Background In dairy cattle, genomic selection has been implemented successfully for purebred populations, but, to date, genomic estimated breeding values (GEBV) for crossbred cows are rarely available, although they are valuable for rotational crossbreeding schemes that are promoted as efficient strategies. An attractive approach to provide GEBV for crossbreds is to use estimated marker effects from the genetic evaluation of purebreds. The effects of each marker allele in crossbreds can depend on the breed of origin of the allele (BOA), thus applying marker effects based on BOA could result in more accurate GEBV than applying only proportional contribution of the purebreds. Application of BOA models in rotational crossbreeding requires methods for detecting BOA, but the existing methods have not been developed for rotational crossbreeding. Therefore, the aims of this study were to develop and test methods for detecting BOA in a rotational crossbreeding system, and to investigate methods for calculating GEBV for crossbred cows using estimated marker effects from purebreds. Results For detecting BOA in crossbred cows from rotational crossbreeding for which pedigree is recorded, we developed the AllOr method based on the comparison of haplotypes in overlapping windows. To calculate the GEBV of crossbred cows, two models were compared: a BOA model where marker effects estimated from purebreds are combined based on the detected BOA; and a breed proportion model where marker effects are combined based on estimated breed proportions. The methods were tested on simulated data that mimic the first four generations of rotational crossbreeding between Holstein, Jersey and Red Dairy Cattle. The AllOr method detected BOA correctly for 99.6% of the marker alleles across the four crossbred generations. The reliability of GEBV was higher with the BOA model than with the breed proportion model for the four generations of crossbreeding, with the largest difference observed in the first generation. Conclusions In rotational crossbreeding for which pedigree is recorded, BOA can be accurately detected using the AllOr method. Combining marker effects estimated from purebreds to predict the breeding value of crossbreds based on BOA is a promising approach to provide GEBV for crossbred dairy cows.

Genetic parameters for carcass conformation, carcass fat, ultrasound eye muscle depth and ultrasound fat depth over eye muscle were estimated with data from Icelandic farms during three periods, 2001-2003, 2006-2008 and 2011-2013. Heritability ranged from 0.30 to 0.42. Genetic correlation between carcass conformation and carcass fat was 0.41, 0.29 and 0.26 in 2001-2003, 2006-2008 and 2011-2013, respectively.Breeding values based on carcass scoring records of 5,796,474 lambs in 2000-2013 were estimated with a bivariate model and compared to the results of a multitrait model, including 715,771 ultrasound records. The genetic merit of rams for carcass conformation was underestimated in the bivariate analysis in the cases where many offspring were kept for replacement. Applying the multitrait model reduced this bias and gave more accurate results for both traits.The genetic trend was -0.05 and 0.08 genetic standard deviations per year for carcass fat and carcass conformation, respectively, in the period 2000-2013.

A total of 480,495 test-day yield records of 33,052 cows were used to estimate the genetic parameters for daily milk yield (MY), fat yield (FY), protein yield (PY) and somatic cell score (SCS) of Icelandic dairy cows in the first three lactations with a random regression model. Heritability of all traits was lowest in early lactation in all lactations and highest in mid-or late lactation. Heritability of lactation yields for the first lactation was 0.43, 0.39 and 0.41 for MY, FY and PY, respectively, but was estimated as lower when using a lactation model. Heritability of SCS in the first lactation was 0.23 using the random regression model but 0.15 using the lactation model. Heritability of persistency of lactation MY, FY and PY were 0.14-0.24 in all lactations and genetic correlations to the whole lactation SCS were-0.08 to-0.13. Heritability of yields had increased from previous estimates for the breed. Genetic variation of persistency in the population makes change of the lactation curve possible through selection.

Predicted genetic progress in production traits was compared using three different models for genetic evaluation of Icelandic dairy cows. The models were: a random regression (RR) test-day model, model using lactation yields until day 305 from calving (LAC1), and the model currently used for the national evaluation, based on lactation yield from calving to the end of lactation regardless of the length of the lactation (LAC2). Additionally, genetic evaluation for somatic cell score with RR and LAC1 were compared. Predicted genetic progress for protein yield was highest when using RR, or 0.170 σ a /yr, compared with 0.167 σ a /yr and 0.158 σ a /yr for LAC1 and LAC2, respectively. Results for other production traits were similar. The main reason could be the shorter generation interval when records can be utilized before the end of lactation and reliable estimated breeding values thus obtained earlier. Application of an RR model will be beneficial for genetic evaluation for production traits and somatic cell score.

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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.

Genomic evaluations for crossbred animals introduce challenges not encountered for purebred animals. We propose to include effects of local breed proportions (LBP) based on detected breed of origin of alleles in a genomic model for crossbred dairy cows. We tested different segment length for modelling the LBP: 1, 20, 100 and 500 markers, for genomic prediction based on data of first lactation protein yields of 5,176 Danish crossbred dairy cows with contribution from three dairy breeds. The models that included LBP had slightly higher crossvalidation predictive ability, 0.390 for all segment lengths compared with 0.385 for a model without LBP effects. The LBP models also had less dispersion bias. Inclusion of LBP in genomic predictions can therefore improve predictions even when considering the local effects in segments of hundreds of markers.

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.