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

Abstract

BackgroundOptimal 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 () 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 compared to EBV for determining which animals were selected as parents, (2) OCS places more emphasis on 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 compared to EBV for determining the magnitude of r. The population studied also provided an opportunity to investigate the convergence of r over time.ResultsSelection 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 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.ConclusionsThis study of commercial data provides some support to results from theoretical and simulation studies that the source of selective advantage from OCS comes from . The implication that genomic selection (GS) improves estimation of should allow for even greater genetic gains for a predefined rate of inbreeding, once the synergistic benefits of combining OCS and GS are realised.Electronic supplementary materialThe online version of this article (10.1186/s12711-018-0392-z) contains supplementary material, which is available to authorized users.

Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study

“…König et al (2010) demonstrated the benefits of OCS compared to truncation selection in two commercial White Leghorn lines and one experimental line in the context of conventional selection. The advantage of OCS over truncation selection is in optimizing contributions, which are a function of animal Mendelian sampling terms (Woolliams et al, 2015; Howard et al, 2018). This means that OCS and genomic selection work in synergy (Daetwyler et al, 2007; Maltecca et al, 2020; Sonesson et al, 2012; Obšteter et al, 2019).…”

Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study

“… König et al (2010) demonstrated the benefits of OCS compared to truncation selection in two commercial White Leghorn lines and one experimental line in the context of conventional selection. The advantage of OCS over truncation selection is in optimizing contributions, which are a function of animal Mendelian sampling terms ( Woolliams et al, 2015 ; Howard et al, 2018 ). This means that OCS and genomic selection work in synergy ( Daetwyler et al, 2007 ; Sonesson et al, 2012 ; Obšteter et al, 2019 ; Maltecca et al, 2020 ).…”

“…The mean of the selection criterion weighted by the optimised contributions is a measure of future genetic mean, while group coancestry weighted by the optimised contributions is a measure of future group coancestry. The advantage of OCS over truncation selection is its emphasis on managing between and within family (Mendelian sampling) variation ( Howard et al, 2018 ). The usefulness of OCS in conventional layer breeding programs was presented by König et al (2010) .…”

AlphaPart—R implementation of the method for partitioning genetic trends

“…This shows the importance of proper base population specification (including unknown parent groups) for meaningful partitioning. This long-term dynamic of contributions is related to the dynamics of “long-term genetic contributions” in the context of genetic gain and inbreeding [ 12 , 13 ], but it should be noted that the “long-term genetic contributions” are trait agnostic (depend only on the pedigree). On a related note, with the implemented method in AlphaPart, we can evaluate (long-term) genetic contributions by setting breeding values to 1 for all animals and partitioning the breeding values by paths [ 6 ].…”