我国奶牛基因组选择技术应用进展

doi:10.11843/j.issn.0366-6964.2023.10.003

Abstract

Abstract: The basic principle of genomic selection (GS) is to evaluate and select the genetically excellent individuals based on genome-wide high-density markers (mainly SNPs). By using genomic selection technology, it is available to accurately select young bulls and heifers early in life without relying on phenotypic information, thereby reducing the generation interval from 5-6 years to around 2 years, and drastically shortening generation intervals, thereby significantly reducing breeding costs and accelerating genetic progress in the population. In 2009, genomic selection technology has been first applied to the genetic evaluation of Holstein young bulls, marking the beginning of a new era in dairy cattle breeding. Herein, this review systematically summerizes the basic process and advantages of genomic selection, the current status of genomic selection applications in developed countries in the dairy industry, including the reference population size, selection traits and application effects, and the establishment and application effects of the genomic selection molecular breeding technology system for Chinese Holstein cattle, with the aim of providing references and directions for dairy cattle breeding program in China.

Key words: dairy cattle, Chinese Holstein, genomic selection, genetic progress, application effects

CLC Number:

S823.91

SUN Dongxiao, ZHANG Shengli, ZHANG Qin, LI Jiao, ZHANG Guixiang, LIU Chousheng, ZHENG Weijie. Application Progress on Genomic Selection Technology for Dairy Cattle in China[J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(10): 4028-4039.

