全基因组选择模型研究进展及展望
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摘要
全基因组选择是一种利用覆盖全基因组的高密度标记进行选择育种的新方法,可通过早期选择缩短世代间隔,提高育种值估计准确性等加快遗传进展,尤其对低遗传力、难测定的复杂性状具有较好的预测效果,真正实现了基因组技术指导育种实践。随着芯片和测序技术日趋成熟,高密度标记芯片检测成本不断降低,全基因组选择模型的不断升级和优化,预测准确性不断提高,全基因组选择已成为动物遗传改良的重要手段和研究热点。目前,全基因组选择已经成为奶牛遗传评估的标准方法,并取得重要进展,在其它物种中的应用正在逐步开展。本文主要对全基因组选择的统计模型发展进行综述,总结全基因组选择在动物遗传育种中的应用现状,讨论当前存在的问题,并对全基因组选择模型的发展方向和应用前景进行展望。
Genomic selection(GS)is a new approach for selection breeding using high-density single nucleotide polymorphisms(SNPs)across the whole genome,which can shorten the generation interval by early selection,accelerate genetic progress by improving the accuracy of breeding value estimation.GS has a good predictive effect,especially for the complex traits with low heritability and difficult to be measured,and which truly realizes the genomic technology guiding breeding program.With the development of chip and sequencing technology,the detection cost of high-density SNP chips is continuously reduced,the genome-wide selection model is continuously upgraded and optimized,and the prediction accuracy is continuously improved,the whole genome selection has become an important means and research hotspot in animal genetic improvement.At present,genomic selection has made significant progress and becomes a standard method for genetic evaluation in dairy cattle,and its application in other species is being carried out.In this paper,we reviewed the statistical models of genomic selection,summarized its applications in animal breeding,discussed the existing problems,and prospected its development direction and application prospects.
Genomic selection(GS)is a new approach for selection breeding using high-density single nucleotide polymorphisms(SNPs)across the whole genome,which can shorten the generation interval by early selection,accelerate genetic progress by improving the accuracy of breeding value estimation.GS has a good predictive effect,especially for the complex traits with low heritability and difficult to be measured,and which truly realizes the genomic technology guiding breeding program.With the development of chip and sequencing technology,the detection cost of high-density SNP chips is continuously reduced,the genome-wide selection model is continuously upgraded and optimized,and the prediction accuracy is continuously improved,the whole genome selection has become an important means and research hotspot in animal genetic improvement.At present,genomic selection has made significant progress and becomes a standard method for genetic evaluation in dairy cattle,and its application in other species is being carried out.In this paper,we reviewed the statistical models of genomic selection,summarized its applications in animal breeding,discussed the existing problems,and prospected its development direction and application prospects.
引文
[1]MEUWISSEN T,HAYES B,GODDARD M.Genomic selection:A paradigm shift in animal breeding[J].Anim Front,2016,6(1):6-14.
[2]VISSCHER P M,WRAY N R,ZHANG Q,et al.10years of GWAS discovery:biology,function,and translation[J].Am J Hum Genet,2017,101(1):5-22.
[3]MEUWISSEN T H E,HAYES B J,GODDARD ME.Prediction of total genetic value using genome-wide dense marker maps[J].Genetics,2001,157(4):1819-1829.
[4]HAYES B J,BOWMAN P J,CHAMBERLAIN A J,et al.Invited review:Genomic selection in dairy cattle:progress and challenges[J].J Dairy Sci,2009,92:433-443.
[5]WELLER J I,EZRA E,RON M.Invited review:Aperspective on the future of genomic selection in dairy cattle[J].J Dairy Sci,2017,100(11):8633-8644.
[6]GARCA-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 SA,2016,113(28):E3995-E4004.
