北京地区大白猪基因组联合育种研究
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摘要
【目的】利用基因组选择技术,进行北京地区大白猪基因组联合遗传评估,并实施基因组选择分子育种,预测刚出生的小公猪基因组估计育种值,提高选种准确性。【方法】利用北京地区3家核心育种场英、美系大白猪2007-2017年场内性能测定记录,筛选4 020头个体构建基因组选择混合参考群,性状包括达100kg体重日龄、100kg活体背膘厚和总产仔数,参考群个体和候选公猪个体基因型信息主要采用Illumina 80K SNP芯片进行测定。基因组育种值采用同时利用系谱信息和基因组信息的一步法(SSGBLUP)方法,对3家核心场猪只进行基因组联合遗传评估,分别在公猪去势前和性能测定结束时预测大白公猪生长性状和繁殖性状基因组估计育种值(GEBV),并进行相应选种。3个场之间的场间遗传联系用关联率衡量。【结果】场间关联率计算结果表明,由于遗传背景差异,北京地区3家核心场场间遗传联系偏低,无法开展传统联合遗传评估,但基于基因组信息的G矩阵亲缘关系结果显示,不同群体间个体存在亲缘关系,说明通过基因组选择可以实现3个育种场间的基因组联合遗传评估。基因组选择实施后,累计基因组预测大白公猪1789头。与传统育种方式相比,基因组选择准确性大幅提高。实施第一次基因组选择或早期选择时(公猪去势前),达100 kg体重日龄、100 kg活体背膘厚和总产仔数系谱指数的准确性分别为0.55,0.56和0.41,而3个性状GEBV的准确性分别为0.65,0.70和0.60,提高了10、14和19个百分点。终选(性能测定结束)时,3个性状的传统育种值(EBV)准确性为0.70、0.72和0.41,GEBV准确性进一步提高至0.78、0.84和0.60,提高了8、12和19个百分点。低遗传力的总产仔数准确性提高幅度最大。公猪去势前初选时基因组选择准确性与常规性能测定结束时的常规育种值选择准确性几乎一致,表明基因组选择早期选种效果与常规育种相当,节省了育种时间和成本。338头完成性能测定的候选公猪两次基因组选择准确性表明,第二次基因组选择由于加入了候选公猪的测定信息,达100kg体重日龄和100kg活体背膘厚的GEBV准确性由第一次的0.55和0.62分别提高到0.72和0.84,提高了17和22个百分点。无偏性系数在0.82-1.00之间,两性状GEBV的无偏性由第一次基因组选择的0.82、0.96分别提高到0.91、1.00,说明第二次估计的偏差更小,结果可信度更高,能更准确选出优秀的种公猪。【结论】基因组选择可以建立场间遗传联系,实现常规育种不能进行的联合遗传评估,能够进行更大范围的联合育种。基因组选择的准确性高于传统的系谱指数和育种值选种,且低遗传力性状提高幅度最大。基因组选择能够实现早期选择,提高育种效益。
【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.
【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.
引文
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[13]GUTIERREZ A P.Genomic selection for improvement of Atlantic salmon aquaculture:[Doctor of Philosophy].University of Concepcion Department 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(Chlamys farreri):[D].Qingdao:Ocean University of China,2015.(in Chinese)
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[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.
[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)
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