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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.

2012-5158 ABSTRACT. We reasoned that marginal returns from genomic selection diminish as the proportion of genotyped selection candidates increases and breeding values (BV) based on a priori information are used to choose candidates that are genotyped. We tested this premise by stochastic simulation of breeding schemes that resembled those used for pigs. We estimated rates of genetic gain and inbreeding realized by genomic selection in breeding schemes where candidates were phenotyped before genotyping and 0 to 100% of the candidates were genotyped based on predicted BV. Genotyping was allocated to male and female candidates at ratios of 100:0, 75:25, 50:50, 25:75, and 0:100. For genotyped candidates, a direct-genomic value (DGV) was sampled with reliabilities 0.10, 0.50, and 0.90. Ten sires and 300 dams with the largest BV after genotyping were selected at each generation. Selection was for a single trait with heritability 0.20. We found that the marginal returns did diminish as genotyping proportion was increased, while the rate at which the returns diminished slowed as DGV became more reliable. With DGV reliability 0.10, genotyping as little as 5% of the selection candidates realized 86% of the additional genetic gain and 67% of the reduction in inbreeding that was realized by genotyping 100% of the candidates. All of the genetic gain and reduction in inbreeding was realized by genotyping 40 and 50% of the candidates. When the reliability was increased to 0.90, genotyping 20% of the candidates was required to realize 76% of the genetic gain and 85% of the reduction in inbreeding. Genotyping 50% of the selection candidates with DGV reliability 0.90 realized 91% of the genetic gain and 94% of the reduction in inbreeding. Regardless of DGV reliability, returns at small genotyping proportions of 0.5 to 10% were maximized when only male candidates were genotyped. At the large genotyping proportions of 20 to 50%, returns were maximized by genotyping both males and females. Our fi ndings indicate that, provided a priori information is available, only 5 to 20% of the selection candidates need to be genotyped to realize most of the benefi ts from genomic selection. At these genotyping proportions, it is best to target males in schemes when selection intensity for males is greater than females. Our fi ndings should benefi t breeders because they suggest that large investments in genotyping are not required to reap most of the benefi ts from genomic selection.

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.

Genetic variance for complex traits in animal breeding are often estimated using linear mixed-models that incorporate information from SNP-markers using a realized genomic-relationship matrices. In these models, individual genetic markers are weighted equally and the variation in the genome is treated as a “black box”. While this approach has proved useful in selecting animals with high genetic potential, it does not generate insight into the biological mechanisms underlying trait variation. We propose to build a linear mixed model approach to evaluate the collective effects of sets of SNPs in genomic features and open the “black box”. Using data on ADG and BF from 6,112 entire Duroc boars and a high-density SNP chip, we show here, that the QTL categories with highest relative importance of the SNP set were indeed biological meaningful

A recently developed single-step method allows genetic evaluation based on information from phenotypes, pedigree and markers simultaneously. This paper compared reliabilities of predicted breeding values obtained from single-step method and the traditional pedigree-based method for two litter size traits, total number of piglets born (TNB), and litter size at five days after birth (Ls 5) in Danish Landrace and Yorkshire pigs. The results showed that the single-step method combining phenotypic and genotypic information provided more accurate predictions than the pedigree-based method, not only for genotyped animals but also for non-genotyped animals.

Background The extent of linkage disequilibrium (LD) is of critical importance for genomic selection and marker assisted selection. The primary purpose of this study is to examine patterns of LD in three Danish pig breeds (Duroc, Landrace and Yorkshire); we also examine patterns of persistence of phase between the breeds. We quantify local LD by fitting a model relating LD to physical distance between markers in sliding windows, and use this to visualize how LD varies according to physical position. We use a similar method to examine local persistence of phase. Results Average LD decay over distance for Duroc was significantly different from Landrace and Yorkshire, that showed similar patterns. Persistence of phase between Landrace and Yorkshire was much higher than between these breeds and Duroc. Local r2 over the chromosomes showed more variation between breeds than average r2 decay across whole genome. Also local persistence of phase was higher between Landrace and Yorkshire than between these breeds and Duroc. Conclusions The results concerning genome-wide LD indicated that Duroc had “old inbreeding”, and confirmed the mixture history of Landrace and Yorkshire, which is also implied by the higher level of persistence of phase between Landrace and Yorkshire. The method to estimate and visualize local pattern of LD and persistence of phase provides insight into how these quantities vary along chromosomes and between breeds.

