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Background Single-step genomic best linear unbiased prediction (SSGBLUP) method is an effective approach for genetic evaluation. To overcome the issue that pedigree and genomic relationship matrices refer to different base populations, a single-step genomic method with a metafounder (MF-SSGBLUP) was put forward. The aim of this study was to compare the predictive abilities and bias of genomic evaluation based on MF-SSGBLUP compared with standard SSGBLUP. We did this for feed conversion ratio (FCR) and average daily gain (ADG) in DanBred Landrace (LL) and DanBred Yorkshire (YY) pigs using both univariate and bivariate models. Additionally, optimal weighting factors (ω) on pedigree-based relationship matrices for ADG and FCR in both SSGBLUP and MF-SSGBLUP were also investigated. Results In general, SSGBLUP and MF-SSGBLUP methods showed similar predictive abilities and biasedness of GEBVs. In LL, for ADG, the predictive ability for SSGBLUP was up to 0.355, while MF-SSGBLUP reached a highest predictive ability of 0.356; for FCR, the predictive ability for MF-SSGBLUP together with a bivariate model was highest (0.196) among all the different scenarios. In YY, performances of genomic prediction for ADG were almost similar among different scenarios and predictive ability for ADG was up to 0.347; for FCR, the highest predictive ability (0.355) was obtained in MF-SSGBLUP. In biasedness, SSGBLUP and MF-SSGBLUP performed nearly the same, and bivariate models generally had less biasedness than univariate models. Additionally, in terms of the optimal weighting factors (ω), in LL, regardless of a univariate or a bivariate model was used, the optimal ω for ADG were around 0.2 in both SSGBLUP and MF-SSGBLUP; for FCR, 0.70 and 0.55 were the optimal ω in SSGBLUP and MF-SSGBLUP method, respectively. In YY, the optimal ω ranged from 0.25 to 0.35 for ADG and from 0.10 to 0.30 for FCR in different scenarios. Conclusions This study indicated that MF-SSGBLUP method performed at least as good as SSGBLUP for genomic evaluation. The optimal weighting factors (ω) in SSGBLUP and MF-SSGBLUP are not dramatically different. Overall, MF-SSGBLUP in combination with a bivariate model was recommended for single-step genomic evaluation for ADG and FCR in DanBred Landrace and Yorkshire pigs.

Background Several studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Accounting for these indirect genetic effects in a selection program may increase genetic progress for growth rate. However, indirect genetic effects are small and difficult to predict accurately. Genomic information may increase the ability to predict indirect genetic effects. Thus, the objective of this study was to test whether including indirect genetic effects in the animal model increases the predictive performance when genetic effects are predicted with genomic relationships. In total, 11,255 pigs were phenotyped for average daily gain between 30 and 94 kg, and 10,995 of these pigs were genotyped. Two relationship matrices were used: a numerator relationship matrix ($${\mathbf{A}}$$ A ) and a combined pedigree and genomic relationship matrix ($${\mathbf{H}}$$ H ); and two different animal models were used: an animal model with only direct genetic effects and an animal model with both direct and indirect genetic effects. The predictive performance of the models was defined as the Pearson correlation between corrected phenotypes and predicted genetic levels. The predicted genetic level of a pig was either its direct genetic effect or the sum of its direct genetic effect and the indirect genetic effects of its group members (total genetic effect). Results The highest predictive performance was achieved when total genetic effects were predicted with genomic information (21.2 vs. 14.7%). In general, the predictive performance was greater for total genetic effects than for direct genetic effects (0.1 to 0.5% greater; not statistically significant). Both types of genetic effects had greater predictive performance when they were predicted with $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when $${\mathbf{H}}$$ H was used rather than $${\mathbf{A}}$$ A . Conclusions This study provides evidence that: (1) corrected phenotypes are better predicted with total genetic effects than with direct genetic effects only; (2) both direct genetic effects and indirect genetic effects are better predicted with $${\mathbf{H}}$$ H than $${\mathbf{A}}$$ A ; (3) using $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A primarily improves the predictive performance of direct genetic effects.

