Combined purebred and crossbred information for genomic evaluation in pig
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This PhD thesis has two aims: first, apply single-step genomic evaluation method for purebred and crossbred performances in different scenarios with data records and genotypes in Danish Landrace, Yorkshire and F1 crossbred pig populations; second, investigate the impact of non-additive genetic effects on genomic evaluation for crossbred performance. In chapter 2, performances of genotype imputation in low density SNP-panels were compared in both purebred and crossbred populations. Imputation for crossbreds worked as well as for purebreds if both parental breeds were included in the reference population. In chapter 3, the single-step GBLUP method was applied to a combined purebred and crossbred dataset, focusing on evaluating genetic ability for crossbred performance of total number of piglets born (TNB). Additive genetic effects in crossbred animals were split into two breed-specific gametic effects. The analysis confirmed the existence of a moderate, positive genetic correlation between purebred and crossbred performances for TNB. Models with genomic information, especially from crossbred animals, improved model-based reliabilities for crossbred performance of purebred boars and also improve predictive abilities on crossbred animals in a validation population. This method requires tracing the breed origin of crossbred alleles, which may be inconvenient. Therefore, in chapter 4, this dataset was reanalysed using a single relationship matrix with metafounders to relate all the involved animals in the three populations. This method did not need tracing the breed origin of crossbred alleles. Estimates of genetic parameters were similar to those in chapter 3 and the predictive abilities for crossbred performance were at least as good as in chapter 3. Both chapters 3 and 4 indicate that the single-step method for combined purebred and crossbred performances is applicable for genomic evaluation. In chapter 5, genomic evaluation using a model including dominance effects and inbreeding depression was investigated for genotyped animals in GBLUP context. The estimated correlations between allele substitution effects of markers for different breeds were reported for the first time. Results indicated that the accuracies of predictions were not improved by including dominance effects in the model, but inclusion of genomic inbreeding depression effects did improve the performance of prediction.Keywords:
Purebred
Imputation (statistics)
Animal Breeding
Until recently, Pietrain boars in the Walloon Region of Belgium were evaluated using performances recorded on their purebred progeny. However, these boars are mostly used in crossbreeding programs. As genetic correlation between purebred and crossbred performances are considered to be rather low and varying between 0.4 and 0.7 for pigs (Dekkers (2007)), the genetic merit of boars should be established for crossbred performances. Therefore since 2007, a new genetic evaluation system has been developed in the Walloon Region of Belgium. Pietrain boars are now only evaluated on performances recorded on their crossbred progeny with Landrace sows. These crossbred progeny are fattened in a central test station between about 20 and 110 kg live weight.
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Relevance. To ensure the profitability of production, it is necessary to use certain schemes for crossing pigs of different breeds. Since the choice of the correct crossing scheme can significantly affect the final result. Crossbred animals, due to the effect of heterosis, are superior in productive qualities to the original maternal and paternal breeds. The paper presents an assessment of the meat qualities of local young pigs obtained as a result of industrial three-breed crossing. Methods. To implement the scientific and economic experience, three groups of sows of pairs-analogues of a large white breed from the company Hypor (KB Hypor) were formed. Sows of the 1st group were crossed with boars of the Landrace breed from PIC (Landrace Pic), the 2nd — with boars of the Landrace breed from Genesus Genetics (Landrace Genesus), the 3rd — with boars of the Landrace breed from Hypor (Landrace Hypor), as a result, two-breed crossbreeds (F1) were obtained. Further, the obtained crossbred sows (F1) of the 1st, 2nd and 3rd experimental groups were crossed with boars of the Duroc breed from Genesus Genetics, as a result, they received commercial young (F2). Results. The highest pre-slaughter live weight was obtained from animals of the 1st group (125.12 kg). This is more than in the 2nd and 3rd experimental groups, respectively, by 5.8% and 4.0%. Significantly, the highest slaughter weight was in the 1st group (93.51 kg), and the lowest — in the 2nd (87.8 kg). Most of the meat was obtained from animals of the 1st group — 62.85 kg ( p ≤ 0.05). This is higher than in the 2nd and 3rd experimental groups, respectively, by 8.2% and 5.9%.