[7]王晨,秦珂,薛明,等.全基因组选择在猪育种中的应用[J].畜牧兽医学报,2016,47(1):1-9.WANG C,QIN K,XUE M,et al.Application of genomic selection in swine breeding[J].Acta Veterinaria et Zootechnica Sinica,2016,47(1):1-9.(in Chinese)
[8]SAMORAB,FONTANESI L.Genomic selection in pigs:state of the art and perspectives[J].Ital J Anim Sci,2016,15(2):211-232.
[9]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.
[10]WOLC A,ZHAO H H,ARANGO J,et al.Response and inbreeding from a genomic selection experiment in layer chickens[J].Genet Sel Evol,2015,47:59.
[11]LPEZ M E,NEIRA R,YEZ J M.Applications in the search for genomic selection signatures in fish[J].Front Genet,2015,5:458.
[12]HENDERSON C R.Best linear unbiased estimation and prediction under a selection model[J].Biometrics,1975,31(2):423-447.
[13]LANDE R,THOMPSON R.Efficiency of marker-assisted selection in the improvement of quantitative traits[J].Genetics,1990,124(3):743-756.
[14]MEUWISSEN T.Genomic selection:marker assisted selection on a genome wide scale[J].J Anim Breed Genet,2007,124(6):321-322.
[15]YANG J A,BENYAMIN B,MCEVOY B P,et al.Common SNPs explain a large proportion of the heritability for human height[J].Nat Genet,2010,42(7):565-569.
[16]GODDARD M E,HAYES B J,MEUWISSEN T HE.Using the genomic relationship matrix to predict the accuracy of genomic selection[J].J Anim Breed Genet,2011,128(6):409-421.
[17]ZHANG Z,ZHANG Q,DING X D.Advances in genomic selection in domestic animals[J].Chin Sci Bull,2011,56(25):2655-2663.
[18]MISZTAL I,LEGARRA A.Invited review:efficient computation strategies in genomic selection[J].Animal,2017,11(5):731-736.
[19]VANRADEN P M.Efficient methods to compute genomic predictions[J].J Dairy Sci,2008,91(11):4414-4423.
[20]HBIERA D,FERNANDO R L,DEKKERS J C M.The impact of genetic relationship information on genome-assisted breeding values[J].Genetics,2007,177(4):2389-2397.
[21]MUIR W M.Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters[J].J Anim Breed Genet,2007,124(6):342-355.
[22]GODDARD M E,KEMPER K E,MACLEOD I M,et al.Genetics of complex traits:prediction of phenotype,identification of causal polymorphisms and genetic architecture[J].Proc Biol Sci,2016,283:20160569.
[23]YANG J,ZENG J,GODDARD M E,et al.Concepts,estimation and interpretation of SNP-based heritability[J].Nat Genet,2017,49(9):1304-1310.
[24]ZHANG Z,LIU J F,DING X D,et al.Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix[J].PLoS One,2010,5(9):e12648.
[25]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.
[26]ZHANG Z,ERBE M,HE J L,et al.Accuracy of whole-genome prediction using agenetic architectureenhanced variance-covariance matrix[J].G3(Bethesda),2015,5(4):615-627.
[27]MOORE J K,MANMATHAN H K,ANDERSON VA,et al.Improving genomic prediction for pre-harvest sprouting tolerance in wheat by weighting large-effect quantitative trait loci[J].Crop Sci,2017,57(3):1315-1324.
[28]SCHRAG T A,WESTHUES M,SCHIPPRACK W,et al.Beyond genomic prediction:combining different types of omics data can improve prediction of hybrid performance in maize[J].Genetics,2018,208(4):1373-1385.
[29]AN N R,LEE S S,PARK J E,et al.Current status of genomic prediction using Multi-omics data in livestock[J].J Biomed Translat Res,2017,18(4):151-156.
[30]HAO X J,ZENG P,ZHANG S J,et al.Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies[J].PLoS Genet,2018,14(1):e1007186.
[31]GAO N,TENG J Y,YE S P,et al.Genomic prediction of complex phenotypes using genic similarity based relatedness matrix[J].Front Genet,2018,9:364.
