Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep
Autor: | 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 |
---|---|
Přispěvatelé: | Al Kalaldeh, Mohammad, Gibson, John, Duijvesteijn, Naomi, Daetwyler, Hans D., MacLeod, Iona, Moghaddar, Nasir, Lee, Sang Hong, van der Werf, Julius H.J. |
Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
Genetic Markers
Male lcsh:QH426-470 [SDV]Life Sciences [q-bio] Quantitative Trait Loci Sheep Diseases Genome-wide association study Single-nucleotide polymorphism Computational biology Biology Quantitative trait locus nematode resistance Polymorphism Single Nucleotide 03 medical and health sciences Genetics Animals SNP Genetic Testing Parasite Egg Count genotype imputation Ecology Evolution Behavior and Systematics Disease Resistance underlying variation 030304 developmental biology Genetic association lcsh:SF1-1100 2. Zero hunger Whole genome sequencing variants 0303 health sciences Sheep Whole Genome Sequencing Australia 0402 animal and dairy science Genetic Variation 04 agricultural and veterinary sciences General Medicine Heritability 040201 dairy & animal science lcsh:Genetics Genetic marker quantitative trait loci Female Animal Science and Zoology lcsh:Animal culture mixed-model analysis Research Article Genome-Wide Association Study |
Zdroj: | 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 |
ISSN: | 0999-193X 1297-9686 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |