Economic aspects of implementing genomic evaluations in a pig sire line breeding scheme

Application of Genomic Data for Reliability Improvement of Pig Breeding Value Estimates

Replacement pigs' genomic prediction for reproduction (total number and born alive piglets in the first parity), meat, fatness and growth traits (muscle depth, days to 100 kg and backfat thickness over 6-7 rib) was tested using single-step genomic best linear unbiased prediction ssGBLUP methodology. These traits were selected as the most economically significant and different in terms of heritability. The heritability for meat, fatness and growth traits varied from 0.17 to 0.39 and for reproduction traits from 0.12 to 0.14. We confirm from our data that ssGBLUP is the most appropriate method of genomic evaluation. The validation of genomic predictions was performed by calculating the correlation between preliminary GEBV (based on pedigree and genomic data only) with high reliable conventional estimates (EBV) (based on pedigree, own phenotype and offspring records) of validating animals. Validation datasets include 151 and 110 individuals for reproduction, meat and fattening traits, respectively. The level of correlation (r) between EBV and GEBV scores varied from +0.44 to +0.55 for meat and fatness traits, and from +0.75 to +0.77 for reproduction traits. Average breeding value (EBV) of group selected on genomic evaluation basis exceeded the group selected on parental average estimates by 22, 24 and 66% for muscle depth, days to 100 kg and backfat thickness over 6-7 rib, respectively. Prediction based on SNP markers data and parental estimates showed a significant increase in the reliability of low heritable reproduction traits (about 40%), which is equivalent to including information about 10 additional descendants for sows and 20 additional descendants for boars in the evaluation dataset.

Machine Learning Prediction of Crossbred Pig Feed Efficiency and Growth Rate From Single Nucleotide Polymorphisms

This research assessed the ability of a Support Vector Machine (SVM) regression model to predict pig crossbred (CB) performance from various sources of phenotypic and genotypic information for improving crossbreeding performance at reduced genotyping cost. Data consisted of average daily gain (ADG) and residual feed intake (RFI) records and genotypes of 5,708 purebred (PB) boars and 5,007 CB pigs. Prediction models were fitted using individual PB genotypes and phenotypes (trn.1); genotypes of PB sires and average of CB records per PB sire (trn.2); and individual CB genotypes and phenotypes (trn.3). The average of CB offspring records was the trait to be predicted from PB sire’s genotype using cross-validation. Single nucleotide polymorphisms (SNPs) were ranked based on the Spearman Rank correlation with the trait. Subsets with an increasing number (from 50 to 2,000) of the most informative SNPs were used as predictor variables in SVM. Prediction performance was the median of the Spearman correlation (SC, interquartile range in brackets) between observed and predicted phenotypes in the testing set. The best predictive performances were obtained when sire phenotypic information was included in trn.1 (0.22 [0.03] for RFI with SVM and 250 SNPs, and 0.12 [0.05] for ADG with SVM and 500–1,000 SNPs) or when trn.3 was used (0.29 [0.16] with Genomic best linear unbiased prediction (GBLUP) for RFI, and 0.15 [0.09] for ADG with just 50 SNPs). Animals from the last two generations were assigned to the testing set and remaining animals to the training set. Individual’s PB own phenotype and genotype improved the prediction ability of CB offspring of young animals for ADG but not for RFI. The highest SC was 0.34 [0.21] and 0.36 [0.22] for RFI and ADG, respectively, with SVM and 50 SNPs. Predictive performance using CB data for training leads to a SC of 0.34 [0.19] with GBLUP and 0.28 [0.18] with SVM and 250 SNPs for RFI and 0.34 [0.15] with SVM and 500 SNPs for ADG. Results suggest that PB candidates could be evaluated for CB performance with SVM and low-density SNP chip panels after collecting their own RFI or ADG performances or even earlier, after being genotyped using a reference population of CB animals.

