Search

We aimed to estimate genetic parameters for semen quality and quantity traits as well as for within-boar variation of these traits to evaluate their inclusion in breeding goals. Genetic parameters were estimated within line using a multiple-trait (4 × 4) repeatability animal model fitted for 5 pig lines, considering 4 semen traits: sperm motility (MOT), sperm progressive motility (PROMOT), log-transformed number of sperm cells per ejaculate (lnNcells), and total morphological abnormalities (ABN). The within-boar variation of these traits was analyzed based on a multiple-trait (2 × 2) approach for SD and average (AVG) and a single-trait analysis for CV. The average heritabilities across the 5 lines estimated by multiple-trait analysis were 0.18 ± 0.07 (MOT), 0.22 ± 0.08 (PROMOT), 0.16 ± 0.04 (lnNcells), and 0.20 ± 0.04 (ABN). The average genetic correlations were favorable between MOT and PROMOT (0.86 ± 0.10), between MOT and ABN (-0.66 ± 0.25), and between PROMOT and ABN (-0.65 ± 0.25). As determined by within-boar variation analysis, AVG exhibited the greatest heritabilities followed by SD and CV, respectively, for the traits MOT and ABN. For PROMOT, average SD heritability was lower than CV heritability, whereas for lnNcells, they were the same. The average genetic correlations between AVG and SD were favorable for MOT (-0.60 ± 0.13), PROMOT (-0.79 ± 0.14), and ABN (0.78 ± 0.17). The moderate heritabilities indicate the possibility of effective selection of boars based on semen traits. Average and SD are proposed as appropriate traits for selection regarding uniformity. [ABSTRACT FROM AUTHOR]

Seasonal infertility is often observed as anestrus and a lower conception rate resulting in a reduced farrowing rate (FR) during late summer and early autumn. This is often regarded as an effect of heat stress; however, we observed a reduction in the FR of sows even after correcting for ambient temperature in our data. Therefore, we added change in photoperiod in the analysis of FR considering its effect on sow fertility. Change in photoperiod was modeled using the cosine of the day of first insemination within a year. On an average, the FR decreased by 2% during early autumn with decreasing daily photoperiod compared with early summer with almost no change in daily photoperiod. It declined 0.2% per degree Celsius of ambient temperature above 19.2°C. This result is a step forward in disentangling the 2 environmental components responsible for seasonal infertility. Our next aim was to estimate the magnitude of genetic variation in FR in response to change in photoperiod and ambient temperature to explore opportunities for selecting pigs to have a constant FR throughout the year. We used reaction norm models to estimate additive genetic variation in response to change in photoperiod and ambient temperature. The results revealed a larger genetic variation at stressful environments when daily photoperiod decreased and ambient temperatures increased above 19.2°C compared with neutral environments. Genetic correlations between stressful environments and nonstressful environments ranged from 0.90 (±0.03) to 0.46 (±0.13) depending on the severity of the stress, indicating changes in expression of FR depending on the environment. The genetic correlation between responses of pigs to changes in photoperiod and to those in ambient temperature were positive, indicating that pigs tolerant to decreasing daily photoperiod are also tolerant to high ambient temperatures. Therefore, selection for tolerance to decreasing daily photoperiod should also increase tolerance to high ambient temperatures or vice versa. [ABSTRACT FROM AUTHOR]

