Selection History Models in a Population under Ongoing Directional Selection

Abstract

The aim of animal breeding is to select the genetically best animals in the current generation to improve the performance of future generations for a specific breeding goal. With the continuous shift in breeding goals towards more balanced breeding, new traits may become of interest. Knowledge of the (indirect) selection history of these traits would be insightful before a trait is included in the breeding goal. Two models, BayesS and [G], have been developed to assess the selection history of traits. BayesS estimates a parameter (s) that reflects the relationship between estimated additive effects and minor allele frequency, while [G] calculates the expected genetic change of a trait based on allele frequency changes and estimated additive marker effects. The aim of this study was to evaluate the performance of estimating s-values (based on BayesS) and [G] in an animal breeding context, focusing on their ability to detect selection for a trait with low heritability. Both [G] and s-value estimation were applied to a simulated dataset of a commercial pig breeding program under phenotypic selection, with varying heritabilities (0.05, 0.1, 0.3) and 30 generations of ongoing selection. Overall, both models were able to detect selection, where higher heritabilities and a larger sample size (for s-value estimation) or a larger selection interval (for [G]) resulted in increased detection of selection. The preferred model to identify selection varied based on the available data of the breeding population.