[32]AGUILAR I,MISZTAL I,JOHNSON D L,et al.Hot topic:A unified approach to utilize phenotypic,full pedigree,and genomic information for genetic evaluation of Holstein final score[J].J Dairy Sci,2010,93(2):743-752.
[33]CHRISTENSEN O F,LUND M S.Genomic prediction when some animals are not genotyped[J].Genet Sel Evol,2010,42:2.
[34]CHRISTENSEN O F,MADSEN P,NIELSEN B,et al.Single-step methods for genomic evaluation in pigs[J].Animal,2012,6(10):1565-1571.
[35]GUO X,CHRISTENSEN O F,OSTERSEN T,et al.Improving genetic evaluation of litter size and piglet mortality for both genotyped and nongenotyped individuals using a single-step method[J].J Anim Sci,2015,93(2):503-512.
[36]MIAR Y.Genomic selection for pork quality and carcass traits in both cross-and pure-bred populations[D].Edmonton,AB,Canada:University of Alberta,2015.
[37]SARUP P,JENSEN J,OSTERSEN T,et al.Increased prediction accuracy using agenomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs[J].BMC Genet,2016,17:11.
[38]SPEED D,BALDING D J.MultiBLUP:improved SNP-based prediction for complex traits[J].Genome Res,2014,24(9):1550-1557.
[39]WEISSBROD O,GEIGER D,ROSSET S.Multikernel linear mixed models for complex phenotype prediction[J].Genome Res,2016,26(7):969-979.
[40]王重龙,丁向东,刘剑锋,等.基因组育种值估计的贝叶斯方法[J].遗传,2014,36(2):111-118.WANG C L,DING X D,LIU J F,et al.Bayesian methods for genomic breeding value estimation[J].Hereditas,2014,36(2):111-118.(in Chinese)
[41]WANG C L,DING X D,WANG J Y,et al.Bayesian methods for estimating GEBVs of threshold traits[J].Heredity,2013,110(3):213-219.
[42]WHITTAKER J C,THOMPSON R,DENHAM MC.Marker-assisted selection using ridge regression[J].Genet Res,2000,75(2):249-252.
[43]GODDARD M.Genomic selection:prediction of accuracy and maximisation of long term response[J].Genetica,2009,136(2):245-257.
[44]HAYES B J,VISSCHER P M,GODDARD M E.Increased accuracy of artificial selection by using the realized relationship matrix[J].Genet Res,2009,91(1):47-60.
[45]HABIER D,FERNANDO R L,KIZILKAYA K,et al.Extension of the bayesian alphabet for genomic selection[J].BMC Bioinformatics,2011,12:186.
[46]YI N J,XU S Z.Bayesian LASSO for quantitative trait loci mapping[J].Genetics,2008,179(2):1045-1055.
[47]ZHOU X,CARBONETTO P,STEPHENS M.Polygenic modeling with bayesian sparse linear mixed models[J].PLoS Genet,2013,9(2):e1003264.
[48]LOGSDON B A,HOFFMAN G E,MEZEY J G.Avariational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis[J].BMCBioinformatics,2010,11:58.
[49]HABIER D,FERNANDO R L,KIZILKAYA K,et al.Extension of the bayesian alphabet for genomic selection[J].BMC Bioinformatics,2011,12:186.
[50]MOSER G,LEE S H,HAYES B J,et al.Simultaneous discovery,estimation and prediction analysis of complex traits using a bayesian mixture model[J].PLoS Genet,2015,11(4):e1004969.
[51]FIKERE M,BARBULESCU D M,MALMBERG MM,et al.Genomic prediction using prior quantitative trait loci information reveals a large reservoir of underutilised blackleg resistance in diverse canola(Brassica napus L.)lines[J].Plant Genome,2018,11:170100.
[52]HAYES B J,CORBET N J,ALLEN J M,et al.Towards multi-breed genomic evaluations for female fertility of tropical beef cattle[J].J Anim Sci,2018,doi:1 0.1093/jas/sky417.