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Carcass weight
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Abstract Background In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question. In this study, we aimed at providing methodologies for the use of data from crossbred individuals with an admixed genetic background together with data from multiple pure breeds, for the purpose of genomic evaluations for both purebred and crossbred animals. A three-breed rotational crossbreeding system was mimicked using simulations based on animals genotyped with the 50 K single nucleotide polymorphism (SNP) chip. Results For purebred populations, within-breed genomic predictions generally led to higher accuracies than those from multi-breed predictions using combined data of pure breeds. Adding admixed population’s (MIX) data to the combined pure breed data considering MIX as a different breed led to higher accuracies. When prediction models were able to account for breed origin of alleles, accuracies were generally higher than those from combining all available data, depending on the correlation of quantitative trait loci (QTL) effects between the breeds. Accuracies varied when using SNP effects from any of the pure breeds to predict the breeding values of MIX. Using those breed-specific SNP effects that were estimated separately in each pure breed, while accounting for breed origin of alleles for the selection candidates of MIX, generally improved the accuracies. Models that are able to accommodate MIX data with the breed origin of alleles approach generally led to higher accuracies than models without breed origin of alleles, depending on the correlation of QTL effects between the breeds. Conclusions Combining all available data, pure breeds’ and admixed population’s data, in a multi-breed reference population is beneficial for the estimation of breeding values for pure breeds with a small reference population. For MIX, such an approach can lead to higher accuracies than considering breed origin of alleles for the selection candidates, and using breed-specific SNP effects estimated separately in each pure breed. Including MIX data in the reference population of multiple breeds by considering the breed origin of alleles, accuracies can be further improved. Our findings are relevant for breeding programs in which crossbreeding is systematically applied, and also for populations that involve different subpopulations and between which exchange of genetic material is routine practice.
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Animal Breeding
SNP
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Abstract 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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The majority of the commercial slaughter pigs are crossbred animals. However, breeding efforts have been mainly focused on increasing genetic progress of purebred populations. The aim of this work is to evaluate different strategies to improve genomic prediction of crossbred performance taking into account the breed origin of alleles in crossbred populations (breed-specific effects). Previous work showed that marker effects estimated in one breed cannot predict performance in another breed (across-breed prediction). This might be due to breed-specific effects caused by differences in linkage disequilibrium between the marker and the QTL, as well as differences in allele frequencies and in genetic background of the breeds. For prediction of crossbred performance, marker effects estimated in single-breed data showed some predictive value but training on crossbred data achieved higher accuracies, although the breed origin of alleles was ignored. In this study, prediction accuracies of breeding values from a traditional genomic selection model (GS) were compared with prediction accuracies of breeding values from a model that accounts for breed-specific effects (BS). The population evaluated consisted of a two-way (Large White and Landrace) crossbred population. As both parents of all crossbred animals were known, the breed origin of alleles was easily determined after phasing of the data. The trait evaluated was gestation length (GL), for which a genetic correlation between purebred and crossbred performance (rpc) of 0.90 was estimated. Prediction accuracy of BS breeding values was slightly greater than prediction accuracy of GS breeding values (0.53 and 0.52, respectively). Additional benefits of BS over GS are expected for traits with lower rpc and when crosses of more distant purebred populations are evaluated. As a step further, a method based on long-range phasing for determining the breed origin of alleles in three-way crossbred data was developed. In a simulation study, the accuracy of breed of origin assignment was determined for 400 three-way crossbred animals with 95% correct assignments, 3% unassigned and < 2% incorrect assignments. Application of this method to real data, including 14,000 genotyped purebred animals and 1700 genotyped three-way crossbred animals, achieved 93% assignments of breed of origin of alleles without using pedigree information. Genotypic data from purebred animals was required to define the haplotypes of the three breeds contributing to the crossbreds. Currently, analyses are underway to use this breed origin information of the three-way crossbred population to estimate breed-specific effects for genomic prediction.
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Animal Breeding
Linkage Disequilibrium
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For some species, animal production systems are based on the use of crossbreeding to take advantage of the increased performance of crossbred compared to purebred animals. Effects of single nucleotide polymorphisms (SNPs) may differ between purebred and crossbred animals for several reasons: (1) differences in linkage disequilibrium between SNP alleles and a quantitative trait locus; (2) differences in genetic backgrounds (e.g., dominance and epistatic interactions); and (3) differences in environmental conditions, which result in genotype-by-environment interactions. Thus, SNP effects may be breed-specific, which has led to the development of genomic evaluations for crossbred performance that take such effects into account. However, to estimate breed-specific effects, it is necessary to know breed origin of alleles in crossbred animals. Therefore, our aim was to develop an approach for assigning breed origin to alleles of crossbred animals (termed BOA) without information on pedigree and to study its accuracy by considering various factors, including distance between breeds.The BOA approach consists of: (1) phasing genotypes of purebred and crossbred animals; (2) assigning breed origin to phased haplotypes; and (3) assigning breed origin to alleles of crossbred animals based on a library of assigned haplotypes, the breed composition of crossbred animals, and their SNP genotypes. The accuracy of allele assignments was determined for simulated datasets that include crosses between closely-related, distantly-related and unrelated breeds. Across these scenarios, the percentage of alleles of a crossbred animal that were correctly assigned to their breed origin was greater than 90 %, and increased with increasing distance between breeds, while the percentage of incorrectly assigned alleles was always less than 2 %. For the remaining alleles, i.e. 0 to 10 % of all alleles of a crossbred animal, breed origin could not be assigned.The BOA approach accurately assigns breed origin to alleles of crossbred animals, even if their pedigree is not recorded.
