Genomic-Based Optimum Contribution in Conservation and Genetic Improvement Programs with Antagonistic Fitness and Productivity Traits

Abstract

Animal selection for genetic improvement of productivity may lead to an increase in inbreeding through the use of techniques that enhance the reproductive capability of selected animals. Therefore, breeding strategies aim to balance maintaining genetic variability and acceptable fitness levels with increasing productivity. The present study demonstrates the effectiveness of genomic-based optimum contribution strategies at addressing this objective when fitness and productivity are genetically antagonistic traits. Strategies are evaluated in directional selection (increasing productivity) or conservation (maintaining fitness) scenarios. In the former case, substantial rates of genetic gain can be achieved while greatly constraining the rate of increase in inbreeding. Under a conservation approach, inbreeding depression can be effectively halted while also achieving a modest rate of genetic gain for productivity. Furthermore, the use of optimum contribution strategies when combined with a simple non-random mating scheme (minimum kinship method) showed an additional delay in the increase of inbreeding in the short term. In conclusion, genomic-based optimum contribution methods can be effectively used to control inbreeding and inbreeding depression, and still allow genetic gain for productivity traits even when fitness and productivity are antagonistically correlated.

Novel optimum contribution selection methods accounting for conflicting objectives in breeding programs for livestock breeds with historical migration

“…For all scenarios and both breeds, the constraint settings were stricter than in the scenarios that optimized male contributions. The performance of all scenarios improved when both male and female selection were optimized, which is consistent with Sánchez-Molano et al [ 8 ], who used OCS to improve fitness and productivity traits. To achieve these improvements, however, additional reproductive techniques must be applied due to the limited reproduction rate of female animals.…”

“…Breeding values estimated by genomic approaches are also more accurate and show more within-family variation compared with breeding values estimated via traditional approaches [ 31 ]. Furthermore, compared to the use of pedigree kinship, the use of genomic kinship is substantially more efficient in maintaining genetic diversity when optimizing genetic contributions [ 8 , 12 , 16 , 32 ]. Moreover, new methods to estimate kinship at native alleles, i.e.…”

“…This selection process maximizes genetic gain in the next generation while constraining the rate of inbreeding via restriction of relatedness among offspring [ 4 – 6 ]. The superiority of OCS has been demonstrated with both simulated [ 7 , 8 ] and real data [ 9 – 11 ]. The objective function for OCS has been optimized using Lagrange multipliers [ 4 , 8 , 12 ], evolutionary algorithms [ 7 , 13 , 14 ], and semidefinite programming algorithms [ 9 , 15 , 16 ].…”

“…The superiority of OCS has been demonstrated with both simulated [ 7 , 8 ] and real data [ 9 – 11 ]. The objective function for OCS has been optimized using Lagrange multipliers [ 4 , 8 , 12 ], evolutionary algorithms [ 7 , 13 , 14 ], and semidefinite programming algorithms [ 9 , 15 , 16 ]. A similar related optimization problem was expressed as a mixed-integer quadratically constrained optimization problem and solved with branch-and-bound algorithms [ 17 ].…”

Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters

“…When considering multi-trait selection, over- or under-weighted indices can result in sub-optimal profitability of the breeding program. Sánchez-Molano et al. (2016) investigated productivity and fitness traits based on different indexes (50/50, 75/25, and 100/0 index weights between the two objectives, respectively).…”