动物基因组选配方法与应用
|
摘要
基因组选择(genomic selection, GS)是利用覆盖基因组的分子标记预测动物个体的估计育种值,可以提高选择的准确度和选择强度,缩短世代间隔,做到早选、准选,使动物育种发生了巨大变革。过去的10多年间,基因组选择技术应用于奶牛等动物的育种中,使种用动物的选择更为准确,遗传进展得到大幅提高。但基因组选择通常重视目标性状的遗传进展,而忽略了配种亲本个体间的遗传关系,因此也没有考虑到后代群体中近交程度的增加、遗传多样性的降低以及有害基因的纯合等问题,因此难以维持长期的遗传进展。2016年,一种具有可持续性的遗传选择方法被正式提出,称为基因组选配(genomicmating,GM)。该方法利用待选种用个体的基因组信息实施优化的选种和选配,可以控制群体近交的增长速率,实现长期且可持续的遗传进展。因此基因组选配方法比基因组选择的方法更适合于现代动物育种,尤其适用于地方品种的保护和遗传改良。本文综述了基因组选配的基本概念、方法和应用,并通过模拟的方法比较了6种选配方案的选择效果,旨在为动物育种方法的应用提供参考。
Genomic selection(GS) is a powerful tool which can be used to estimate the breeding value of individual animals by using the molecular markers of the animal's entire genome. GS improves the accuracy and intensity of selection,reduces the interval of generation, and realizes the effects of early accuracy selection contributing to a significant evolution in animal breeding. In the past decade, GS was successfully applied in the genetic improvement of dairy animals with improved selection accuracy and genetic gain of breeding animals. However, GS focuses on the genetic gain of target traits while it ignores the genetic relationship between mating pairs such that it ignores long term genetic merits such as an increase in inbreeding coefficient of offspring population, a decrease of genetic diversity and the homozygous presentation of harmful genes. In 2016, genomic mating(GM) was proposed as a sustainable genetic selection method using genomic information of the breeding candidate individuals to optimize selection and mating with resultant control of the growth rate of population inbreeding coefficient and achieving long-term and sustainable genetic progress. Therefore, GM is more suitable for modern animal breeding than GS, especially for the genetic improvement of indigenous species. In this review,we summarize the basic concepts, methods, and applications of GM, and then present examples comparing the effects of six simulated mating schemes. This review serves as a valuable reference for the applications of animal breeding methods.
Genomic selection(GS) is a powerful tool which can be used to estimate the breeding value of individual animals by using the molecular markers of the animal's entire genome. GS improves the accuracy and intensity of selection,reduces the interval of generation, and realizes the effects of early accuracy selection contributing to a significant evolution in animal breeding. In the past decade, GS was successfully applied in the genetic improvement of dairy animals with improved selection accuracy and genetic gain of breeding animals. However, GS focuses on the genetic gain of target traits while it ignores the genetic relationship between mating pairs such that it ignores long term genetic merits such as an increase in inbreeding coefficient of offspring population, a decrease of genetic diversity and the homozygous presentation of harmful genes. In 2016, genomic mating(GM) was proposed as a sustainable genetic selection method using genomic information of the breeding candidate individuals to optimize selection and mating with resultant control of the growth rate of population inbreeding coefficient and achieving long-term and sustainable genetic progress. Therefore, GM is more suitable for modern animal breeding than GS, especially for the genetic improvement of indigenous species. In this review,we summarize the basic concepts, methods, and applications of GM, and then present examples comparing the effects of six simulated mating schemes. This review serves as a valuable reference for the applications of animal breeding methods.
引文
[1]Gianola D,Rosa GJ.One hundred years of statistical developments in animal breeding.Annu Rev Anim Biosci,2014,3:19-56.
[2]Henderson C R.Best linear unbiased estimation and prediction under a selection model.Biometrics,1975,31(2):423-447.
[3]Vergara OD,Elzo MA,Cerón-Mu?oz MF.Genetic parameters and genetic trends for age at first calving and calving interval in an angus-blanco orejinegro-zebu multibreed cattle population in colombia.Livest Sci,2009,126(1):318-322.
