ArticleJournal of animal science and biotechnology2020
The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs.
Article in Journal of animal science and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 21 citations in OpenAlex.
- Effects of Application of Lactobacilli on Lipid Metabolism in Monogastric and Aquatic Animals: Benefits and Underlying Mechanism.Animals : an open access journal from MDPI · 2026Review
- Evaluating Adjusted ssGBLUP Models for Genomic Prediction and Matrix Compatibility in South African Holstein Cattle.Animals : an open access journal from MDPI · 2026Article
- Genetic connectedness and genotype-by-environment interaction in a maternal pig line across countries.Journal of animal science · 2026Article
- Improving multibreed genomic prediction for breeds with small populations by modeling heterogeneous genetic (co)variance blockwise accounting for linkage disequilibrium.Journal of animal science and biotechnology · 2025Article
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework.Journal of animal science and biotechnology · 2024Article
- Genome-Wide Gene-Environment Interaction Analysis Identifies Novel Candidate Variants for Growth Traits in Beef Cattle.Animals : an open access journal from MDPI · 2024Article
- Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information.Briefings in bioinformatics · 2024Article
- Improving Genomic Prediction Accuracy in the Chinese Holstein Population by Combining with the Nordic Holstein Reference Population.Animals : an open access journal from MDPI · 2023Article
- Using machine learning to improve the accuracy of genomic prediction of reproduction traits in pigs.Journal of animal science and biotechnology · 2022Article
- Strategies to improve the accuracy and reduce costs of genomic prediction in aquaculture species.Evolutionary applications · 2022Article
- G × EBLUP: A novel method for exploring genotype by environment interactions and genomic prediction.Frontiers in genetics · 2022Article
- The theory on and software simulating large-scale genomic data for genotype-by-environment interactions.BMC genomics · 2021Article
- Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations.Frontiers in genetics · 2021Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferent production systems and climates could lead to genotype-by-environment (G × E) interactions between populations, and the inclusion of G × E interactions is becoming essential in breeding decisions. The objective of this study was to investigate the performance of multi-trait models in genomic prediction in a limited number of environments with G × E interactions.
resultsIn total, 2,688 and 1,384 individuals with growth and reproduction phenotypes, respectively, from two Yorkshire pig populations with similar genetic backgrounds were genotyped with the PorcineSNP80 panel. Single- and multi-trait models with genomic best linear unbiased prediction (GBLUP) and BayesC
conclusionsIn conclusion, our findings confirmed that directly combining populations to enlarge the reference population is not efficient in improving the accuracy of genomic prediction in the presence of G × E interactions, while multi-trait models perform better in a limited number of environments with G × E interactions.
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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.