References 65

[1]	VISSCHER P M, MEDLAND S E, FERREIRA M A R, et al.Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings[J].PLoS Genet, 2006, 2(3):e41.
[2]	SELLNER E M, KIM J W, MCCLURE M C, et al.Board-invited review:applications of genomic information in livestock[J].J Anim Sci, 2007, 85(12):3148-3158.
[3]	VANRADEN P M, VAN TASSELL C P, WIGGANS G R, et al.Invited review:reliability of genomic predictions for North American Holstein bulls[J].J Dairy Sci, 2009, 92(1):16-24.
[4]	MEUWISSEN T H E, HAYES B J, GODDARD M E.Prediction of total genetic value using genome-wide dense marker maps[J].Genetics, 2001, 157(4):1819-1829.
[5]	SCHAEFFER L R.Strategy for applying genome-wide selection in dairy cattle[J].J Anim Breed Genet, 2006, 123(4):218-223.
[6]	MATUKUMALLI L K, LAWLEY C T, SCHNABEL R D, et al.Development and characterization of a high density SNP genotyping assay for cattle[J].PLoS One, 2009, 4(4):e5350.
[7]	Illumina.BovineSNP50 Genotyping BeadChip[R].San Diego:Illumina, 2016.
[8]	WIGGANS G R, COLE J B, HUBBARD S M, et al.Genomic selection in dairy cattle:the USDA experience[J].Annu Rev Anim Biosci, 2017, 5:309-327.
[9]	CUYABANO B C, SU G S, LUND M S.Selection of haplotype variables from a high-density marker map for genomic prediction[J]. Genet Sel Evol, 2015, 47(1):61.
[10]	HAYES B J, BOWMAN P J, CHAMBERLAIN A J, et al.Invited review:genomic selection in dairy cattle:progress and challenges[J]. J Dairy Sci, 2009, 92(2):433-443.
[11]	LIU Z T, SEEFRIED F R, REINHARDT F, et al.Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction[J].Genet Sel Evol, 2011, 43(1):19.
[12]	张胜利, 孙东晓.奶牛种业的昨天、今天和明天[J].中国畜牧业, 2021(15):22-26.ZHANG S L, SUN D X.Past, now and future of daory breeding in dustry[J].China Animal Industry, 2021(15):22-26.(in Chinese)
[13]	郑伟杰, 李厚诚, 苏丁然, 等.国际奶牛遗传评估体系概况[J].中国畜牧杂志, 2020, 56(6):161-168.ZHENG W J, LI H C, SU D R, et al.General status of international genetic evaluation system of dairy cattle[J].Chinese Journal of Animal Science, 2020, 56(6):161-168.(in Chinese)
[14]	DING X, ZHANG Z, LI X, et al.Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows[J].J Dairy Sci, 2013, 96(8):5315-5323.
[15]	张哲, 张勤, 丁向东.畜禽基因组选择研究进展[J].科学通报, 2011, 56(26):2212-2222.ZHANG Z, ZHANG Q, DING X D.Advances in genomic selection in domestic animals[J].Chinese Science Bulletin, 2011, 56(26):2212-2222.(in Chinese)
[16]	MA P P, LUND M S, AAMAND G P, et al.Use of a Bayesian model including QTL markers increases prediction reliability when test animals are distant from the reference population[J].J Dairy Sci, 2019, 102(8):7237-7247.
[17]	DRUET T, MACLEOD I M, HAYES B J.Toward genomic prediction from whole-genome sequence data:impact of sequencing design on genotype imputation and accuracy of predictions[J].Heredity (Edinb), 2014, 112(1):39-47.
[18]	ERBE M, HAYES B J, MATUKUMALLI L K, et al.Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels[J].J Dairy Sci, 2012, 95(7):4114-4129.
[19]	VAN BINSBERGEN R, CALUS M P L, BINK M C A M, et al.Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle[J].Genet Sel Evol, 2015, 47(1):71.
[20]	CALUS M P L, MEUWISSEN T H E, WINDIG J J, et al.Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values[J].Genet Sel Evol, 2009, 41(1):11.
[21]	CALUS M P L, MEUWISSEN T H E, DE ROOS A P W, et al.Accuracy of genomic selection using different methods to define haplotypes[J].Genetics, 2008, 178(1):553-561.
[22]	DOUBLET A C, CROISEAU P, FRITZ S, et al.The impact of genomic selection on genetic diversity and genetic gain in three French dairy cattle breeds[J].Genet Sel Evol, 2019, 51(1):52.