[53]ZENG P,ZHOU X.Non-parametric genetic prediction of complex traits with latent dirichlet process regression models[J].Nat Commun,2017,8:456.
[54]ZENG J,GARRICK D,DEKKERS J,et al.A nested mixture model for genomic prediction using whole-genome SNP genotypes[J].PLoS One,2018,13(3):e0194683.
[55]ZHOU X,STEPHENS M.Efficient multivariate linear mixed model algorithms for genome-wide association studies[J].Nat Methods,2014,11(4):407-409.
[56]WRAY N R,WIJMENGA C,SULLIVAN P F,et al.Common disease is more complex than implied by the core gene omnigenic model[J].Cell,2018,173(7):1573-1580.
[57]张巧霞,张玲妮,刘飞,等.基于GBLUP与惩罚类回归方法的猪血液性状基因组选择研究[J].畜牧兽医学报,2017,48(12):2258-2267.ZHANG Q X,ZHANG L N,LIU F,et al.A study of genomic selection on porcine hematological traits using GBLUP and penalized regression methods[J].Acta Veterinaria et Zootechnica Sinica,2017,48(12):2258-2267.(in Chinese)
[58]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.WANG Y H,ZHU B,LI J Y.Bayesian models including dominant effects for genomic selection[J].Acta Veterinaria et Zootechnica Sinica,2017,48(1):60-67.(in Chinese)
[2]VISSCHER P M,WRAY N R,ZHANG Q,et al.10years of GWAS discovery:biology,function,and translation[J].Am J Hum Genet,2017,101(1):5-22.
[3]MEUWISSEN T H E,HAYES B J,GODDARD ME.Prediction of total genetic value using genome-wide dense marker maps[J].Genetics,2001,157(4):1819-1829.
[4]HAYES B J,BOWMAN P J,CHAMBERLAIN A J,et al.Invited review:Genomic selection in dairy cattle:progress and challenges[J].J Dairy Sci,2009,92:433-443.
[5]WELLER J I,EZRA E,RON M.Invited review:Aperspective on the future of genomic selection in dairy cattle[J].J Dairy Sci,2017,100(11):8633-8644.
[6]GARCA-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 SA,2016,113(28):E3995-E4004.
[7]王晨,秦珂,薛明,等.全基因组选择在猪育种中的应用[J].畜牧兽医学报,2016,47(1):1-9.WANG C,QIN K,XUE M,et al.Application of genomic selection in swine breeding[J].Acta Veterinaria et Zootechnica Sinica,2016,47(1):1-9.(in Chinese)
[8]SAMORAB,FONTANESI L.Genomic selection in pigs:state of the art and perspectives[J].Ital J Anim Sci,2016,15(2):211-232.
[9]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.
[10]WOLC A,ZHAO H H,ARANGO J,et al.Response and inbreeding from a genomic selection experiment in layer chickens[J].Genet Sel Evol,2015,47:59.
[11]LPEZ M E,NEIRA R,YEZ J M.Applications in the search for genomic selection signatures in fish[J].Front Genet,2015,5:458.
[12]HENDERSON C R.Best linear unbiased estimation and prediction under a selection model[J].Biometrics,1975,31(2):423-447.
[13]LANDE R,THOMPSON R.Efficiency of marker-assisted selection in the improvement of quantitative traits[J].Genetics,1990,124(3):743-756.
[14]MEUWISSEN T.Genomic selection:marker assisted selection on a genome wide scale[J].J Anim Breed Genet,2007,124(6):321-322.
[15]YANG J A,BENYAMIN B,MCEVOY B P,et al.Common SNPs explain a large proportion of the heritability for human height[J].Nat Genet,2010,42(7):565-569.
[16]GODDARD M E,HAYES B J,MEUWISSEN T HE.Using the genomic relationship matrix to predict the accuracy of genomic selection[J].J Anim Breed Genet,2011,128(6):409-421.