Practical oxygen therapy for newborn piglets

Aims: To evaluate the effect of a novel method of practical oxygen therapy on physiological parameters related to survival, weaning weight and preweaning mortality of neonatal piglets under commercial farm conditions. Methods: Piglets from hyperprolific sows born with signs of asphyxia, (n = 109; <6 on a score of respiration, meconium staining and activity) or very low birth weight (VLBW; n = 112; <1.05 kg) were selected for the study. Approximately half of each group (n = 55 VLBW piglets and n = 57 piglets with asphyxia) received 100% oxygen immediately after birth using a specially designed facemask for 45 seconds (VLBW) or 1 minute (asphyxiated). Physiological parameters (peripheral blood oxygen saturation (SpO₂) blood glucose concentration and rectal temperature) were measured before oxygen treatment 5 minutes after birth (SpO₂) and 24 hours later (SpO₂, blood glucose concentration, temperature). Weight at birth, at 24 hours and at 21 days of age, preweaning mortality, and estimated colostrum intake were also recorded. Results: A significant treatment effect on SpO₂ was observed (p = 0.013 and p < 0.001 for VLBW and asphyxiated piglets respectively). VLBW and asphyxiated piglets that received oxygen treatment had higher SpO₂ after treatment (measured 5 minutes after birth, 97.7 and 97.8% respectively) compared to immediately after birth (93.3 and 86.8% respectively) while untreated piglets showed no variation. Blood glucose concentrations increased in all piglets between birth and 24 hours of age (p = 0.003 and p < 0.001 for asphyxiated and VLBW piglets respectively) and this was higher in asphyxiated piglets that received oxygen than those that did not (5.6 (SE 0.2) mmol/L; p < 0.05). Estimated colostrum intake was higher in asphyxiated (401.6 (SD 24.4) g/kg) and VLBW (374.9 (SE 23.4 g/kg) piglets that received oxygen than those that did not (273.2 (SE 24.1) g/kg; p < 0.001 and 249.0 (SE 22.5) g/kg; p < 0.001 respectively). Similarly weight at weaning was higher in asphyxiated (5.8 (SE 0.2) kg) and VLBW (4.9 (SE 0.2) kg) piglets that received oxygen therapy than control animals (4.9 (SE 0.2) kg; = 0.005 and 4.1 (SE 0.2) kg; p = 0.008 respectively). Furthermore, oxygen treatment markedly reduced preweaning mortality from 9/52 (17%) untreated to 1/57 (1.7%) oxygen-treated piglets suffering asphyxia at birth (p = 0.006). Conclusions: Oxygen therapy improves physiological and productive parameters in piglets born with signs of asphyxia or VLBW. The incorporation of this strategy as part of the farrowing routine enhances the advantages of rearing hyperprolific sows.

Development of a low-density panel for genomic selection of pigs in Russia

Genomic selection is routinely used worldwide in agricultural breeding. However, in Russia, it is still not used to its full potential partially due to high genotyping costs. The use of genotypes imputed from the low-density chips (LD-chip) provides a valuable opportunity for reducing the genotyping costs. Pork production in Russia is based on the conventional 3-tier pyramid involving 3 breeds; therefore, the best option would be the development of a single LD-chip that could be used for all of them. Here, we for the first time have analyzed genomic variability in 3 breeds of Russian pigs, namely, Landrace, Duroc, and Large White and generated the LD-chip that can be used in pig breeding with the negligible loss in genotyping quality. We have demonstrated that out of the 3 methods commonly used for LD-chip construction, the block method shows the best results. The imputation quality depends strongly on the presence of close ancestors in the reference population. We have demonstrated that for the animals with both parents genotyped using high-density panels high-quality genotypes (allelic discordance rate < 0.05) could be obtained using a 300 single nucleotide polymorphism (SNP) chip, while in the absence of genotyped ancestors at least 2,000 SNP markers are required. We have shown that imputation quality varies between chromosomes, and it is lower near the chromosome ends and drops with the increase in minor allele frequency. Imputation quality of the individual SNPs correlated well across breeds. Using the same LD-chip, we were able to obtain comparable imputation quality in all 3 breeds, so it may be suggested that a single chip could be used for all of them. Our findings also suggest that the presence of markers with extremely low imputation quality is likely to be explained by wrong mapping of the markers to the chromosomal positions.

TRIENNIAL GROWTH AND DEVELOPMENT SYMPOSIUM: Genetics and breeding for intramuscular fat and oleic acid content in pigs

The intramuscular fat (IMF) and oleic acid (OL) content have been favorably related to pork quality and human health. This influences the purchasing behavior of consumers and, therefore, also shifts the attention of breeding companies toward whether these traits are included into the breeding goal of the lines producing for high-valued markets. Because IMF and OL are unfavorably associated with lean content, a key economic trait, the real challenge for the industry is not simply to increase IMF and OL, but rather to come up with the right trade-off between them and lean content. In this paper we review the efforts performed to genetically improve IMF and OL, with particular reference to the research we conducted in a Duroc line aimed at producing high quality fresh and dry-cured pork products. Based on this research, we conclude that there are selection strategies that lead to response scenarios where IMF, OL, and lean content can be simultaneously improved. Such scenarios involve regular recording of IMF and OL, so that developing a cost-efficient phenotyping system for these traits is paramount. With the economic benefits of genomic selection needing further assessment in pigs, selection on a combination of pedigree-connected phenotypes and genotypes from a panel of selected genetic markers is presented as a suitable alternative. Evidence is provided supporting that at least a polymorphism in the leptin receptor and another in the stearoyl-CoA desaturase genes should be in that panel. Selection for IMF and OL results in an opportunity cost on lean growth. The extent to which it is affordable relies on the consumers' willingness to pay for premium products and on the cost to benefit ratio of alternative management strategies, such as specific dietary manipulations. How the genotype can influence the effect of the diet on IMF and OL remains a topic for further research.

