ArticleGenetics, selection, evolution : GSE2025
Effect of using preselected markers from imputed whole-genome sequence for genomic prediction in Angus cattle.
Article in Genetics, selection, evolution : GSE, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Systematic Approach for Compound Angus Populations Revealing Positional Candidate Genes and Improving Prediction Accuracy in Carcass Traits.Animals : an open access journal from MDPI · 2026Article
- Incorporating GO/KEGG Functional Annotations Improves the Accuracy and Stability of Genomic Prediction Across Diverse Beef Cattle Populations.Animals : an open access journal from MDPI · 2026Article
- Genome-Wide Development and Validation of KASP-Based SNP Markers inAnimals : an open access journal from MDPI · 2026Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe advent of next-generation sequencing enables the opportunity to use denser marker tools, up to whole-genome sequences (WGS), for genomic prediction in livestock. Improvement in genomic prediction (GP) accuracy from using WGS has been observed in simulation studies. In contrast, such advantage has found to be inconsistent once implemented in practice. The benefit of WGS appears to be from markers that are significant for the trait of interest. Thus, the main objective of this study was to investigate the predictive ability of adding preselected markers to the standard-industry 50k genotype for GP of economically important traits in Angus cattle, namely, birth weight (BW), scrotal circumference (SC), carcass weight (CWT) and carcass intramuscular fat (CIMF). Animals were genotyped with either commercial or customised SNP-genotyping arrays; then, the genotypes were imputed to WGS. The 50k genotype was used as the control group. Informative markers associated with the desired traits were extracted from WGS, then were added to the 50k genotype. Several methods were chosen to select different sets of informative markers, including LD-based pruning, top SNP from a genome-wide association study (GWAS), functional annotation based on Gene Ontology, cattle QTL database, and sequence annotation. In total, eight different sets of genotypes were investigated. We applied different statistical models to predict genomic breeding values, including GBLUP, BayesR, and BayesRC, and two-GRM GBLUP constructed separately from the 50k and the preselected genotype set.
resultsHeritability (h
conclusionsThe findings suggest that potential improvements may be achieved by using preselected SNPs from the GWAS, a method that has proven within the population. The performance of preselected markers on GP influenced by several factors, including population structure, method used to select significant markers, and genetic architecture of traits.
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