[23]	HABIER D, TETENS J, SEEFRIED F R, et al.The impact of genetic relationship information on genomic breeding values in German Holstein cattle[J].Genet Sel Evol, 2010, 42(1):5.
[24]	SONESSON A K, MEUWISSEN T H E.Testing strategies for genomic selection in aquaculture breeding programs[J].Genet Sel Evol, 2009, 41(1):37.
[25]	VANRADEN P M.Efficient methods to compute genomic predictions[J].J Dairy Sci, 2008, 91(11):4414-4423.
[26]	CAI W T, LI C, LIU S L, et al.Genome wide identification of novel long non-coding RNAs and their potential associations with milk proteins in Chinese Holstein cows[J].Front Genet, 2018, 9:281.
[27]	PRYCE J E, HAILE-MARIAM M.Symposium review:genomic selection for reducing environmental impact and adapting to climate change[J].J Dairy Sci, 2020, 103(6):5366-5375.
[28]	WIGGANS G R, COOPER T A, VAN TASSELL C P, et al.Technical note:characteristics and use of the Illumina BovineLD and GeneSeek genomic profiler low-density bead chips for genomic evaluation[J].J Dairy Sci, 2013, 96(2):1258-1263.
[29]	VAN DOORMAAL B J, KISTEMAKER G J, SULLIVAN P G, et al.Canadian implementation of genomic evaluations[C]//Proceedings of the 2009 Interbull Meeting.Barcelona:Interbull Bulletin, 2009:214-218.
[30]	DAVID X, DE VRIES A, FEDDERSEN E, et al.International genomic cooperation-Euro Genomics significantly improves reliability of genomic evaluations[C]//Proceedings of the Interbull International Workshop.Paris:Interbull Bulletin, 2010:77-78.
[31]	DRUET T, SCHROOTEN C, ROOS A P W D.In silico genotyping of thousands of SNP in dairy cattle for the EuroGenomics project.2010.
[32]	方美英, 刘剑锋, 张勤, 等.加速优秀基因传递开辟动物遗传育种新时代[J].中国农村科技, 2016(6):61-63.FANG M Y, LIU J F, ZHANG Q, et al.Accelerating the transfer of excellent genes and opening up a new era of animal genetic breeding[J].China Rural Science & Technology, 2016(6):61-63.(in Chinese)
[33]	吴晓平.基于SNP芯片和全测序数据的奶牛全基因组关联分析和基因组选择研究[D].北京:中国农业大学, 2014.WU X P.GWAS and genomic prediction based on markers of SNP-Chips and sequence data in dairy cattle populations[D]. Beijing:China Agricultural University, 2014.(in Chinese)
[34]	张哲.畜禽基因组选择方法及其应用研究[D].北京:中国农业大学, 2011.ZHANG Z.Development and application of genomic selection methodology in livestock[D].Beijing:China Agricultural University, 2011.(in Chinese)
[35]	ZHANG Q Q, SAHANA G, SU G S, et al.Impact of rare and low-frequency sequence variants on reliability of genomic prediction in dairy cattle[J].Genet Sel Evol, 2018, 50(1):62.
[36]	郑伟杰.比较研究不同密度芯片和估计方法对我国奶牛基因组预测准确性的影响[D].北京:中国农业大学, 2021.ZHENG W J.Comparative study on accuracy of genomic prediction based on different chips and estimation methods for Holstein cows[D].Beijing:China Agricultural University, 2021.(in Chinese)
[37]	ZHANG Z, DING X D, LIU J F, et al.Genomic selection for QTL-MAS data using a trait-specific relationship matrix[J].BMC Proc, 2011, 5 Suppl 3(Suppl 3):S15.
[38]	DE ROOS A P W, HAYES B J, GODDARD M E.Reliability of genomic predictions across multiple populations[J].Genetics, 2009, 183(4):1545-1553.
[39]	GODDARD M.Genomic selection:prediction of accuracy and maximisation of long term response[J].Genetica, 2009, 136(2):245-257.
[40]	GODDARD M E, HAYES B J.Genomic selection[J].J Anim Breed Genet, 2007, 124(6):323-330.
[41]	农业农村部种业管理司, 全国畜牧总站.中国乳用种公牛遗传评估概要2021[M].北京:中国农业出版社, 2022.Ministry of Agriculture and Rural Affairs of the People's Republic of China, National Animal Husbandry Services.Sire summaries on national dairy genetic evaluation 2021[M].Beijing:China Agriculture Press, 2022.(in Chinese)
