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Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep

Authors :
Hans D. Daetwyler
N. Duijvesteijn
John P. Gibson
Nasir Moghaddar
Mohammad Al Kalaldeh
Sang Hong Lee
Iona M. MacLeod
Julius H. J. van der Werf
Al Kalaldeh, Mohammad
Gibson, John
Duijvesteijn, Naomi
Daetwyler, Hans D.
MacLeod, Iona
Moghaddar, Nasir
Lee, Sang Hong
van der Werf, Julius H.J.
Source :
Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2019, 51 (1), pp.32. ⟨10.1186/s12711-019-0476-4⟩, Genetics Selection Evolution, Vol 51, Iss 1, Pp 1-13 (2019), Genetics, Selection, Evolution : GSE
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

Abstract

International audience; AbstractBackgroundThis 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 -log10(pvalue)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$- log_{10} (p\,value)$$\end{document} 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 -log10(pvalue)\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$- log_{10} (p\,value)$$\end{document} 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.

Details

Language :
English
ISSN :
0999193X and 12979686
Database :
OpenAIRE
Journal :
Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2019, 51 (1), pp.32. ⟨10.1186/s12711-019-0476-4⟩, Genetics Selection Evolution, Vol 51, Iss 1, Pp 1-13 (2019), Genetics, Selection, Evolution : GSE
Accession number :
edsair.doi.dedup.....b3becf6698062d11488500c49c504d23