Methods to detect selection history in a population under ongoing directional selection

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Livestock Breeding

Background:

• Animal breeding aims to enhance future generations through genetic selection.
• Understanding selection history is crucial for setting effective breeding goals.
• Two methods, BayesS and G^, exist to assess selection history using marker data.

Purpose of the Study:

• To evaluate the performance of BayesS and G^ in detecting selection in animal breeding.
• To assess the methods' ability to detect direct and indirect selection, especially for low heritability traits.
• To compare the effectiveness of these methods under varying simulation parameters.

Main Methods:

• Simulated direct selection in pigs under phenotypic selection with low heritabilities (0.05-0.3) over 30 generations.
• Simulated indirect selection using a correlated trait (heritability 0.05, genetic correlation 0.4-0.7).
• Applied BayesS (estimating s-value) and G^ (calculating expected genetic change) to simulated data.

Main Results:

• Both BayesS and G^ successfully detected selection.
• Increased heritability, larger sample size (for BayesS), and longer selection intervals (for G^) enhanced detection.
• Indirect selection detection was challenging; G^ showed limited success in some scenarios, improved by estimating marker effects in the initial generation.

Conclusions:

• BayesS and G^ show potential for identifying selection history in animal populations.
• Method choice depends on the specific population's available data.
• Detecting indirect selection remains a challenge, requiring further methodological refinement.