[4]Aguilar I,Misztal I,Johnson DL,Legarra A,Tsuruta S,Lawlor TJ.Hot topic:a unified approach to utilize phenotypic,full pedigree,and genomic information for genetic evaluation of Holstein final score.J dairy sci,2010,93(2):743-752
[5]Caetano SL,Savegnago RP,Boligon AA,Ramos SB,Chud TCS,L?bo RB,Munari DP.Estimates of genetic parameters for carcass,growth and reproductive traits in nellore cattle.Livest Sci,2013,155(1):1-7.
[6]Lopes FB,da Silva MC,Magnabosco CU,Goncalves Narciso MG,Sainz RD.Selection indices and multivariate analysis show similar results in the evaluation of growth and carcass traits in beef cattle.PLoS One,2016,11(1):e0147180.
[7]Meuwissen TH,Hayes BJ,Goddard ME.Prediction of total genetic value using genome-wide dense marker maps.Genetics,2001,157(4):1819-1829.
[8]Meuwissen T.Genomic selection:the future of marker assisted selection and animal breeding.In:Proceedings of Electronic forum on biotechnology in food and agriculture.MAS:a fast track to increase genetic gain in plant and animal breeding,Session II:MAS in animals.FAO,Conference 10.University of Turin via L.da Vinci 44,Grugliasco(TO),Italy.2003,54-59.
[9]Goddard ME,Hayes BJ.Genomic selection.J Anim breed Genet,2007,124(6):323-330.
[10]VanRaden PM.Practical implications for genetic modeling in the genomics era.J Dairy Sci,2016,99(3):2405-2412.
[11]Tan C,Bian C,Yang D,Li N,Wu ZF,Hu XX.Application of genomic selection in farm animal breeding.Hereditas(Beijing),2017,39(11):1033-1045.谈成,边成,杨达,李宁,吴珍芳,胡晓湘,李明洲.基因组选择技术在农业动物育种中的应用.遗传,2017,39(11):1033-1045.
[12]Wiggans GR,Cole JB,Hubbard SM,Sonstegard TS.Genomic selection in dairy cattle:The USDA experience.Annu Rev Anim Biosci,2017,5:309-327.
[13]Meuwissen T,Hayes B,Goddard M.Accelerating improvement of livestock with genomic selection.Annu Rev Anim Biosci,2013,1:221-237.
[14]de los Campos G,Hickey JM,Pong-Wong R,Daetwyler HD,Calus MP.Whole-genome regression and prediction methods applied to plant and animal breeding.Genetics,2013,193(2):327-345.
[15]Lourenco DA,Tsuruta S,Fragomeni BO,Masuda Y,Aguilar I,Legarra A,Bertrand JK,Amen TS,Wang L,Moser DW,Misztal I.Genetic evaluation using single-step genomic best linear unbiased predictor in american angus,J Anim Sci,2015,93(6):2653-2662.
[16]Jannink JL.Dynamics of long-term genomic selection.Genet Sel Evol,2010,42:35.
[17]Akdemir D,Beavis W,Fritsche-Neto R,Singh AK,IsidroSánchez J.Multi-objective optimized genomic breeding strategies for sustainable food improvement.Heredity(Edinb),2019,122(5):672-683.
[18]De Beukelaer H,Badke Y,Fack V,De Meyer G.Moving beyond managing realized genomic relationship in longterm genomic selection.Genetics,2017,206(2):1127-1138.
[19]Wray NR,Goddard ME.Increasing long-term response to selection.Genet Sel Evol,1994,26(5):431-451.
[20]Meuwissen TH.Maximizing the response of selection with a predetermined rate of inbreeding.J Anim Sci,1997,75(4):934-940.
[21]Grundy B,Villanueva B,Woolliams JA.Dynamic selection procedures for constrained inbreeding and their consequences for pedigree development.Genet Res,1998,72(2):159-168.
[22]Grundy B,Villanueva B,Woolliams JA.Dynamic selection for maximizing response with constrained inbreeding in schemes with overlapping generations.Anim Sci,2000,70(3),373-382.
