国内外肉牛遗传评估体系概况

doi:10.11843/j.issn.0366-6964.2021.06.001

畜牧兽医学报 ›› 2021, Vol. 52 ›› Issue (6): 1447-1460.doi: 10.11843/j.issn.0366-6964.2021.06.001

国内外肉牛遗传评估体系概况

朱波^1,2,3*, 李宏伟¹, 周姵诺¹, 李倩¹, 高翰¹, 王泽昭¹, 汪聪勇³, 蔡文涛¹, 徐凌洋¹, 陈燕¹, 张路培¹, 高雪¹, 高会江^1,2,3, 李俊雅^1,2,3*

1. 中国农业科学院北京畜牧兽医研究所, 北京 100193;
2. 国家肉牛遗传评估中心, 北京 100193;
3. 肉用西门塔尔牛育种联合会, 北京 100193

收稿日期:2020-11-11 出版日期:2021-06-23 发布日期:2021-06-22
通讯作者: 朱波,主要从事肉牛分子数量遗传学研究,E-mail:zhubo@caas.cn;李俊雅,主要从事肉牛分子数量遗传学研究,E-mail:lijunya@caas.cn
作者简介:朱波(1989-),男,河南南阳人,助理研究员,博士,主要从事肉牛分子数量遗传学研究,Tel:010-62816065,E-mail:zhubo@caas.cn
基金资助:
国家自然科学基金（31802049；31672384）；财政部和农业农村部：国家现代农业产业技术体系资助；中国农业科学院创新工程（ASTIP-IAS03；CAAS-XTCX2016010）；中国农业科学院北京畜牧兽医研究所基本科研业务费（2019-YWF-YTS-11）

Overview of Genetic Evaluation System of Beef Cattle in China and Abroad

ZHU Bo^1,2,3*, LI Hongwei¹, ZHOU Peinuo¹, LI Qian¹, GAO Han¹, WANG Zezhao¹, WANG Congyong³, CAI Wentao¹, XU Lingyang¹, CHEN Yan¹, ZHANG Lupei¹, GAO Xue¹, GAO Huijiang^1,2,3, LI Junya^1,2,3*

1. Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China;
2. National Centre of Beef Cattle Genetic Evaluation, Beijing 100193, China;
3. Beef Simmental Breeding Alliance, Beijing 100193, China

Received:2020-11-11 Online:2021-06-23 Published:2021-06-22

摘要/Abstract

摘要： 种公牛的选育是肉牛育种工作的核心。传统选育肉用种公牛需要经过后裔测定进行选择，其优点是准确性高，但存在周期长、屠宰和肉质性状难以收集、成本高等问题，致其选择效率低。自2001年全基因组选择概念提出后，该技术迅速成为动植物育种领域研究的热点。利用全基因组选择进行肉用种公牛的选育，进行早期选择从而大幅度缩短世代间隔，可以提高繁殖性状等低遗传力性状的选择准确性，加快遗传进展，并大大降低育种成本。2014年，美国安格斯协会开始应用全基因组选择技术，其他欧美发达国家也陆续使用，肉牛育种进入基因组时代。中国自2017年开始使用全基因组选择技术选择青年肉用种公牛，并于2020年在全国范围内使用该技术进行基因组遗传评估。本文综述了国内外肉牛遗传评估现状，以期为我国肉牛育种工作提供参考和借鉴。

