北京地区大白猪基因组联合育种研究

doi:10.3864/j.issn.0578-1752.2019.12.013

Abstract

Abstract:

【Objective】In this study, the molecular breeding via genomic selection was carried out in the joint genomic evaluation on Yorkshire population in Beijing, predicting the breeding value of the new born boars and making selection, so as to improve the selection accuracy of breeding. 【Method】 An admixed population consisting of 4020 individuals from three Yorkshire breeding farms with different genetic background in Beijing was established as the reference group, and the reference animals were selected according to the performance testing records between 2007-2017 in those three pig farms. Three economic traits age at 100 kg (AGE), backfat thickness at 100 kg (BF) and total number born (TNB) were taken into account. The reference and candidate animals were genotyped with Illumina Porcine80K SNP chip. GEBV was estimated by single-step GBLUP (SSGBLUP) method which could make use of both pedigree information and genomic information. GEBVs of candidate boars on the growth traits and reproductive traits were predicted before castration and after performance testing, respectively. Afterwards, the elite candidates were selected according to their GEBVs. Meanwhile, the genetic connectedness among three pig farms was measured by connectedness rating.【Result】Our results showed that the genetic connectedness based on pedigree information among three Yorkshire breeding farms was too low to carry out traditional joint genetic evaluation. However,the genomic relationship coefficients of individuals between farms in G-matrix indicated that genetic links existed among different farms. The genomic selection could realize the joint genomic evaluation through establishing the genetic connectedness via genome-wide markers. A total of 1789 boars were genomic predicted.The accuracy of genomic prediction was largely improved, compared to traditional breeding methods. At the first time of implementing genomic selection or early selection (before the castration of boars), the accuracies of Pedigree Index (PI) for three traits, age at 100 kg (AGE), backfat thickness at 100 kg (BF) and total number born (TNB) were 0.55, 0.56 and 0.41, respectively. However, the accuracies of GEBV from genomic selection were increased to 0.65, 0.70 and 0.60 with improvement of 10, 14 and 19 percentage compared to PI selection, respectively. At the second time of implementing genomic selection (after performance testing), the accuracies of GEBV for AGE, BF and TNB were further increased to 0.78, 0.84 and 0.60, respectively, yielding 8, 12 and 19 percentage higher accuracy than EBV, respectively, in which the accuracies were 0.70, 0.72 and 0.41, respectively. The largest gain of genomic selection was on trait of TNB with low heritability. The early selection based on genomic selection had the same accuracy as traditional selection based on estimated breeding values calculated from performance testing, implying genomic selection could save breeding time and cost with keeping the same accuracy. The comparison of two implementations of genomic selection on 338 boars at different stage showed that the second genomic prediction after performance testing yielded higher accuracy, because the phenotypic records of these boars were also utilized. The accuracies of GEBV for AGE and BF were improved from 0.55, 0.62 to 0.72, 0.84 by increasing 17 and 22 percentage point, respectively. The unbiasedness coefficient was between 0.82 and 1.00, and the unbiasedness of GEBV on traits of AGE and BF were increased from 0.82 and 0.96 to 0.91 and 1.00, respectively. The lower unbiasedness of second genomic selection indicated that the reliability of selecting elite boars was higher.【Conclusion】 Genomic selection could establish genetic connectedness between different farms, enabling joint genetic evaluation which was not feasible in traditional breeding plausible and more breeding farms involved. Compared to traditional PI or EBV selection, genomic selection generated much higher accuracy, and the greatest improvement was obtained on the traits with low heritability. Genomic selection was useful to achieve early selection and to improve the breeding efficiency.

Key words: genomic selection, Yorkshire, admixed population, joint breeding, early selection

ZHANG JinXin, TANG ShaoQing, SONG HaiLiang, GAO Hong, JIANG Yao, JIANG YiFan, MI ShiRong, MENG QingLi, YU Fan, XIAO Wei, YUN Peng, ZHANG Qing, DING XiangDong. Joint Genomic Selection of Yorkshire in Beijing[J].Scientia Agricultura Sinica, 2019, 52(12): 2161-2170.

