Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers
Received date: 2024-03-11
Revised date: 2024-06-25
Online published: 2024-06-26
Supported by
National Key Research and Development Program of China(2021YFD1200904);Earmarked Fund for CARS36(CARS-36)
Accurate breed classification is required for the conservation and utilization of farm animal genetic resources. Traditional classification methods mainly rely on phenotypic characterization. However, it is difficult to distinguish between the highly similar breeds due to the challenges in qualifying the phenotypic character. Machine learning algorithms show unique advantages in breed classification using genomic information. To evaluate the classification methods for Chinese cattle breeds, this study utilized genomic SNP data from 213 individuals across seven Chinese local breeds and compared the classification accuracies of three feature selection methods (FST value sorting and screening, mRMR, and Relief-F) and three machine learning algorithms (Random Forest, Support Vector Machine, and Naive Bayes). Results showed that: 1) using the FST method to screen more than 1500 SNPs, or using the mRMR algorithm to screen more than 1000 SNPs, the SVM classification algorithm can achieve more than 99.47% classification accuracy; 2) the most effective algorithm was SVM, followed by NB, while the best SNP selection method was FST and mRMR, followed by Relief-F; 3) species misclassification often occurs between breeds with high similarity. This study demonstrates that machine learning classification models combined with genomic data are effective methods for the classification of local cattle breeds, providing a technical basis for the rapid and accurate classification of cattle breeds in China.
Hui Liang, Xue Wang, Jingfang Si, Yi Zhang . Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers[J]. Hereditas(Beijing), 2024 , 46(7) : 530 -539 . DOI: 10.16288/j.yczz.24-059
| [1] | Sun H, Olasege BS, Xu Z, Zhao QB, Ma PP, Wang QS, Lu SX, Pan YC. Genome-wide and trait-specific markers: a perspective in designing conservation programs. Front Genet, 2018, 9: 389. |
| [2] | Maudet C, Luikart G, Taberlet P. Genetic diversity and assignment tests among seven French cattle breeds based on microsatellite DNA analysis. J Anim Sci, 2002, 80(4): 942-950. |
| [3] | Suekawa Y, Aihara H, Araki M, Hosokawa D, Mannen H, Sasazaki S. Development of breed identification markers based on a bovine 50K SNP array. Meat Sci, 2010, 85(2): 285-288. |
| [4] | Lewis J, Abas Z, Dadousis C, Lykidis D, Paschou P, Drineas P. Tracing cattle breeds with principal components analysis ancestry informative SNPs. PLoS One, 2011, 6(4): e18007. |
| [5] | Putnová L, ?tohl R. Comparing assignment-based approaches to breed identification within a large set of horses. J Appl Genet, 2019, 60(2): 187-198. |
| [6] | Gao J, Sun LW, Zhang SS, Xu JH, He MQ, Zhang DF, Wu CF, Dai JJ. Screening discriminating SNPs for Chinese indigenous pig breeds identification using a random forests algorithm. Genes (Basel), 2022, 13(12): 2207. |
| [7] | Sharma A, Dey P. A machine learning approach to unmask novel gene signatures and prediction of Alzheimer's disease within different brain regions. Genomics, 2021, 113(4): 1778-1789. |
| [8] | Zhang ZS, Liu ZP. Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods. BMC Med Genomics, 2021, 14(Suppl 1): 112. |
| [9] | Yang YL, Wang XY, Wang SY, Chen Q, Li M L, Lu SX. Identification of potential sex-specific biomarkers in pigs with low and high intramuscular fat content using integrated bioinformatics and machine learning. Genes (Basel), 2023, 14(9): 1695. |
| [10] | Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution, 1984, 38(6): 1358-1370. |
| [11] | Schiavo G, Bertolini F, Galimberti G, Bovo S, Dall’Olio S, Nanni Costa L, Gallo M, Fontanesi L. A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds. Animal, 2020, 14(2): 223-232. |
| [12] | Zhao CH, Wang D, Teng J, Yang C, Zhang XY, Wei XM, Zhang Q. Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data. J Anim Sci Biotechnol, 2023, 14(1): 85. |
| [13] | Kumar H, Panigrahi M, Chhotaray S, Parida S, Chauhan A, Bhushan B, Gaur GK, Mishra BP, Singh RK. Comparative analysis of five different methods to design a breed- specific SNP panel for cattle. Anim Biotechnol, 2021, 32(1): 130-136. |