[23]Woolliams JA,Thompson R.A theory of genetic contributions.In:Proceedings of 5th World Congress of Genetics Applied to Livestock Production,University of Guelph,Guelph,Ontario,Canada.1994,25:127-134.
[24]Avenda?o S,Woolliams JA,Villanueva B.Mendelian sampling terms as a selective advantage in optimum breeding schemes with restrictions on the rate of inbreeding.Genet Res,2004,83(1),55-64.
[25]Daetwyler HD,Villanueva B,Bijma P,Woolliams JA.Inbreeding in genome‐wide selection.J Anim Breed Genet,2007,124(6):369-376.
[26]Henryon M,Berg P,S?rensen AC.Animal-breeding schemes using genomic information need breeding plans designed to maximise long-term genetic gains.Livest Sci,2014,166:38-47.
[27]Sonesson AK,Woolliams JA,Meuwissen THE.Genomic selection requires genomic control of inbreeding.Genet Sel Evol,2012,44:27.
[28]Akdemir D,Sánchez JI.Efficient breeding by genomic mating.Front Genet,2016,7:210
[29]Pryce JE,Hayes BJ,Goddard ME.Novel strategies to minimize progeny inbreeding while maximizing genetic gain using genomic information.J Dairy Sci,2012,95(1):377-388.
[30]Jansen GB,Wilton JW.Selecting mating pairs with linear programming techniques.J Dairy Sci,1985,68(5):1302-1305.
[31]Schierenbeck S,Pimentel ECG,Tietze M,K?rte J,Reents R,Reinhardt F,Simianer H,K?nig S.Controlling inbreeding and maximizing genetic gain using semidefinite programming with pedigree-based and genomic relationships.J Dairy Sci,2011,94(12):6143-6152.
[32]Hill WG,Weir BS.Variation in actual relationship as a consequence of mendelian sampling and linkage.Genet Res,2011,93(1):47-64.
[33]Clark SA,Kinghorn BP,Hickey JM,van der Werf JH.The effect of genomic information on optimal contribution selection in livestock breeding programs.Genet Sel Evol,2013,45:44.doi:10.1186/1297-9686-45-44.
[34]Weigel KA,Lin SW.Use of computerized mate selection programs to control inbreeding of holstein and jersey cattle in the next generation.J Dairy Sci,2000,83(4):822-828.
[35]Meuwissen THE.GENCONT:An operational tool for controlling inbreeding in selection and conservation schemes.In:Proceedings of 7th World Congress on Genetics Applied to Livestock Production.Montpellier,France.2002,CD-ROM communication no 28-20.
[36]Woolliams JA,Berg P,Dagnachew BS,Meuwissen TH.Genetic contributions and their optimization.J Anim Breed Genet,2015,132(2):89-99.
[37]Pong-Wong R,Woolliams JA.Optimisation of contribution of candidate parents to maximise genetic gain and restricting inbreeding using semidefinite programming.Genet Sel Evol,2007,39(1):3-25.
[38]Ahlinder J,Mullin TJ,Yamashita M.Using semidefinite programming to optimize unequal deployment of genotypes to a clonal seed orchard.Tree Genet Genomes,2014,10(1):27-34.
[39]Carvalheiro R,Queiroz SAD,Kinghorn B.Optimum contribution selection using differential evolution.R Bras Zootec,2010,39(7):1429-1436.
[40]Storn R,Price K.Differential evolution a simple and efficient heuristic for global optimization over continuous spaces.J Global Optim,1997,11(4):341-359.
[41]Kinghorn BP.An algorithm for efficient constrained mate selection.Genet Sel Evol,2011,43(1):4.
[42]Mullin TJ,Belotti P.Using branch-and-bound algorithms to optimize selection of a fixed-size breeding population under a relatedness constraint.Tree Genet Genomes,2016,12(1):4.
[43]Goddard M.Genomic selection:prediction of accuracy and maximisation of long term response.Genetica,2009,136(2):245-257.
