Including crossbred pigs in the genomic relationship matrix through utilization of both linkage disequilibrium and linkage analysis.

In pig breeding, the final product is a crossbred (CB) animal, while selection is performed at the purebred (PB) level using mainly PB data. However, incorporating CB data in genetic evaluations is expected to result in greater genetic progress at the CB level. Currently, there is no optimal way to include CB genotypes into the genomic relationship matrix. This is because, in single-step genomic BLUP, which is the most commonly used method, genomic and pedigree relationships must refer to the same base. This may not be the case when several breeds and CB are included. An alternative to overcome this issue may be to use a genomic relationship matrix (G matrix) that accounts for both linkage disequilibrium (LD) and linkage analysis (LA), called G. The objectives of this study were to further develop the G matrix approach to utilize both PB and CB genotypes simultaneously, to investigate its performance, and the general added value of including CB genotypes in genomic evaluations. Data were available on Dutch Landrace, Large White, and the F1 cross of those breeds. In total, 7 different G matrix compositions (PB alone, PB together, each PB with the CB, all genotypes across breeds, and G) were tested on 3 maternal traits: total number born (TNB), live born (LB), and gestation length (GL). Results show that G gave the greatest prediction accuracy of all the relationship matrices tested for PB prediction, but not for CB prediction. Including CB genotypes in general increased prediction accuracy for all breeds. However, in some cases, these increases in prediction accuracy were not significant (at < 0.05). To conclude, CB genotypes increased prediction accuracy for some of the traits and breeds, but not for all. The G matrix had significantly greater prediction accuracy in PB than the other G matrix with both PB and CB genotypes, except in one case. While for CB, the G matrix with genotypes across all breeds gave the greatest accuracy, though this was not significantly different from G. Computation time was high for G, and research will be needed to reduce its computational costs to make it feasible for use in routine evaluations. The main conclusion is that inclusion of CB genotypes is beneficial for both PB and CB animals.

Genetic trends of conformation traits and genetic correlations to osteochondrosis in boars.

The objective of our study was to investigate the heritabilities and genetic correlations between traits from a linear exterior assessment system and osteochondrosis (OC) measured by computed tomography (CT), and in addition, to study the genetic trend in a population where the conformation traits have been included in the breeding goal. The data material consisted of phenotypes from a total of 4571 Norsvin Landrace test boars. At the end of the test period, all boars were subjected to a detailed exterior assessment system. Within 10 days of the assessment, the boars were CT scanned for measuring OC. The total score of osteochondrosis (OCT), used in this study, is the sum of phenotypes from the assessment on the medial and lateral condyles at the distal end of both the humerus and the femur of the right and the left leg of the boar based on images from CT. The exterior assessment traits included in the study were; 'front leg knee' (FKNE), 'front leg pasterns' (FPAS), 'front leg stance' (FSTA), 'front leg twisted pasterns' (FFLK), 'hind leg stance', 'hind leg pasterns' (HPAS), 'hind leg standing under' (HSTU), 'hind leg small inner toe', 'dipped back', 'arched back' (ARCH) and 'waddling hindquarters' (WADL). The estimation of (co)variance components and breeding values were performed using bivariate animal genetic models. Breeding values for HSTU, HPAS, FPAS, WADL and OCT traits were additional outputs from the same bivariate analyses. The lowest heritability was found for FFLK (h 2 FFLK=0.05), whereas FPAS was estimated to have the highest heritability (h 2 FPAS=0.36), and OCT demonstrating a heritability of 0.29. Significant genetic correlations were found between several traits; the strongest correlation was between FSTA and FFLK (0.94), which was followed by the correlation between FPAS and FKNE (0.69). The traits ARCH and FSTA had significant genetic correlations to OCT, whereas all other genetic correlations between OCT and the conformation traits were low and not significantly different from 0. Our study shows positive genetic trends for the conformation traits included in the breeding goal. In general, low genetic correlations between conformation traits and OC were observed in our study.

Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc.

In this study, computed tomography (CT) technology was used to measure body composition on live pigs for breeding purposes. Norwegian Landrace (L; n = 3835) and Duroc (D; n = 3139) boars, selection candidates to be elite boars in a breeding programme, were CT-scanned between August 2008 and August 2010 as part of an ongoing testing programme at Norsvin's boar test station. Genetic parameters in the growth rate of muscle (MG), carcass fat (FG), bone (BG) and non-carcass tissue (NCG), from birth to ∼100 kg live weight, were calculated from CT data. Genetic correlations between growth of different body tissues scanned using CT, lean meat percentage (LMP) calculated from CT and more traditional production traits such as the average daily gain (ADG) from birth to 25 kg (ADG1), the ADG from 25 kg to 100 kg (ADG2) and the feed conversion ratio (FCR) from 25 kg to 100 kg were also estimated from data on the same boars. Genetic parameters were estimated based on multi-trait animal models using the average information-restricted maximum likelihood (AI-REML) methodology. The heritability estimates (s.e. = 0.04 to 0.05) for the various traits for Landrace and Duroc were as follows: MG (0.19 and 0.43), FG (0.53 and 0.59), BG (0.37 and 0.58), NCG (0.38 and 0.50), LMP (0.50 and 0.57), ADG1 (0.25 and 0.48), ADG2 (0.41 and 0.42) and FCR (0.29 and 0.42). Genetic correlations for MG with LMP were 0.55 and 0.68, and genetic correlations between MG and ADG2 were -0.06 and 0.07 for Landrace and Duroc, respectively. LMP and ADG2 were clearly unfavourably genetically correlated (L: -0.75 and D: -0.54). These results showed the difficulty in jointly improving LMP and ADG2. ADG2 was unfavourably correlated with FG (L: 0.84 and D: 0.72), thus indicating to a large extent that selection for increased growth implies selection for fatness under an ad libitum feeding regime. Selection for MG is not expected to increase ADG2, but will yield faster growth of the desired tissues and a better carcass quality. Hence, we consider MG to be a better biological trait in selection for improved productivity and carcass quality. CT is a powerful instrument in conjunction with breeding, as it combines the high accuracy of CT data with measurements taken from the selection candidates. CT also allows the selection of new traits such as real body composition, and in particular, the actual MG on living animals.

