Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection1

Samuel E. Aggrey,El Hamidi Hay,Ashley S. Ling,Romdhane Rekaya

doi:10.1186/s12863-021-00979-y

Abstract

BackgroundUse 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 FST and absolute estimated marker effect as preselection statistics to discriminate between linked and unlinked markers and the corresponding impact on accuracy.ResultsWe 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 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 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 absolute estimated marker effects but the consistency between the two statistics regarding false positives is weak.ConclusionIdentification 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.

Highlights

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
Druet et al [10] found that accuracies could be increased by up to 28% using sequence data compared to the equivalent of a bovine 50 k Single nucleotide polymorphism (SNP) chip when the trait was controlled by rare quantitative trait loci (QTL); these gains were largely lost when the sequence genotypes were imputed, likely as a result of lower imputation accuracy of rare markers that would be most effective in tracking causal loci with low minor allele frequencies
50 to 60 k markers are typically necessary for many livestock species before reaching a plateau in accuracy, the smaller number of SNPs required in this study is likely due to the unconventional

Summary

Introduction

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. Whole-genome marker information has been successfully utilized through genomic selection (GS) in many livestock and plant genetic improvement programs for the prediction of genomic merit and has led to a significant increase in the rate of genetic gain in these species [1] This has been partly a result of increased prediction accuracy for selection candidates, for individuals with no phenotypes or progeny of their own [2]. Druet et al [10] found that accuracies could be increased by up to 28% using sequence data compared to the equivalent of a bovine 50 k SNP chip when the trait was controlled by rare QTL; these gains were largely lost when the sequence genotypes were imputed, likely as a result of lower imputation accuracy of rare markers that would be most effective in tracking causal loci with low minor allele frequencies

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