Selective advantage of implementing optimal contributions selection and timescales for the convergence of long-term genetic contributions

Abstract

Abstract Background Optimal contributions selection (OCS) provides animal breeders with a framework for maximising genetic gain for a predefined rate of inbreeding. Simulation studies have indicated that the source of the selective advantage of OCS is derived from breeding decisions being more closely aligned with estimates of Mendelian sampling terms ($$\hat{a}$$ a^ ) of selection candidates, rather than estimated breeding values (EBV). This study represents the first attempt to assess the source of the selective advantage provided by OCS using a commercial pig population and by testing three hypotheses: (1) OCS places more emphasis on $$\hat{a}$$ a^ compared to EBV for determining which animals were selected as parents, (2) OCS places more emphasis on $$\hat{a}$$ a^ compared to EBV for determining which of those parents were selected to make a long-term genetic contribution (r), and (3) OCS places more emphasis on $$\hat{a}$$ a^ compared to EBV for determining the magnitude of r. The population studied also provided an opportunity to investigate the convergence of r over time. Results Selection intensity limited the number of males available for analysis, but females provided some evidence that the selective advantage derived from applying an OCS algorithm resulted from greater weighting being placed on $$\hat{a}$$ a^ during the process of decision-making. Male r were found to converge initially at a faster rate than female r, with approximately 90% convergence achieved within seven generations across both sexes. Conclusions This study of commercial data provides some support to results from theoretical and simulation studies that the source of selective advantage from OCS comes from $$\hat{a}$$ a^ . The implication that genomic selection (GS) improves estimation of $$\hat{a}$$ a^ should allow for even greater genetic gains for a predefined rate of inbreeding, once the synergistic benefits of combining OCS and GS are realised.