Controlling Coancestry and Thereby Future Inbreeding by Optimum-Contribution Selection Using Alternative...

Area of Science:

• Animal breeding and genetics
• Quantitative genetics
• Genomic selection

Background:

• Genomic selection utilizes genomic relationship matrices (GRMs) to predict genomic breeding values (GEBVs).
• Optimum contribution selection (OCS) aims to maximize genetic gain while controlling inbreeding.
• Genomic optimum contribution selection (GOCS) combines these approaches, but the choice of GRM for GEBV prediction and coancestry control can vary.

Purpose of the Study:

• To investigate the consequences of using different GRMs for GEBV prediction and coancestry control in GOCS.
• To evaluate the impact on genetic gain and true genomic inbreeding under various genetic architectures.

Main Methods:

• A stochastic simulation study was conducted using GOCS.
• Three GRMs were used for GEBV prediction (marker-based [G_M], QTL-based [G_Q], marker and QTL-based [G_A]).
• Two GRMs (G_M, G_A) were used for coancestry control, with varying numbers of QTL (7702, 1000, 500).

Main Results:

• With 7702 QTL, no significant differences in genetic gain or inbreeding were observed across GRM strategies.
• With 1000 QTL, using G_Q for GEBV prediction and G_M for coancestry control yielded 29.7% higher genetic gain compared to using G_M for both.
• Increased GEBV accuracy accounted for 43% of the enhanced genetic gain in the latter scenario.

Conclusions:

• The choice of GRM becomes critical for GEBV estimation when the number of quantitative trait loci (QTL) is small.
• Utilizing GRMs that emphasize QTL regions is recommended for GEBV estimation in GOCS with few QTL.
• Marker-based relationships are preferred for controlling coancestry in GOCS.