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References 40

[1]	彭中镇 . 《全国生猪遗传改良计划》解读及实施中有关问题的讨论(续完).猪业科学, 2011(10):106-108.
	PENG Z Z . Discussion on the relevant issues in the interpretation and implementation of the "National Swine Improvement Program"(Continued). Swine Industry Science , 2011(10):106-108.(in Chinese)
[2]	张勤, 丁向东, 陈瑶生 . 种猪遗传评估技术研发与评估系统应用. 中国畜牧杂志, 2015,51(8):61-65.
	ZHANG Q, DING X D, CHEN Y S . Development and application of swine genetic evaluation system in China. Chinese Journal of Animal Science, 2015,51(8):61-65.(in Chinese)
[3]	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. 畜牧兽医学报, 2017,48(09):1591-1601.
	ZHANG J X, ZHANG S Y, QIU X T, GAO H, WANG C C, WANG Y, ZHANG Q, WANG Z G, YANG H J, DING X D . The genetic connectedness of Duroc, Landrace and Yorkshire pigs in China. ActaVeterinaria et ZootechnicaSinica, 2017,48(9):1591-1601.(in Chinese)
[4]	张哲, 张勤, 丁向东 . 畜禽基因组选择研究进展. 2011,56(26):2212-2222.
	ZHANG Z, ZHANG Q, DING X D . Advances in genomic selection in domestic animals. Chinese Sci Bull, 2011,56(26):2212-2222.(in Chinese)
[5]	SCHAEFFER L R . Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics, 2006,123(4):381-391.
[6]	FORNI S, AGUILAR I, MISZTAL I. Genomic relationships and biases in the evaluation of sow litter size. 2010: Liepzig, Germany. 2-6.
[7]	SONG H L, ZHANG J X, JIANG Y, GAO H, TANG S Q, MI S R, YU F, MENG Q L, XIAO W, ZHANG Q, DING X D . Genomic prediction for growth and reproduction traits in pig using an admixed reference population. Journal of Animal Science, 2017,95(8):3415-3424.
[8]	SAATCHI M, MCCLURE M C, MCKAY S D, ROLF M M, KIM J, DECKER J E, TAXIS T M, CGAPPLE R H, RAMEY H R, NORTHCUTT S L, BAUCK S, WOODWARD B, DEKKERS J C, FERNANDO R L, SCHNABEL R D, GARRICK D J . Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genetics Selection Evolution, 2011,43(1):40-43. doi: 10.1186/1297-9686-43-40
[9]	LONG N, GIANOLA D, ROSA GJ, WEIGEL K A, AVENDANO S . Machine learning classification procedure for selecting SNPs in genomic selection: Application to Early Mortality in Broilers. Journal of Animal Breeding and Genetics, 2007,124(6):377-389. doi: 10.1111/j.1439-0388.2007.00694.x
[10]	RIEDELSHEIMER C, CZEDIKEYSENBERG A, GRIEDER C, LISEC J, TECHNOW F, SULPICE R, ALTMANN T, STITT M, WILLMITZER L, MELCHINGER A E . Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nature Genetics, 2012,44(2):217-220. doi: 10.1038/ng.1033
[11]	GUO T T, LI H H, YAN J B, TANG J H, LI J S, ZHANG Z W, ZHANG L Y, WANG J K . Performance prediction of F1 hybrids between recombinant inbred lines derived from two elite maize inbred lines. Theoretical and Applied Genetics, 2013,126(1):189-201. doi: 10.1007/s00122-012-1973-9
[12]	郭亮虎, 逯腊虎, 张婷, 史晓芳, 袁凯, 王镇, 杨三维 . 植物全基因组选择育种研究进展与前景. 山西农业科学, 2015,43(11):1558-1562.
	GUO L H, LU L H, ZHANG T, SHI X F, YUAN K, WANG Z, YANG S W . Research Progress and Prospects of Genome-wide Selection Breeding in Plants. Journal of Shanxi Agricultural Sciences. 2015,43(11):1558-1562.(in Chinese)
[13]	GUTIERREZ A P . Genomic selection for improvement of Atlantic salmon aquaculture: [Doctor of Philosophy]. University ofConcepcionDepartment of Molecular Biology and Biochemistry Faculty of Science, 2015.
[14]	张璐 . 全基因组选择在栉孔扇贝育种中的应用: [D], 青岛:中国海洋大学, 2015.
	ZHANG L . Application of genome selection in breeding of Zhikong Scallop (Chlamysfarreri):[D]. Qingdao: OceanUniversity of China, 2015.(in Chinese)
[15]	MEUWISSEN T HE, HAYES B J, GODDARD M E . Prediction of total genetic value using genome-wide dense marker maps . Genetics, 2001,157(4):1819-1829.
[16]	SAMORE A B, FONTANESI L . Genomic selection in pigs: state of the art and perspectives. Italian Journal of Animal Science, 2016,15(2):211-232. doi: 10.1080/1828051X.2016.1172034
[17]	周磊, 杨华威, 赵祖凯, 杨红杰, 刘剑锋 . 基因组选择在我国种猪育种中应用的探讨. 中国畜牧杂志, 2018,54(3):4-8.
	ZHOU L, YANG H W, ZHAO Z K, YANG H J, LIU J F . The Application of Genomic Selection in Chinese Pig Breeding Industry. Chinese Journal of Animal Science. 2018,54(3):4-8.(in Chinese)
[18]	CHANG C C, CHOW C C, TELLIER L C, VATTIKUTI S, PURCELL S M, LEE J J . Second-Generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience, 2015,4(1):4-7.
[19]	BROWNING B L, BROWNING S R . A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. American Journal of Human Genetics, 2009,84(2):210-223. doi: 10.1016/j.ajhg.2009.01.005
