A method of calculating weighted values of objective traits for BLUP of breeding values to achieve relative desired changes.

A method of calculating weighted values for objective traits from the phenotypic records of all animals in a population was devised as an alternative to the conventional method of calculating weighted values from a family selection index. The genetic improvement of this method was verified by Monte Carlo computer simulation. A base population consisting of 10 males and 50 females, and five separate generations, other than the base population that had been randomly selected, was bred for two traits with different heritabilities. The breeding values of animals in generation five were estimated using the bivariate BLUP method. The three different weighted values obtained from this method and two conventional methods for estimated breeding values of the objective traits were used to estimate aggregate breeding values for selection. The results showed that selection using weighted values calculated from all animals in a population resulted in a greater response to selection, especially when the genetic correlation between the two traits was positive, than selection using other conventional methods. The use of the method devised in this study was expected to result in a greater genetic improvement than the conventional family selection index method for pig breeding programs applied in closed herds in Japan.

An Assessment of the Genetic Parameters of Boars' Reproductive Traits.

The aim of this study was to estimate genetic parameters for the reproductive traits of boars based on single-nucleotide polymorphism data. A total of 109,836 semen samples from 2249 boars were collected between 2010 and 2022. Five basic traits were assessed: sperm volume, sperm concentration, motility, number of abnormal sperm, and, for the first time for the local population, libido. In addition, two derived traits were assessed: total sperm count and number of functional sperm. Genetic parameters were estimated using the single-step genomic best linear unbiased prediction method (ssGBLUP). Dam and sire breeds were evaluated separately. The five basic traits were evaluated using five-trait models, while the two derived traits were evaluated using single-trait models. The heritability coefficients had lower values for all sperm quality traits with both methods. For the basic traits, the heritability ranged from 0.099 to 0.342. The greatest difference between dam and sire breeds was observed for the heritability of the sperm concentration trait (0.099 and 0.271, respectively). The heritability of the libido trait was twice as high for boars of sire breeds as it was for boars of dam breeds. The genetic parameters estimated with ssGBLUP can be used in routine genetic evaluations to improve the pig breeding process.

Identification of Selection Signatures and Loci Associated with Important Economic Traits in Yunan Black and Huainan Pigs.

Henan Province is located in central China and rich in domestic pig populations; Huainan (HN) pigs are one of three Henan indigenous breeds with great performance, including early maturation, strong disease resistance and high meat quality. Yunan (YN) black pigs are a typical, newly cultivated breed, synthesized between HN pigs and American Duroc, and are subjected to selection for important traits, such as fast growth and excellent meat quality. However, the genomic differences, selection signatures and loci associated with important economic traits in YN black pigs and HN pigs are still not well understood. In this study, based on high-density SNP chip analysis of 159 samples covering commercial DLY (Duroc × Landrace × Large White) pigs, HN pigs and YN black pigs, we performed a comprehensive analysis of phylogenetic relationships and genetic diversity among the three breeds. Furthermore, we used composite likelihood ratio tests (CLR) and F-statistics (Fst) to identify specific signatures of selection associated with important economic traits and potential candidate genes. We found 147 selected regions (top 1%) harboring 90 genes based on genetic differentiation (Fst) in the YN-DLY group. In the HN-DLY group, 169 selected regions harbored 58 genes. In the YN-HN group, 179 selected regions harbored 77 genes. In addition, the QTLs database with the most overlapping regions was associated with triglyceride level, number of mummified pigs, hemoglobin and loin muscle depth for YN black pigs, litter size and intramuscular fat content for HN pigs, and humerus length, linolenic acid content and feed conversion ratio mainly in DLY pigs. Of note, overlapping 14 tissue-specific promoters' annotation with the top Fst 1% selective regions systematically demonstrated the muscle-specific and hypothalamus-specific regulatory elements in YN black pigs. Taken together, these results contribute to an accurate knowledge of crossbreeding, thus benefitting the evaluation of production performance and improving the genome-assisted breeding of other important indigenous pig in the future.

Simulating the Commercial Implementation of Gene-Editing for Influenza A Virus Resistance in Pigs: An Economic and Genetic Analysis.

