FST-based marker prioritization within quantitative trait loci regions and its impact on genomic selection accuracy: Insights from a simulation study with high-density marker panels for bovines

Authors: Toghiani, Sajjad; AGGREY, SAMUEL - University Of Georgia; REKAYA, ROMDHANE - University Of Georgia

Submitted to: Genes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/8/2025
Publication Date: 5/10/2025
Citation: Toghiani, S., Aggrey, S.E., Rekaya, R. 2025. FST-based marker prioritization within quantitative trait loci regions and its impact on genomic selection accuracy: Insights from a simulation study with high-density marker panels for bovines. Genes. 16(5):563. https://doi.org/10.3390/genes16050563.
DOI: https://doi.org/10.3390/genes16050563

Interpretive Summary: Farmers aim to breed healthier and more productive livestock. Scientists can use information from an animal's DNA to predict which animals will have the best traits. However, using all the genetic information available does not always make these predictions better. There is a need to figure out the most important genetic markers to focus on. A new method, developed and tested through computer simulation, identifies genetic markers around genes that influence simulated traits. By prioritizing on a specific set of these genetic markers, it was shown that predicting animals using this subset can be more accurate than using all available genetic markers. However, this method still needs to be tested on real-world data to confirm its effectiveness. This research presents a potentially more efficient way for farmers to use genetic information, potentially leading to faster improvements in livestock like dairy cows. By focusing on the most relevant genetic markers, as shown in the simulations, farmers can breed animals that are healthier and more productive, leading to better food production. Moreover, this research helps scientists better understand how to use genetic information to improve breeding programs by highlighting the importance of looking at genetic differences caused by selection pressures. While the simulation results are promising, further testing on real-world data is necessary to confirm its effectiveness and explore its application in different livestock species and complex traits.

Technical Abstract: Genomic selection (GS) has improved accuracy compared to traditional methods. However, accuracy tends to plateau beyond a certain marker density. Prioritizing influential SNPs could further enhance the accuracy of GS. The fixation index (F_ST) allows the identification of SNPs under selection pressure. Although the F_ST method was shown to be able to prioritize SNPs across the whole genome and to increase accuracy, its performance could be further improved by focusing the prioritization process within QTL regions. A trait with heritability of 0.1 and 0.4 was generated under different simulation scenarios (number of QTL, size of SNP windows around QTL, and number of selected SNPs within a QTL region). In total, 6 simulation scenarios were analyzed. Each scenario was replicated 5 times. The population comprised 30K animals from the last 2 generations (G9-G10) of a 10 generations (G1-G10) selection process. All animals in G9-10 were genotyped with a 600K SNP panel. F_ST scores were calculated for all 600K SNPs. Two prioritization scenarios were used: 1) selecting the top 1% SNPs with the highest F_ST scores, and 2) selecting a predetermined number of SNPs within each QTL window. GS accuracy was evaluated using the correlation between true and estimated breeding values for 5,000 randomly selected animals from G10. Prioritizing SNPs using F_ST scores within QTL window regions increased accuracy by 5 to 18% with the 50 SNP-windows showing the best performance. The increase in GS accuracy warrants the testing of the algorithm when the number and position of QTL are unknown.