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Linkage Disequilibrium
SNP
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The purpose of this work was to determine the efficiency of using crossbred rams(? Kalmyk fat-tailed + ? Dorper) for crossing with Stavropol breed ewes in order to obtain lamb meat. In order to carry out the research and production experiment, two groups of Stavropol breed ewes with 40 heads in each group were formed according to the paired comparison method. In late October – early November 2019, ewes from group 1 were inseminated with the sperm of Stavropol breed rams and ewes from group 2 were inseminated with the sperm of crossbred rams (? Kalmyk fat-tailed + ? Dorper). It was found that the conception rate of ewes from the 2nd experimental group was 2.5% higher than in the control group. 44 lambs were obtained from the ewes of the 2nd experimental group, which was 4 heads or 10% more than from the 1st control group. Crossbred lambshave increased growth and they are significantly superior to purebred herdmates of Stavropol breed in live weight at the age of five months by 3.3 kg (P> 0.999), at the age of six months by 4.2 kg (P> 0.999), and at the age of seven months by 5.1 kg (P> 0.999). The study of exterior traits indicates that crossbred animals have higher indices of format, chest, blockiness and massiveness, while purebred animals of Stavropol breed are distinguished by higher indices of long legs and overgrowth.
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Commercial pig producers generally use a terminal crossbreeding system with three breeds. Many pig breeding organisations have started to use genomic selection for which genetic evaluation is often done by applying single-step methods for which the pedigree-based additive genetic relationship matrix is replaced by a combined relationship matrix based on both marker genotypes and pedigree. Genomic selection is implemented for purebreds, but it also offers opportunities for incorporating information from crossbreds and selecting for crossbred performance. However, models for genetic evaluation for the three-way crossbreeding system have not been developed. Four-variate models for three-way terminal crossbreeding are presented in which the first three variables contain the records for the three pure breeds and the fourth variable contains the records for the three-way crossbreds. For purebred animals, the models provide breeding values for both purebred and crossbred performances. Heterogeneity of genetic architecture between breeds and genotype by environment interactions are modelled through genetic correlations between these breeding values. Specification of the additive genetic relationships is essential for these models and can be defined either within populations or across populations. Based on these two types of additive genetic relationships, both pedigree-based, marker-based and combined relationships based on both pedigree and marker information are presented. All these models for three-way crossbreeding can be formulated using Kronecker matrix products and therefore fitted using Henderson's mixed model equations and standard animal breeding software. Models for genetic evaluation in the three-way crossbreeding system are presented. They provide estimated breeding values for both purebred and crossbred performances, and can use pedigree-based or marker-based relationships, or combined relationships based on both pedigree and marker information. This provides a framework that allows information from three-way crossbred animals to be incorporated into a genetic evaluation system.
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Animal Breeding
Genomic Selection
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The breeding performance and subsequent productivity and progeny performance of 835 purebred and two-breed cross Duroc, Hampshire and Yorkshire females were analyzed to compare the productivity of purebred and crossbred females and the performance of two-breed and three-breed cross pigs. A total of 406 purebred and 429 crossbred females were saved for breeding with 148 purebred and 194 crossbred gilts slaughtered 30 days postbreeding. Litter productivity was measured on 193 two-breed and 199 three-breed cross litters. Growth and feed efficiency data included 1,246 two-breed and 1,599 three-breed cross barrows and gilts and carcass merit was evaluated on 252 two-breed and 261 three-breed cross barrows. Conception rate and ovulation rate of purebred and crossbred females were very similar. However, crossbred gilts slaughtered 30 days postbreeding had .71 ± .38 more embryos per gilt. These gilts were also 5.8 ± 1.4 days younger at 100 kg and 11.7 ± 2.1 days younger at breeding than purebred gilts. Crossbred females farrowed .93 ± .32 and weaned 1.24 ± .27 more pigs per litter than purebred females. Survival rate of three-breed cross pigs was significantly higher than two-breed crosses, both early in gestation and from birth to weaning. Total litter weight weaned was 19.6% heavier for crossbred females with three-breed cross litters than for purebred females with two-breed cross litters. There was very little difference between two-breed and three-breed cross pigs for growth rate, feed efficiency, probe backfat or carcass merit.
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