The development of swine Influenza A Virus resistance along with genetic technologies could complement current control measures to help to improve animal welfare standards and the economic efficiency of pig production. We have created a simulation model to assess the genetic and economic implications of various gene-editing methods that could be implemented in a commercial, multi-tiered swine breeding system. Our results demonstrate the length of the gene-editing program was negatively associated with genetic progress in commercial pigs and that the time required to reach fixation of resistance alleles was reduced if the efficiency of gene-editing is greater. The simulations included the resistance conferred in a digenic model, the inclusion of genetic mosaicism in progeny, and the effects of selection accuracy. In all scenarios, the level of mosaicism had a greater effect on the time required to reach resistance allele fixation and the genetic progress of the herd than gene-editing efficiency and zygote survival. The economic analysis highlights that selection accuracy will not affect the duration of gene-editing and the investment required compared to the effects of gene-editing-associated mosaicism and the swine Influenza A Virus control strategy on farms. These modelling results provide novel insights into the economic and genetic implications of targeting two genes in a commercial pig gene-editing program and the effects of selection accuracy and mosaicism.

Performance of the No-U-Turn sampler in multi-trait variance component estimation using genomic data.

BACKGROUND: Multi-trait genetic parameter estimation is an important topic for target traits with few records and with a low heritability and when the genetic correlation between target and secondary traits is strong. However, estimating correlations between multiple traits is difficult for both Bayesian and non-Bayesian inferences. We extended a Hamiltonian Monte Carlo approach using the No-U-Turn Sampler (NUTS) to a multi-trait animal model and investigated the performance of estimating (co)variance components and breeding values, compared to those for restricted maximum likelihood and Gibbs sampling with a population size of 2314 and 578 in a simulated and real pig dataset, respectively. For real data, we used publicly available data for three traits from the Pig Improvement Company (PIC). For simulation data, we generated two quantitative traits by using the genotypes of the PIC data. For NUTS, two prior distributions were adopted: Lewandowski-Kurowicka-Joe (LKJ) and inverse-Wishart distributions. RESULTS: For the two simulated traits with heritabilities of 0.1 and 0.5, most estimates of the genetic and residual variances for NUTS with the LKJ prior were closer to the true values and had smaller root mean square errors and smaller mean absolute errors, compared to NUTS with inverse-Wishart priors, Gibbs sampling and restricted maximum likelihood. The accuracies of estimated breeding values for lowly heritable traits for NUTS with LKJ and inverse-Wishart priors were 14.8% and 11.1% higher than those for Gibbs sampling and restricted maximum likelihood, respectively, with a population size of 578. For the trivariate animal model with real pig data, the estimates of the genetic correlations for Gibbs sampling and restricted maximum likelihood were strongly affected by population size, compared to NUTS. For both the simulated and pig data, the genetic variances and heritabilities for NUTS with an inverse-Wishart prior were overestimated for low-heritability traits when the population size was 578. CONCLUSIONS: The accuracies of variance components and breeding values estimates for a multi-trait animal model using NUTS with the LKJ prior were equal to or higher than those obtained with restricted maximum likelihood or Gibbs sampling. Therefore, when the population size is small, NUTS with an LKJ prior could be an alternative sampling method for multi-trait analysis in animal breeding.

Whole-genome sequence analysis reveals selection signatures for important economic traits in Xiang pigs.

Xiang pig (XP) is one of the best-known indigenous pig breeds in China, which is characterized by its small body size, strong disease resistance, high adaptability, favorite meat quality, small litter sizes, and early sexual maturity. However, the genomic evidence that links these unique traits of XP is still poorly understood. To identify the genomic signatures of selection in XP, we performed whole-genome resequencing on 25 unrelated individual XPs. We obtained 876.70 Gb of raw data from the genomic libraries. The LD analysis showed that the lowest level of linkage disequilibrium was observed in Xiang pig. Comparative genomic analysis between XPs and other breeds including Tibetan, Meishan, Duroc and Landrace revealed 3062, 1228, 907 and 1519 selected regions, respectively. The genes identified in selected regions of XPs were associated with growth and development processes (IGF1R, PROP1, TBX19, STAC3, RLF, SELENOM, MSTN), immunity and disease resistance (ZCCHC2, SERPINB2, ADGRE5, CYP7B1, STAT6, IL2, CD80, RHBDD3, PIK3IP1), environmental adaptation (NR2E1, SERPINB8, SERPINB10, SLC26A7, MYO1A, SDR9C7, UVSSA, EXPH5, VEGFC, PDE1A), reproduction (CCNB2, TRPM6, EYA3, CYP7B1, LIMK2, RSPO1, ADAM32, SPAG16), meat quality traits (DECR1, EWSR1), and early sexual maturity (TAC3). Through the absolute allele frequency difference (ΔAF) analysis, we explored two population-specific missense mutations occurred in NR6A1 and LTBP2 genes, which well explained that the vertebrae numbers of Xiang pigs were less than that of the European pig breeds. Our results indicated that Xiang pigs were less affected by artificial selection than the European and Meishan pig breeds. The selected candidate genes were mainly involved in growth and development, disease resistance, reproduction, meat quality, and early sexual maturity. This study provided a list of functional candidate genes, as well as a number of genetic variants, which would provide insight into the molecular basis for the unique traits of Xiang pig.

