The effect of including genomic relationships in the estimation of genetic parameters of functional traits in pigs.

The term functionality in animal breeding is used for traits that increase the efficiency of production by lowering the input cost, such as animal health and leg weakness related to longevity. The main objective of the study was to investigate the impact of genomic information, in a multivariate variance component analysis, on some of these traits. In addition, the effect of the inclusion was studied by testing the model's prediction ability based on best linear unbiased estimates for fixed and random effects. The material in this study consists of phenotypes from 76,683 animals, of which 4933 animals are genotyped. The heritabilities for front leg conformation, stayability, osteochondrosis and arched back, estimated using the traditional pedigree, were found to be between 0.12 and 0.29. When using the combined genomic and pedigree relationship matrix, the heritabilities were between 0.14 and 0.36. The results show that the combined relationship matrix can be used for the estimation of (co)variance components, and that the predictive ability of the model in this study marginally increases with the inclusion of genomic information.

The genetic correlation between scapula shape and shoulder lesions in sows.

Shoulder lesions and body condition of sows at weaning have both environmental and genetic causes. The traits can be scored at farm level, and following recording, the traits can be included in the breeding goal and directional selection can be applied. However, to further increase the genetic progress of these traits, it is advantageous to develop indicator traits on the selection candidates (test boars or gilts, not yet exhibiting the phenotype themselves). It has previously been suggested that the scapula morphology and the spine of scapula might be a key factor for the sow to develop shoulder lesions. In this study, we developed 11 novel traits describing the morphology of the shoulder blade based on computed tomography images from scanned test boars. These traits include the area, length, width, height, and volume of the shoulder blade as well as 6 traits obtained from principal component analysis, describing 80% of the variation observed for the scapula spine profile. The analyzed traits have moderate to high heritability (h2 from 0.29 to 0.78, SE = 0.06), low to medium genetic correlations with shoulder lesions (up to 0.4, SE = 0.1), and body condition scoring at weaning (up to 0.25, SE = 0.1). These novel phenotypes can now be recorded automatically and accurately prior to selection of the AI boars. If such recordings are included in multivariate genomic selection models, it is expected to improve the genetic progress of shoulder lesions and body condition score by weaning.

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.

Using markers with large effect in genetic and genomic predictions.

The first attempts of applying marker-assisted selection (MAS) in animal breeding were not very successful because the identification of markers closely linked to QTL using low-density microsatellite panels was difficult. More recently, the use of high-density SNP panels in genome-wide association studies (GWAS) have increased the power and precision of identifying markers linked to QTL, which offer new possibilities for MAS. However, when GWAS started to be performed, the focus of many breeders had already shifted from the use of MAS to the application of genomic selection (using all available markers without any preselection of markers linked to QTL). In this study, we aimed to evaluate the prediction accuracy of a MAS approach that accounts for GWAS findings in the prediction models by including the most significant SNP from GWAS as a fixed effect in the marker-assisted BLUP (MA-BLUP) and marker-assisted genomic BLUP (MA-GBLUP) prediction models. A second aim was to compare the prediction accuracies from the marker-assisted models with those obtained from a Bayesian variable selection (BVS) model. To compare the prediction accuracies of traditional BLUP, MA-BLUP, genomic BLUP (GBLUP), MA-GBLUP, and BVS, we applied these models to the trait "number of teats" in 4 distinct pig populations, for validation of the results. The most significant SNP in each population was located at approximately 103.50 Mb on chromosome 7. Applying MAS by accounting for the most significant SNP in the prediction models resulted in improved prediction accuracy for number of teats in all evaluated populations compared with BLUP and GBLUP. Using MA-BLUP instead of BLUP, the increase in prediction accuracy ranged from 0.021 to 0.124, whereas using MA-GBLUP instead of GBLUP, the increase in prediction accuracy ranged from 0.003 to 0.043. The BVS model resulted in similar or higher prediction accuracies than MA-GBLUP. For the trait number of teats, BLUP resulted in the lowest prediction accuracies whereas the highest were observed when applying MA-GBLUP or BVS. In the same data set, MA-BLUP can yield similar or superior accuracies compared with GBLUP. The superiority of MA-GBLUP over traditional GBLUP is more pronounced when training populations are smaller and when relationships between training and validation populations are smaller. Marker-assisted GBLUP did not outperform BVS but does have implementation advantages in large-scale evaluations.

Treatment of strabismus and nystagmus with botulinum toxin type A. An evaluation of effects and complications.

PURPOSE: Injection of botulinum toxin type A into eye muscles leads to a temporary paralysis and the effects have been evaluated in strabismus or nystagmus. METHOD: A total of 112 patients with different types of concomitant and paralytic strabismus and acquired nystagmus were treated with botulinum toxin, according to well-established indications. RESULTS: The lasting effects of the injections on strabismic angle were largest in esotropia, consecutive exotropia and abducens palsy, and amounted to, on an average, 12 prism diopters or 6 degrees. The larger the strabismus the better was the effect. Repeated injections reduced the angle further. In complex nystagmus forms retrobulbar injections could be used. The side effects were mostly due to spread of botulinum toxin to the levator, producing ptosis (8%), or the inferior rectus muscle, causing vertical strabismus (10%). On an average 42% of the patients were later operated for strabismus and nystagmus. CONCLUSION: Injection of botulinum toxin A into eye muscles is a valuable adjunct to surgery in the treatment of strabismus and nystagmus.