Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data
Abstract Background Breed 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 t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | Journal of Animal Science and Biotechnology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40104-023-00880-x |
| _version_ | 1797811374232961024 |
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| author | Changheng Zhao Dan Wang Jun Teng Cheng Yang Xinyi Zhang Xianming Wei Qin Zhang |
| author_facet | Changheng Zhao Dan Wang Jun Teng Cheng Yang Xinyi Zhang Xianming Wei Qin Zhang |
| author_sort | Changheng Zhao |
| collection | DOAJ |
| description | Abstract Background Breed 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 ST, and I n) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated the accuracy of breed identification using SNP chip data of different densities. Results We 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. Conclusions The 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. |
| first_indexed | 2024-03-13T07:22:45Z |
| format | Article |
| id | doaj.art-e8e760fbca104921a194d750d708e124 |
| institution | Directory Open Access Journal |
| issn | 2049-1891 |
| language | English |
| last_indexed | 2024-03-13T07:22:45Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Animal Science and Biotechnology |
| spelling | doaj.art-e8e760fbca104921a194d750d708e1242023-06-04T11:35:33ZengBMCJournal of Animal Science and Biotechnology2049-18912023-06-0114111310.1186/s40104-023-00880-xBreed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip dataChangheng Zhao0Dan Wang1Jun Teng2Cheng Yang3Xinyi Zhang4Xianming Wei5Qin Zhang6Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural UniversityShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural UniversityShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural UniversityShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural UniversityShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural UniversityShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural UniversityShandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural UniversityAbstract Background Breed 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 ST, and I n) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated the accuracy of breed identification using SNP chip data of different densities. Results We 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. Conclusions The 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.https://doi.org/10.1186/s40104-023-00880-xBreed identificationBreed-informative SNPsGenomic breed compositionMachine learningWhole genome sequence data |
| spellingShingle | Changheng Zhao Dan Wang Jun Teng Cheng Yang Xinyi Zhang Xianming Wei Qin Zhang Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data Journal of Animal Science and Biotechnology Breed identification Breed-informative SNPs Genomic breed composition Machine learning Whole genome sequence data |
| title | Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data |
| title_full | Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data |
| title_fullStr | Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data |
| title_full_unstemmed | Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data |
| title_short | Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data |
| title_sort | breed identification using breed informative snps and machine learning based on whole genome sequence data and snp chip data |
| topic | Breed identification Breed-informative SNPs Genomic breed composition Machine learning Whole genome sequence data |
| url | https://doi.org/10.1186/s40104-023-00880-x |
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