Genetic Marker Discovery in Complex Traits: A Field Example on Fat Content and Composition in Pigs

Among the large number of attributes that define pork quality, fat content and composition have attracted the attention of breeders in the recent years due to their interaction with human health and technological and sensorial properties of meat. In livestock species, fat accumulates in different depots following a temporal pattern that is also recognized in humans. Intramuscular fat deposition rate and fatty acid composition change with life. Despite indication that it might be possible to select for intramuscular fat without affecting other fat depots, to date only one depot-specific genetic marker (PCK1 c.2456C>A) has been reported. In contrast, identification of polymorphisms related to fat composition has been more successful. For instance, our group has described a variant in the stearoyl-coA desaturase (SCD) gene that improves the desaturation index of fat without affecting overall fatness or growth. Identification of mutations in candidate genes can be a tedious and costly process. Genome-wide association studies can help in narrowing down the number of candidate genes by highlighting those which contribute most to the genetic variation of the trait. Results from our group and others indicate that fat content and composition are highly polygenic and that very few genes explain more than 5% of the variance of the trait. Moreover, as the complexity of the genome emerges, the role of non-coding genes and regulatory elements cannot be disregarded. Prediction of breeding values from genomic data is discussed in comparison with conventional best linear predictors of breeding values. An example based on real data is given, and the implications in phenotype prediction are discussed in detail. The benefits and limitations of using large SNP sets versus a few very informative markers as predictors of genetic merit of breeding candidates are evaluated using field data as an example.

Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale

A breeding strategy with moderate nursery selection followed by genomic selection and one-stage phenotypic selection maximizes annual selection gain for grain yield across a wide range of hybrid breeding scenarios. Genomic selection (GS) is a promising method for the selection of quantitatively inherited traits but its most effective implementation in routine hybrid breeding schemes requires further research. We compared five breeding strategies and varied their available budget, the costs for doubled haploid (DH) line and hybrid seed production as well as variance components for grain yield in a wide range. In contrast to previous studies, we included a nursery selection for disease resistance just before GS on grain yield. The breeding strategy GSrapid with moderate nursery selection followed by one stage GS and one final stage with phenotypic selection on grain yield had the highest annual selection gain across all strategies, budgets, costs and variance components considered and we, therefore, highly recommend its use in hybrid breeding of cereals. Although selecting on traits not correlated with grain yield in the observation nursery, this selection reduced the selection gain of grain yield, especially in the breeding schemes with GS and for selected fractions smaller than 0.3. Owing to the very high number of test candidates entering breeding strategies with GS, the costs for DH line production had a larger impact on the annual selection gain than the hybrid seed production costs. The optimum allocation of test resources maximizing annual selection gain in classical two-stage phenotypic selection on grain yield and for the recommended breeding strategy GSrapid is finally explored for maize, wheat, rye, barley, rice and triticale.

Genomic selection in a pig population including information from slaughtered full sibs of boars within a sib-testing program