[44]Sun C,VanRaden PM,O’Connell JR,Weigel KA,Gianola D.Mating programs including genomic relationships and dominance effects.J Dairy Sci,2013,96(12):8014-8023.
[45]Liu H,Henryon M,S?rensen AC.Mating strategies with genomic information reduce rates of inbreeding in animal breeding schemes without compromising genetic gain.Animal,2017,11(4):547-555.
[46]Liu AYH,Woolliams JA.Continuous approximations for optimizing allele trajectories.Genet Res(Camb),2010,92:157-166.
[47]Li B,Leal SM.Methods for detecting associations with rare variants for common diseases:application to analysis of sequence data.Am J Hum Genet,2008,83(3):311-321.
[48]Sargolzaei M,Schenkel FS.QMSim:a large-scale genome simulator for livestock.Bioinformatics,2009,25(5):680-681.
[49]Brito FV,Neto JB,Sargolzaei M,Cobuci JA,Schenkel FS.Accuracy of genomic selection in simulated populations mimicking the extent of linkage disequilibrium in beef cattle.BMC Genet,2011,12(1):80.
[50]Wellmann R.Optimum contribution selection for animal breeding and conservation:the R package optiSel.BMCBioinformatics,2019,20(1):25.
[2]Henderson C R.Best linear unbiased estimation and prediction under a selection model.Biometrics,1975,31(2):423-447.
[3]Vergara OD,Elzo MA,Cerón-Mu?oz MF.Genetic parameters and genetic trends for age at first calving and calving interval in an angus-blanco orejinegro-zebu multibreed cattle population in colombia.Livest Sci,2009,126(1):318-322.
[4]Aguilar I,Misztal I,Johnson DL,Legarra A,Tsuruta S,Lawlor TJ.Hot topic:a unified approach to utilize phenotypic,full pedigree,and genomic information for genetic evaluation of Holstein final score.J dairy sci,2010,93(2):743-752
[5]Caetano SL,Savegnago RP,Boligon AA,Ramos SB,Chud TCS,L?bo RB,Munari DP.Estimates of genetic parameters for carcass,growth and reproductive traits in nellore cattle.Livest Sci,2013,155(1):1-7.
[6]Lopes FB,da Silva MC,Magnabosco CU,Goncalves Narciso MG,Sainz RD.Selection indices and multivariate analysis show similar results in the evaluation of growth and carcass traits in beef cattle.PLoS One,2016,11(1):e0147180.
[7]Meuwissen TH,Hayes BJ,Goddard ME.Prediction of total genetic value using genome-wide dense marker maps.Genetics,2001,157(4):1819-1829.
[8]Meuwissen T.Genomic selection:the future of marker assisted selection and animal breeding.In:Proceedings of Electronic forum on biotechnology in food and agriculture.MAS:a fast track to increase genetic gain in plant and animal breeding,Session II:MAS in animals.FAO,Conference 10.University of Turin via L.da Vinci 44,Grugliasco(TO),Italy.2003,54-59.
[9]Goddard ME,Hayes BJ.Genomic selection.J Anim breed Genet,2007,124(6):323-330.
[10]VanRaden PM.Practical implications for genetic modeling in the genomics era.J Dairy Sci,2016,99(3):2405-2412.
[11]Tan C,Bian C,Yang D,Li N,Wu ZF,Hu XX.Application of genomic selection in farm animal breeding.Hereditas(Beijing),2017,39(11):1033-1045.谈成,边成,杨达,李宁,吴珍芳,胡晓湘,李明洲.基因组选择技术在农业动物育种中的应用.遗传,2017,39(11):1033-1045.
[12]Wiggans GR,Cole JB,Hubbard SM,Sonstegard TS.Genomic selection in dairy cattle:The USDA experience.Annu Rev Anim Biosci,2017,5:309-327.
[13]Meuwissen T,Hayes B,Goddard M.Accelerating improvement of livestock with genomic selection.Annu Rev Anim Biosci,2013,1:221-237.
[14]de los Campos G,Hickey JM,Pong-Wong R,Daetwyler HD,Calus MP.Whole-genome regression and prediction methods applied to plant and animal breeding.Genetics,2013,193(2):327-345.
