Abstract

BackgroundThis study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel.ResultsThe accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS - log_{10} (p,value) threshold of 3 across the entire genome were selected, the prediction accuracy improved by 5% (up to 0.21 ± 0.01), whereas when selection was limited to sequence variants that passed the same GWAS - log_{10} (p,value) threshold of 3 in regions identified by RHM, the accuracy improved by 9% (up to 0.25 ± 0.01).ConclusionsOur results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep.

Highlights

  • This study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel

  • The objectives of this study were to: (1) compare the accuracies of genomic prediction for parasite resistance based on WGS data to those based on 50k and HD SNP panels; (2) investigate whether the use of variants in QTL regions that were selected from WGS data would improve the accuracy more than variants selected from the HD SNP panel; and (3) compare the prediction accuracies between variants selected from RHM, GWAS, or GWAS in genomic regions detected by RHM

  • GWAS results for the selected regions based on WGS variants generally have both increasingly higher and sharper peaks than GWAS results based on HD or 50k variants

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Summary

Introduction

This study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel. Genetic improvement in livestock species has relied on the use of phenotypes and pedigree information of animals to predict their breeding values. This approach has resulted in substantial genetic gains for most production traits. Genomic selection is increasingly applied in breeding programs, offering an alternative to conventional methods; it can potentially increase the rates of genetic gain [2] and could be useful for traits that are difficult to improve using traditional methods. In practice the use of all the variants from WGS data in cattle populations resulted in little to no improvement in the accuracy of genomic predictions when investigated within breed [3, 4]. For small values of Ne, increasing marker density will have a limited impact on the accuracy of genomic predictions [6]

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