Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection 1 .

Autor: Ling AS; Department of Animal and Dairy Science, The University of Georgia, 30602, Athens, GA, USA. asling@uga.edu., Hay EH; USDA Agricultural Research Service, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT, 59301, USA., Aggrey SE; Department of Poultry Science, The University of Georgia, 30602, Athens, GA, USA.; Institute of Bioinformatics, The University of Georgia, 30602, Athens, GA, USA., Rekaya R; Department of Animal and Dairy Science, The University of Georgia, 30602, Athens, GA, USA.; Institute of Bioinformatics, The University of Georgia, 30602, Athens, GA, USA.; Department of Statistics, The University of Georgia , 30602, Athens, GA, USA.
Jazyk: angličtina
Zdroj: BMC genomic data [BMC Genom Data] 2021 Aug 11; Vol. 22 (1), pp. 26. Date of Electronic Publication: 2021 Aug 11.
DOI: 10.1186/s12863-021-00979-y
Abstrakt: 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 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 nonrelevant 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 F ST and absolute estimated marker effect as preselection 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. F ST -preselected marker sets of 10 k 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 F ST scores and absolute estimated marker effects was 0.77 and 0.27 among only linked and only unlinked markers, respectively. The sensitivity of F ST scores to detect truly linked markers is comparable to 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 F ST presents an efficient and effective tool for preselection of trait-relevant markers.
(© 2021. The Author(s).)
Databáze: MEDLINE
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