ArticleGenetics, selection, evolution : GSE2018
Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants.
Article in Genetics, selection, evolution : GSE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it, 94 citations in OpenAlex.
- Multi-ancestry genome-wide association meta-analysis identifies candidate genes for computed tomography-based carcass composition traits in pigs.Genetics, selection, evolution : GSE · 2025Pooled it
- Whole-Genome Resequencing of 299 Large White Pigs Identifies RASAL2 as a Candidate Gene for Feed Efficiency Traits.Animal genetics · 2026Article
- Silk_40K SNP array facilitates the genomic prediction of complex traits in silkworm (Bombyx mori).Journal of advanced research · 2026Article
- Genome-wide association studies for feed efficiency, production and feeding behavior traits in Canadian purebred Duroc pigs.Journal of animal science · 2026Article
- Effect of using preselected markers from imputed whole-genome sequence for genomic prediction in Angus cattle.Genetics, selection, evolution : GSE · 2025Article
- Prioritized SNP Selection from Whole-Genome Sequencing Improves Genomic Prediction Accuracy in Sturgeons Using Linear and Machine Learning Models.International journal of molecular sciences · 2025Article
- A Comparative Study of Optimizing Genomic Prediction Accuracy in Commercial Pigs.Animals : an open access journal from MDPI · 2025Article
- Genomic and Gut Microbiome Evaluations of Growth and Feed Efficiency Traits in Broilers.Animals : an open access journal from MDPI · 2024Article
- Integrating Gene Expression Data into Single-Step Method (ssBLUP) Improves Genomic Prediction Accuracy for Complex Traits of Duroc × Erhualian FCurrent issues in molecular biology · 2024Article
- Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework.Journal of animal science and biotechnology · 2024Article
- Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs.Evolutionary applications · 2024Article
- Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs.Animals : an open access journal from MDPI · 2023Article
- Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs.Animals : an open access journal from MDPI · 2023Article
- Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs.Genetics, selection, evolution : GSE · 2023Article
- Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study.Genetics, selection, evolution : GSE · 2023Article
- Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs.Journal of animal science and biotechnology · 2023Article
- Utilizing Variants Identified with Multiple Genome-Wide Association Study Methods Optimizes Genomic Selection for Growth Traits in Pigs.Animals : an open access journal from MDPI · 2023Article
- Multi-line ssGBLUP evaluation using preselected markers from whole-genome sequence data in pigs.Frontiers in genetics · 2023Article
- Genomic prediction with whole-genome sequence data in intensely selected pig lines.Genetics, selection, evolution : GSE · 2022Article
- Infrared Predictions Are a Valuable Alternative to Actual Measures of Dry-Cured Ham Weight Loss in the Training of Genome-Enabled Prediction Models.Animals : an open access journal from MDPI · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundIncreasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs.
resultsPhenotypes of average daily feed intake (ADFI), average daily gain (ADG), ultrasound backfat depth (FAT), and loin muscle depth (LMD) were available for 1363 Duroc boars from a commercial breeding program. Genotype imputation accuracies reached 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence variants. Average accuracies across methods and marker densities of genomic prediction of ADFI, FAT, LMD and ADG were 0.40, 0.65, 0.30 and 0.15, respectively. For ADFI and FAT, BayesB outperformed GBLUP, but increasing marker density had little advantage for genomic prediction. For ADG and LMD, GBLUP outperformed BayesB, while BayesRC based on whole-genome sequence data gave the best accuracies and reached up to 0.35 for LMD and 0.25 for ADG.
conclusionsUse of genomic information was beneficial for prediction of ADFI and FAT but not for that of ADG and LMD compared to pedigree-based estimates. BayesB based on 80K SNPs gave the best genomic prediction accuracy for ADFI and FAT, while BayesRC based on whole-genome sequence data performed best for ADG and LMD. We suggest that these differences between traits in the effect of marker density and method on accuracy of genomic prediction are mainly due to the underlying genetic architecture of the traits.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.