Whole-genome sequencing of 217 animals from three Danish commercial pig breeds (Duroc, Landrace [LL], and Yorkshire [YY]) was performed. Twenty-six million single-nucleotide polymorphisms (SNPs) and 8 million insertions or deletions (indels) were uncovered. Among the SNPs, 493,099 variants were located in coding sequences, and 29,430 were predicted to have a high functional impact such as gain or loss of stop codon. Using the whole-genome sequence dataset as the reference, the imputation accuracy for pigs genotyped with high-density SNP chips was examined. The overall average imputation accuracy for all biallelic variants (SNP and indel) was 0.69, while it was 0.83 for variants with minor allele frequency > 0.1. This study provides whole-genome reference data to impute SNP chip-genotyped animals for further studies to fine map quantitative trait loci as well as improving the prediction accuracy in genomic selection. Signatures of selection were identified both through analyses of fixation and differentiation to reveal selective sweeps that may have had prominent roles during breed development or subsequent divergent selection. However, the fixation indices did not indicate a strong divergence among these three breeds. In LL and YY, the integrated haplotype score identified genomic regions under recent selection. These regions contained genes for olfactory receptors and oxidoreductases. Olfactory receptor genes that might have played a major role in the domestication were previously reported to have been under selection in several species including cattle and swine.

Longevity in commercial sows is often selected for through stayability traits measured in purebred animals. However, this may not be justifiable because longevity and stayability may be subject to both genotype by environment interaction (G × E) and genotype by genotype interaction (G × G). This study tested the hypothesis that stayability to service after first parity is more strongly genetically correlated with longevity in commercial herds when stayability is measured in commercial herds rather than multiplier herds. The analysis was based on farrowing- and service-records from 470,824 sows (189,263 multiplier; 281,561 commercial) and 300 herds (156 multiplier; 144 commercial sows). Multiplier sows were either purebred Landrace or Yorkshire and commercial sows were mainly rotationally crossbreds between the two breeds. Commercial longevity was defined as age in days when culled (LongC), and stayability to service after first parity was defined for both commercial sows (StayC) and multiplier sows (StayM). The genetic correlations between LongC, StayC, and StayM were estimated by restricted maximum likelihood using linear mixed models. Genetic parameters were estimated separately for Landrace and Yorkshire. In Landrace, the genetic correlations between LongC and StayC, LongC and StayM, and StayC and StayM were 0.86 ± 0.02, 0.24 ± 0.05, and 0.34 ± 0.06, respectively. In Yorkshire, the genetic correlations between LongC and StayC, LongC and StayM, and StayC and StayM were 0.81 ± 0.03, 0.17 ± 0.05, and 0.18 ± 0.7, respectively. Conclusively, longevity in commercial herds is more strongly correlated with stayability when stayability is measured in commercial herds rather than multiplier herds.

Records on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.

A single-step method allows genetic evaluation using information of phenotypes, pedigree, and markers from genotyped and nongenotyped individuals simultaneously. This paper compared genomic predictions obtained from a single-step BLUP (SSBLUP) method, a genomic BLUP (GBLUP) method, a selection index blending (SELIND) method, and a traditional pedigree-based method (BLUP) for total number of piglets born (TNB), litter size at d 5 after birth (LS5), and mortality rate before d 5 (Mort; including stillbirth) in Danish Landrace and Yorkshire pigs. Data sets of 778,095 litters from 309,362 Landrace sows and 472,001 litters from 190,760 Yorkshire sows were used for the analysis. There were 332,795 Landrace and 207,255 Yorkshire animals in the pedigree data, among which 3,445 Landrace pigs (1,366 boars and 2,079 sows) and 3,372 Yorkshire pigs (1,241 boars and 2,131 sows) were genotyped with the Illumina PorcineSNP60 BeadChip. The results showed that the 3 methods with marker information (SSBLUP, GBLUP, and SELIND) produced more accurate predictions for genotyped animals than the pedigree-based method. For genotyped animals, the average of reliabilities for all traits in both breeds using traditional BLUP was 0.091, which increased to 0.171 w+hen using GBLUP and to 0.179 when using SELIND and further increased to 0.209 when using SSBLUP. Furthermore, the average reliability of EBV for nongenotyped animals was increased from 0.091 for traditional BLUP to 0.105 for the SSBLUP. The results indicate that the SSBLUP is a good approach to practical genomic prediction of litter size and piglet mortality in Danish Landrace and Yorkshire populations.