[20]	MATHUR P K, SSLLIVAN B P, CHESNAIS J P . Measuring connectedness: concept and application to a large industry breeding program. Ottawa, Canada: Canadian Centre for Swine Improvement, 2002.
[21]	MAYHUR P K, SULLIVAN B, CHESNAIS J . Estimation of the degree of connectedness between herds or management groups in the Canadian swine population. Ottawa, Canada: Canadian Centre for Swine Improvement, 1999.
[22]	张越, 杨宇泽, 张金鑫, 张锁宇, 邱小田, 唐韶青, 丁向东 . DMU、GBS和Herdsman 3个软件育种值估计的比较. 中国畜牧杂志, 2017,53(7):29-35.
	ZHANG Y, YANG Y Z, ZHANG J X, ZHANG S Y, QIU X T, TANG S Q, DING X D . Comparison of Breeding Value Estimation by DMU,GBS and Herdsman. Chinese Journal of Animal Science, 2017,53(7):29-35.(in Chinese)
[23]	白俊艳, 杨又兵, 李广录 . GBS育种分析系统在家畜育种学实验教学中的应用. 畜牧与饲料科学, 2018,39(03):97-100.
	BAI J Y, YANG Y B, LI G L . Application of GBS Breeding Analysis System in the Teaching of Livestock Breeding. Animal Husbandry and Feed Science, 2018,39(03):97-100.(in Chinese)
[24]	CHRISTENSEN O F, LUND M S . Genomic prediction when some animals are not genotyped. Genetics Selection Evolution, 2010,42(1):2. doi: 10.1186/1297-9686-42-2
[25]	CHRISTENSEN O F, MADSEN P, NIELSEN B, OSTERSEN T, SU G . Single-step methods for genomic evaluation in pigs. Animal, 2012,6(10):1565-1571. doi: 10.1017/S1751731112000742
[26]	HENDERSON C R . Best linear unbiased estimation and prediction under a selection model. Biometrics, 1975,31(2):423-447. doi: 10.2307/2529430
[27]	BALOCHE G, LEGARRA A, SALLE G, LARROQUE H, ASTRUC J M, ROBERT-GRANIE C, BARILLET F . Assessment of accuracy of genomic prediction for French Lacaune dairy sheep. Journal of Dairy Science, 2014,97(2):1107-1116. doi: 10.3168/jds.2013-7135
[28]	ZHONG S Q, DEKKERS J C, FERNANDO R L, JANNINK J L . Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics, 2009,182(1):355-364. doi: 10.1534/genetics.108.098277
[29]	MEUWISSEN T H . Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genetics Selection Evolution, 2009: 35-41.
[30]	CALUS M P, MEUWISSEN T H ,DE ROOS A P W, VEERKAMP R F. Accuracy of genomic selection using different methods to define haplotypes. Genetics, 2008,178(1):553-561. doi: 10.1534/genetics.107.080838
[31]	LUND M S ,ROOS A P D, VRIES A G D, DRUET T, DUCROCQ V, FRITZ S, GUILLAUME F, GULDRANDSEN B, LIU Z, REENTS R, SCHROOTEN C, SEEFRIED F, SU G S. A common reference population from four europeanholstein populations increases reliability of genomic predictions. Genetics Selection Evolution, 2011,43(1):43-51. doi: 10.1186/1297-9686-43-43
[32]	BEN J H, PHILLIP J B, AMANDA C C, VERBYLA K, GODDARD M E . Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution, 2009: 41-51.
[33]	WEBER K L, THALLMAN R M, KEELE J W, SNELLING W M, BENNETT G L ,SMITH T P L, MCDANELD T G, ALLAN M F, EENENNAAM A L V, KUEHN L A. Accuracy of genomic breeding values in multibreed beef cattle populations derived from deregressed breeding values and phenotypes. Journal of Animal Science, 2012,90(12):4177-4190. doi: 10.2527/jas.2011-4586
[34]	GUO X, CHRISTENSEN O F, OSTERSEN T, WANG Y, LUND M S, SU G . Improving genetic evaluation of litter size and piglet mortality for both genotyped and nongenotyped individuals using a single-step method. Journal of Animal Science, 2015,93(2):503-512. doi: 10.2527/jas.2014-8331
[35]	MIAR Y, PLASTOW G S, BRUCE H L , KEMP B . Genomic selection of pork pH in purebred pigs for crossbred performance. 2014: VancouverBC,Canada.
[36]	OU Z, TEMPELMAN R J, STEIBEL J P, ERNST C W, BATES R O, BELLO N M . Genomic prediction accounting for residual heteroskedasticity. G3-Genes Genomes Genetics, 2015(6):1-13.
[37]	YI N, XU S . Bayesian LASSO for quantitative trait loci mapping. Genetics, 2008,179(2):1045-1055. doi: 10.1534/genetics.107.085589
[38]	朱波, 王延晖, 牛红, 陈燕, 张路培, 高会江, 高雪, 李俊雅, 孙少华 . 畜禽基因组选择中贝叶斯方法及其参数优化策略. 中国农业科学, 2014,47(22):4495-4505.
	ZHU B, WANG Y H, NIU H, CHEN Y, ZHANG L P, GAO H J, GAO X, LI J Y, SUN S H . The Strategy of Parameter Optimization of Bayesian Methods for Genomic Selection in Livestock. ScientiaAgriculturaSinica, 2014,47(22):4495-4505.(in Chinese)
[39]	VANRADEN P M . Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 2008,91(11):4414-4423. doi: 10.3168/jds.2007-0980
[40]	AGUILAR I, MISZTAL I, JOHNSON D L, LEGARRA A, TSURUTA S, LAWLOR T J . Hot Topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score1. Journal of Dairy Science, 2010,93(2):743-752. doi: 10.3168/jds.2009-2730

