Genomic predictions for crossbred dairy cows by combining solutions from purebred evaluation based on breed origin of alleles

Abstract

Genomic predictions have been applied for dairy cattle for more than a decade with great success, but genomic estimated breeding values (GEBV) are not widely available for crossbred dairy cows. The large reference populations already in place for genomic evaluations of many pure breeds makes it interesting to use the accurate solutions, in particular the estimated marker effects, from these evaluations for calculation of GEBV for crossbred heifers and cows. Effects of marker alleles in crossbred animals can depend on breed origin of the alleles (BOA). Therefore, our aim was to investigate if reliable GEBV for crossbred dairy cows can be obtained by combining estimated marker effects from purebred evaluations based on BOA. We used data on 5,467 Danish crossbred dairy cows with contributions from Holstein, Jersey, and Red Dairy Cattle breeds. We assessed BOA assignment on their genotypes and found that we could assign 99.3% of the alleles to a definite breed of origin. We compared GEBV for 2 traits, protein yield and interval between first and last insemination of cows, with 2 models that both combine estimated marker effects from the genomic evaluations of the pure breeds: a breed of origin model that accounts for BOA and a breed proportion model that only accounts for genomic breed proportions in the crossbred animals. We accounted for the difference in level between the purebred evaluations by including intercepts in the models based on phenotypic averages. The predictive ability for protein yield was significantly higher from the breed of origin model, 0.45 compared with 0.43 from the breed proportion model. Furthermore, for the breed proportion model, the GEBVs had level bias, which made comparison across groups with different breed composition skewed. We therefore concluded that reliable genomic predictions for crossbred dairy cows can be obtained by combining estimated marker effects from the genomic evaluations of purebreds using a model that accounts for BOA.

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

“…In addition, the predictive ability for crossbreds increased as the number of crossbred animals in the reference population increased. In our study, the predictive abilities of the methods that use SNP solutions from separate within-breed evaluations were not significantly different in most cases, while the BOM method proposed by Eiríksson et al [ 7 ] outperformed BPM in simulations and real data [ 7 , 8 ]. This may be due to the fact that in our study, GBP were calculated based on allele assignments, while Eiríksson et al [ 7 ] calculated them based on a regression on the markers of the pure breeds.…”

“…For a rotational crossbred simulated population (using purebred sires), the same software assigned 99.8% of the alleles [ 7 ]. For a real population, including simple crosses of Holstein, Jersey, and Red Dairy Cattle (i.e., first-generation crosses, three-way crosses, and backcrosses primarily), it assigned 99.3% of the alleles [ 8 ]. In our study, some alleles that could not be assigned to a specific breed had a 75% chance of originating from Limousin or Charolais; others had a 50% chance of originating from one of these breeds.…”

“…Using simulations, Eiríksson et al [ 7 ] reported that a method combining SNP effects that are estimated from separate within-breed genomic evaluations based on breed-of-origin of genome regions outperformed the method that was proposed by VanRaden et al [ 6 ] across the four generations evaluated, with the largest difference observed in the first generation. The same methods were compared in a real dairy crossbred population, with similar results [ 8 ]. The method based on the breed-of-origin of genome regions used in Eiríksson et al [ 7 ] relies on the estimation of the breed-of-origin (proportion) for each SNP allele (BOA), which, for a certain allele, can take only the values 0 or 1 when the allele can be assigned to a specific breed.…”

Developing flexible models for genetic evaluations in smallholder crossbred dairy farms

“…Our study has some limitations. First, we did not fit breed of origin models ( Vandenplas et al, 2016 ), because they only show small improvements in accuracy of estimates compared with models that ignore breed of origin ( Sevillano et al, 2017 ; VanRaden et al, 2020 ; Eiríksson et al, 2022 ). In smallholder settings, crossbreeding is more complex than in intensive or pasture-based production systems.…”

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

“…We also obtained genotypes and assigned BOA for three crossbred dairy cows, which had crossbred dams and the same pedigree structure as individuals 7 to 9 in Table 1 . The genotypes are a part of the dataset used by Eiríksson et al [ 18 ], where further details can be found. Based on these data, we constructed the matrices, which were then used to construct the genomic BS similarity matrices using Eq.…”