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ARS Home » Plains Area » Miles City, Montana » Livestock and Range Research Laboratory » Research » Publications at this Location » Publication #380889
Research Project: Alleviating Rate Limiting Factors that Compromise Beef Production Efficiency

Location: Livestock and Range Research Laboratory

Title: Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection

Author
item LING, ASHLEY - University Of Georgia
item Hay, El Hamidi
item AGGREY, SAMUEL - University Of Georgia
item REKAYA, ROMDHANE - University Of Georgia

Submitted to: BioMed Central (BMC) Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/18/2021
Publication Date: 8/11/2021
Citation: Ling, A., Hay, E.A., Aggrey, S., Rekaya, R. 2021. Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection. BioMed Central (BMC) Genetics. 22. Article 26. https://doi.org/10.1186/s12863-021-00979-y.
DOI: https://doi.org/10.1186/s12863-021-00979-y

Interpretive Summary: The ability to accurately select genetically superior animals is of great importance to the beef cattle industry. The advent and use of genomic information greatly increased the prediction accuracy of animal’s performance through genomic selection and therefore improved selection and mating decisions. Genomic selection still suffers from several limitations such as the high dimensionality of data. In this study, a simulation was carried out to evaluate genomic predictions in the presence of single nucleotide polymorphism markers unlinked with trait-relevant quantitative trait loci and the use of FST as a preselection of markers to reduce the number of predictors and its impact on accuracy. We found that the accuracy of genomic predictions decreased as the proportion of unlinked markers used to calculate the genomic relationships increased. Using all, only linked, and only unlinked marker sets yielded prediction accuracies of 0.62, 0.89, and 0.22, respectively. Identification and exclusion of markers that have little to no relevance to the trait of interest may significantly increase genomic prediction accuracies. The population statistic FST presents an efficient and effective tool for preselection of trait-relevant markers.

Technical Abstract: Background: Use of genomic information has resulted in an undeniable improvement in prediction accuracies and an increase in genetic gain in animal and plant genetic selection programs in spite of the oversimplified assumptions about the true biological processes. Even for complex traits, a large portion of markers do not segregate with or effectively track genomic regions contributing to trait variation; yet it is not clear how genomic prediction accuracies are impacted by such potentially non-relevant markers. In this study, a simulation was carried out to evaluate genomic predictions in the presence of markers unlinked with trait-relevant QTL. Further, we compared the ability of the population statistic FST and absolute estimated marker effect as pre-selection statistics to discriminate between linked and unlinked markers and the corresponding impact on accuracy. Results: We found that the accuracy of genomic predictions decreased as the proportion of unlinked markers used to calculate the genomic relationships increased. Using all, only linked, and only unlinked marker sets yielded prediction accuracies of 0.62, 0.89, and 0.22, respectively. Furthermore, it was found that prediction accuracies are severely impacted by unlinked markers with large spurious associations. FST-preselected marker sets of 10k and larger yielded accuracies 8.97 to 17.91% higher than those achieved using preselection by absolute estimated marker effects, despite selecting 5.1 to 37.7% more unlinked markers and explaining 2.4 to 5.0% less of the genetic variance. This was attributed to false positives selected by absolute estimated marker effects having a larger spurious association with the trait of interest and more negative impact on predictions. The Pearson correlation between FST scores and absolute estimated marker effects was 0.77 and 0.27 among only linked and only unlinked markers, respectively. The sensitivity of FST scores to detect truly linked markers is comparable to that of absolute estimated marker effects but the consistency between the two statistics regarding false positives is weak. Conclusion: Identification and exclusion of markers that have little to no relevance to the trait of interest may significantly increase genomic prediction accuracies. The population statistic FST presents an efficient and effective tool for pre-selection of trait-relevant markers.