[42]	农业农村部种业管理司, 全国畜牧总站.中国畜禽种业发展报告2020-2021[M].北京:中国农业出版社, 2022.Ministry of Agriculture and Rural Affairs of the People's Republic of China, National Animal Husbandry Station.China livestock and poultry breeding industry development report 2020-2021[M].Beijing:China Agriculture Press, 2022.(in Chinese)
[43]	苏丁然, 彭朋, 闫青霞, 等.我国荷斯坦青年公牛基因组选择效果分析[J].畜牧兽医学报, 2021, 52(6):1550-1562.SU D R, PENG P, YAN Q X, et al.Analysis of the effects for genomic selection in Holstein young bulls in China[J].Acta Veterinaria et Zootechnica Sinica, 2021, 52(6):1550-1562.(in Chinese)
[44]	VAN VLECK L D.Selection index and introduction to mixed model methods[M].Boca Raton:CRC Press, 1993.
[45]	HENDERSON C R.Sire evaluation and genetic trends[J].J Anim Sci, 1973, 1973(S1):10-41.
[46]	GIANOLA D, DE LOS CAMPOS G, HILL W G, et al.Additive genetic variability and the Bayesian alphabet[J].Genetics, 2009, 183(1):347-363.
[47]	HABIER D, FERNANDO R L, KIZILKAYA K, et al.Extension of the Bayesian alphabet for genomic selection[J].BMC Bioinformatics, 2011, 12:186.
[48]	GIANOLA D.Priors in whole-genome regression:the bayesian alphabet returns[J].Genetics, 2013, 194(3):573-596.
[49]	MOSER G, LEE S H, HAYES B J, et al.Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model[J].PLoS Genet, 2015, 11(4):e1004969.
[50]	PARK T, CASELLA G.The Bayesian lasso[J].J Am Stat Assoc, 2008, 103(482):681-686.
[51]	TIBSHIRANI R.Regression shrinkage and selection via the lasso[J].J Roy Stat Soc B Methodol, 1996, 58(1):267-288.
[52]	BOSER B E, GUYON I M, VAPNIK V N.A training algorithm for optimal margin classifiers[C]//Proceedings of the Fifth Annual Workshop on Computational Learning Theory.Pittsburgh:ACM, 1992:144-152.
[53]	MVLLER A C, GUIDO S.Introduction to machine learning with python:a guide for data scientists[M].Sebastopol:O'Reilly Media, 2016.
[54]	SAUNDERS C, GAMMERMAN A, VOVK V.Ridge regression learning algorithm in dual variables[C]//Proceedings of the Fifteenth International Conference on Machine Learning.Madison:ICML, 1998:515-521.
[55]	EXTERKATE P, GROENEN P J F, HEIJ C, et al.Nonlinear forecasting with many predictors using kernel ridge regression[J].Int J Forecast, 2016, 32(3):736-753.
[56]	BREIMAN L.Random forests[J].Mach Learn, 2001, 45(1):5-32.
[57]	GONZÁLEZ-CAMACHO J M, ORNELLA L, PÉREZ-RODRÍGUEZ P, et al.Applications of machine learning methods to genomic selection in breeding wheat for rust resistance[J].Plant Genome, 2018, 11(2):170104.
[58]	KONONENKO I, KUKAR M.Machine learning and data mining:introduction to principles and algorithms[M].Chichester:Horwood Pub, 2007.
[59]	LONG N Y, GIANOLA D, ROSA G J M, et al.Application of support vector regression to genome-assisted prediction of quantitative traits[J].Theor Appl Genet, 2011, 123(7):1065-1074.
[60]	EIRÍKSSON J H, STRANDÉN I, SU G S, et al.Local breed proportions and local breed heterozygosity in genomic predictions for crossbred dairy cows[J].J Dairy Sci, 2022, 105(12):9822-9836.
[61]	GUILLENEA A, SU G S, LUND M S, et al.Genomic prediction in Nordic Red dairy cattle considering breed origin of alleles[J].J Dairy Sci, 2022, 105(3):2426-2438.
[62]	VANRADEN P M.Symposium review:how to implement genomic selection[J].J Dairy Sci, 2020, 103(6):5291-5301.
[63]	WELLER J I, EZRA E, RON M.Invited review:a perspective on the future of genomic selection in dairy cattle[J].J Dairy Sci, 2017, 100(11):8633-8644.
[64]	DE HAAS Y, PSZCZOLA M, SOYEURT H, et al.Invited review:phenotypes to genetically reduce greenhouse gas emissions in dairying[J].J Dairy Sci, 2017, 100(2):855-870.
[65]	SCHÖPKE K, SWALVE H H.Review:opportunities and challenges for small populations of dairy cattle in the era of genomics[J]. Animal, 2016, 10(6):1050-1060.