| [14] | Mahendran N, Durai Raj Vincent PM, Srinivasan K, Chang CY. Machine learning based computational gene selection models: a survey, performance evaluation, open issues, and future research directions. Front Genet, 2020, 11: 603808 |
| [15] | Liu RQ, Xu ZT, Teng JY, Pan XC, Lin Q, Cai XD, Diao SQ, Feng XY, Yuan XL, Li JQ, Zhang Z. Evaluation of six machine learning classification algorithms in pig breed identification using SNPs array data. Anim Genet, 2023, 54(2): 113-122. |
| [16] | Pasupa K, Rathasamuth W, Tongsima S. Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique. BMC Bioinformatics, 2020, 21(1): 216. |
| [17] | Hayah I, Ababou M, Botti S, Badaoui B. Comparison of three statistical approaches for feature selection for fine-scale genetic population assignment in four pig breeds. Trop Anim Health Prod, 2021, 53(3): 395. |
| [18] | Hulsegge B, Calus MPL, Windig JJ, Hoving-Bolink AH, Maurice-van Eijndhoven MHT, Hiemstra SJ. Selection of SNP from 50K and 777K arrays to predict breed of origin in cattle. J Anim Sci, 2013, 91(11): 5128-5134. |
| [19] | Judge MM, Kelleher MM, Kearney JF, Sleator RD, Berry DP. Ultra-low-density genotype panels for breed assignment of Angus and Hereford cattle. Animal, 2017, 11(6): 938-947. |
| [20] | Liu YX, Zhang NN, He Y, Lun LJ. Prediction of core cancer genes using a hybrid of feature selection and machine learning methods. Genet Mol Res, 2015, 14(3): 8871-8882. |
| [21] | Shreem SS, Abdullah S, Nazri MZA, Alzaqebah MA. Hybridizing ReliefF, MRMR filters and GA wrapper approaches for gene selection. J Theor Appl Inf Technol, 2012, 46(2): 1034-1039. |
| [22] | Gao YH, Gautier M, Ding XD, Zhang H, Wang YC, Wang X, Faruque MO, Li JY, Ye SH, Gou X, Han JL, Lenstra JA, Zhang Y. Species composition and environmental adaptation of indigenous Chinese cattle. Sci Rep, 2017, 7(1): 16196. |
| [23] | Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 2007, 81(3): 559-575. |
| [24] | Wickham H. ggplot2: elegant graphics for data analysis. Springer New York, 2009. |
| [25] | Hanchuan P, Fuhui L, Chris D. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell, 2005, 27(8): 1226-1238. |
| [26] | De Jay N, Papillon-Cavanagh S, Olsen C, El-Hachem N, Bontempi G, Haibe-Kains B. mRMRe: an R package for parallelized mRMR ensemble feature selection. Bioinformatics, 2013, 29(18): 2365-2368. |
| [27] | Robnik-?ikonja M, Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn, 2003, 53(1): 23-69. |
| [28] | Robnik-?ikonja M, Savicky P. CORElearn: classification, regression and feature evaluation. 2020. |
| [29] | Liaw A, Wiener M. Classification and regression by RandomForest. Forest, 2001, 23(2/3): 18-22. |
| [30] | Meyer D. Support Vector Machines? the interface to libsvm in package e1071. 2001. |
| [31] | Zhang ZH. Na?ve Bayes classification in R. Ann Transl Med, 2016, 4(12): 241. |
| [32] | Xu ZT, Diao SQ, Teng JY, Chen ZT, Feng XY, Cai XT, Yuan XL, Zhang H, Li JQ, Zhang Z. Breed identification of meat using machine learning and breed tag SNPs. Food Control, 2021, 125: 107971. |
| [33] | Bertolini F, Galimberti G, Calò DG, Schiavo G, Matassino D, Fontanesi L. Combined use of principal component analysis and random forests identify population- informative single nucleotide polymorphisms: application in cattle breeds. J Anim Breed Genet, 2015, 132(5): 346-356. |
| [34] | Li B, Zhang NX, Wang YG, George AW, Reverter A, Li YT. Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Front Genet, 2018, 9: 237. |
| [35] | Yang JF, Qiao PR, Li YM, Wang N. A review of machine-learning classification and algorithms. Statistics & Decision, 2019, 35(06): 36-40. |
| 杨剑锋, 乔佩蕊, 李永梅, 王宁. 机器学习分类问题及算法研究综述. 统计与决策, 2019, 35(6): 36-40. | |
| [36] | Zhang Y, Ding C, Li T. Gene selection algorithm by combining reliefF and mRMR. BMC Genomics, 2008, 9(2): S27. |
| [37] | Wilmot H, Bormann J, Soyeurt H, Hubin X, Glorieux G, Mayeres P, Bertozzi C, Gengler N. Development of a genomic tool for breed assignment by comparison of different classification models: application to three local cattle breeds. J Anim Breed Genet, 2022, 139(1): 40-61. |
| [38] | Huang JJ. Identify pig breeds with different methods based on SNP chip[Dissertation]. South China Agricultural University, 2019. |
| 黄进杰. 基于SNP芯片利用不同方法鉴定个体猪品种[学位论文]. 华南农业大学, 2019. |
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