Assessment of the Genetic Diversity of a Local Pig Breed Using Pedigree and SNP Data.

Herein, the genetic diversity of the local Prestice Black-Pied pig breed was assessed by the simultaneous analysis of the pedigree and single nucleotide polymorphism (SNP) data. The information about sire line, dam, date of birth, sex, breeding line, and herd for 1971 individuals was considered in the pedigree analysis. The SNP analysis (n = 181) was performed using the Illumina PorcineSNP60 BeadChip kit. The quality of pedigree and SNPs and the inbreeding coefficients (F) and effective population size (Ne) were evaluated. The correlations between inbreeding based on the runs of homozygosity (FROH) and pedigree (FPED) were also calculated. The average FPED for all animals was 3.44%, while the FROH varied from 10.81% for a minimum size of 1 Mbp to 3.98% for a minimum size of 16 Mbp. The average minor allele frequency was 0.28 ± 0.11. The observed and expected within breed heterozygosities were 0.38 ± 0.13 and 0.37 ± 0.12, respectively. The Ne, obtained using both the data sources, reached values around 50 animals. Moderate correlation coefficients (0.49-0.54) were observed between FPED and FROH. It is necessary to make decisions that stabilize the inbreeding rate in the long-term using optimal contribution selection based on the available SNP data.

Investigating pig survival in different production phases using genomic models.

Pig survival is an economically important trait with relevant social welfare implications, thus standing out as an important selection criterion for the current pig farming system. We aimed to estimate (co)variance components for survival in different production phases in a crossbred pig population as well as to investigate the benefit of including genomic information through single-step genomic best linear unbiased prediction (ssGBLUP) on the prediction accuracy of survival traits compared with results from traditional BLUP. Individual survival records on, at most, 64,894 crossbred piglets were evaluated under two multi-trait threshold models. The first model included farrowing, lactation, and combined postweaning survival, whereas the second model included nursery and finishing survival. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods. Furthermore, prediction accuracy, bias, and dispersion were accessed using the linear regression validation method. Direct heritability estimates for survival in all studied phases were low (from 0.02 to 0.08). Survival in preweaning phases (farrowing and lactation) was controlled by the dam and piglet additive genetic effects, although the maternal side was more important. Postweaning phases (nursery, finishing, and the combination of both) showed the same or higher direct heritabilities compared with preweaning phases. The genetic correlations between survival traits within preweaning and postweaning phases were favorable and strong, but correlations between preweaning and postweaning phases were moderate. The prediction accuracy of survival traits was low, although it increased by including genomic information through ssGBLUP compared with the prediction accuracy from BLUP. Direct and maternal breeding values were similarly accurate with BLUP, but direct breeding values benefited more from genomic information. Overall, a slight increase in bias was observed when genomic information was included, whereas dispersion of breeding values was greatly reduced. Combined postweaning survival presented higher direct heritability than in the preweaning phases and the highest prediction accuracy among all evaluated production phases, therefore standing out as a candidate trait for improving survival. Survival is a complex trait with low heritability; however, important genetic gains can still be obtained, especially under a genomic prediction framework.

Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy.