关键词: 肉牛, 遗传评估, 综合选择指数, 全基因组选择

Abstract: Beef bull selection is the core of beef cattle breeding. Traditional breeding of beef bulls requires progeny testing, which has the advantages of high accuracy, however, it has the problems of long cycle for breeding, difficult to collect the slaughter and meat quality traits, and high cost, resulting in low selection efficiency. Since the concept of genomic selection was put forward in 2001, it has quickly become a hot spot in animal and plant breeding. When genomic selection was used to select the beef bulls, it can select individuals at early age, greatly shorten the generation interval, improve the prediction accuracy of low heritability traits such as reproduction traits, accelerate the genetic progress, and greatly reduce the breeding cost. In 2014, the American Angus Association began to apply the genomic selection technology, and other developed countries in Europe and the United States also successively used it. Beef cattle breeding entered the genomic era. Since 2017, China began to use the genomic selection technology to select young beef bulls. In 2020, this technology was implemented for national beef cattle genomic evaluation. The current situation of genetic evaluation in China and abroad was summarized in this article, which was respected to provide references for beef cattle breeding in China.

Key words: beef cattle, genetic evaluation, comprehensive selection index, genomic selection

中图分类号:

朱波, 李宏伟, 周姵诺, 李倩, 高翰, 王泽昭, 汪聪勇, 蔡文涛, 徐凌洋, 陈燕, 张路培, 高雪, 高会江, 李俊雅. 国内外肉牛遗传评估体系概况[J]. 畜牧兽医学报, 2021, 52(6): 1447-1460.

ZHU Bo, LI Hongwei, ZHOU Peinuo, LI Qian, GAO Han, WANG Zezhao, WANG Congyong, CAI Wentao, XU Lingyang, CHEN Yan, ZHANG Lupei, GAO Xue, GAO Huijiang, LI Junya. Overview of Genetic Evaluation System of Beef Cattle in China and Abroad[J]. Acta Veterinaria et Zootechnica Sinica, 2021, 52(6): 1447-1460.