Genomic selection is becoming a common practise in dairy cattle, but only few works have studied its introduction in pig selection programs. Results described for this species are highly dependent on the considered traits and the specific population structure. This paper aims to simulate the impact of genomic selection in a pig population with a training cohort of performance-tested and slaughtered full sibs. This population is selected for performance, carcass and meat quality traits by full-sib testing of boars. Data were simulated using a forward-in-time simulation process that modeled around 60K single nucleotide polymorphisms and several quantitative trait loci distributed across the 18 porcine autosomes. Data were edited to obtain, for each cycle, 200 sires mated with 800 dams to produce 800 litters of 4 piglets each, two males and two females (needed for the sib test), for a total of 3200 newborns. At each cycle, a subset of 200 litters were sib tested, and 60 boars and 160 sows were selected to replace the same number of culled male and female parents. Simulated selection of boars based on performance test data of their full sibs (one castrated brother and two sisters per boar in 200 litters) lasted for 15 cycles. Genotyping and phenotyping of the three tested sibs (training population) and genotyping of the candidate boars (prediction population) were assumed. Breeding values were calculated for traits with two heritability levels (h 2=0.40, carcass traits, and h 2=0.10, meat quality parameters) on simulated pedigrees, phenotypes and genotypes. Genomic breeding values, estimated by various models (GBLUP from raw phenotype or using breeding values and single-step models), were compared with the classical BLUP Animal Model predictions in terms of predictive ability. Results obtained for traits with moderate heritability (h 2=0.40), similar to the heritability of traits commonly measured within a sib-testing program, did not show any benefit from the introduction of genomic selection. None of the considered genomic models provided improvements in prediction ability of pigs with no recorded phenotype. However, a few advantages were found for traits with low heritability (h 2=0.10). These heritability levels are characteristic for meat quality traits recorded after slaughtering or for reproduction or health traits, typically recorded on field and not in performance stations. Other scenarios of data recording and genotyping should be evaluated before considering the implementation of genomic selection in a pig-selection scheme based on sib testing of boars.

Methods to address poultry robustness and welfare issues through breeding and associated ethical considerations

As consumers and society in general become more aware of ethical and moral dilemmas associated with intensive rearing systems, pressure is put on the animal and poultry industries to adopt alternative forms of housing. This presents challenges especially regarding managing competitive social interactions between animals. However, selective breeding programs are rapidly advancing, enhanced by both genomics and new quantitative genetic theory that offer potential solutions by improving adaptation of the bird to existing and proposed production environments. The outcomes of adaptation could lead to improvement of animal welfare by increasing fitness of the animal for the given environments, which might lead to increased contentment and decreased distress of birds in those systems. Genomic selection, based on dense genetic markers, will allow for more rapid improvement of traits that are expensive or difficult to measure, or have a low heritability, such as pecking, cannibalism, robustness, mortality, leg score, bone strength, disease resistance, and thus has the potential to address many poultry welfare concerns. Recently selection programs to include social effects, known as associative or indirect genetic effects (IGEs), have received much attention. Group, kin, multi-level, and multi-trait selection including IGEs have all been shown to be highly effective in reducing mortality while increasing productivity of poultry layers and reduce or eliminate the need for beak trimming. Multi-level selection was shown to increases robustness as indicated by the greater ability of birds to cope with stressors. Kin selection has been shown to be easy to implement and improve both productivity and animal well-being. Management practices and rearing conditions employed for domestic animal production will continue to change based on ethical and scientific results. However, the animal breeding tools necessary to provide an animal that is best adapted to these changing conditions are readily available and should be used, which will ultimately lead to the best possible outcomes for all impacted.

Total cost estimation for implementing genome-enabled selection in a multi-level swine production system

BACKGROUND

Determining an animal's genetic merit using genomic information can improve estimated breeding value (EBV) accuracy; however, the magnitude of the accuracy improvement must be large enough to recover the costs associated with implementing genome-enabled selection. One way to reduce costs is to genotype nucleus herd selection candidates using a low-density chip and to use high-density chip genotyping for animals that are used as parents in the nucleus breeding herd. The objective of this study was to develop a tool to estimate the cost structure associated with incorporating genome-enabled selection into multi-level commercial breeding programs.

RESULTS

For the purpose of this deterministic study, it was assumed that a commercial pig is created from a terminal line sire and a dam that is a cross between two maternal lines. It was also assumed that all male and female selection candidates from the 1000 sow maternal line nucleus herds were genotyped at low density and all animals used for breeding at high density. With the assumptions used in this analysis, it was estimated that genome-enabled selection costs for a maternal line would be approximately US$0.082 per weaned pig in the commercial production system. A total of US$0.164 per weaned pig is needed to incorporate genome-enabled selection into the two maternal lines. Similarly, for a 600 sow terminal line nucleus herd and genotyping only male selection candidates with the low-density panel, the cost per weaned pig in the commercial herd was estimated to be US$0.044. This means that US$0.21 per weaned pig produced at the commercial level and sired by boars obtained from the nucleus herd breeding program needs to be added to the genetic merit value in order to break even on the additional cost required when genome-enabled selection is used in both maternal lines and the terminal line.

CONCLUSIONS

By modifying the input values, such as herd size and genotyping strategy, a flexible spreadsheet tool developed from this work can be used to estimate the additional costs associated with genome-enabled selection. This tool will aid breeders in estimating the economic viability of incorporating genome-enabled selection into their specific breeding program.