BackgroundFeed efficiency is one of the major components determining costs of animal production. Residual feed intake (RFI) is defined as the difference between the observed and the expected feed intake given a certain production. Residual feed intake 1 (RFI1) was calculated based on regression of individual daily feed intake (DFI) on initial test weight and average daily gain. Residual feed intake 2 (RFI2) was as RFI1 except it was also regressed with respect to backfat (BF). It has been shown to be a sensitive and accurate measure for feed efficiency in livestock but knowledge of the genomic regions and mechanisms affecting RFI in pigs is lacking. The study aimed to identify genetic markers and candidate genes for RFI and its component traits as well as pathways associated with RFI in Danish Duroc boars by genome-wide associations and systems genetic analyses.ResultsPhenotypic and genotypic records (using the Illumina Porcine SNP60 BeadChip) were available on 1,272 boars. Fifteen and 12 loci were significantly associated (p < 1.52 x 10-6) with RFI1 and RFI2, respectively. Among them, 10 SNPs were significantly associated with both RFI1 and RFI2 implying the existence of common mechanisms controlling the two RFI measures. Significant QTL regions for component traits of RFI (DFI and BF) were detected on pig chromosome (SSC) 1 (for DFI) and 2 for (BF). The SNPs within MAP3K5 and PEX7 on SSC 1, ENSSSCG00000022338 on SSC 9, and DSCAM on SSC 13 might be interesting markers for both RFI measures. Functional annotation of genes in 0.5 Mb size flanking significant SNPs indicated regulation of protein and lipid metabolic process, gap junction, inositol phosphate metabolism and insulin signaling pathway are significant biological processes and pathways for RFI, respectively.ConclusionsThe study detected novel genetic variants and QTLs on SSC 1, 8, 9, 13 and 18 for RFI and indicated significant biological processes and metabolic pathways involved in RFI. The study also detected novel QTLs for component traits of RFI. These results improve our knowledge of the genetic architecture and potential biological pathways underlying RFI; which would be useful for further investigations of key candidate genes for RFI and for development of biomarkers.

Genome wide association studies (GWAS) are being extensively used in revealing genetic architecture of complex traits. However, GWAS offer limited understanding of the biological role of significant single nucleotide polymorphisms (SNPs) affecting complex traits. Pathway analysis using GWAS results is an important step where we firstly detect genes located near GWAS-detected SNPs and subsequently we detect enrichment of these genes in various biological processes and pathways. The objective of this study was to apply these steps to identify relevant pathways involved in residual feed intake (RFI) in pigs. Residual feed intake is a feed efficiency measure and is highly economically important in animal production. In our study, a total of 596 Yorkshire boars had phenotypic and genotypic records. After quality control, 37,915 SNPs were available for GWAS which was implemented in the DMU software package. Genome wide association analysis detected 3,450 SNPs (P < 0.05) significantly associated with this trait. Using the R-NCBI2R package, 7,169 nearby genes were found in 0.5 Mb flanking the SNP positions for all 3,450 SNPs. Among them, 2,934 genes were functionally recognized in pigs and were used as input in Database for Annotation, Visualization and Integrated Discovery (DAVID). Neuroactive ligand-receptor interaction, SNARE interactions in vesicular transport, pyruvate metabolism and Wnt signaling pathway were detected by using KEGG tools, growth hormone and phospholipase C signaling pathway were detected by using BioCarta pathway analysis tools and asynaptic_vesicle_trafficking and thyrotropin-releasing hormone receptor signaling pathways were identified by using Panther-pathway profiling tools. Several of these pathways such as Wnt, growth hormone and thyrotropin-releasing hormone receptor signaling pathway are known to be involved in biological processes closely related to regulation of feed intake or residual feed intake. These results provide insights into the genetic architecture as well as the systems biological mechanisms of this complex trait in pigs.

This study was aimed at identifying genomic regions controlling feeding behavior in Danish Duroc boars and its potential implications for eating behavior in humans. Data regarding individual daily feed intake (DFI), total daily time spent in feeder (TPD), number of daily visits to feeder (NVD), average duration of each visit (TPV), mean feed intake per visit (FPV) and mean feed intake rate (FR) were available for 1130 boars. All boars were genotyped using the Illumina Porcine SNP60 BeadChip. The association analyses were performed using the GenABEL package in the R program. Sixteen SNPs were found to have moderate genome-wide significance (p

