This study investigated the reliability of genomic prediction (GP) using breed origin of alleles (BOA) approach in the Nordic Red (RDC) population, which has an admixed population structure. The RDC population consists of animals with varying degrees of genetic materials from the Danish Red (RDM), Swedish Red (SRB), Finnish Ayrshire (FAY), and Holstein (HOL) because bulls have been used across the breeds. The BOA approach was tested using 39,550 RDC animals in the reference population and 11,786 in the validation population. Deregressed proofs (DRP) of milk, fat and protein were used as response variable for GP. Direct genomic breeding values (DGV) for animals in the validation population were calculated with (BOA model) or without (joint model) considering breed origin of alleles. The joint model assumed homogeneous marker effects and a single set of marker effects were estimated, whereas BOA model assumed heterogeneous marker effects, and different sets of marker effects were estimated across the breeds. For the BOA approach, we tested scenarios assuming both correlated (BOA_cor) and uncorrelated (BOA_uncor) marker effects between the breeds. Additionally, we investigated GP using a standard Illumina 50K chip and including SNP selected from imputed whole-genome sequencing (50K+WGS). We also studied the effect of estimating (co)variances for genome regions of different sizes to exploit the information of the genome regions contributing to the (co) variance between the breeds. Region sizes were set as 1 SNP, a group of 30 or 100 adjacent SNP, or the whole genome. Reliability of DGV was measured as squared correlations between DGV and DRP divided by the reliability of DRP. Across the 3 traits, in general, RS30 and RS100 SNP yielded the highest reliabilities. Including WGS SNP improved reliabilities in almost all scenarios (0.297 on average for 50K and 0.307 on average for 50K+WGS). The BOA_uncor (0.233 on average) was inferior to the joint model (0.339 on average), but the reliabilities obtained using BOA_cor (0.334 on average) in most cases were not significantly different from those obtained using the joint model. The results indicate that both including additional whole-genome sequencing SNP and dividing the genome into fixed regions improve GP in the RDC. The BOA models have the potential to increase the reliability of GP, but the benefit is limited in populations with a high exchange of genetic material for a long time, as is the case for RDC.
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.
This study investigates genomic prediction using a breed origin of alleles (BOA) model which accounts for BOA with a (i) definitive assignment to a breed or (ii) probabilities of assignments to each breed. Our BOA model estimates breed-specific marker effects based on genotypic and phenotypic information from the purebred and crossbred animals. A traditional combined analysis of all breeds' data implicitly assumes a correlation of one between the marker effects of the breeds, whereas our BOA model allows the estimation of these correlations. We used de-regresed proofs of production traits from the admixed Nordic Red Cattle population to evaluate the model and performed the analysis assuming marker effects between breeds are correlated or assuming they are uncorrelated. We found that using probabilities in the BOA model outperformed the BOA model, assuming the origin of each marker is known with certainty, especially when the marker effects were assumed uncorrelated between breeds.
The objective of this study was to evaluate genomic prediction using a breed origin of alleles (BOA) model for a multi-breed Irish beef cattle population. Our BOA model uses one matrix with allele counts for each breed to predict breed-specific marker effects, enabling to use crossbred and purebred animals in the reference population. Pedigree-based best linear unbiased prediction (PBLUP), a genomic model assuming a homogeneous population (SNP-BLUP) and BOA model were compared. Accuracy was estimated as the correlation between breeding values and corrected phenotype divided by the square root of heritability. The accuracy of predictions using BOA increased by 82% and 42% for crossbred and purebred animals, respectively, compared with PBLUP, and improvements of BOA over SNP-BLUP were of 6% for crossbred animals.
Selection has emphasized animal growth, leading to an increase in their mature size affecting in some cases the pregnancy of the cows and the efficiency of the systems. Usually, crossbreeding improve productivity because of the genetic effects that the cows exploit, but the impact on mature weight (MW) has not been studied. The present study aimed at estimating MW and genetic parameters associated with the MW in crosses between two British breeds: Hereford (H/H) and Angus (A/A), a Continental: Salers (S/S), and a Zebu: Nelore (N/N). MW was analyzed at 4; 4.5; 5; 5.5 and 6 years of age using a repeated-measure sire model. For parameters estimation, an additive – dominant model was used including the fixed effects of breed group, contemporary group, and age as covariate linear and quadratic, with the linear regression fitted by breed group. Permanent environmental and sire were included as random effects. According to the results, it is expected to observe heterosis between H/H and N/N, however, the structure of the data may not be enough for estimate accurately the genetic parameters in this trait. The A/H, N/H, S/H, S/SH and H/NH cows were heavier than the H/H cows. All the breed groups continue gaining weight until six years of age. The results revealed that British crossbred animals are heavier than H/H at the first crossing but not in the following. Crossbred cows with proportions of 0.5 and greater for the Continental breed are heavier than H/H cows. Crosses between British and Zebu cows have higher mature weight than H/H at the first crossing and in backcrosses toward the British in all ages.
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