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群体类型 Population type	性状名称 Trait name	北京六马 LM	顺鑫农业 XD	养猪中心 ZX	总计 Total number
参考群体 Reference population	达100 kg体重日龄Age at 100 kg live weight	2260	874	886	4020
	达100 kg活体背膘厚Backfat adjusted to 100 kg	2260	874	886	4020
	总产仔数Total number born	1687	874	854	3415
候选公猪 Candidate population	--	979	259	641	1879

猪场编号Farm Code	BJXD1	BJXD2	BJLM1	BJLM6	BJLM4	BJLM5	BBSCB
BJXD1	1	--	--	--	--	--	--
BJXD2	0.602	1	--	--	--	--	--
BJLM1	0	0	1	--	--	--	--
BJLM6	0	0	0.649	1	--	--	--
BJLM4	0	0	0.874	0.727	1	--	--
BJLM5	0	0	0.681	0.692	0.76	1	--
BBSCB	0	0	0	0	0	0	1

性状 Trait	选择阶段Selection stage	准确性Accuracy	无偏性Unbiasedness
达100 kg体重日龄 Age at 100 kg live weight	第一次基因组选择（公猪去势前） First genomic selection (before castrated)	0.55	0.82
达100 kg体重日龄 Age at 100 kg live weight	第二次基因组选择（性能测定结束） Second genomic selection (end of performance measurement)	0.72	0.96
100 kg活体背膘厚 Backfat adjusted to 100 kg	第一次基因组选择（公猪去势前） First genomic selection (before castrated)	0.62	0.91
100 kg活体背膘厚 Backfat adjusted to 100 kg	第二次基因组选择（性能测定结束） Second genomic selection (end of performance measurement)	0.84	1.00

选择阶段 Selection stage	选择标准 Selection criteria	达100 kg体重日龄 Age at 100 kg live weight	100 kg活体背膘厚 Backfat adjusted to 100 kg	总产仔数 Total number born
公猪去势前 Castration stage	基因组育种值Genomic estimated breeding value	0.65±0.04	0.70±0.03	0.60±0.04
公猪去势前 Castration stage	系谱指数 Pedigree index	0.55±0.03	0.56±0.04	0.41±0.05
测定结束 Performance testing	基因组育种值Genomic estimated breeding value	0.78±0.02	0.84±0.02	0.60±0.05
测定结束 Performance testing	育种值Estimated breeding value	0.70±0.04	0.72±0.04	0.41±0.05

Joint Genomic Selection of Yorkshire in Beijing

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