Optimising Pig Breeding Programs Using Genomic Selection

Abstract

<p>Optimisation of pig breeding programs aims to increase the genetic gain in pig populations and to decrease the rate of inbreeding in the pig nucleus population. Genomic selection is a potential breeding strategy that can increase genetic gain and is also expected to decrease the rate of inbreeding in livestock breeding programs. Pigs are selected based on multiple correlated traits in the nucleus population. It might be difficult to improve response to selection in favourable direction for individual breeding objective traits because of an interplay between complex correlation structure and the economic value of each trait. On top of that, genomic selection might also shift genetic gain towards hard-to-measure traits. More work is needed on how genomic selection benefits the overall merit of breeding objectives and individual breeding objective traits.</p> <p>Post-weaning survival (PWS) is an important breeding objective trait in the sire line of pigs. The benefits of genomic selection for PWS depend on the structure of the reference population, which should have both genotypes and phenotypes. Animal breeders might not be interested in genotyping dead pigs because dead pigs cannot be selection candidates. However, genotyping dead and live pigs might increase the genetic gain for PWS in comparison to genotyping live animals alone. While improving genetic gain, it is also important to reduce the rate of inbreeding because pigs are selected in a closed elite herd. Genomic markers might also increase genetic diversity because genomic relationships are more accurate than pedigree relationships. With the availability of genomic marker information, it is also easier to account for the dominance effect than pedigree information in the genetic evaluation model in the presence of dominance. Therefore, the broad objective of this thesis was to investigate the benefits of using genomic selection in pig breeding programs. This thesis explored multiple new avenues of using genomic information to increase the rate of genetic gain and decrease the rate of inbreeding in pig breeding programs. </p> <p>In <b>chapter 3</b>, a premise was tested that the overall pig breeding objective achieves additional genetic gain in genomic selection compared to pedigree selection, but some traits achieve larger additional genetic gain than other traits. Results in a deterministic simulation study showed that genomic selection scenarios based on different sizes of reference populations increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively, compared to pedigree selection. In the dam line of pigs, reproductive traits such as sow mature weight, number born alive, and sow longevity achieved 123% to 403%, 73 % to 351%, and 58% to 278% larger genetic gain in genomic selection compared to the pedigree selection respectively. In comparison, backfat thickness, average daily gain, and feed conversion ratio achieved 6% to 14%, 4% to 11%, and 7% to 9% smaller genetic gain in genomic selection than pedigree selection, respectively. In the sire line of pigs, post-weaning survival, drip loss, and middle portion percentage achieved larger genetic gain in genomic selection than the pedigree selection. Achieving larger genetic gain for reproduction traits in the dam line and post-weaning survival and meat and carcass quality traits in the sire line increased the overall merit of pig breeding objectives in genomic selection compared to the pedigree selection. </p> <p>In <b>chapter 4</b>, a premise was tested that genotyping both live and dead animals realises more genetic gain for PWS (assuming a PWS of 90%) in pigs compared to the scenario where only live animals are genotyped. Stochastic simulation was conducted to compare genetic gain in the scenarios of either genotyping live and dead animals or genotyping live animals only. Genetic gain for PWS in these genotyping strategies was compared at 1% pedigree inbreeding in optimum contribution selection. Results showed that genetic gain with genotyping all live animals was 52% higher than pedigree selection. On top of that, genetic gain with genotyping live and 20 to 100% of dead animals was 14 to 33% higher than genotyping only live animals. Genotyping live and dead animals increased the accuracy of the genomic breeding values of live animals compared to genotyping only live animals, which resulted in a larger genetic gain for PWS.</p> <p>In <b>chapter 5</b>, a premise was tested that optimum-contribution selection with genomic relationships using only low MAF (minor allele frequency) markers below a predefined threshold to control inbreeding realises less rate of true inbreeding than optimum-contribution selection (OCS) with pedigree relationships. Genetic gain in genomic and pedigree-based OCS was fixed at a predefined value while comparing the rate of inbreeding. Results showed that pedigree-based OCS realised a lower rate of inbreeding than genomic-based OCS at the same rate of genetic gain. Genomic-based OCS fixed more favourable quantitative trait loci than pedigree-based OCS. In addition, genomic-based OCS selected more closely related animals than pedigree-based OCS. Therefore, pedigree-based OCS realised a lower rate of inbreeding than genomic-based OCS at the same rate of genetic gain. </p> <p>Finally, in <b>chapter 6</b>, a premise was tested that genetic gain in pig breeding programs using dominance models that accounted for both random additive genetic and dominance effects was higher than additive models that included only random additive genetic effects under the presence of dominance. The stochastic simulation was conducted to compare models in thedam and sire line of pigs. In the sire line, similar additive genetic variances were estimated by the two models but with the additive model, the litter and residual variances were biased upward by 42% and 23%, respectively. When the model did not include a common litter effect in the dam line, the additive genetic variance was 10% smaller in comparison to the additive genetic variance estimated using the dominance model. Despite overestimating variance components using additive models, both models realised a similar rate of total true genetic gain. Since animals were selected based on additive genetic merit, the dominance model did not impact the rate of total true genetic gain. Therefore, the additive genetic model can be used for estimating breeding values if animals are selected based on additive genetic merit, even under the presence of dominance.</p> <p>The results mentioned above showed the potential of genomic selection to increase genetic gain in pig breeding programs. This study investigated multiple new avenues of using genomic information for the genetic improvement in pigs. However, there are still many unanswered question. Use of genomics is beneficial for improving the accuracy of selection and genetic gain, it is not clear how to use genomics to control inbreeding. To take the advantage of genomics, more work is needed to investigate how to use genomics to control inbreeding. In addition, genomics can be useful for accounting non-additive genetic effects such as dominance and epistasis in the model. As more research emerges, use of genomics will be more useful for optimising the pig breeding programs because genomics opens up further opportunities to reveal the biology of traits. </p>