Moving Beyond Managing Realized Genomic Relationship in Long-Term Genomic Selection

Area of Science:

• Animal breeding and genetics
• Quantitative genetics
• Genomic selection strategies

Background:

• Long-term genomic selection (GS) requires balancing genetic gain with population diversity to sustain breeding progress.
• Existing strategies like genomic optimal contributions selection (GOCS) and weighted genomic selection (WGS) have limitations in achieving this balance.

Purpose of the Study:

• To compare GOCS and WGS head-to-head in a simulation model for recurrent selection.
• To introduce and evaluate two new set selection methods (IND-HE and IND-RA) for long-term genomic selection.

Main Methods:

• Simulation model for a recurrent selection scheme.
• Head-to-head comparison of GOCS and WGS.
• Development and testing of two new set selection methods (IND-HE and IND-RA) maximizing a weighted index.

Main Results:

• Both GOCS and WGS increased long-term genetic gain and decreased inbreeding compared to standard GS, but had limitations.
• GOCS failed to optimally balance genetic gain and inbreeding, while WGS struggled with rare allele frequency management.
• The new IND-HE and IND-RA methods outperformed GOCS and WGS in balancing genetic gain and diversity.

Conclusions:

• IND-HE and IND-RA offer a significant advancement for efficient long-term genomic selection.
• These methods show promise for practical application in breeding programs, improving the balance between genetic gain and diversity.
• Further testing is recommended, but the inherent benefits suggest broad applicability.