Prioritization of SNP markers for genomic prediction in closed beef cattle populations

Authors: Hay, El Hamidi

Submitted to: Livestock Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/6/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary: This study tested whether choosing the most informative DNA markers improves prediction of breeding values for growth in two beef cattle populations. Two marker-prioritization methods were compared (one using genetic differentiation, Fst, and one using estimated marker effects) in a Line 1 Hereford herd (1,208 animals) and a composite breed (2,853 animals). Using a smaller set of prioritized DNA markers improved prediction accuracy for growth traits in the inbred Hereford population, while weighting the genomic relationship matrix by Fst and marker effects did not help and sometimes reduced accuracy. Overall, results show that choosing informative DNA markers can improve prediction of the breeding values accuracy, but the benefit depends on the population structure, the trait, and the prediction model.

Technical Abstract: With the advances in high-throughput technologies, genomic information is becoming readily available. This has led to whole genome sequences and denser single nucleotide polymorphism (SNP) panels being generated for more individuals. However, the increase in genomic information has shown little benefit in improving the prediction accuracy of genomic estimated breeding values (GEBV). One method to best utilize the increased amount of SNP information is to optimize the selection of informative SNP markers. In this study, genomic prediction of growth traits in two closed beef cattle populations using various prioritization techniques was evaluated. The first population used is Line 1 Hereford. The data consisted of 1,208 animals with genotypes and phenotypes. The second population is a composite breed (50% Red Angus, 25% Charolais, 25% Tarentaise) and consisted of 2,853 genotypes and phenotypes. The SNP prioritization methods adopted in this study were based on fixation index (Fst) and SNP marker effects. Using a subset of prioritized SNP markers increased the accuracy for all three traits for the Line 1 Hereford population. On the other hand, using a weighted G matrix based on Fst and SNP effects did not increase the accuracy and in some instances decreased. Furthermore, the predication accuracy was higher in the Line Hereford which is an inbred population compared to the composite population. The study showed that prediction accuracy of GEBV can be improved with SNP prioritization, however it is population specific, trait specific and model specific. Moreover, this study highlights the importance of population structure in the prediction accuracy of GEBV.