[17]ZHANG Z,ZHANG Q,DING X D.Advances in genomic selection in domestic animals[J].Chin Sci Bull,2011,56(25):2655-2663.
[18]MISZTAL I,LEGARRA A.Invited review:efficient computation strategies in genomic selection[J].Animal,2017,11(5):731-736.
[19]VANRADEN P M.Efficient methods to compute genomic predictions[J].J Dairy Sci,2008,91(11):4414-4423.
[20]HBIERA D,FERNANDO R L,DEKKERS J C M.The impact of genetic relationship information on genome-assisted breeding values[J].Genetics,2007,177(4):2389-2397.
[21]MUIR W M.Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters[J].J Anim Breed Genet,2007,124(6):342-355.
[22]GODDARD M E,KEMPER K E,MACLEOD I M,et al.Genetics of complex traits:prediction of phenotype,identification of causal polymorphisms and genetic architecture[J].Proc Biol Sci,2016,283:20160569.
[23]YANG J,ZENG J,GODDARD M E,et al.Concepts,estimation and interpretation of SNP-based heritability[J].Nat Genet,2017,49(9):1304-1310.
[24]ZHANG Z,LIU J F,DING X D,et al.Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix[J].PLoS One,2010,5(9):e12648.
[25]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.
[26]ZHANG Z,ERBE M,HE J L,et al.Accuracy of whole-genome prediction using agenetic architectureenhanced variance-covariance matrix[J].G3(Bethesda),2015,5(4):615-627.
[27]MOORE J K,MANMATHAN H K,ANDERSON VA,et al.Improving genomic prediction for pre-harvest sprouting tolerance in wheat by weighting large-effect quantitative trait loci[J].Crop Sci,2017,57(3):1315-1324.
[28]SCHRAG T A,WESTHUES M,SCHIPPRACK W,et al.Beyond genomic prediction:combining different types of omics data can improve prediction of hybrid performance in maize[J].Genetics,2018,208(4):1373-1385.
[29]AN N R,LEE S S,PARK J E,et al.Current status of genomic prediction using Multi-omics data in livestock[J].J Biomed Translat Res,2017,18(4):151-156.
[30]HAO X J,ZENG P,ZHANG S J,et al.Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies[J].PLoS Genet,2018,14(1):e1007186.
[31]GAO N,TENG J Y,YE S P,et al.Genomic prediction of complex phenotypes using genic similarity based relatedness matrix[J].Front Genet,2018,9:364.
[32]AGUILAR I,MISZTAL I,JOHNSON D L,et al.Hot topic:A unified approach to utilize phenotypic,full pedigree,and genomic information for genetic evaluation of Holstein final score[J].J Dairy Sci,2010,93(2):743-752.
[33]CHRISTENSEN O F,LUND M S.Genomic prediction when some animals are not genotyped[J].Genet Sel Evol,2010,42:2.
[34]CHRISTENSEN O F,MADSEN P,NIELSEN B,et al.Single-step methods for genomic evaluation in pigs[J].Animal,2012,6(10):1565-1571.
[35]GUO X,CHRISTENSEN O F,OSTERSEN T,et al.Improving genetic evaluation of litter size and piglet mortality for both genotyped and nongenotyped individuals using a single-step method[J].J Anim Sci,2015,93(2):503-512.
[36]MIAR Y.Genomic selection for pork quality and carcass traits in both cross-and pure-bred populations[D].Edmonton,AB,Canada:University of Alberta,2015.
[37]SARUP P,JENSEN J,OSTERSEN T,et al.Increased prediction accuracy using agenomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs[J].BMC Genet,2016,17:11.
[38]SPEED D,BALDING D J.MultiBLUP:improved SNP-based prediction for complex traits[J].Genome Res,2014,24(9):1550-1557.
[39]WEISSBROD O,GEIGER D,ROSSET S.Multikernel linear mixed models for complex phenotype prediction[J].Genome Res,2016,26(7):969-979.