BACKGROUND: Uncovering the genetic architecture of economic traits in pigs is important for agricultural breeding. However, high-density haplotype reference panels are unavailable in most agricultural species, limiting accurate genotype imputation in large populations. Moreover, the infinitesimal model of quantitative traits implies that weak association signals tend to be spread across most of the genome, further complicating the genetic analysis. Hence, there is a need to develop new methods for sequencing large cohorts without large reference panels. RESULTS: We describe a Tn5-based highly accurate, cost- and time-efficient, low-coverage sequencing method to obtain 11.3 million whole-genome single-nucleotide polymorphisms in 2,869 Duroc boars at a mean depth of 0.73×. On the basis of these single-nucleotide polymorphisms, a genome-wide association study was performed, resulting in 14 quantitative trait loci (QTLs) for 7 of 21 important agricultural traits in pigs. These QTLs harbour genes, such as ABCD4 for total teat number and HMGA1 for back fat thickness, and provided a starting point for further investigation. The inheritance models of the different traits varied greatly. Most follow the minor-polygene model, but this can be attributed to different reasons, such as the shaping of genetic architecture by artificial selection for this population and sufficiently interconnected minor gene regulatory networks. CONCLUSIONS: Genome-wide association study results for 21 important agricultural traits identified 14 QTLs/genes and showed their genetic architectures, providing guidance for genetic improvement harnessing genomic features. The Tn5-based low-coverage sequencing method can be applied to large-scale genome studies for any species without a good reference panel and can be used for agricultural breeding.

Economic and environmental assessments of combined genetics and nutrition optimization strategies to improve the efficiency of sustainable pork production.

We evaluated the economic and environmental impacts of strategies that incorporated selection for pig feed efficiency and dietary optimization based on a single or multiple objectives tailored to meet the population nutritional requirements, with the goal to optimize sustainable farm feed efficiency. The economic and environmental features of the strategy were evaluated using life cycle assessment (LCA) and bio-economic models. An individual trait-based LCA model was applied to evaluate global warming potential, terrestrial acidification potential, freshwater eutrophication potential (EP), and land occupation of the combined genetics and nutrition optimization to produce 1 kg of live pig weighing 120 kg at the farm gate. A parametric individual trait-based bio-economic model was developed and applied to determine the cost breakdown, revenue, and profit to be gained from a 120-kg live pig at the farm gate. Data from two genetic lines with contrasted levels of feed efficiency were used to apply the combined genetics and nutrition optimization: accounting for the average nutritional requirements for each line, the individual pig responses to diets formulated for least cost, least environmental impacts, or minimum combination of costs and environmental impacts objectives were predicted with INRAPorc. Significant differences in the environmental impacts (P < 0.0001) and profit (P < 0.05) between lines predicted with the same reference diet showed that selection for feed efficiency (residual feed intake) in pigs improves pig production sustainability. When pig responses were simulated with their line-optimized diets, except for EP, all the line environmental impacts were lower (P < 0.05) than with the reference diet. The high correlations of feed conversion ratio with the environmental impacts (> 0.82) and the profit (< -0.88) in both lines underlined the importance of feed efficiency as a lever for the sustainability of pig production systems. Implementing combined genetics and nutrition optimization, the inherent profit and environmental differences between the genetic lines were predicted to be reduced from 23.4% with the reference diet to 7.6% with the diet optimized jointly for economic and environmental objectives (joint diet). Consequently, for increased pig sustainability, diet optimization for sustainability objectives should be applied to cover the specific nutritional requirements arising in the herd from the pigs genetic level.

Evaluation of the CRISPR/Cas9 Genetic Constructs in Efficient Disruption of Porcine Genes for Xenotransplantation Purposes Along with an Assessment of the Off-Target Mutation Formation.

The increasing life expectancy of humans has led to an increase in the number of patients with chronic diseases and organ failure. However, the imbalance between the supply and the demand for human organs is a serious problem in modern transplantology. One of many solutions to overcome this problem is the use of xenotransplantation. The domestic pig (Sus scrofa domestica) is currently considered as the most suitable for human organ procurement. However, there are discrepancies between pigs and humans that lead to the creation of immunological barriers preventing the direct xenograft. The introduction of appropriate modifications to the pig genome to prevent xenograft rejection is crucial in xenotransplantation studies. In this study, porcine GGTA1, CMAH, ß4GalNT2, vWF, ASGR1 genes were selected to introduce genetic modifications. The evaluation of three selected gRNAs within each gene was obtained, which enabled the selection of the best site for efficient introduction of changes. Modifications were examined after nucleofection of porcine primary kidney fibroblasts with CRISPR/Cas9 system genetic constructs, followed by the tracking of indels by decomposition (TIDE) analysis. In addition, off-target analysis was carried out for selected best gRNAs using the TIDE tool, which is new in the research conducted so far and shows the utility of this tool in these studies.

Estimation of economic weights for number of teats and sperm quality traits in pigs.

