Optimal Contribution Selection in Highly Fecund Species With Overlapping Generations.

Area of Science:

• Animal Breeding and Genetics
• Quantitative Genetics
• Population Genetics

Background:

• Optimal contribution selection (OCS) is crucial for maximizing genetic gains while minimizing inbreeding in closed populations.
• Traditional OCS methods face challenges in highly fecund outcrossing species, especially with overlapping generations and unknown juvenile numbers.
• Existing OCS procedures require modifications or extensive use of dummy individuals, increasing computational burden.

Purpose of the Study:

• To present a modified OCS approach suitable for highly fecund outcrossing species.
• To address the computational challenges posed by overlapping generations and large, unknown family sizes in juvenile populations.
• To provide a more efficient method for optimizing parental selection in complex breeding programs.

Main Methods:

• Developed an OCS strategy utilizing 'between-family relationship matrices' instead of 'between-individual relationship matrices'.
• Adapted computational procedures to accommodate large, potentially unknown numbers of individuals per family in juvenile age groups.
• Ensured applicability to breeding programs with overlapping generations and high fecundity.

Main Results:

• The proposed method effectively handles the complexities of highly fecund outcrossing species.
• It alleviates the computational burden associated with conventional OCS in large, overlapping generations.
• The approach is viable even when the exact number of juveniles per family is not precisely known.

Conclusions:

• The novel OCS approach using between-family relationships offers a practical solution for managing genetic improvement in challenging breeding scenarios.
• This method enhances the efficiency of genetic selection in species with high fecundity and overlapping generations.
• It provides a valuable tool for breeders aiming to balance genetic gain and inbreeding control in complex populations.