Genetic parameters of fat quality in pigs measured by near-infrared spectroscopy.

Subcutaneous fat from Norwegian Landrace (n=3230) and Duroc (n=1769) pigs was sampled to investigate the sources of variation and genetic parameters of various fatty acids, fat moisture percentage and fat colour, with the lean meat percentage (LMP) also included as a trait representing the leanness of the pig. The pigs were from half-sib groups of station-tested boars included in the Norwegian pig breeding scheme. They were fed ad libitum to obtain an average of 113 kg live weight. Near-infrared spectroscopy (NIRS) was applied for prediction of the fatty acids and fat moisture percentage, and Minolta was used for the fat colour measurements. Heritabilities and genetic correlations were estimated with a multi-trait animal model using average information-restricted maximum likelihood (AI-REML) methodology. Fat from Landrace pigs had considerably more monounsaturated fatty acids, polyunsaturated fatty acids (PUFAs) and fat moisture, as well as less saturated fatty acids (SFAs) than fat from Duroc pigs. The heritability estimates (s.e. 0.03 to 0.08) for the various fatty acids were as follows: Palmitic, C16:0 (0.39 and 0.51 for Landrace and Duroc pigs, respectively); Palmitoleic, C16:1n-7 (0.41 and 0.50); Steric, C18:0 (0.46 and 0.54); Oleic, C18:1n-9 (0.67 and 0.57); Linoleic, C18:2n-6 (0.44 and 0.46); α-linolenic, C18:3n-3 (0.37 and 0.25) and n-6/n-3 ratio (0.06 and 0.01). The other fat quality traits revealed the following heritabilities: fat moisture (0.28 and 0.33), colour values in subcutaneous fat: L* (whiteness; 0.22 and 0.21), a* (redness; 0.13 and 0.24) and b* (yellowness; 0.07 and 0.17) and LMP (0.46 and 0.47). LMP showed high positive genetic correlations to PUFA (C18:2n-6 and C18:3n-3), which implies that selecting leaner pigs changes the fatty acid composition and deteriorates the quality of fat. Higher concentrations of PUFA are not beneficial as the ratio of n-6 and n-3 fatty acids becomes unfavourably high. Owing to the high genetic correlation between C18:2n-6 and C18:3n-3 and a low heritability for this ratio, the latter is difficult to change through selection. However, a small reduction in the ratio should be expected if selection aims at reducing the level of C18:2n-6. Selection for more C18:1n-9 is possible in view of the genetic parameters, which are favourable for eating quality, technological quality and human nutrition. The NIRS technology and the high heritabilities found in this study make it possible to implement fat quality traits to achieve the breeding goal in the selection of a lean pig with better fat quality.

Prediction of fat quality in pig carcasses by near-infrared spectroscopy.

This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R(2)(cv)) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n = 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R(2)(val)) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R(2)(cv), RMSECV, R(2)(val) and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (α-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.

Genetic parameters of meat quality traits in two pig breeds measured by rapid methods.

To study genetic variation in meat quality traits measured by rapid methods, data were recorded between 2005 and 2008 on samples of M. longissimus dorsi (LD) in Landrace (n = 3838) and Duroc (n = 2250) pigs included in the Norwegian pig breeding scheme. In addition, ultimate pH levels in the glycolytic LD (loin muscle) and M. gluteus medius (GM, ham muscle), and in the oxidative m. gluteus profundus (GP, ham muscle) were recorded as an extended data set (n = 16 732 and n = 7456 for Landrace and Duroc, respectively) from 1998 to 2008. Data were analysed with a multi-trait animal model using AI-REML methodology. Meat from Duroc had considerably more intramuscular fat (IMF), less moisture and protein, appeared darker with higher colour intensity and had lower drip loss than meat from Landrace. The heritability estimates (s.e. 0.01 to 0.07) for pH in LD (0.19 and 0.27 for Landrace and Duroc, respectively), GM (0.12 and 0.22) and GP (0.19 and 0.38), drip loss (0.23 and 0.33), colour values: L* (lightness) (0.41 and 0.28), a* (redness) (0.46 and 0.43), b* (yellowness) (0.31 and 0.33), IMF (0.50 and 0.62), muscle moisture (0.31 and 0.50) and muscle protein content (0.40 and 0.54) in LD all demonstrated moderate-to-high genetic variation for these traits in both breeds. Near infrared spectroscopy and EZ-DripLoss are modern technologies used in this study for the determination of chemical components and drip loss in meat. These methods gave higher heritabilities than more traditional methods used to measure these traits. The estimated genetic correlations between moisture and IMF in Duroc, and pH and drip loss in Duroc were both -0.89. Interesting differences between the two breeds in numerical value of some genetic correlations were observed, probably reflecting the differences in physiology and selection history between Landrace and Duroc. The estimated genetic correlation between drip loss and pH was much stronger in Duroc than in Landrace (-0.89 and -0.63, respectively). This might be due to the high pH in Duroc, whereas Landrace had a lower pH closer to the iso-electric point for muscle proteins. The positive genetic correlation between the L* value in meat and IMF in Duroc (0.50) was an effect of differences in visible marbling, rather than meat colour. For Landrace, this correlation was negative (-0.20). IMF content showed favourable genetic correlations to drip loss (-0.36 and -0.35 for Landrace and Duroc, respectively).