For many decades, pig breeding has been based only on purebred animals. In more recent times, research has shown that traits measured on purebred animals in a high-health environment are not necessarily the same trait when measured on crossbreds in a commercial environment. The benefit of having good genetic progress in purebred populations is limited if only part of this progress is expressed in the crossbreds. The investment in purebred populations is something that is done to produce a better crossbred product, such as hybrid sows (multipliers) or finishers (meat production).
A new statistical tool has been developed and tested on crossbred data. This method builds a genomic relationship matrix (G-matrix) that can include genotyped crossbreds, several purebred lines, and animals without genotypes. This matrix (GLDLA) combines linkage disequilibrium (LD) and linkage analysis (LA). Through LD, relationships with common ancestors are estimated by analysing for similarity in haplotype fragments, and with LA, the markers (SNPs) are used directly to estimate the relationship between animals. This method is expected to be beneficial when crossbred animals are included in the relationship matrix because the relationship between breeds is considered. Some Dutch breeds are used for training. Results show that inclusion of genotypes from other breeds and crossbreds in the relationship matrix increases the accuracy of estimated breeding values (EBVs). The use of the GLDLA matrix increased accuracy for purebreds more than any of the other relationship matrices that were tested, with 4-11 percentage point compared to a purebred relationship matrix. For the crossbreds, it was sufficient to include crossbred genotypes to increase the accuracy, also without using the GLDLA matrix This indicates that use of crossbred genotypes in the relationship matrix is valuable regardless of whether the GLDLA matrix is used.
Heterozygosity, that is, the proportion of heterozygote SNP per individual is generally higher in crossbreds than in purebreds. Therefore, work is being done to investigate the value of including heterozygosity in breeding value estimations, both in terms of accuracy of EBVs, and the effect of increasing heterozygosity in the population. Results indicate that an increase in heterozygosity has a positive effect on maternal traits, both for the Norwegian Landrace, and for Dutch lines and their crosses. For production traits, heterozygosity is positive for most of the traits, but not all. The effect on prediction accuracy when including heterozygosity in the model varies with each trait. For maternal traits, it increases when including heterozygosity for total number born, litter weight at 3 weeks, number of live born (most lines) and gestation length. For crossbreds, there was no effect on prediction accuracy of including heterozygosity in the model. For production traits, traits that showed an increased prediction accuracy included weight at 21 days and 150d, age at 40 kg, days from 40-120kg and feed intake.
A study has been performed where the effect of including data from TN70 sows on longevity has been evaluated. In addition, a new model for longevity was tested. The results show that longevity cannot be considered the same trait in purebred and crossbred pigs. Still, treating the traits as different did not improve prediction accuracy compared to assuming the traits were the same. The new model for longevity seems to be favourable for young animals compared to existing models. It is clear from the results that if the goal is to improve crossbred performance, it will be necessary to include data from crossbred animals.
The advantage of the new model is that we get data on animals earlier than with the existing method. This is a repeatability model, so animals will have their first observation already after the first parity, instead of after 5 parities. Animals with more observations (older animals) will naturally have a more accurate estimate of their breeding value than young animals, but it is still an advantage to use a trait that can give information on the animal itself while it is still a selection candidate.
The PhD thesis, which is part of the project, has been approved, and the thesis is completed. Both parts had very good feedback.
Now as Norsvin succeeds in selecting for Norwegian Landrace animals with better crossbred traits, this will benefit the food industry in Norway at an early stage in the production chain for meat, and give Norsvin an important boost internationally in terms of genetic progress for production, quality and longevity.
Project implementation and use of resources have been according to the original plan, and in cooperation between Norsvin, Topigs Norsvin, the NMBU and the university in Wageningen. New cooperation is established, and a new PhD are developed.
Breeding a robust and healthy sow is important for national and international partners, and it is important for animal welfare. We will, through the CCPS-platform make the animals less sensitive to genotype by environment interactions.
In this project the CCPS-producers has learned about quality in data collection, and the importance of different traits involved in the project. The general knowledge on pig breeding and the value of our crossbred F1 sow TN70 has been built up in commercial farms, and this has led to increased interest and competence on high quality pig production. This can be seen by increased interest for cross breed animals, and improved production results which gives better economy, and lower GHG-emissions.
Production systems based on crossbreeding are predominant in commercial pig production. Gain in performance therefore need to take place in the purebred populations, although genetic selection has so far primarily been performed with data from purebreds only. Several studies have shown that selection of purebreds for increased performance of their crossbred descendants under field conditions is disadvantaged by low genetic correlations between purebred and commercial crossbred (CC) performance. Genomic selection (GS) has successfully applied for selection within purebred populations. Moreover, GS also offers greater opportunities for incorporating information from crossbreds and selecting purebreds for crossbred performance. This is a growing research area worldwide, but it is not straightforward to utilize crossbred data in an operative breeding program. Initially, we are going to estimate the genetic correlations between purebred and CC performance. If this correlation is below 0.7-0.8, the benefit of using purebred animals in the training population is smaller than the benefit of using crossbred animals. In some markets and some traits, particularly sows performance at CC animals, we see needs for improvement. Therefore, in this study, crossbred training populations will be designed, traits and samples collected and sows genotyped, in order to design an optimal crossbred breeding program for the international markets. By using semen from Norsvins Landrace elite boars to various markets directly on sows of the complementary breed (TOPIGS Z-line), our selection candidates will have crossbreed half sibs in commercial herds abroad. Consequently, this project aims at elaborating strategies for using GS with selection in purebreds and data collection in crossbred populations. Models with haplotypes will be compared to single SNP models regarding to genetic variance, prediction ability and accuracy in cross validation studies.