Fertility of Liquid Boar Semen as Influenced by Breed and Season
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Landrace, Duroc, Yorkshire, Pietrain, Hampshire and crossbred females were inseminated with liquid semen containing 5 × 109 spermatozoa in 50 ml glucose-milk extender one or two times during 1,883 estrous cycles. The semen was collected from 27 Landrace, 35 Duroc, 29 Yorkshire, 22 Minnesota No. 2, 14 Pietrain and seven Hampshire boars. Only semen exhibiting over 70% motile spermatozoa was used. This report includes data from one year's inseminations with purebred semen at the Taiwan Pig Research Institute. The data were analyzed for effect of breed, age of females and season of the year on fertility. The farrowing rates in crossbred and Landrace females (78.2 and 72.3%, respectively) were higher (P<.05) than for the other breeds (60.0 to 63.1%) except in Pietrain (68.8%). Inseminations with Landrace, Minnesota No. 2 and Duroc semen resulted in higher (P<.05) farrowing rates (74.3 to 71.3%) than Yorkshire semen (63.2%). Pietrain and Hampshire boars were not different from any of the other breeds. Regardless of breed, inseminations in females of the same breed as the boar resulted in lower (P<.05) farrowing rate (65.4%) than inseminations in purebred females of a different breed (70.7%) or crossbred females (78.2%). The number of pigs born alive per litter was affected by the breed of females but not the breed of males. Crossbred females produced significantly (P<.01) more live pigs per litter (9.51) than Landrace females (8.59). The latter breed farrowed more live pigs (P<.01) than the other breeds (6.80 to 8.02) with the exception of Pietrain (8.34). The correlation between conception rate and litter size for females was .66 between breeds. Within breeds the correlation was poor (.20). There was no significant effect due to age of the female with the age grouping used or to the season of the year on fertility.Keywords:
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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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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.
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Abstract 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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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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Study on growth performance of purebred and crossbred Tellicherry kids revealed that the overall means for body weight of purebred Tellicherry kids at birth, 6 and 12 months of age were 2.2±0.1, lO.5±O.4 and 16.9±O.6 kg respectively and the values for crossbred kids were 2.1±O.1, lO.O±O.4 and 15.3±O.4 kg respectively. In general, the purebred Tellicherry kids were heavier than crossbred kids but it was not significant. Further study on reproduction performance and adaptation to the extensive system ofmanagement (prevailing in the farmers field condition) are warranted before introduction of the crossbred kjds into the farmers fields conditions.
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Boar breed, sow cross and breed group effects were evaluated for sow productivity traits including conception rate and litter averages for: litter size, pig livability, pig weight and litter weight. The litters were from crossbred Duroc × Yorkshire (DY), Hampshire × Yorkshire (HY) and Landrace × Yorkshire (LY) sows mated to purebred Duroc (DD), purebred Hampshire (HH), and crossbred Duroc × Hampshire and Hampshire × Duroc (DH) boars. Litter average records were measured for 380 litters represented at marketing. Conception rate averaged 95.1%, through five 30-day breeding periods, for the 505 sow exposures. Differences in conception rate among the different boar and sow cross groups were not significant. Boar breed effects were significant only for one trait-pig market weight; whereas sow cross effects were significant for all litter size traits. The DY sows had the largest litter sizes and weights at 0, 35 and 210 days. Litters from DY and LY sows had 9.8 and 14.4%, respectively, greater livability at 35 days than litters from HY sows. On a per litter marketed basis, the DY sows exhibited advantages in litter weight per sow over HY and LY sows, respectively, of 12.6 and 7.3 kg at 35 days, and 208 and 102 kg at 210 days. Breed group effects were significant for all litter average traits, except litter birth weight and pig weaning weight. The DD × DY group had the largest litter sizes and weights at 0, 35 and 210 days.
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Feedlot and carcass performance of cross-bred progeny of purebred and crossbred sires were compared. The eight crossbred sires were three-breed crosses of Minn. No. 3 boars bred to Minn. No. 2 times Minn. No. 1 crossbred females. The purebred sires consisted of four Minn. No. 3's, two Minn. No. 2's and two Minn. No. 1's in order to equalize the breed composition of the two kinds of pigs. The dams were all Minn. No. 1's. Differences in performance between progeny of purebred and crossbred sires were found only for backfat thickness and daily gain. The difference for these two traits favored the purebred sires. The difference in daily gain may be explained by the greater selection practiced for this trait in the purebred boars. The difference in backfat thickness is not entirely explainable. The variances of progency performance were similar for purebred and crossbred sires. This indicates that the use of crossbred sires in systematic crossing systems need not result in increased variation among the progeny. In general, it may be stated that progeny of crossbred boars will perform at a level equal to the average performance of progeny sired by the parent breeds of the crossbred sires.
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A single-step genomic BLUP method (ssGBLUP) has been successfully developed and applied for purebred and crossbred performance in pigs. However, it requires phasing the genotypes and inferring the breed origin of alleles in crossbred animals, which is somewhat inconvenient. Recently, a new concept of metafounders that considers the relationship within and across base populations was developed. With this concept of metafounders, regular methods to build and invert the pedigree relationships matrix can be used with only minor modifications and, moreover, genomic relationships and pedigree-based relationships are automatically compatible in the ssGBLUP. In this study, data for the total number of piglets born in Danish Landrace, Yorkshire, and 2-way crossbred pigs and models for purebred and crossbred performance were revisited by use of ssGBLUP with 2 metafounders. Genetic variances and genetic correlations between purebred and crossbred performances were first reestimated. Then, model-based reliabilities of purebred boars for their crossbred performance and predictive abilities for crossbred animals were compared in different scenarios. Results in this study were compared to those in a previous study with identical data but with models that required known breed origin of crossbred genotypes. Results show that relationships for base individuals within Landrace and within Yorkshire are similar and that the ancestor populations for Landrace and Yorkshire are related. In terms of model-based reliabilities and predictive abilities, ssGBLUP with metafounders performs at least as well as the single-step method requiring phasing at a lower complexity.
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