Genetic evaluation based on information from phenotypes, pedigree and markers can be implemented using a recently developed single-step method. In this paper we compare accuracies of predicted breeding values for daily gain and feed conversion ratio (FCR) in Danish Duroc pigs obtained from different versions of single-step methods, the traditional pedigree-based method and the genomic BLUP (GBLUP) method. In particular, we present a single-step method with an adjustment of the genomic relationship matrix so that it is compatible to the pedigree-based relationship matrix. Comparisons are made for both genotyped and non-genotyped animals and univariate and bivariate models. The results show that the three methods with marker information (two single-step methods and GBLUP) produce more accurate predictions of genotyped animals than the pedigree-based method. In addition, single-step methods provide more accurate predictions for non-genotyped animals. The results also show that the single-step method with adjusted genomic relationship matrix produce more accurate predictions than the original single-step method. Finally, the results for the bivariate analyses show a somewhat improved accuracy and reduced inflation of predictions for FCR for the two single-step methods compared with the univariate analyses. The conclusions are: first, the methods with marker information improve prediction compared with the pedigree-based method; second, a single-step method, contrary to GBLUP, provides improved predictions for all animals compared to the pedigree-based method; and third, a single-step method should be used with an adjustment of the genomic relationship matrix.

Dominance and imprinting effects which may be important for complex traits are usually ignored in the genomic prediction of complex traits in livestock. The objectives of this study were to estimate genetic parameters of additive, dominance and imprinting effects and assess genomic predictions considering dominance and imprinting effects for lean meat Percentage (LMP) and average daily gain (ADG) in Danish Duroc pigs.Corrected phenotypes of 8,113 genotyped pigs in both breeding and multiplier herds were used for the analysis. Four genomic BLUP models were compared with regard to prediction accuracy and bias: 1) additive model (GA), 2) additive and dominance model (GAD), 3) additive and imprinting model (GAI), 4) additive, dominance and imprinting model (GADI).Based on GADI model, the estimated heritabilities were 0.37 (LMP) and 0.21 (ADG). GADI resulted to the smallest estimates of additive, litter and residual variances, respectively, while GA led the largest. Dominance is significant and accounted for about 3% of the phenotypic variances while imprinting is not significant and accounted for less than 1% of the phenotypic variances. Among all the models, GADI led to the highest reliability (51.4%) of predicted total genetic values (GTV) for LMP, and GAD led to the highest (27.7%) for ADG. The most unbiased predictions of GTV were provided by GAD model.The results indicate that dominance effects significantly contribute to the genetic variation of LMP and ADG in Danish Duroc pigs, but imprinting effects are not. Models including dominance effects can improve accuracy and reduce bias of genomic predictions.

This study was aimed at identifying genomic regions controlling feeding behaviors inDanish Duroc boars and its potential implications for eating behaviors in humans.Individual daily feed intake (DFI), total daily time spent in feeder (TPD), number of dailyvisits to feeder (NVD), time spent to eat per visit (TPV), mean feed intake per visit(FPV) and mean feed intake rate (FR) were available on 1130 boars. All boars weregenotyped using the Illumina Porcine SNP60 BeadChip. The association analyseswere performed using the GenABEL package in R. Sixteen SNPs had moderategenome-wide significant (p < 5E-05) and 76 SNPs had suggestive (p < 5E-04)association with feeding behavior traits. Locus M1GA0016584 located close to theMSI2 gene on chromosome (SSC) 14 was very strongly associated with NVD (p =9.6E-07). Thirty six SNPs were located in genome regions where QTLs havepreviously been reported for behaviors and/or feed intake traits in pigs. The regions:64-65Mb on SSC 1, 124-130Mb on SSC 8, 63-68Mb on SSC 11, 32-39Mb and 59-60Mb on SSC 12 harbored several significant SNPs. Three and two haplotypes thatwere detected in significant regions on SSC1 and SSC12 affected DFI and NVD,respectively. Synapse, dephosphorylation and positive regulation of peptide secretiongenes were found highly significantly associated with feeding behavior traits byfunctional annotation. This is the first GWAS to identify genetic variants and biologicalmechanisms for feeding behavior in pigs and these results are important for geneticimprovement of pig feed efficiency. The results of pig-human comparative genemapping revealed some important genomic regions and/or genes on the humangenome that may influence eating behavior in human and consequently affect thedevelopment of obesity and metabolic syndromes. This is the first such translationalgenomics study to report potential candidate genes for eating behavior in humans

