A comparison between the use of pedigree or genomic relationships to control inbreeding in optimum-contribution selection
Stochastic simulation was used to test the hypothesis that optimum-contribution selection with genomic relationships using marker loci with low minor allele frequency (MAF) below a predefined threshold (referred as TGOCS) to control inbreeding maintained more genetic variation than pedigree relationships (POCS) at the same rate of true genetic gain (∆Gtrue). Criteria to measure genetic variation were the number of segregating QTL loci (quantitative trait loci) and the average number of founder alleles per locus. Marker alleles having a MAF below 0.025 were used in forming the genomic relationships in TGOCS strategy. For centering in establishing genomic relationships, when the allele frequency of marker loci with low MAF set to 0.5 the TGOCS strategy maintained 66% fewer founder alleles than POCS and there were 30% fewer QTL segregating. This TGOCS strategy maintained 61% fewer founder alleles than GOCS and 28% fewer segregating QTL loci. When the allele frequency of marker loci with low MAF was set to observed allele frequency these figures were 8%, 2%, 5% and 2%, respectively. Using marker loci with low MAF in the TGOCS strategy was inferior to both GOCS and POCS. Both TGOCS and GOCS were affected by the same constraint that is LD (linkage disequilibrium) between markers and QTL. Therefore, POCS is a more efficient method to maintain genetic variation in the population until a better way to use genomic information in optimum-contribution selection is identified.
Genotyping dead animals improves post-weaning survival of pigs in breeding programs
A premise was tested that genotyping both surviving and dead pigs will realise more genetic gain in post-weaning survival (PWS) than genotyping only surviving animals. Stochastic simulation was used to estimate the rate of true genetic gain in different genotyping scenarios that differed in varying proportions of genotyping dead animals. Selection was for only PWS that had heritability of 0.02. Mortality was assumed 10%. The trait was controlled by 7,702 biallelic quantitative trait loci distributed across a 30 Morgan genome. We used 54,218 biallelic single nucleotide polymorphisms (SNPs) that were used in genomic prediction. Genotyping both surviving and dead animals realised 12 to 24% more genetic gain than genotyping only surviving animals. The power of detecting SNP effects increased when animals of extreme phenotypes are genotyped. Therefore, genotyping both surviving and dead pigs realised more genetic gain than genotyping only surviving animals.
The predicted benefits of genomic selection on pig breeding objectives
The premise was tested that the additional genetic gain was achieved in the overall breeding objective in a pig breeding program using genomic selection (GS) compared to a conventional breeding program, however, some traits achieved larger gain than other traits. GS scenarios based on different reference population sizes were evaluated. The scenarios were compared using a deterministic simulation model to predict genetic gain in scenarios with and without using genomic information as an additional information source. All scenarios were compared based on selection accuracy and predicted genetic gain per round of selection for objective traits in both sire and dam lines. The results showed that GS scenarios increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively. The difference in response resulted from differences in the size of the reference population. Although all traits achieved higher selection accuracy in GS, traits with limited phenotypic information at the time of selection or with low heritability, such as sow longevity, number of piglets born alive, pre- and post-weaning survival, as well as meat and carcass quality traits achieved the largest additional response. This additional response came at the expense of smaller responses for traits that are easy to measure, such as back fat and average daily gain in GS compared to the conventional breeding program. Sow longevity and drip loss percentage did not change in a favourable direction in GS with a reference population of 500 pigs. With a reference population of 1000 pigs or onwards, sow longevity and drip loss percentage began to change in a favourable direction. Despite the smaller responses for average daily gain and back fat thickness in GS, the overall breeding objective achieved additional gain in GS.
Comparing pedigree and genomic relationships to control inbreeding in optimum-contribution selection restricting the number of sires in pigs
Introduction Pedigree relationships to control inbreeding in optimum-contribution selection (POCS) realised a higher rate of true genetic gain (ΔG) than use of genomic relationships for optimum-contribution selection (GOCS) at the same rate of true inbreeding (ΔF) (Henryon et al., 2019). Recently, Gautason et al. (2022) found that GOCS realised just as much ΔG as POCS but at lower ΔF when they fixed the number of selected sires in their simulations of a breeding scheme for dairy cattle. The striking difference with the study of Gautason et al. (2022) is that they restricted ΔF in POCS and GOCS to the same rate but did so on different scales based either on pedigree or genomic information. However, if DF based on the same scale in POCS and GOCS is compared at the same ΔG, POCS realises less ΔF by allocating matings to more sires and dams from more full-sib families than GOCS. This suggests that POCS may not be as good as GOCS when the number of sires and dams allocated to matings is fixed. Based on this information, it was hypothesised that GOCS would realise less ΔF at the same ΔG than POCS when number of sires and dams allocated to matings is fixed.
The predicted responses to genomic selection in growing pigs
The responses to genomic selection in breeding programs for growing pigs were predicted using a selection index approach. Genomic selection increased overall predicted response by 2.6 (500 reference population) to 27.8% (5000 reference population) for a breeding objective consisting of backfat thickness (BFT), average daily gain (ADG), post-weaning survival (PWS) and feed conversion ratio (FCR) in growing pigs . Predicted response in PWS increased by 147% with genomic selection (5000 reference population) at the expense of the other traits like BFT, ADG, and FCR which had 14.5, 1.6, and 2.8% less genetic gain compared to the response in a conventional breeding program without genomic selection. The higher loss in genetic gain for BFT was due to a stronger genetic correlation with FCR in comparison to ADG. The predicted additional responses in the breeding objective is a guideline for the implementation of genomic selection in pig breeding programs.