参考文献 52

[1]	朱波,李姣,汪聪勇,等.我国肉用西门塔尔牛群体生长发育性状遗传参数估计及其遗传进展[J].畜牧兽医学报,2020,51(8):1833-1844.ZHU B,LI J,WANG C Y,et al.Genetic parameter and genetic gain estimation for growth and development traits in Chinese Simmental beef cattle[J].Acta Veterinaria et Zootechnica Sinica,2020,51(8):1833-1844.(in Chinese)
[2]	MIDDLETON B K,GIBB J B.An overview of beef cattle improvement programs in the United States[J].J Anim Sci,1991,69(9):3861-3871.
[3]	BARKHOUSE K L,VAN VLECK L D,CUNDIFF L V,et al.Comparison of sire breed solutions for growth traits adjusted by mean expected progeny differences to a 1993 base[J].J Anim Sci,1998,76(9):2287-2293.
[4]	ZAHRÁDKOVÁ R,BARTOÑ L,BUREŠ D,et al.Comparison of growth performance and slaughter characteristics of Limousin and Charolais heifers[J].Arch Anim Breed,2010,53(5):520-528.
[5]	CHAMBAZ A,SCHEEDER M R L,KREUZER M,et al.Meat quality of Angus,Simmental,Charolais and Limousin steers compared at the same intramuscular fat content[J].Meat Sci,2003,63(4):491-500.
[6]	LUNT D K,RILEY R R,SMITH S B.Growth and carcass characteristics of Angus and American Wagyu steers[J].Meat Sci,1993,34(3):327-334.
[7]	DE MELO COSTA C C,MAIA A S C,NASCIMENTO S T,et al.Thermal balance of Nellore cattle[J].Int J Biometeorol, 2018,62(5):723-731.
[8]	FAÇANHA D A E,FERREIRA J B,LEITE J H G M,et al.The dynamic adaptation of Brazilian Brahman bulls[J].J Therm Biol,2019,81:128-136.
[9]	PHELPS K J,JOHNSON D D,ELZO M A,et al.Effect of Brahman genetics on myofibrillar protein degradation,collagen crosslinking,and tenderness of the longissimus lumborum[J].J Anim Sci,2017,95(12):5397-5406.
[10]	HENDERSON C R.Rapid method for computing the inverse of a relationship matrix[J].J Dairy Sci,1975,58(11):1727-1730.
[11]	CR R.Multivariate analysis[J].Indian J Stat,1983,45(2):284-299.
[12]	HARVILLE D A.Maximum likelihood approaches to variance component estimation and to related problems[J].J Am Stat Assoc,1977,72(358):320-338.
[13]	PATTERSON H D,THOMPSON R.Recovery of inter-block information when block sizes are unequal[J].Biometrika, 1971,58(3):545-554.
[14]	CHALONER K.A bayesian approach to the estimation of variance components for the unbalanced one-way random model[J].Technometrics,1987,29(3):323-337.
[15]	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)
[16]	BOLDMAN K G,KRIESE L A,VAN VLECK L D,et al.A manual for use of MTDFREML:a set of programs to obtain estimates of variances and covariances[DRAFT] [R].Washington,DC:ARS-USDA,1995.
[17]	LOURENCO D,LEGARRA A,TSURUTA S,et al.Single-step genomic evaluations from theory to practice:using SNP chips and sequence data in BLUPF90[J].Genes (Basel),2020,11(7):790.
[18]	GILMOUR A R,GOGEL B J,CULLIS B R,et al.ASReml user guide release 4.1 structural specification[M].Hemel Hempstead:VSN International Ltd.,2015.
[19]	MADSEN P,JENSEN J.DMU:a user's guide.a package for analysing multivariate mixed models[M].Denmark:DJF,Foulum,2008.
[20]	GARRICK D J.The nature,scope and impact of genomic prediction in beef cattle in the United States[J].Genet Sel Evol,2011,43(1):17.
[21]	MEUWISSEN T H,HAYES B J,GODDARD M E.Prediction of total genetic value using genome-wide dense marker maps[J].Genetics,2001,157(4):1819-1829.
[22]	OLIVEIRA H R,BRITO L F,SILVA F F,et al.Genomic prediction of lactation curves for milk,fat,protein,and somatic cell score in Holstein cattle[J].J Dairy Sci,2019,102(1):452-463.
[23]	ZHU B,GUO P,WANG Z,et al.Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle[J].Anim Genet,2019,50(6):634-643.
[24]	SONG H L,ZHANG Q,DING X D.The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs[J].J Anim Sci Biotechnol,2020,11:88.
[25]	HERRY F,DRUET D P,HÉRAULT F,et al.Interest of using imputation for genomic evaluation in layer chicken[J].Poult Sci,2020,99(5):2324-2336.
[26]	VANRADEN P M.Efficient methods to compute genomic predictions[J].J Dairy Sci,2008,91(11):4414-4423.
[27]	LEGARRA A,AGUILAR I,MISZTAL I.A relationship matrix including full pedigree and genomic information[J].J Dairy Sci,2009,92(9):4656-4663.
