Benefits from marker-assisted selection under an additive polygenic genetic model

Area of Science:

• Quantitative genetics
• Animal breeding
• Genomic selection

Background:

• Marker-assisted selection (MAS) uses genetic markers alongside pedigree data to improve breeding value estimation.
• Previous studies often used mixed inheritance models, differing from the infinitesimal model with linkage used here.
• Understanding MAS benefits across various genome sizes is crucial for livestock populations.

Purpose of the Study:

• To quantify the extra genetic gains from marker-assisted selection (MAS) compared to standard BLUP.
• To investigate the impact of genome size and linkage on MAS effectiveness.
• To evaluate MAS performance over multiple generations in an infinitesimal model.

Main Methods:

• Stochastic computer simulations were employed to model selection.
• An infinitesimal model with 2,000 additive loci distributed across c chromosomes (c=5, 10, 20, 30) was used.
• Marker and pedigree information were combined for relationship matrix computation in Best Linear Unbiased Prediction (BLUP) evaluations.

Main Results:

• Sufficient marker density (approx. 1 marker/10 cM) increased selection accuracy with MAS.
• MAS resulted in extra genetic gains compared to standard BLUP across all simulated genome sizes.
• Benefits of MAS increased over generations, with up to 11% greater response in Generation 10 for smaller genomes.

Conclusions:

• Marker-assisted selection provides detectable, albeit small, extra genetic gains in livestock populations.
• The effectiveness of MAS is influenced by marker density and genome size.
• MAS demonstrates a progressive benefit over generations, enhancing selection response.