Genomic prediction in Nordic Red dairy cattle considering breed origin of alleles

Segregation between breeds and local breed proportions in genetic and genomic models for crossbreds

BACKGROUND

The breeding value of a crossbred individual can be expressed as the sum of the contributions from each of the contributing pure breeds. In theory, the breeding value should account for segregation between breeds, which results from the difference in the mean contribution of loci between breeds, which in turn is caused by differences in allele frequencies between breeds. However, with multiple generations of crossbreeding, how to account for breed segregation in genomic models that split the breeding value of crossbreds based on breed origin of alleles (BOA) is not known. Furthermore, local breed proportions (LBP) have been modelled based on BOA and is a concept related to breed segregation. The objectives of this study were to explore the theoretical background of the effect of LBP and how it relates to breed segregation and to investigate how to incorporate breed segregation (co)variance in genomic BOA models.

RESULTS

We showed that LBP effects result from the difference in the mean contribution of loci between breeds in an additive genetic model, i.e. breed segregation effects. We found that the (co)variance structure for BS effects in genomic BOA models does not lead to relationship matrices that are positive semi-definite in all cases. However, by setting one breed as a reference breed, a valid (co)variance structure can be constructed by including LBP effects for all other breeds and assuming them to be correlated. We successfully estimated variance components for a genomic BOA model with LBP effects in a simulated example.

CONCLUSIONS

Breed segregation effects and LBP effects are two alternative ways to account for the contribution of differences in the mean effects of loci between breeds. When the covariance between LBP effects across breeds is included in the model, a valid (co)variance structure for LBP effects can be constructed by setting one breed as reference breed and fitting an LBP effect for each of the other breeds.

Multibreed genomic prediction using summary statistics and a breed-origin-of-alleles approach

Because of an increasing interest in crossbreeding between dairy breeds in dairy cattle herds, farmers are requesting breeding values for crossbred animals. However, genomically enhanced breeding values are difficult to predict in crossbred populations because the genetic make-up of crossbred individuals is unlikely to follow the same pattern as for purebreds. Furthermore, sharing genotype and phenotype information between breed populations are not always possible, which means that genetic merit (GM) for crossbred animals may be predicted without the information needed from some pure breeds, resulting in low prediction accuracy. This simulation study investigated the consequences of using summary statistics from single-breed genomic predictions for some or all pure breeds in two- and three-breed rotational crosses, rather than their raw data. A genomic prediction model taking into account the breed-origin of alleles (BOA) was considered. Because of a high genomic correlation between the breeds simulated (0.62-0.87), the prediction accuracies using the BOA approach were similar to a joint model, assuming homogeneous SNP effects for these breeds. Having a reference population with summary statistics available from all pure breeds and full phenotype and genotype information from crossbreds yielded almost as high prediction accuracies (0.720-0.768) as having a reference population with full information from all pure breeds and crossbreds (0.753-0.789). Lacking information from the pure breeds yielded much lower prediction accuracies (0.590-0.676). Furthermore, including crossbred animals in a combined reference population also benefitted prediction accuracies in the purebred animals, especially for the smallest breed population.

Development and Validation of a 54K Genome-Wide Liquid SNP Chip Panel by Target Sequencing for Dairy Goat

As an important genotyping platform, SNP chips are essential for implementing genomic selection. In this article, we introduced the development of a liquid SNP chip panel for dairy goats. This panel contains 54,188 SNPs based on genotyping by targeted sequencing (GBTS) technology. The source of SNPs in the panel were from the whole-genome resequencing of 110 dairy goats from three European and two Chinese indigenous dairy goat breeds. The performance of this liquid SNP chip panel was evaluated by genotyping 200 additional goats. Fifteen of them were randomly selected for whole-genome resequencing. The average capture ratio of the panel design loci was 98.41%, and the genotype concordance with resequencing reached 98.02%. We further used this chip panel to conduct genome-wide association studies (GWAS) to detect genetic loci that affect coat color in dairy goats. A single significant association signal for hair color was found on chromosome 8 at 31.52-35.02 Mb. The TYRP1 gene, which is associated with coat color in goats, was identified to be located at this genomic region (chromosome 8: 31,500,048-31,519,064). The emergence of high-precision and low-cost liquid microarrays will improve the analysis of genomics and breeding efficiency of dairy goats.

A breed-of-origin of alleles model that includes crossbred data improves predictive ability for crossbred animals in a multi-breed population

BACKGROUND

Recently, crossbred animals have begun to be used as parents in the next generations of dairy and beef cattle systems, which has increased the interest in predicting the genetic merit of those animals. The primary objective of this study was to investigate three available methods for genomic prediction of crossbred animals. In the first two methods, SNP effects from within-breed evaluations are used by weighting them by the average breed proportions across the genome (BPM method) or by their breed-of-origin (BOM method). The third method differs from the BOM in that it estimates breed-specific SNP effects using purebred and crossbred data, considering the breed-of-origin of alleles (BOA method). For within-breed evaluations, and thus for BPM and BOM, 5948 Charolais, 6771 Limousin and 7552 Others (a combined population of other breeds) were used to estimate SNP effects separately within each breed. For the BOA, the purebreds' data were enhanced with data from ~ 4K, ~ 8K or ~ 18K crossbred animals. For each animal, its predictor of genetic merit (PGM) was estimated by considering the breed-specific SNP effects. Predictive ability and absence of bias were estimated for crossbreds and the Limousin and Charolais animals. Predictive ability was measured as the correlation between PGM and the adjusted phenotype, while the regression of the adjusted phenotype on PGM was estimated as a measure of bias.

RESULTS

With BPM and BOM, the predictive abilities for crossbreds were 0.468 and 0.472, respectively, and with the BOA method, they ranged from 0.490 to 0.510. The performance of the BOA method improved as the number of crossbred animals in the reference increased and with the use of the correlated approach, in which the correlation of SNP effects across the genome of the different breeds was considered. The slopes of regression for PGM on adjusted phenotypes for crossbreds showed overdispersion of the genetic merits for all methods but this bias tended to be reduced by the use of the BOA method and by increasing the number of crossbred animals.

CONCLUSIONS

For the estimation of the genetic merit of crossbred animals, the results from this study suggest that the BOA method that accommodates crossbred data can yield more accurate predictions than the methods that use SNP effects from separate within-breed evaluations.