Assessment of sire contribution and breed-of-origin of alleles in a three-way crossbred broiler dataset

Calus, M.P.L.; Vandenplas, Jérémie; Hulsegge, Ina; Borg, Randy; Henshall, John; Hawken, Rachel

doi:10.3382/ps/pez458

Cited by 6 publications

(10 citation statements)

References 33 publications

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“…The BOA program of Vandenplas et al [ 13 ] showed a higher proportion of alleles in real genotypes that could not be assigned than in most of the simulated scenarios that they studied. Application of their program on three-way pig data assigned 95.2% of the alleles to breed of origin [ 10 ], but on chicken data 91.8% of the alleles could be assigned to breed of origin for the three-way crosses and 96.9% for the F1 crosses [ 27 ]. Our preliminary results of the application of AllOr on real genotypes of crossbred dairy cows are promising with more than 99% of the alleles assigned to breed of origin (personal communication).…”

Section: Discussionmentioning

confidence: 99%

Breed of origin of alleles and genomic predictions for crossbred dairy cows

Eiríksson

Karaman

et al. 2021

Genet Sel Evol

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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.

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Section: Discussionmentioning

confidence: 99%

Breed of origin of alleles and genomic predictions for crossbred dairy cows

Eiríksson

Karaman

et al. 2021

Genet Sel Evol

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“…The number of multiplication steps that occur in the purebred lines prior to crossing has no effect on the assignment of line-origin, because it only depends on the composition of the crossbred genome. In studies using real data, 95.2% of the alleles of three-way crossbreds pigs [ 45 ] and 91.1% of the alleles of three-way crossbred broilers [ 46 ] could be assigned to one of the lines using this method. The lower percentage in broilers was probably because the number of purebred genotypes available was small for one line [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

“…In studies using real data, 95.2% of the alleles of three-way crossbreds pigs [ 45 ] and 91.1% of the alleles of three-way crossbred broilers [ 46 ] could be assigned to one of the lines using this method. The lower percentage in broilers was probably because the number of purebred genotypes available was small for one line [ 46 ]. Since the correct line origin is not known in real data, the accuracy of assigning the line-origin was investigated in a simulation study, which showed that the line-origin of an allele could be correctly assigned for 94.3 to 97.2% of the alleles in three-way crossbreds, with a higher percentage when the lines were more distantly related [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Optimizing genomic reference populations to improve crossbred performance

2020

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Background In pig and poultry breeding, the objective is to improve the performance of crossbred production animals, while selection takes place in the purebred parent lines. One way to achieve this is to use genomic prediction with a crossbred reference population. A crossbred reference population benefits from expressing the breeding goal trait but suffers from a lower genetic relatedness with the purebred selection candidates than a purebred reference population. Our aim was to investigate the benefit of using a crossbred reference population for genomic prediction of crossbred performance for: (1) different levels of relatedness between the crossbred reference population and purebred selection candidates, (2) different levels of the purebred-crossbred correlation, and (3) different reference population sizes. We simulated a crossbred breeding program with 0, 1 or 2 multiplication steps to generate the crossbreds, and compared the accuracy of genomic prediction of crossbred performance in one generation using either a purebred or a crossbred reference population. For each scenario, we investigated the empirical accuracy based on simulation and the predicted accuracy based on the estimated effective number of independent chromosome segments between the reference animals and selection candidates. Results When the purebred-crossbred correlation was 0.75, the accuracy was highest for a two-way crossbred reference population but similar for purebred and four-way crossbred reference populations, for all reference population sizes. When the purebred-crossbred correlation was 0.5, a purebred reference population always resulted in the lowest accuracy. Among the different crossbred reference populations, the accuracy was slightly lower when more multiplication steps were used to create the crossbreds. In general, the benefit of crossbred reference populations increased when the size of the reference population increased. All predicted accuracies overestimated their corresponding empirical accuracies, but the different scenarios were ranked accurately when the reference population was large. Conclusions The benefit of a crossbred reference population becomes larger when the crossbred population is more related to the purebred selection candidates, when the purebred-crossbred correlation is lower, and when the reference population is larger. The purebred-crossbred correlation and reference population size interact with each other with respect to their impact on the accuracy of genomic estimated breeding values.

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“…This study reuses data from a broiler three-way cross experiment that was previously analysed [13][14][15][16]. The main advantages of this dataset are that (1) all the phenotyped animals are genotyped, (2) the pedigree and the observed breed composition of all the crossbred animals were previously derived by Calus et al [13], and (3) the three purebred lines have been shown to be distantly related [15,16]. Furthermore, Duenk et al [14] analysed the same dataset using models based on the breed-oforigin (BOA) of alleles, which enabled us to compare our results to those obtained with that model.…”

Section: Methodsmentioning

confidence: 99%

Validation with single-step SNPBLUP shows that evaluations can continue using a single mean of genotyped individuals, even with multiple breeds

et al. 2023

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Background In genomic prediction, it is common to centre the genotypes of single nucleotide polymorphisms based on the allele frequencies in the current population, rather than those in the base generation. The mean breeding value of non-genotyped animals is conditional on the mean performance of genotyped relatives, but can be corrected by fitting the mean performance of genotyped individuals as a fixed regression. The associated covariate vector has been referred to as a ‘J-factor’, which if fitted as a fixed effect can improve the accuracy and dispersion bias of sire genomic estimated breeding values (GEBV). To date, this has only been performed on populations with a single breed. Here, we investigated whether there was any benefit in fitting a separate J-factor for each breed in a three-way crossbred population, and in using pedigree-based expected or genome-based estimated breed fractions to define the J-factors. Results For body weight at 7 days, dispersion bias decreased when fitting multiple J-factors, but only with a low proportion of genotyped individuals with selective genotyping. On average, the mean regression coefficients of validation records on those of GEBV increased with one J-factor compared to none, and further increased with multiple J-factors. However, for body weight at 35 days this was not observed. The accuracy of GEBV remained unchanged regardless of the J-factor method used. Differences between the J-factor methods were limited with correlations approaching 1 for the estimated covariate vector, the estimated coefficients of the regression on the J-factors, and the GEBV. Conclusions Based on our results and in the particular design analysed here, i.e. all the animals with phenotype are of the same type of crossbreds, fitting a single J-factor should be sufficient, to reduce dispersion bias. Fitting multiple J-factors may reduce dispersion bias further but this depends on the trait and genotyping rate. For the crossbred population analysed, fitting multiple J-factors has no adverse consequences and if this is done, it does not matter if the breed fractions used are based on the pedigree-expectation or the genomic estimates. Finally, when GEBV are estimated from crossbred data, any observed bias can potentially be reduced by including a straightforward regression on actual breed proportions.

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Assessment of sire contribution and breed-of-origin of alleles in a three-way crossbred broiler dataset

Cited by 6 publications

References 33 publications

Breed of origin of alleles and genomic predictions for crossbred dairy cows

Breed of origin of alleles and genomic predictions for crossbred dairy cows

Optimizing genomic reference populations to improve crossbred performance

Validation with single-step SNPBLUP shows that evaluations can continue using a single mean of genotyped individuals, even with multiple breeds

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