ArticleJournal of animal science and biotechnology2023
Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data.
Article in Journal of animal science and biotechnology, 2023. 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, 20 citations in OpenAlex.
- Integrating landscape genomics and machine learning to analyze the genetic structure and specific molecular markers of Chinese dairy goats (Capra hircus).Journal of animal science and biotechnology · 2026Article
- Genomic Selection for Milk Yield and Milk Composition Traits in Dairy Goats Using Machine Learning and Prior-Information Models.Animals : an open access journal from MDPI · 2026Article
- A Boruta-SMOTE Integrated Approach for Rapid Donkey Breed Classification Using SNP Data: Addressing High-Dimensionality and Small Sample Challenges.Biochemical genetics · 2026Article
- Interpretable machine learning for cattle breed classification and SNP prioritization.Genetics, selection, evolution : GSE · 2026Article
- Machine Learning-Based Accurate Full-Sib Family Assignment in Sturgeon Using Whole-Genome Sequencing Data.International journal of molecular sciences · 2026Article
- Big data and artificial intelligence in animal nutrition: a new era of precision feeding.Tropical animal health and production · 2026Review
- Machine learning-based discovery of informative SNPs for population assignment through whole genome sequencing.BMC genomics · 2025Article
- A deep learning strategy for accurate identification of purebred and hybrid pigs across SNP chips.Journal of animal science and biotechnology · 2025Article
- A high-throughput screening method for selecting feature SNPs to evaluate breed diversity and infer ancestry.Genome research · 2025Article
- Design and verification of a 25 K multiple-SNP liquid-capture chip by target sequencing for dairy goat.BMC genomics · 2025Article
- rPIMS: a ShinyR package for the precision identification and modelling of livestock breeds using genomic data and machine learning approaches.Bioinformatics advances · 2025Article
- Identifying low-density, ancestry-informative SNP markers through whole genome resequencing in Indian, Chinese, and wild yak.BMC genomics · 2024Article
- Population structure and breed identification of Chinese indigenous sheep breeds using whole genome SNPs and InDels.Genetics, selection, evolution : GSE · 2024Article
- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundBreed identification is useful in a variety of biological contexts. Breed identification usually involves two stages, i.e., detection of breed-informative SNPs and breed assignment. For both stages, there are several methods proposed. However, what is the optimal combination of these methods remain unclear. In this study, using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project, we compared the combinations of three methods (Delta, F
resultsWe found that all combinations performed quite well with identification accuracies over 95% in all scenarios. However, there was no combination which performed the best and robust across all scenarios. We proposed to integrate the three breed-informative detection methods, named DFI, and integrate the three machine learning methods, KNN, SVM, and RF, named KSR. We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99% in most cases and was very robust in all scenarios. The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.
conclusionsThe current study showed that the combination of DFI and KSR was the optimal strategy. Using sequence data resulted in higher accuracies than using chip data in most cases. However, the differences were generally small. In view of the cost of genotyping, using chip data is also a good option for breed identification.
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