International audience; AbstractBackgroundDominance and imprinting genetic effects have been shown to contribute to genetic variance for certain traits but are usually ignored in genomic prediction of complex traits in livestock. The objectives of this study were to estimate variances of additive, dominance and imprinting genetic effects and to evaluate predictions of genetic merit based on genomic data for average daily gain (DG) and backfat thickness (BF) in Danish Duroc pigs.MethodsCorrected phenotypes of 8113 genotyped pigs from breeding and multiplier herds were used. Four Bayesian mixture models that differed in the type of genetic effects included: (A) additive genetic effects, (AD) additive and dominance genetic effects, (AI) additive and imprinting genetic effects, and (ADI) additive, dominance and imprinting genetic effects were compared using Bayes factors. The ability of the models to predict genetic merit was compared with regard to prediction reliability and bias.ResultsBased on model ADI, narrow-sense heritabilities of 0.18 and 0.31 were estimated for DG and BF, respectively. Dominance and imprinting genetic effects accounted for 4.0 to 4.6 and 1.3 to 1.4 % of phenotypic variance, respectively, which were statistically significant. Across the four models, reliabilities of the predicted total genetic values (GTV, sum of all genetic effects) ranged from 16.1 (AI) to 18.4 % (AD) for DG and from 30.1 (AI) to 31.4 % (ADI) for BF. The least biased predictions of GTV were obtained with model AD, with regression coefficients of corrected phenotypes on GTV equal to 0.824 (DG) and 0.738 (BF). Reliabilities of genomic estimated breeding values (GBV, additive genetic effects) did not differ significantly among models for DG (between 16.5 and 16.7 %); however, for BF, model AD provided a significantly higher reliability (31.3 %) than model A (30.7 %). The least biased predictions of GBV were obtained with model AD with regression coefficients of 0.872 for DG and 0.764 for BF.ConclusionsDominance and genomic imprinting effects contribute significantly to the genetic variation of BF and DG in Danish Duroc pigs. Genomic prediction models that include dominance genetic effects can improve accuracy and reduce bias of genomic predictions of genetic merit.

International audience; BackgroundIn many animal breeding programs, with the increasing number of genotyped animals, estimation of genomic breeding values by the single-step method is becoming limited by excessive computing requirements. A recently proposed algorithm for proven and young animals (APY) is an approximation that reduces computing time drastically by dividing genotyped animals into core and non-core animals, with only computations for core animals being time-consuming. We hypothesized that choosing core animals based on representing all generations, minimizing the relatedness within the core group, or maximizing the number of genotyped offspring, would result in greater accuracies of estimated breeding values (EBV).MethodsWe compared eight different core groups for the three pig breeds DanAvl Duroc, DanAvl Landrace and DanAvl Yorkshire. These eight sparse approximations of the single-step method were evaluated based on correlations of EBV for genotyped animals obtained from the sparse methods with those obtained from the usual version of the single-step method. We used a single-trait model with daily gain as trait.ResultsFor core groups that distributed animals across generations, correlations for genotyped animals (from 0.977 to 0.989) were higher than for those that did not distribute core animals across generations (from 0.934 to 0.956). For core groups that maximized the number of genotyped offspring, correlations for genotyped animals (from 0.983 to 0.989) were higher than for other core groups (from 0.934 to 0.981). There was no clear association between low relatedness within the core group and accuracy of approximations.ConclusionsWe found that for core groups that represent all generations and that maximize the number of genotyped offspring, accurate approximations of EBV were obtained. However, we did not find a clear association between accuracy and relatedness within the core group. For the APY method, this is the first study that reports systematic criteria for the creation of core groups that result in more accurate EBV than a similar-sized random core group. Random core groups only ensure across-generation representation. Therefore, we recommend choosing a core group that represents all generations and that maximizes the number of genotyped offspring for single-step genomic evaluation using the APY method.

