bimodal only for net merit, which had the widest 90% CI (eight haplotype segments). This may be due to the fact that net merit is a composite trait. For all other chromosomes, the CI spanned less than a third of the chromosome. The narrowest CI (a single haplotype segment) was found for SCS. It is likely that analysis by more advanced methods could further reduce CIs at least by half. These results can be used as a first step to determine the actual polymorphisms responsible for observed quantitative variation in dairy cattle.