Accurate predictive modelling facilitates efficient and effective trait selection in animal breeding and can decrease costs while maximizing profits when raising economically important animals. The objective of this study was to extend a previously developed bioeconomic model and computer program to calculate the marginal economic values (MEVs) and economic weights (EWs) for direct and maternal pig traits affected by new reproductive traits, namely the number of sow functional teats (NFTs) and boar sperm quality traits (SQTs) that included sperm volume, sperm concentration, motility percentage and percentage of abnormal spermatozoa. The MEV of NFTs represented the cost differences between naturally and artificially reared piglets until weaning and the cost differences between naturally and artificially reared finished animals. The MEVs of SQTs expressed the saved costs for artificial insemination, assuming a decreased price per insemination dose when improving the SQTs. The absolute and relative EWs for the newly defined complex of traits in the breeding objectives for pig breeds involved in the Czech national three-way crossing system (Czech Large White [CLW], Czech Landrace [CL] and Pietrain [PN]) were calculated using gene flow methods. The NFT trait was included only for dam breeds, and the relative EW averaged 3.6% of the total economic importance based on the genetic standard deviations of all 19 simultaneously evaluated traits in CLW and CL breeds. The relative EWs of the four SQTs comprised 2.0% of the total economic importance of the 19 traits in the CLW and CL dam breeds and 8% of the total economic importance of the 18 traits in the PN sire breed. Therefore, inclusion of the NFTs for dam breeds and SQTs for sire breeds in the breeding goal is recommended to aid in obtaining ideal outcomes with optimal economic values.

An assessment of genetic diversity and population structures of fifteen Vietnamese indigenous pig breeds for supporting the decision making on conservation strategies.

The study aimed to characterize genetic diversity, genetic clusters, and phylogenetic relationships of 15 Vietnamese indigenous pig breeds across the country for supporting the decision making of the conservation strategies. For this purpose, 638 samples from the breeds together with two wild pig breeds and an exotic breed were genotyped with 19 microsatellite markers recommended from FAO/ISAG for diversity studies. The higher genetic diversity was observed for indigenous breeds (mean He = 0.67) and wild breeds (mean He = 0.74); the indigenous CoAluoi breed compared the out-breed Landrace (He = 0.59). Fifteen percent of the genetic variation came from differences among breeds. The unrooted neighbor-joining dendrogram obtained from Nei's genetic distances showed three nodes with 100% supported bootstrap values. The first node included the three indigenous breeds (Hung, LungPu, and MuongKhuong), the second node included the indigenous BaXuyen and the exotic Landrace, and the third node included the two wild Thailand and Vietnam pig breeds. The discriminant analysis of principal component (DAPC) of 18 studied breeds resulted in 12 genetic clusters. Unlike the other indigenous breeds, the BaXuyen was in the same genetic cluster with the exotic Landrace-which agreed with the 100% bootstrap value of their node-so the BaXuyen should not be conserved. The five indigenous pig breeds-Huong, VanPa, Soc, ChuProng, and CoAluoi-were assigned to their own clusters, which agreed with the low supported bootstrap values of their nodes. These five breeds should be in the high conservation priority. Finally, the 9 indigenous pig breeds (MuongKhuong, LungPu, Hung, TapNa, MongCai, HaLang, Lung, Meo, and Ban breeds) formed four genetic admixture structures. These results suggest the conservation strategies should be built based on from five to nine pig groups thus reducing the cost of conservation whereas still remaining the genetic diversity of the studied breeds.

Assessment of relationships between pigs based on pedigree and genomic information.

Relationships play a very important role in studies on quantitative genetics. In traditional breeding, pedigree records are used to establish relationships between animals; while this kind of relationship actually represents one kind of relatedness, it cannot distinguish individual specificity, capture the variation between individuals or determine the actual genetic superiority of an animal. However, with the popularization of high-throughput genotypes, assessments of relationships among animals based on genomic information could be a better option. In this study, we compared the relationships between animals based on pedigree and genomic information from two pig breeding herds with different genetic backgrounds and a simulated dataset. Two different methods were implemented to calculate genomic relationship coefficients and genomic kinship coefficients, respectively. Our results show that, for the same kind of relative, the average genomic relationship coefficients (G matrix) were very close to the pedigree relationship coefficients (A matrix), and on average, the corresponding values were halved in genomic kinship coefficients (K matrix). However, the genomic relationship yielded a larger variation than the pedigree relationship, and the latter was similar to that expected for one relative with no or little variation. Two genomic relationship coefficients were highly correlated, for farm1, farm2 and simulated data, and the correlations for the parent-offspring, full-sib and half-sib were 0.95, 0.90 and 0.85; 0.93, 0.96 and 0.89; and 0.52, 0.85 and 0.77, respectively. When the inbreeding coefficient was measured, the genomic information also yielded a higher inbreeding coefficient and a larger variation than that yielded by the pedigree information. For the two genetically divergent Large White populations, the pedigree relationship coefficients between the individuals were 0, and 62 310 and 175 271 animal pairs in the G matrix and K matrix were greater than 0. Our results demonstrated that genomic information outperformed the pedigree information; it can more accurately reflect the relationships and capture the variation that is not detected by pedigree. This information is very helpful in the estimation of genomic breeding values or gene mapping. In addition, genomic information is useful for pedigree correction. Further, our findings also indicate that genomic information can establish the genetic connection between different groups with different genetic background. In addition, it can be used to provide a more accurate measurement of the inbreeding of an animal, which is very important for the assessment of a population structure and breeding plan. However, the approaches for measuring genomic relationships need further investigation.

Performance of Hamiltonian Monte Carlo and No-U-Turn Sampler for estimating genetic parameters and breeding values.

BACKGROUND: Hamiltonian Monte Carlo is one of the algorithms of the Markov chain Monte Carlo method that uses Hamiltonian dynamics to propose samples that follow a target distribution. The method can avoid the random walk behavior to achieve a more effective and consistent exploration of the probability space and sensitivity to correlated parameters, which are shortcomings that plague many Markov chain Monte Carlo methods. However, the performance of Hamiltonian Monte Carlo is highly sensitive to two hyperparameters. The No-U-Turn Sampler, an extension of Hamiltonian Monte Carlo, was recently introduced to automate the tuning of these hyperparameters. Thus, this study compared the performances of Gibbs sampling, Hamiltonian Monte Carlo, and the No-U-Turn Sampler for estimating genetic parameters and breeding values as well as sampling qualities in both simulated and real pig data. For all datasets, we used a pedigree-based univariate linear mixed model. RESULTS: For all datasets, the No-U-Turn Sampler and Gibbs sampling performed comparably regarding the estimation of heritabilities and accuracies of breeding values. Compared with Gibbs sampling, the estimates of effective sample sizes for simulated and pig data with the No-U-Turn Sampler were 3.2 to 22.6 and 3.5 to 5.9 times larger, respectively. Autocorrelations decreased more quickly with the No-U-Turn Sampler than with Gibbs sampling. When true heritability was low in the simulated data, the skewness of the marginal posterior distributions with the No-U-Turn Sampler was smaller than that with Gibbs sampling. The performance of Hamiltonian Monte Carlo for sampling quality was inferior to that of No-U-Turn Sampler in the simulated data. Moreover, Hamiltonian Monte Carlo could not estimate genetic parameters because of difficulties with the hyperparameter settings with pig data. CONCLUSIONS: The No-U-Turn Sampler is a promising sampling method for animal breeding because of its good sampling qualities: large effective sample sizes, low autocorrelations, and low skewness of marginal posterior distributions, particularly when heritability is low. Meanwhile, Hamiltonian Monte Carlo failed to converge with a simple univariate model for pig data. Thus, it might be difficult to use Hamiltonian Monte Carlo for usual complex models in animal breeding.

Impact of merging commercial breeding lines on the genetic diversity of Landrace pigs.

BACKGROUND: The pig breeding industry has undergone a large number of mergers in the past decades. Various commercial lines were merged or discontinued, which is expected to reduce the genetic diversity of the pig species. The objective of the current study was to investigate the genetic diversity of different former Dutch Landrace breeding lines and quantify their relationship with the current Dutch Landrace breed that originated from these lines. RESULTS: Principal component analysis clearly divided the former Landrace lines into two main clusters, which are represented by Norwegian/Finnish Landrace lines and Dutch Landrace lines. Structure analysis revealed that each of the lines that are present in the Dutch Gene bank has a unique genetic identity. The current Dutch Landrace breed shows a high level of admixture and is closely related to the six former lines. The Dumeco N-line, which is conserved in the Dutch Gene bank, is poorly represented in the current Dutch Landrace. All seven lines (the six former and the current line) contribute almost equally to the genetic diversity of the Dutch Landrace breed. As expected, the current Dutch Landrace breed comprises only a small proportion of unique genetic diversity that was not present in the other lines. The genetic diversity level, as measured by Eding's core set method, was equal to 0.89 for the current Dutch Landrace breed, whereas total genetic diversity across the seven lines, measured by the same method, was equal to 0.99. CONCLUSIONS: The current Dutch Landrace breed shows a high level of admixture and is closely related to the six former Dutch Landrace lines. Merging of commercial Landrace lines has reduced the genetic diversity of the Landrace population in the Netherlands, although a large proportion of the original variation is maintained. Thus, our recommendation is to conserve breeding lines in a gene bank before they are merged.