Application Progress on Genomic Selection Technology for Dairy Cattle in China

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References 65

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	CAO Jinkang, ZHANG Chun, WANG Jiayao, LI Xiaotong, WANG Pengyu, FANG Yingyan, ZHANG Yu, DING Ning, JIANG Li. Proteomic Analysis of Sperm with Different Freezability in Chinese Holstein Bulls [J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(3): 1052-1061.
[2]	ZHONG Xin, ZHANG Hui, ZHANG Chong, LIU Xiaohong. Research Progress on Genetic Breeding of Reproductive Performance in Sows [J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(2): 438-450.
[3]	CAO Jianhua, YANG Baigao, ZHANG Peipei, FENG Xiaoyi, ZHANG Hang, YU Zhou, NIU Yifan, HAO Haisheng, DU Weihua, ZHU Huabin, YANG Ling, ZHAO Xueming. Mechanisms of Negative Energy Balance Affects Follicular Development in Dairy Cattle [J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(1): 22-30.
[4]	WANG Wannian, CHEN Sijia, GAO Jinrong, WEN Zhonghao, YUAN Mengjiao, ZHANG Hongzhi, PANG Zhixu, QIAO Liying, LIU Wenzhong. Simulation Study on Genomic Selection of Sex-limited Traits Using Multilayer Perceptron in Sheep [J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(7): 2824-2835.
[5]	WANG Zhenyu, ZHANG Saibo, LIU Wenhui, LIANG Dong, REN Xiaoli, YAN Lei, YAN Yuefei, GAO Tengyun, ZHANG Zhen, HUANG Hetian. Genomic Inbreeding Coefficient Analysis and Functional Gene Screening in Different Dairy Farms Based on SNP Chip Data [J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(7): 2848-2857.
[6]	YANG Kai, LU Zhuoda, HE Jian, ZHANG Ruiqi, WANG Suqing, LI Kebiao, ZHAO Yunxiang, ZHU Xiaoping, GUO Jinbiao. The Population Effect of Commercial Pigs on the Accuracy of Genomic Selection for Carcass Traits in Duroc Pigs [J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(12): 4943-4951.
[7]	MA Haoran, ZHANG Lupei, JIN Shengyun, BAO Jinshan, LI Hongyan, GAO Huijiang, XU Lingyang, WANG Zezhao, LI Junya. Assessment of the Genomic Relationships for Chinese Indigenous Beef Cattle Using High-density SNP Chip [J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54(10): 4174-4185.
[8]	SHI Rui, SU Guosheng, CHEN Ziwei, LI Xiang, LUO Hanpeng, LIU Lin, GUO Gang, ZHANG Yi, WANG Yachun, ZHANG Shengli, ZHANG Qin. Comparisons of Genomic Predictions for Fertility Traits in Chinese Holstein Cattle [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(9): 2944-2954.
[9]	YANG Yang, ZHOU Ziwei, ZHANG Jingyi, YANG Shuo, WANG Boyu, GE Nan, LIN Ye, HOU Xiaoming. Analysis on SP1 Gene Structure and Its Function on Milk Fat Synthesis in Holstein Dairy Cows [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(9): 2970-2981.
[10]	PANG Zhixu, ZHANG Hongzhi, QIAO Liying, WANG Wannian, PAN Yangyang, LIU Wenzhong. Simulation Study on Joint Genomic Breeding Using Metafounders [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(7): 2172-2181.
[11]	DING Jiqiang, LI Qinghe, ZHANG Gaomeng, LI Sen, ZHENG Maiqing, WEN Jie, ZHAO Guiping. Comparing the Accuracy of Estimated Breeding Value by Several Algorithms on Laying Traits in Broilers [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(5): 1364-1372.
[12]	SONG Yuetong, ZHANG Rumei, LI Yanqin, LI Rongling, GAO Yundong, ZHONG Jifeng, XUE Guanghui, WANG Yudong, LI Jianbin, SUN Dongxiao. Analysis of Genetic Parameters of Type Traits and Influence of Genealogical Generation of Holstein Cows in Shandong Province [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(5): 1384-1395.
[13]	LIU Jiamin, YU Baojun, MU Tong, ZHANG Di, FENG Xiaofang, ZHANG Juan, WANG Ying, WEN Wan, GU Yaling. Screening and Identification of Key miRNAs for Milk Fat Metabolism in Dairy Cattle [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(12): 4244-4257.
[14]	DU Yongwang, HUANG Chao, WANG Yidong, LI Sen, WEN Jie, CHEN Zhiwu, ZHAO Guiping, ZHENG Maiqing. Genomic Selection for RFI in Broiler Combining GWAS Prior Marker Information [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(10): 3403-3411.
[15]	WANG Chuanchuan, MU Tong, FENG Xiaofang, YU Baojun, ZHANG Juan, GU Yaling. Identification of Key Candidate Genes for Milk Fat Metabolism in Dairy Cows Based on Transcriptome Sequencing [J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(10): 3421-3433.