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
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