Assessment of meat quality defect genes in indigenous pigs of Bareilly region.

This investigation was undertaken to assess the population of indigenous (Bareilly local) pigs for meat quality genes (RYR1, PRKAG3, HFABP, MYF-5, and MC4R). The results showed that indigenous pigs were monomorphic at RYR1locus (100% NN genotype), HFABP locus (100% HH genotype), and MYF-5 locus (100% DD genotype). Homozygote RR and heterozygote QR genotypes were observed at PRKAG3 (c.599 G>A) SNP locus with 89 and 11% frequency. The frequency of wild (R) and mutant (Q) allele at the said locus was 95 and 5%. The MC4R SNP had three genotypes; homozygote AA with 5% frequency, heterozygote AG with 53% frequency, and homozygote GG with 42% frequency. Corresponding frequency of A and G allele was 32 and 68%, respectively. Monomorphic status at RYR1locus for NN genotype, HFABP locus for HH genotype, and MYF-5 locus for DD genotype indicated that favorable genes for quality pork production have been fixed in the population. The higher frequency of RR genotype (89%) at PRKAG3 and GG genotype (42%) at MC4R locus further explained the existence of favorable genotypes in indigenous pigs.

Bayes Factor-Based Regulatory Gene Network Analysis of Genome-Wide Association Study of Economic Traits in a Purebred Swine Population.

: Early stage prediction of economic trait performance is important and directly linked to profitability of farm pig production. Genome-wide association study (GWAS) has been applied to find causative genomic regions of traits. This study established a regulatory gene network using GWAS for critical economic pig characteristics, centered on easily measurable body fat thickness in live animals. We genotyped 2,681 pigs using Illumina Porcine SNP60, followed by GWAS to calculate Bayes factors for 47,697 single nucleotide polymorphisms (SNPs) of seven traits. Using this information, SNPs were annotated with specific genes near genome locations to establish the association weight matrix. The entire network consisted of 226 nodes and 6,921 significant edges. For in silico validation of their interactions, we conducted regulatory sequence analysis of predicted target genes of transcription factors (TFs). Three key regulatory TFs were identified to guarantee maximum coverage: AT-rich interaction domain 3B (ARID3B), glial cell missing homolog 1 (GCM1), and GLI family zinc finger 2 (GLI2). We identified numerous genes targeted by ARID3B, associated with cellular processes. GCM1 and GLI2 were involved in developmental processes, and their shared target genes regulated multicellular organismal process. This system biology-based function analysis might contribute to enhancing understanding of economic pig traits.

Response to a selection index including environmental costs and risk preferences of producers.

Genetic improvement of animals plays an important role in improving the economic and environmental sustainability of livestock production systems. This paper proposes a method to incorporate mitigation of environmental impacts and risk preferences of producers into a breeding objective via economic values (EVs). The paper assesses the effects of using these alternative EVs of breeding goal traits on discounted economic response to selection and on environmental impacts at commercial farm level. The application focuses on a Brazilian pig production system. Separate dam- and sire-line breeding programs that supply parents in a 3-tier production system for producing crossbreds (fattening pigs) at commercial level were assumed. Using EVs that are derived from utility functions by incorporating risk aversion increases the cumulative discounted economic response to selection in sire-line selection (6%) while reducing response in dam-line selection (12%) compared with the use of traditional EVs. The use of EVs that include environmental costs increases the cumulative discounted social response to selection in both dam-line (5%) and sire-line (10%) selections. Emission of greenhouse gases, and excretion of nitrogen and phosphorus can be reduced more with genetic improvements of production traits than reproduction traits for the typical Brazilian farrow-to-finish pig farm. Reductions in environmental impacts do not, however, depend on the use of the different EVs (i.e., with and without taking into account environmental costs and risk). Both environmental costs and risk preferences of producers need to be considered in sire-line selection, and only environmental costs in dam-line selection to improve, at the same time, the economic and environmental sustainability of the Brazilian pig production system.