基因组选择在羊育种中的应用研究进展

doi:10.11843/j.issn.0366-6964.2018.12.001

摘要/Abstract

摘要：

基因组选择（genomic selection，GS）已经作为新一代畜禽遗传评估方法应用于奶牛育种中，并取得了显著成效。随着羊参考基因组的不断完善，以及不同密度SNP芯片的推出和商业化推广，新西兰、澳大利亚、法国等相继将基因组选择应用到羊育种中，揭开了羊育种的新时代。本研究总结了国内外羊基因组选择的研究现状、影响因素、优势以及存在问题和展望，以期为中国羊开展基因组选择育种提供参考。

Abstract:

As a new genetic evaluation method in livestock and poultry, genomic selection(GS)has been applied in dairy cattle breeding and gained remarkable success. With the improvement of reference genomes of sheep and goats and the availability of SNP chips with different densities, the application of GS in sheep and goats has also being undertaken in New Zealand,Australia,France,etc.. In this review, we summarized the current status of application of GS, factors affecting the accuracy of GEBVs and the advantages of GS in sheep and goat breeding. Moreover, we also discussed potential problems, aiming to provide a reference for carrying out GS in sheep and goat breeding in China.

中图分类号:

S826.2

张统雨, 魏霞, 张勤, 杜立新, 王立贤, 赵福平. 基因组选择在羊育种中的应用研究进展[J]. 畜牧兽医学报, 2018, 49(12): 2535-2542.

ZHANG Tong-yu, WEI Xia, ZHANG Qin, DU Li-xin, WANG Li-xian, ZHAO Fu-ping. Progress on Application of Genomic Selection in Sheep and Goat Breeding[J]. ACTA VETERINARIA ET ZOOTECHNICA SINICA, 2018, 49(12): 2535-2542.

