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Review

The research progress of genomic selection in livestock

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  • Inner Mongolia Key Laboratory of Animal Genetics, Breeding and Reproduction, Inner Mongolia Agricultural University, Hohhot 0 10018, China

Received date: 2016-10-17

  Revised date: 2017-01-15

  Online published: 2017-12-25

Supported by

the National High Technology Research and Development Program of China(2013AA102506);the Program of Science and Technology Plan of Inner Mongolia Autonomous Region, the National Natural Science Foundation of China(31560619,31660639);the Key Program of Natural Science Foundation of Inner Mongolia of China(2016ZD02);National Special Fund for Agro-scientific Research in the Public Interest(201303059);the Modern Agricultural Industry Technology System of Ministry of Agriculture of China(CARS-40-05)

Abstract

With the development of gene chip and breeding technology, genomic selection in plants and animals has become research hotspots in recent years. Genomic selection has been extensively applied to all kinds of economic livestock, due to its high accuracy, short generation intervals and low breeding costs. In this review, we summarize genotyping technology and the methods for genomic breeding value estimation, the latter including the least square method, RR-BLUP, GBLUP, ssGBLUP, BayesA and BayesB. We also cover basic principles of genomic selection and compare their genetic marker ranges, genomic selection accuracy and operational speed. In addition, we list common indicators, methods and influencing factors that are related to genomic selection accuracy. Lastly, we discuss latest applications and the current problems of genomic selection at home and abroad. Importantly, we envision future status of genomic selection research, including multi-trait and multi-population genomic selection, as well as impact of whole genome sequencing and dominant effects on genomic selection. This review will provide some venues for other breeders to further understand genome selection.

Cite this article

Hongwei Li,Ruijun Wang,Zhiying Wang,Xuewu Li,Zhenyu Wang,Yanjun Zhang,Rui Su,Zhihong Liu,Jinquan Li . The research progress of genomic selection in livestock[J]. Hereditas(Beijing), 2017 , 39(5) : 377 -387 . DOI: 10.16288/j.yczz.16-342

References

[1] Heffner EL, Sorrells ME, Jannink JL. Genomic selection for crop improvement. Crop Sci, 2009, 49(1): 1-12.
[2] Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157(4): 1819-1829.
[3] Botstein D, White RL, Skolnick M, Davis RW. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet, 1980, 32(3): 314-331.
[4] Wang JL. Study on the methods of genomic selection for meat sheep by simulation[D]. Jinzhong: Shanxi Agricultural University, 2014.
[4] 王景霖. 肉用绵羊基因组选择方法的模拟研究[学位论文]. 晋中: 山西农业大学, 2014.
[5] Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics, 1975, 31(2): 423-447.
[6] VanRaden PM. Efficient Methods to compute genomic predictions. J Dairy Sci, 2008, 91(11): 4414-4423.
[7] Zhu M. Application of genomic selection on carcass and meat quality traits in simmental cattle[D]. Beijing: Chinese Academy of Agricultural Sciences, 2013.
[7] 朱淼. 西门塔尔牛部分屠宰和肉质性状基因组选择初步研究[学位论文]. 北京: 中国农业科学院, 2013.
[8] Misztal I, Legarra A, Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci, 2009, 92(9): 4648-4655.
[9] Christensen OF, Lund MS. Genomic prediction when some animals are not genotyped. Genet Sel Evol, 2010, 42: 2.
[10] Whittaker JC, Thompson R, Denham MC. Marker-assisted selection using ridge regression. Genet Res, 2000, 75(2): 249-252.
[11] Gianola D, de los Campos G, Hill WG, Manfredi E, Fernando R. Additive genetic variability and the Bayesian alphabet. Genetics, 2009, 183(1): 347-363.
[12] Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet, 2009, 10(6): 381-391.
[13] Hayes BJ, Visscher PM, Goddard ME. Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res, 2009, 91(1): 47-60.
[14] Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics, 2011, 12: 186.
[15] VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS. Invited Review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci, 2009, 92(1): 16-24.
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