Background Genotype imputation is commonly used as an initial step in genomic selection since the accuracy of genomic selection does not decline if accurately imputed genotypes are used instead of actual genotypes but for a lower cost. Performance of imputation has rarely been investigated in crossbred animals and, in particular, in pigs. The extent and pattern of linkage disequilibrium differ in crossbred versus purebred animals, which may impact the performance of imputation. In this study, first we compared different scenarios of imputation from 5 K to 8 K single nucleotide polymorphisms (SNPs) in genotyped Danish Landrace and Yorkshire and crossbred Landrace-Yorkshire datasets and, second, we compared imputation from 8 K to 60 K SNPs in genotyped purebred and simulated crossbred datasets. All imputations were done using software Beagle version 3.3.2. Then, we investigated the reasons that could explain the differences observed. Results Genotype imputation performs as well in crossbred animals as in purebred animals when both parental breeds are included in the reference population. When the size of the reference population is very large, it is not necessary to use a reference population that combines the two breeds to impute the genotypes of purebred animals because a within-breed reference population can provide a very high level of imputation accuracy (correct rate ≥ 0.99, correlation ≥ 0.95). However, to ensure that similar imputation accuracies are obtained for crossbred animals, a reference population that combines both parental purebred animals is required. Imputation accuracies are higher when a larger proportion of haplotypes are shared between the reference population and the validation (imputed) populations. Conclusions The results from both real data and pedigree-based simulated data demonstrate that genotype imputation from low-density panels to medium-density panels is highly accurate in both purebred and crossbred pigs. In crossbred pigs, combining the parental purebred animals in the reference population is necessary to obtain high imputation accuracy. Electronic supplementary material The online version of this article (doi:10.1186/s12711-015-0134-4) contains supplementary material, which is available to authorized users.

BackgroundWe tested the hypothesis that optimum-contribution selection (OCS) with restrictions imposed during optimisation realises most of the long-term genetic gain realised by OCS without restrictions.MethodsWe used stochastic simulation to estimate long-term rates of genetic gain realised by breeding schemes that applied OCS without and with restrictions imposed during optimisation, where long-term refers to generations 23 to 25 (approximately). Six restrictions were imposed. Five of these removed solutions from the solution space. The sixth removed records of selection decisions made at earlier selection times. We also simulated a conventional breeding scheme with truncation selection as a reference point. Generations overlapped, selection was for a single trait, and the trait was observed for all selection candidates prior to selection.ResultsOCS with restrictions realised 67 to 99% of the additional gain realised by OCS without restrictions, where additional gain was the difference in the long-term rates of genetic gain realised by OCS without restrictions and our reference point with truncation selection. The only exceptions were those restrictions that removed all solutions near the optimum solution from the solution space and the restriction that removed records of selection decisions made at earlier selection times. Imposing these restrictions realised only −12 to 46% of the additional gain.ConclusionsMost of the long-term genetic gain realised by OCS without restrictions can be realised by OCS with restrictions imposed during optimisation, provided the restrictions do not remove all solutions near the optimum from the solution space and do not remove records of earlier selection decisions. In breeding schemes where OCS cannot be applied optimally because of biological and logistical restrictions, OCS with restrictions provides a useful alternative. Not only does it realise most of the long-term genetic gain, OCS with restrictions enables OCS to be tailored to individual breeding schemes.