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参考文献

[1] 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.
[2] 谈成,边成,杨达,等.基因组选择技术在农业动物育种中的应用[J].遗传,2017,39(11):1033-1045.
TAN C,BIAN C,YANG D,et al.Application of genomic selection in farm animal breeding[J].Hereditas (Beijing), 2017, 39(11):1033-1045.(in Chinese)
[3] GARCÍA-RUIZ A,COLE J B,VANRADEN P M,et al.Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection[J].Proc Natl Acad Sci U S A,2016,113(28):E3995-E4004.
[4] KIJAS J W,TOWNLEY D,DALRYMPLE B P,et al.A genome wide survey of SNP variation reveals the genetic structure of sheep breeds[J].PLoS One,2009,4(3):e4668.
[5] TOSSER-KLOPP G,BARDOU P,BOUCHEZ O,et al.Design and characterization of a 52K SNP chip for goats[J].PLoS One,2014,9(1):e86227.
[6] ANDERSON R,MCEWAN J,BRAUNING R,et al.Development of a high density (600K) Illumina Ovine SNP chip and its use to fine map the yellow fat locus[C]//Proceedings of thePlant and Animal Genome XXⅡ Conference.San Diego,CA:Plant and Animal Genome,2014.
[7] VENTURA R V,LEE M,MILLER S P,et al.Assessing imputation accuracy using a 15K low density panel in a multi-breed New Zealand sheep population[C]//Proceedings ofthe 21st Association for the Advancement of Animal Breeding and Genetics.At Lorne,Victoria,Australia:ResearchGate,2015.
[8] BOLORMAA S,GORE K,VAN DER WERF J H J,et al.Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy[J].Anim Genet, 2015, 46(5):544-556.
[9] QIAO X,SU R,WANG Y,et al.Genome-wide target enrichment-aided chip design:a 66 K SNP chip for cashmere goat[J].Sci Rep,2017,7(1):8621.
[10] VAN DER WERF J H J.Potential benefit of genomic selection in sheep[C]//Proceedings of the Association for the Advancement of Animal Breeding and Genetics.Adelaide,Australia:AAABG,2009:38-41.
[11] MRODE R,TAREKEGN G M,MWACHARO J M,et al.Invited review:genomic selection for small ruminants in developed countries:how applicable for the rest of the world?[J].Animal,2018,12(7):1333-1340.
[12] DAETWYLER H D,HICKEY J M,HENSHALL J M,et al.Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population[J].Anim Prod Sci,2010,50(12):1004-1010.
[13] MOGHADDAR N,SWAN A A,VAN DER WERF J H J.Genomic prediction of weight and wool traits in a multi-breed sheep population[J].Anim Prod Sci,2014,54(5):544-549.
[14] BOLORMAA S,SWAN A A,BROWN D J,et al.Multiple-trait QTL mapping and genomic prediction for wool traits in sheep[J].Genet Sel Evol,2017,49(1):62.
[15] DAETWYLER H D,SWAN A A,VAN DER WERF J H J,et al.Accuracy of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data assessed by cross-validation[J].Genet Sel Evol,2012,44(1):33.
[16] DAETWYLER H D,KEMPER K E,VAN DER WERF J H J,et al.Components of the accuracy of genomic prediction in a multi-breed sheep population[J].J Anim Sci,2012,90(10):3375-3384.
[17] SLACK-SMITH A,KINGHORN B P,VAN DER WERF J H J.Accuracy of genomic selection in predicting carcass traits in meat sheep[J].Anim Prod Sci,2010,50(11-12):XⅢ.
[18] BRITO L F,CLARKE S M,MCEWAN J C,et al.Prediction of genomic breeding values for growth,carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip[J].BMC Genet,2017,18(1):7.
[19] SHUMBUSHO F,RAOUL J,ASTRUC J M,et al.Potential benefits of genomic selection on genetic gain of small ruminant breeding programs[J].J Anim Sci,2013,91(8):3644-3657.
[20] WU J,ZHU H,SONG W,et al.Identification of conservative microRNAs in Saanen dairy goat testis through deep sequencing[J].Reprod Domest Anim,2014,49(1):32-40.
[21] CARILLIER C,LARROQUE H,PALHIÈRE I,et al.A first step toward genomic selection in the multi-breed French dairy goat population[J].J Dairy Sci,2013,96(11):7294-7305.
[22] LARROQUE H,BARILLET F,BALOCHE G,et al.Toward genomic breeding programs in French dairy sheep and goats[C]//Proceedings of the 10thWorld Congress on Genetics Applied to Livestock Production.Vancouver, BC,Canada:ASAS,2014.
[23] DUCHEMIN S I,COLOMBANI C,LEGARRA A,et al.Genomic selection in the French Lacaune dairy sheep breed[J].J Dairy Sci,2012,95(5):2723-2733.
[24] BALOCHE G,ASTRUC J M,BOULENC P,et al.Genomic selection experiment in Lacaune dairy sheep:Progeny test results of rams initially selected either on parent average or on genomic prediction[C]//Proceedings of the 10th World Congress on Genetics Applied to Livestock Production. Vancouver, BC,Canada:ASAS,2014.
[25] BALOCHE G,LEGARRA A,SALLÉ G,et al.Assessment of accuracy of genomic prediction for French Lacaune dairy sheep[J].J Dairy Sci,2014,97(2):1107-1116.
[26] MUCHA S,MRODE R,MACLAREN-LEE I,et al.Estimation of genomic breeding values for milk yield in UK dairy goats[J]. J Dairy Sci,2015,98(11):8201-8208.
[27] MOLINA A,MUÑOZ E,MENÉNDEZ-BUXADERA A,et al.Goat genomic selection:impact of the integration of genomic information in the genetic evaluations of the Spanish Florida goats[J].Small Rumin Res,2018,163:72-75.