Background In animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix. In such models, individual genetic markers are weighted equally and genomic variation is treated as a “black box.” This approach is useful for selecting animals with high genetic potential, but it does not generate or utilise knowledge of the biological mechanisms underlying trait variation. Here we propose a linear mixed-model approach that can evaluate the collective effects of sets of SNPs and thereby open the “black box.” The described genomic feature best linear unbiased prediction (GFBLUP) model has two components that are defined by genomic features. Results We analysed data on average daily gain, feed efficiency, and lean meat percentage from 3,085 Duroc boars, along with genotypes from a 60 K SNP chip. In addition information on known quantitative trait loci (QTL) from the animal QTL database was integrated in the GFBLUP as a genomic feature. Our results showed that the most significant QTL categories were indeed biologically meaningful. Additionally, for high heritability traits, prediction accuracy was improved by the incorporation of biological knowledge in prediction models. A simulation study using the real genotypes and simulated phenotypes demonstrated challenges regarding detection of causal variants in low to medium heritability traits. Conclusions The GFBLUP model showed increased predictive ability when enough causal variants were included in the genomic feature to explain over 10 % of the genomic variance, and when dilution by non-causal markers was minimal. In the observed data set, predictive ability was increased by the inclusion of prior QTL information obtained outside the training data set, but only for the trait with highest heritability. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0322-9) contains supplementary material, which is available to authorized users.

We hypothesized that (i) non-genetic correlations between litter size (LS) and daily gain are different from zero, and (ii) modelling non-genetic correlations changes estimates of genetic (co)variances of the traits compared to when they are ignored. We tested these hypotheses using a new multi-trait model that separates genetic and non-genetic correlations by fitting correlations between the residual effects for one trait and non-residual effects of the other trait.We estimated variances by fitting models with and without these non-genetic correlations to LS and daily gain in DanBred Landrace pigs. We found that all non-genetic correlations between LS and daily gain were significantly different from zero. Estimates of genetic (co)variances changed significantly when non-genetic correlations were ignored. Clearly, multi-trait models with production traits and litter size traits should include correlations between residual effects for one trait and non-residual effects of the other trait to separate genetic correlations from non-genetic correlations.

This aim of this study was to identify genomic regions controlling feed efficiency defined by 2 estimated residual feed intake (RFI) measures in Danish Duroc boars. RFI1 was calculated based on regression of individual average daily feed intake (30-100 kg) on initial test weight and average daily gain (30-100 kg). RFI2 was the same as RFI1 except that it was also regressed on backfat (BF). A total of 868 boars had phenotypic and genotype (i.e. Illumina Porcine SNP60 BeadChip) records. A total of 33945 SNPs were available for genome wide association studies (GWAS) after quality control. Deregressed EBVs were used as response variables in GWAS implemented in the R-GenABEL package. A total of 16 and 17 loci were significantly associated (p < 5×10-4) with RFI1 and RFI2, respectively. 12 SNPs were in common implying the existence of common genetic mechanisms. A mutation (A/G) at locus ALGA0098358 on CPVL gene on SSC18 was highly associated with both RFI1 (p = 1.8×10-4) and RFI2 (p = 4.5×10-5). Moreover, the genomic region 30.5-31.5Mb on SSC1 contained high numbers of significant SNPs (8 loci) for both RFIs. The SNPs within region of MAP3K5 on SSC1, GTF2IRD2 on SSC3, and WDR70 on SSC16 may be interesting markers for both RFIs. The ANKRD6 on SSC1, CADM1 on SSC9 and RHBDD1 on SSC15 were distinct markers for each RFI1 and RFI2, respectively. Further systems genetics investigations were carried out to reveal functional importance of genes and haplotypes on candidate chromosomes with high numbers of significant SNPs. This study enhanced our biological knowledge of the genes and variants controlling RFIs and might contribute to genetic improvement for feed efficiency traits in pigs

Background Genomic selection can be implemented by a multi-step procedure, which requires a response variable and a statistical method. For pure-bred pigs, it was hypothesised that deregressed estimated breeding values (EBV) with the parent average removed as the response variable generate higher reliabilities of genomic breeding values than EBV, and that the normal, thick-tailed and mixture-distribution models yield similar reliabilities. Methods Reliabilities of genomic breeding values were estimated with EBV and deregressed EBV as response variables and under the three statistical methods, genomic BLUP, Bayesian Lasso and MIXTURE. The methods were examined by splitting data into a reference data set of 1375 genotyped animals that were performance tested before October 2008, and 536 genotyped validation animals that were performance tested after October 2008. The traits examined were daily gain and feed conversion ratio. Results Using deregressed EBV as the response variable yielded 18 to 39% higher reliabilities of the genomic breeding values than using EBV as the response variable. For daily gain, the increase in reliability due to deregression was significant and approximately 35%, whereas for feed conversion ratio it ranged between 18 and 39% and was significant only when MIXTURE was used. Genomic BLUP, Bayesian Lasso and MIXTURE had similar reliabilities. Conclusions Deregressed EBV is the preferred response variable, whereas the choice of statistical method is less critical for pure-bred pigs. The increase of 18 to 39% in reliability is worthwhile, since the reliabilities of the genomic breeding values directly affect the returns from genomic selection.