Robustness is an important issue in the pig production industry. Since pigs from international breeding organizations have to withstand a variety of environmental challenges, selection of pigs with the inherent ability to sustain their productivity in diverse environments may be an economically feasible approach in the livestock industry. The objective of this study was to estimate genetic parameters and breeding values across different levels of environmental challenge load. The challenge load (CL) was estimated as the reduction in reproductive performance during different weeks of a year using 925,711 farrowing records from farms distributed worldwide. A wide range of levels of challenge, from favorable to unfavorable environments, was observed among farms with high CL values being associated with confirmed situations of unfavorable environment. Genetic parameters and breeding values were estimated in high-and low-challenge environments using a bivariate analysis, as well as across increasing levels of challenge with a random regression model using Legendre polynomials. Although heritability estimates of number of pigs born alive were slightly higher in environments with extreme CL than in those with intermediate levels of CL, the heritabilities of number of piglet losses increased progressively as CL increased. Genetic correlations among environments with different levels of CL suggest that selection in environments with extremes of low or high CL would result in low response to selection. Therefore, selection programs of breeding organizations that are commonly conducted under favorable environments could have low response to selection in commercial farms that have unfavorable environmental conditions. Sows that had experienced high levels of challenge at least once during their productive life were ranked according to their EBV. The selection of pigs using EBV ignoring environmental challenges or on the basis of records from only favorable environments resulted in a sharp decline in productivity as the level of challenge increased. In contrast, selection using the random regression approach resulted in limited change in productivity with increasing levels of challenge. Hence, we demonstrate that the use of a quantitative measure of environmental CL and a random regression approach can be comprehensively combined for genetic selection of pigs with enhanced ability to maintain high productivity in harsh environments. [ABSTRACT FROM AUTHOR]

The objective of this study was to estimate the genetic variance for within-litter variation of birth weight (BW0) using genomic (GRM) or pedigree relationship matrices (PRM) and to compare the accuracy of estimated breeding values (EBV) for within-litter variation of BW0 using GRM and PRM. The BW0 and residual variance of BW0 were modeled by the double hierarchical generalized linear model using GRM or PRM. Data came from 2 dam lines: Landrace and Large White. After editing, the data set in Landrace consisted of 748 sows with 1,938 litters and 29,430 piglets and in Large White of 989 sows with 3,320 litters and 51,818 piglets. To construct GRM, 46,466 (Landrace) and 44,826 (Large White) single nucleotide polymorphisms were used, whereas to construct PRM, 5 generations of pedigree were used. The accuracy of EBV with GRM was estimated with 8-fold cross-validation and compared to PRM. Estimated variance components were highly similar for GRM and PRM. The maternal genetic variance in residual variance of BW0 in Landrace was 0.05 with GRM and 0.06 with PRM. In Large White these were 0.04 with GRM and 0.05 with PRM. The genetic coefficient of variation (GCVSDe) was about 0.10 in both dam lines. This indicates a change of 10% in residual SD of BW0 when achieving a genetic response of 1 genetic standard deviation. The genetic correlation between birth weight and its residual variance was about 0.6 in both dam lines. The accuracies of selection for within-litter variation of birth weight were 0.35 with GRM and 0.23 with PRM in Landrace and 0.29 with GRM and 0.34 with PRM in Large White. In this case, using GRM did not significantly increase accuracies of selection. Results, however, show good opportunities to select for reduced within-litter variation of BW0. Genomic selection can increase accuracy of selection when reference populations contain at least 2,000 sows. [ABSTRACT FROM AUTHOR]

Porcine reproductive and respiratory syndrome (PRRS) is a viral disease with negative impacts on reproduction of sows. Genetic selection to improve the response of sows to PRRS could be an approach to control the disease. Detennining sow response to PRRS requires knowing pathogen burden and sow performance. In practice, though, records of pathogen burden are unavailable. We develop a statistical method to distinguish healthy and disease phases and to develop a method to quantify sows' responses to PRRS without having individual pathogen burden. We analyzed 10,910 sows with 57,135 repeated records of reproduction performance. Disease phases were recognized as strong deviation of herd-year-week estimates for reproduction traits using two methods: Method 1 used raw weekly averages of the herd; Method 2 used a linear model with fixed effects for seasonality, parity, and year, and random effects for herd-year-week and sow. The variation of sows in response to PRRS was quantified using 2 models on the traits number of piglets bom alive (NBA) and number of piglets bom dead (LOSS): 1) bivariate model considering the trait in healthy and disease phases as different traits, and 2) reaction norm model modeling the response of sows as a linear regression of the trait on herd-year-week estimates of NBA. The linear model for NBA had the highest sensitivity (78%) for disease phases. Residual variances of both were more than doubled in the disease phase com-pared with the healthy phase. Trait correlations between healthy and disease phases deviated from unity (0.57 ± 0.13 - 0.87 ± 0.18). In the bivariate model, repeatabilities were lower in disease phase compared with healthy phase (0.07 ± 0.027 and 0.16 ± 0.005 for NBA; 0.07 ± 0.027 and 0.09 ± 0.004 for LOSS). The reaction norm model fitted the data better than the bivariate model based on Akaike's information criterion, and had also higher pre-dictive ability in disease phase based on cross validation. Our results show that the linear model is a practical meth-od to distinguish between healthy and disease phases in farm data. We showed that there is variation among sows in response to PRRS, implying possibilities for selection, and the reaction norm model is a good model to study the response of animals toward diseases. [ABSTRACT FROM AUTHOR]