[28] DI MENNA M E,SMITH B L,MILES C O.A history of facial eczema (pithomycotoxicosis) research[J]. New Zeal J Agric Res, 2009,52(4):345-376.
[29] PHUA S H,HYNDMAN D L,BAIRD H J,et al.Towards genomic selection for facial eczema disease tolerance in the New Zealand sheep industry[J].Anim Genet,2014,45(4):559-564.
[30] NEWTON J E,BROWN D J,DOMINIK S,et al.Impact of young ewe fertility rate on risk and genetic gain in sheep-breeding programs using genomic selection[J].Anim Prod Sci,2016,57(8):1653-1664.
[31] PICKERING N K,DODDS K G,AUVRAY B,et al.The impact of genomic selection on genetic gain in the New Zealand sheep dual purpose selection index[C]//Proceedings of theAssociation for the Advancement of Animal Breeding and Genetics.At Napier,New Zealand:AAABG,2013:2761-2779.
[32] BOLORMAA S,BROWN D J,SWAN A A,et al.Genomic prediction of reproduction traits for Merino sheep[J].Anim Genet,2017,48(3):338-348.
[33] ROWE S J,MCEWAN J C,HICKEY S M,et al.Genomic selection as a tool to decrease greenhouse gas emission from dual purpose New Zealand Sheep[C]//Proceedings of the10th World Congress of Genetics Applied to Livestock Production. Vancouver, BC,Canada:ASAS,2014.
[34] TODD D L.Implementation of genomic selection in UK beef and sheep breeding[D]. Edinburgh:University of Edinburgh, 2013.
[35] MOGHADDAR N,SWAN A A,VAN DER WERF J H J.Comparing genomic prediction accuracy from purebred,crossbred and combined purebred and crossbred reference populations in sheep[J].Genet Sel Evol, 2014,46(1):58.
[36] DODDS K G,BENOÎT A,NEWMAN S A N,et al.Genomic breed prediction in New Zealand sheep[J]. BMC Genet,2014,15(1):92.
[37] CARILLIER-JACQUIN C.Evaluation of a reference population in dairy goats for genomic selection[D].Toulouse:Céline Carillier-Jacquin at L'Institut National De La Recherche Agronomique,2012.
[38] CARILLIER-JACQUIN C,ROBERT-GRANIÉ C,LARROQUE H.Genomic selection in the multi-breed French dairy goat population[C]//Proceedings of the Book of Abstracts of the,Meeting of the European Federation of Animal Science.At Nantes, France:EAAP,2013.
[39] AUVRAY B,MCEWAN J C,NEWMAN S A N,et al.Genomic prediction of breeding values in the New Zealand sheep industry using a 50K SNP chip[J].J Anim Sci,2014,92(10):4375-4389.
[40] RAOUL J,SWAN A A,ELSEN J M.Using a very low-density SNP panel for genomic selection in a breeding program for sheep[J].Genet Sel Evol,2017,49(1):76.
[41] CLARKE S,ROWE S,DODDS K.Genomic selection in dairy goats (Capra hircus) using genotyping by sequencing[C]//Proceedings of the Plant & Animal Genome XX Conference.San Diego,CA:Plant and Animal Genome,2012.
[42] GIANOLA D,ROSA G J M.One hundred years of statistical developments in animal breeding[J].Annu Rev Anim Biosci, 2015,3:19-56.
[43] CROSSA J,PÉREZ-RODRÍGUEZ P,CUEVAS J,et al.Genomic selection in plant breeding:methods, models, and perspectives[J].Trends Plant Sci,2017,22(11):961-975.
[44] MISZTAL I.Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size[J].Genetics,2016,202(2):401-409.
[45] XU S Z.Derivation of the shrinkage estimates of quantitative trait locus effects[J]. Genetics, 2007,177(2):1255-1258.
[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] ZHAO F P,XU S Z.An expectation and maximization algorithm for estimating Q×E interaction effects[J]. Theor Appl Genet,2012,124(8):1375-1387.
[48] XU S.An expectation-maximization algorithm for the Lasso estimation of quantitative trait locus effects[J].Heredity, 2010, 105(5):483-494.
[49] WANG T T,CHEN Y P P,BOWMAN P J,et al.A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping[J].BMC Genomics,2016,17(1):744.
[50] WU X L,SUN C Y,BEISSINGER T M,et al.Parallel Markov chain Monte Carlo-bridging the gap to high-performance Bayesian computation in animal breeding and genetics[J].Genet Sel Evol,2012,44(1):29.
[51] GUO P,ZHU B,NIU H,et al.Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis[J].BMC Bioinformat,2018,19(1):3.
[52] ZHANG Z,OBER U,ERBE M,et al.Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies[J].PLoS One,2014,9(3):e93017.
[53] EDWARDS S M,SØRENSEN I F,SARUP P,et al.Genomic prediction for quantitative traits is improved by mapping variants to gene ontology categories in drosophila melanogaster[J].Genetics,2016,203(4):1871-1883.
[54] KADARMIDEEN H N.Genomics to systems biology in animal and veterinary sciences:Progress,lessons and opportunities[J]. Livest Sci,2014,166:232-248.
[55] MACLEOD I M,BOWMAN P J,JAGT C J V,et al.Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits[J].BMC Genomics,2016,17(1):144.
[56] GAO N,MARTINI J W R,ZHANG Z,et al.Incorporating gene annotation into genomic prediction of complex phenotypes[J].Genetics,2017,207(2):489-501.
[57] HICKEY J M,CHIURUGWI T,MACKAY I,et al.Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery[J].Nat Genet,2017,49(9):1297-1303.
[58] LACHANCE J,TISHKOFF S A.SNP ascertainment bias in population genetic analyses:Why it is important,and how to correct it[J].Bioessays,2013,35(9):780-786.