[40]王重龙,丁向东,刘剑锋,等.基因组育种值估计的贝叶斯方法[J].遗传,2014,36(2):111-118.WANG C L,DING X D,LIU J F,et al.Bayesian methods for genomic breeding value estimation[J].Hereditas,2014,36(2):111-118.(in Chinese)
[41]WANG C L,DING X D,WANG J Y,et al.Bayesian methods for estimating GEBVs of threshold traits[J].Heredity,2013,110(3):213-219.
[42]WHITTAKER J C,THOMPSON R,DENHAM MC.Marker-assisted selection using ridge regression[J].Genet Res,2000,75(2):249-252.
[43]GODDARD M.Genomic selection:prediction of accuracy and maximisation of long term response[J].Genetica,2009,136(2):245-257.
[44]HAYES B J,VISSCHER P M,GODDARD M E.Increased accuracy of artificial selection by using the realized relationship matrix[J].Genet Res,2009,91(1):47-60.
[45]HABIER D,FERNANDO R L,KIZILKAYA K,et al.Extension of the bayesian alphabet for genomic selection[J].BMC Bioinformatics,2011,12:186.
[46]YI N J,XU S Z.Bayesian LASSO for quantitative trait loci mapping[J].Genetics,2008,179(2):1045-1055.
[47]ZHOU X,CARBONETTO P,STEPHENS M.Polygenic modeling with bayesian sparse linear mixed models[J].PLoS Genet,2013,9(2):e1003264.
[48]LOGSDON B A,HOFFMAN G E,MEZEY J G.Avariational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis[J].BMCBioinformatics,2010,11:58.
[49]HABIER D,FERNANDO R L,KIZILKAYA K,et al.Extension of the bayesian alphabet for genomic selection[J].BMC Bioinformatics,2011,12:186.
[50]MOSER G,LEE S H,HAYES B J,et al.Simultaneous discovery,estimation and prediction analysis of complex traits using a bayesian mixture model[J].PLoS Genet,2015,11(4):e1004969.
[51]FIKERE M,BARBULESCU D M,MALMBERG MM,et al.Genomic prediction using prior quantitative trait loci information reveals a large reservoir of underutilised blackleg resistance in diverse canola(Brassica napus L.)lines[J].Plant Genome,2018,11:170100.
[52]HAYES B J,CORBET N J,ALLEN J M,et al.Towards multi-breed genomic evaluations for female fertility of tropical beef cattle[J].J Anim Sci,2018,doi:1 0.1093/jas/sky417.
[53]ZENG P,ZHOU X.Non-parametric genetic prediction of complex traits with latent dirichlet process regression models[J].Nat Commun,2017,8:456.
[54]ZENG J,GARRICK D,DEKKERS J,et al.A nested mixture model for genomic prediction using whole-genome SNP genotypes[J].PLoS One,2018,13(3):e0194683.
[55]ZHOU X,STEPHENS M.Efficient multivariate linear mixed model algorithms for genome-wide association studies[J].Nat Methods,2014,11(4):407-409.
[56]WRAY N R,WIJMENGA C,SULLIVAN P F,et al.Common disease is more complex than implied by the core gene omnigenic model[J].Cell,2018,173(7):1573-1580.
[57]张巧霞,张玲妮,刘飞,等.基于GBLUP与惩罚类回归方法的猪血液性状基因组选择研究[J].畜牧兽医学报,2017,48(12):2258-2267.ZHANG Q X,ZHANG L N,LIU F,et al.A study of genomic selection on porcine hematological traits using GBLUP and penalized regression methods[J].Acta Veterinaria et Zootechnica Sinica,2017,48(12):2258-2267.(in Chinese)
[58]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.WANG Y H,ZHU B,LI J Y.Bayesian models including dominant effects for genomic selection[J].Acta Veterinaria et Zootechnica Sinica,2017,48(1):60-67.(in Chinese)
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