The article discusses linkage disequilibrium (LD) which determines the number of single nucleotide polymorphism (SNP) which can be used for association and genomic studies in a genome. It states that LD also influences the genomic breeding values accuracy. The studies involved the marker reduction by selecting SNP which can be used for haplotype block used in association mapping. The differences in haplotype block can be used for future studies of differences across the animal cell lines.

The sow provides a specific environment to her offspring during gestation and lactation. Certain features in the early life of the sow (Sow history features) may affect her ability to deliver and feed a healthy litter. In genetic analyses of grow-finish traits, these effects are estimated as common litter or permanent sow effects. The objective of this research was to identify sow history features that affect the growth rate (GR) and feed intake (FI) of her offspring during the grow-finish stage. Data from 17,743 grow-finish pigs, coming from 604 sires and 681 crossbred sows, were recorded between May 2001 and February 2010 at the experimental farm of the Institute for Pig Genetics (Beilen, the Netherlands). The grow-finish stage was divided into 2 phases (phase 1: 26 to 75 kg; phase 2: 75 to 115 kg). The sow history features were birth litter size, birth year and season, birth farm, weaning age, age of transfer to the experimental farm, and age at first insemination. The sow features were added to the basic model one at a time to study their effect on the grow-finish traits of the pigs. Subsequently, significant sow features (P < 0.1) were fitted simultaneously in an animal model. With every extra piglet in the birth litter of the sow, the GR of her offspring decreased by g/d and the FT decreased by 4 g/d. Every extra day to the first insemination increased the GR of grow-finish pigs by 0.1 g/d. The heritability estimates for GR an FT (only in phase 2 of the grow-finish stage) decrease after adding the sow features to the model. No differences were found in estimates of the common litter effects between the basic model and the model with a significant sow features. The estimates of the permanent sow effect changed for FI from 0.03 (basic model to 0.00 (model with sow features), and for FI in phase 1, the permanent sow effect decreased from 0.03 (basic model) to 0.01 (model with sow features). In conclusion selected sow features do affect the grow-finish traits ( the pigs, but their estimates are small and explain only a small proportion of the differences in the GR and FI of grow-finish pigs. The sow features partially explained the permanent sow effect of FI-related traits and did not explain the common litter effect. Although the sow early life features can affect piglet traits, they do not predict which sows produce better performing offspring in the grow-finish stage. [ABSTRACT FROM AUTHOR]

In animal breeding, recording of correct pedigrees is essential to achieve genetic progress. Markers on DNA are useful to verify the on-farm pedigree records (parental verification) but can also be used to assign parents retrospectively (parental identification). This approach could reduce the costs of recording for traits with low incidence, such as those related to diseases or mortality. In this study, SNP were used to assign the true sires of 368 purebred animals from a Duroc-based sire line and 140 crossbred offspring from a commercial pig population. Some of the sires were closely related. There were 3 full sibs and 17 half sibs among the true fathers and 4 full sibs and 35 half sibs among all putative fathers. To define the number of SNP necessary, 5 SNP panels (40, 60, 80, 100, and 120 SNP) were assembled from the Illumina PorcineSNP60 Beadchip (Illumina, San Diego, CA) based on minor allele frequency (>0.3), high genotyping call rate (≥90%), and equal spacing across the genome. For paternal identification considering only the 66 true sires in the data set, 60 SNP resulted in 100% correct assignment of the sire. By including additional putative sires (n = 304), 80 SNP were sufficient for 100% correct assignment of the sire. The following criteria were derived to identify the correct sire for the current data set: the logarithm of odds (LOD) score for assigning the correct sire was ≥5, the number of mismatches was ≤1, and the difference in the LOD score between the first and the second most likely sire was >5. If the correct sire was not present among all putative sires, the mean LOD for the most likely sire was close to zero or negative when using 100 SNP. More SNP would be needed for paternal identification if the number of putative sires increased and the degree of relatedness was greater than in the data set used here. The threshold for the number of mismatches can be adjusted according to the practical situation to account for the trade-off between false negatives and false positives. The latter can be avoided efficiently, ensuring that the correct father is being sampled. Nevertheless, a restriction on the number of putative sires is advisable to reduce the risk of assigning close relatives. [ABSTRACT FROM AUTHOR]

