ArticleGenetics, selection, evolution : GSE2017
Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle.
Article in Genetics, selection, evolution : GSE, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- Transition from Conventional to Genomic Selection (ssGBLUP) led to improve in accuracy gains and selection decisions in Sahiwal Cattle.Scientific reports · 2026Article
- Recent progress in genomic prediction for Hanwoo cattle and its implications for beef quality: review.Animal bioscience · 2026Article
- Mutual information-based best linear unbiased prediction for enhanced genomic prediction accuracy.Journal of animal science · 2025Article
- Genomic prediction and genome-wide association studies of morphological traits and distraction index in Korean Sapsaree dogs.PloS one · 2024Article
- Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs.Genetics, selection, evolution : GSE · 2023Article
- Comparison of accuracy of breeding value for cow from three methods in Hanwoo (Korean cattle) population.Journal of animal science and technology · 2023Article
- Genomic evaluation of carcass traits of Korean beef cattle Hanwoo using a single-step marker effect model.Journal of animal science · 2023Article
- Exploring and Identifying Candidate Genes and Genomic Regions Related to Economically Important Traits in Hanwoo Cattle.Current issues in molecular biology · 2022Review
- Genome-wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole-genome sequencing data.Evolutionary applications · 2022Article
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- Genomic Prediction in Local Breeds: The Rendena Cattle as a Case Study.Animals : an open access journal from MDPI · 2021Article
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- Multi-Trait Single-Step GBLUP Improves Accuracy of Genomic Prediction for Carcass Traits Using Yearling Weight and Ultrasound Traits in Hanwoo.Frontiers in genetics · 2021Article
- Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle.BMC genetics · 2020Article
- Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population.Asian-Australasian journal of animal sciences · 2020Article
- Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90.Genes · 2020Review
- Genome-wide association mapping and accuracy of predictions for amoebic gill disease in Atlantic salmon (Salmo salar).Scientific reports · 2020Article
- Bias in estimates of variance components in populations undergoing genomic selection: a simulation study.BMC genomics · 2019Article
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8 authors.
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Abstract
backgroundGenomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals.
methodsCarcass records included 988 genotyped Hanwoo steers with 35,882 SNPs and 1438 non-genotyped steers that were measured for back-fat thickness (BFT), carcass weight (CWT), eye-muscle area, and marbling score (MAR). Single-trait pedigree-based BLUP, Bayesian methods using only genotyped individuals, SSGBLUP and SSBR methods were compared using cross-validation.
resultsMethods using genomic information always outperformed pedigree-based BLUP when the same phenotypic data were modeled from either genotyped individuals only or both genotyped and non-genotyped individuals. For BFT and MAR, accuracies were higher with single-step methods than with BayesB, BayesC and BayesCπ. Gains in accuracy with the single-step methods ranged from +0.06 to +0.09 for BFT and from +0.05 to +0.07 for MAR. For CWT, SSBR always outperformed the corresponding Bayesian methods that used only genotyped individuals. However, although SSGBLUP incorporated information from non-genotyped individuals, prediction accuracies were lower with SSGBLUP than with BayesC (π = 0.9999) and BayesB (π = 0.98) for CWT because, for this particular trait, there was a benefit from the mixture priors of the effects of the single nucleotide polymorphisms.
conclusionsSingle-step methods are the preferred approaches for prediction combining genotyped and non-genotyped animals. Alternative priors allow SSBR to outperform SSGBLUP in some cases.
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