ArticleGenetics, selection, evolution : GSE2019
Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep.
Article in Genetics, selection, evolution : GSE, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- PGS-GS: a framework integrating polygenic scores and genomic selection in animal breeding.Briefings in bioinformatics · 2026Article
- Whole genome copy number variation analysis to detect genomic regions for resistance to Haemonchus contortus in Sheep.BMC genomics · 2026Article
- Opportunities and computational challenges in large-scale whole-genome sequencing data analysis.Journal of animal science · 2026Review
- Fine mapping and functional annotation of a QTL for resistance to tilapia lake virus in Nile tilapia (Oreochromis niloticus).G3 (Bethesda, Md.) · 2026Article
- Effect of using preselected markers from imputed whole-genome sequence for genomic prediction in Angus cattle.Genetics, selection, evolution : GSE · 2025Article
- Genomic selection with GWAS-identified QTL markers enhances prediction accuracy for quantitative traits in poplar (Populus deltoides).Communications biology · 2025Article
- SPDC-HG: An accelerator of genomic hybrid breeding in maize.Plant biotechnology journal · 2025Article
- A bulked segregant analysis tool for out-crossing species (BSATOS) and QTL-based genomics-assisted prediction of complex traits in apple.Journal of advanced research · 2022Article
- Genomic prediction with whole-genome sequence data in intensely selected pig lines.Genetics, selection, evolution : GSE · 2022Article
- Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation.Advanced genetics (Hoboken, N.J.) · 2022Article
- PHARP: a pig haplotype reference panel for genotype imputation.Scientific reports · 2022Article
- Functionally prioritised whole-genome sequence variants improve the accuracy of genomic prediction for heat tolerance.Genetics, selection, evolution : GSE · 2022Article
- Multi-trait GWAS using imputed high-density genotypes from whole-genome sequencing identifies genes associated with body traits in Nile tilapia.BMC genomics · 2021Article
- Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data.Frontiers in genetics · 2021Article
- GMStool: GWAS-based marker selection tool for genomic prediction from genomic data.Scientific reports · 2020Article
- Article
- Genomic Prediction Based on SNP Functional Annotation Using Imputed Whole-Genome Sequence Data in Korean Hanwoo Cattle.Frontiers in genetics · 2020Article
- Genomic prediction based on selected variants from imputed whole-genome sequence data in Australian sheep populations.Genetics, selection, evolution : GSE · 2019Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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 [Formula: see text] 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 [Formula: see text] 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.
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