[28]	GUO Z G,TUCKER D M,LU J W,et al.Evaluation of genome-wide selection efficiency in maize nested association mapping populations[J].Theor Appl Genet,2012,124(2):261-275.
[29]	HABIER D,FERNANDO R L,KIZILKAYA K,et al.Extension of the bayesian alphabet for genomic selection[J].BMC Bioinformatics,2011,12:186.
[30]	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.
[31]	ZHU B,ZHU M,JIANG J C,et al.The impact of variable degrees of freedom and scale parameters in bayesian methods for genomic prediction in Chinese simmental beef cattle[J].PLoS One,2016,11(5):e0154118.
[32]	ZHU B,NIU H,ZHANG W G,et al.Genome wide association study and genomic prediction for fatty acid composition in Chinese Simmental beef cattle using high density SNP array[J].BMC Genomics,2017,18(1):464.
[33]	XU L,GAO N,WANG Z Z,et al.Incorporating genome annotation into genomic prediction for carcass traits in Chinese Simmental beef cattle[J].Front Genet,2020,11:481.
[34]	AN B,XIA J,CHANG T,et al.Genome-wide association study reveals candidate genes associated with body measurement traits in Chinese Wagyu beef cattle[J].Anim Genet,2019,50(4):386-390.
[35]	WANG Z Z,ZHU B,NIU H,et al.Genome wide association study identifies SNPs associated with fatty acid composition in Chinese Wagyu cattle[J].J Anim Sci Biotechnol,2019,10:27.
[36]	GARRICK D J,GOLDEN B L.Producing and using genetic evaluations in the United States beef industry of today[J].J Anim Sci,2009,87(14 Suppl):E11-E18.
[37]	BERRY D P,GARCIA J F,GARRICK D J.Development and implementation of genomic predictions in beef cattle[J].Anim Front,2016,6(1):32-38.
[38]	SAATCHI M,MCCLURE M C,MCKAY S D,et al.Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation[J].Genet Sel Evol,2011,43(1):40.
[39]	SAATCHI M,SCHNABEL R D,ROLF M M,et al.Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle[J].Genet Sel Evol,2012,44(1):38.
[40]	JOURNAUX L,WICKHAM B,VENOT E,et al.Development of routine international genetic evaluation services for beef cattle as an extension of interbull's services[C]//Proceedings of the 2006 Interbull Meeting.2006:146-152.
[41]	VENOT E,PABIOU T,FOUILLOUX M N,et al.Interbeef in practice:example of a joint genetic evaluation between France,Ireland and United Kingdom for pure bred Limousine weaning weights[C]//Proceedings of the Interbull Technical Workshop.2007:41-48.
[42]	FORABOSCO F,PALUCCI V,DÜRR J.Selecting traits for international beef evaluations:survey results[C]//Proceedings of the 2009 Interbull Meeting.Barcelona,Spain,2009.
[43]	MONTALDO H H,CASAS E,FERRAZ J B S,et al.Opportunities and challenges from the use of genomic selection for beef cattle breeding in Latin America[J].Anim Front,2012,2(1):23-29.
[44]	NEVES H H R,CARVALHEIRO R,O'BRIEN A M P,et al.Accuracy of genomic predictions in Bos indicus (Nellore) cattle[J].Genet Sel Evol,2014,46(1):17.
[45]	JÚNIOR G A F,ROSA G J M,VALENTE B D,et al.Genomic prediction of breeding values for carcass traits in Nellore cattle[J].Genet Sel Evol,2016,48:7.
[46]	GRASER H U,TIER B,JOHNSTON D J,et al.Genetic Evaluation for the beef industry in Australia[J].Aust J Exp Agric,2005,45(8):913-921.
[47]	DAETWYLER H D,CAPITAN A,PAUSCH H,et al.Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle[J].Nat Genet,2014,46(8):858-865.
[48]	BOLORMAA S,PRYCE J E,KEMPER K,et al.Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus,Bos indicus,and composite beef cattle[J].J Anim Sci,2013,91(7):3088-3104.
[49]	BANKS R G.Progress in implementation of a beef information nucleus portfolio in the Australian beef industry[J].Proc Assoc Advmt Anim Breed Genet,2011,19:399-402.
[50]	CHEN L,VINSKY M,LI C.Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle[J].Anim Genet,2015,46(1):55-59.
[51]	NIU H,ZHU B,GUO P,et al.Estimation of linkage disequilibrium levels and haplotype block structure in Chinese Simmental and Wagyu beef cattle using high-density genotypes[J].Livest Sci,2016,190:1-9.
[52]	AKDEMIR D,SANCHEZ J I.Efficient breeding by genomic mating[J].Front Genet,2016,7:210.