Meta-analysis of results from multiple studies could lead to more precise quantitative trait loci (QTL) position estimates compared to the individual experiments. As the raw data from many different studies are not readily available, the use of results from published articles may be helpful. In this study, we performed a meta-analysis of QTL on chromosome 4 in pig, using data from 25 separate experiments. First, a meta-analysis was performed for individual traits: average daily gain and backfat thickness. Second, a meta-analysis was performed for the QTL of three traits affecting loin yield: loin eye area, carcass length and loin meat weight. Third, 78 QTL were selected from 20 traits that could be assigned to one of three broad categories: carcass, fatness or growth traits. For each analysis, the number of identified meta-QTL was smaller than the number of initial QTL. The reduction in the number of QTL ranged from 71% to 86% compared to the total number before the meta-analysis. In addition, the meta-analysis reduced the QTL confidence intervals by as much as 85% compared to individual QTL estimates. The reduction in the confidence interval was greater when a large number of independent QTL was included in the meta-analysis. Meta-QTL related to growth and fatness were found in the same region as the FAT1 region. Results indicate that the meta-analysis is an efficient strategy to estimate the number and refine the positions of QTL when QTL estimates are available from multiple populations and experiments. This strategy can be used to better target further studies such as the selection of candidate genes related to trait variation. [ABSTRACT FROM AUTHOR]

Porcine chromosome 4 harbours many quantitative trait loci (QTL) affecting meat quality, fatness and carcass composition traits, detected in resource pig populations previously. However, prior to selection in commercial breeds, QTL identified in an intercross between divergent breeds require confirmation, so that they can be segregated. Consequently, the objective of this study was to validate several QTL on porcine chromosome 4 responsible for meat and carcass quality traits. The experimental population consisted of 14 crossbred paternal half-sib families. The region of investigation was the q arm of SSC4 flanked by the markers S0073 and S0813. Regression analysis resulted in the validation of three QTL within the interval: Minolta a* loin, back fat thickness and the weight of trimmed ham. The results were additionally confirmed by factor analysis. Candidate genes were proposed for meat colour, which was the most evident QTL validated in this study. [ABSTRACT FROM AUTHOR]

In a previous study, QTL for carcass composition and meat quality were identified in a commercial finisher cross. The main objective of the current study was to confirm and fine map the QTL on SSC4 and SSC11 by genotyping an increased number of individuals and markers and to analyze the data using a combined linkage and linkage disequilibrium analysis method. A modified version of the method excludes linkage disequilibrium information from the analysis, enabling the comparison of results based on linkage information only or results based on combined linkage and linkage disequilibrium information. Nine additional paternal half-sib families were genotyped for 18 markers, resulting in a total of 1,855 animals genotyped for 15 and 13 markers on SSC4 and SSC11, respectively. The QTL affecting meat color on SSC4 was confirmed, whereas the QTL affecting LM weight could not be confirmed. The combined linkage and linkage disequilibrium analysis resulted in the identification of new significant effects for 14 traits on the 2 chromosomes. Heritabilities of the QTL effects ranged from 1.8 to 13.2%. The analysis contributed to a more accurate positioning of QTL and further characterized their phenotypic effect. However, results showed that even greater marker densities are required to take full advantage of linkage disequilibrium information and to identify haplotypes associated with favorable QTL alleles. [ABSTRACT FROM AUTHOR]