国内外肉牛遗传评估体系概况

Overview of Genetic Evaluation System of Beef Cattle in China and Abroad

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献 52

相关文章 15

编辑推荐

Metrics

本文评价

[1]	姜玲玲, 牛小霞, 刘强, 张刚, 王璞, 李勇. 宁夏地区肉牛腹泻相关病毒感染状况的分析[J]. 畜牧兽医学报, 2023, 54(9): 3863-3871.
[2]	马浩然, 张路培, 金生云, 宝金山, 李红艳, 高会江, 徐凌洋, 王泽昭, 李俊雅. 利用高密度SNP芯片评估中国地方肉牛品种基因组亲缘关系[J]. 畜牧兽医学报, 2023, 54(10): 4174-4185.
[3]	李宏伟, 徐凌洋, 王泽昭, 蔡文涛, 朱波, 陈燕, 高雪, 张路培, 高会江, 李俊雅. 基于单倍型肉牛屠宰性状全基因组关联分析研究[J]. 畜牧兽医学报, 2022, 53(12): 4232-4243.
[4]	李紫仟, 陈卓, 任强林, 马义诚, 叶倩文, 夏瑞阳, 王金泉, 赵红琼, 马忠祥, 李红波, 姚刚. 安格斯妊娠母牛异食行为的营养代谢异常及其与肠道菌群相关性研究[J]. 畜牧兽医学报, 2022, 53(1): 188-199.
[5]	苏丁然, 彭朋, 闫青霞, 陈绍祜, 张胜利, 李姣, 刘丑生, 孙东晓. 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.
[6]	袁泽湖, 葛玲, 李发弟, 乐祥鹏, 孙伟. 整合生物学先验信息的全基因组选择方法及其在家畜育种中的应用进展[J]. 畜牧兽医学报, 2021, 52(12): 3323-3334.
[7]	张海亮, 常瑶, 穆柏宇, 王凯, 杨明路, 王磊, 马龙刚, 宁景扬, 郭刚, 王雅春. 荷斯坦牛皮肤褶皱厚度及体况评分性状遗传分析[J]. 畜牧兽医学报, 2021, 52(11): 3089-3098.
[8]	王宏浩, 任小康, 张毅, 高会江, 车雷杰, 王曦. 基因芯片技术在晋南牛种公牛选育中的应用[J]. 畜牧兽医学报, 2021, 52(10): 2803-2813.
[9]	刘明, 张磊, 安小鹏, 张恩平, 宋宇轩. 不同氨气浓度对肉牛生产性能、免疫和抗氧化能力的影响[J]. 畜牧兽医学报, 2020, 51(11): 2757-2764.
[10]	尹立林, 马云龙, 项韬, 朱猛进, 余梅, 李新云, 刘小磊, 赵书红. 全基因组选择模型研究进展及展望[J]. 畜牧兽医学报, 2019, 50(2): 233-242.
[11]	张新雨, 刘博, 罗军荣, 徐洋, 尚含乐, 杨食堂, 宋小珍. 中药复方制剂对热应激肉牛生产性能及生理生化指标的影响[J]. 畜牧兽医学报, 2018, 49(3): 620-628.
[12]	高虹, 邱小田, 王长存, 张金鑫, 张锁宇, 王源, 张勤, 王志刚, 杨红杰, 丁向东. 我国杜洛克、长白、大白猪区域性联合遗传评估研究[J]. 畜牧兽医学报, 2018, 49(12): 2567-2575.
[13]	张金鑫, 张锁宇, 邱小田, 高虹, 王长存, 王源, 张勤, 王志刚, 杨红杰, 丁向东. 我国杜洛克、长白和大白猪场间遗传联系分析[J]. 畜牧兽医学报, 2017, 48(9): 1591-1601.
[14]	毛宏祥, 高凤仙, 王敏, 蒋载阳, 张秀敏, 龙栋磊, 王荣, 谭支良. 肉牛胃肠道甲烷排放模型估算精度的评估分析[J]. 畜牧兽医学报, 2017, 48(4): 690-698.
[15]	王延晖,朱波，李俊雅. 基于显性模型的基因组选择中贝叶斯方法研究[J]. 畜牧兽医学报, 2017, 48(1): 60-67.