[15]Lourenco DA,Tsuruta S,Fragomeni BO,Masuda Y,Aguilar I,Legarra A,Bertrand JK,Amen TS,Wang L,Moser DW,Misztal I.Genetic evaluation using single-step genomic best linear unbiased predictor in american angus,J Anim Sci,2015,93(6):2653-2662.
[16]Jannink JL.Dynamics of long-term genomic selection.Genet Sel Evol,2010,42:35.
[17]Akdemir D,Beavis W,Fritsche-Neto R,Singh AK,IsidroSánchez J.Multi-objective optimized genomic breeding strategies for sustainable food improvement.Heredity(Edinb),2019,122(5):672-683.
[18]De Beukelaer H,Badke Y,Fack V,De Meyer G.Moving beyond managing realized genomic relationship in longterm genomic selection.Genetics,2017,206(2):1127-1138.
[19]Wray NR,Goddard ME.Increasing long-term response to selection.Genet Sel Evol,1994,26(5):431-451.
[20]Meuwissen TH.Maximizing the response of selection with a predetermined rate of inbreeding.J Anim Sci,1997,75(4):934-940.
[21]Grundy B,Villanueva B,Woolliams JA.Dynamic selection procedures for constrained inbreeding and their consequences for pedigree development.Genet Res,1998,72(2):159-168.
[22]Grundy B,Villanueva B,Woolliams JA.Dynamic selection for maximizing response with constrained inbreeding in schemes with overlapping generations.Anim Sci,2000,70(3),373-382.
[23]Woolliams JA,Thompson R.A theory of genetic contributions.In:Proceedings of 5th World Congress of Genetics Applied to Livestock Production,University of Guelph,Guelph,Ontario,Canada.1994,25:127-134.
[24]Avenda?o S,Woolliams JA,Villanueva B.Mendelian sampling terms as a selective advantage in optimum breeding schemes with restrictions on the rate of inbreeding.Genet Res,2004,83(1),55-64.
[25]Daetwyler HD,Villanueva B,Bijma P,Woolliams JA.Inbreeding in genome‐wide selection.J Anim Breed Genet,2007,124(6):369-376.
[26]Henryon M,Berg P,S?rensen AC.Animal-breeding schemes using genomic information need breeding plans designed to maximise long-term genetic gains.Livest Sci,2014,166:38-47.
[27]Sonesson AK,Woolliams JA,Meuwissen THE.Genomic selection requires genomic control of inbreeding.Genet Sel Evol,2012,44:27.
[28]Akdemir D,Sánchez JI.Efficient breeding by genomic mating.Front Genet,2016,7:210
[29]Pryce JE,Hayes BJ,Goddard ME.Novel strategies to minimize progeny inbreeding while maximizing genetic gain using genomic information.J Dairy Sci,2012,95(1):377-388.
[30]Jansen GB,Wilton JW.Selecting mating pairs with linear programming techniques.J Dairy Sci,1985,68(5):1302-1305.
[31]Schierenbeck S,Pimentel ECG,Tietze M,K?rte J,Reents R,Reinhardt F,Simianer H,K?nig S.Controlling inbreeding and maximizing genetic gain using semidefinite programming with pedigree-based and genomic relationships.J Dairy Sci,2011,94(12):6143-6152.
[32]Hill WG,Weir BS.Variation in actual relationship as a consequence of mendelian sampling and linkage.Genet Res,2011,93(1):47-64.
[33]Clark SA,Kinghorn BP,Hickey JM,van der Werf JH.The effect of genomic information on optimal contribution selection in livestock breeding programs.Genet Sel Evol,2013,45:44.doi:10.1186/1297-9686-45-44.
[34]Weigel KA,Lin SW.Use of computerized mate selection programs to control inbreeding of holstein and jersey cattle in the next generation.J Dairy Sci,2000,83(4):822-828.
[35]Meuwissen THE.GENCONT:An operational tool for controlling inbreeding in selection and conservation schemes.In:Proceedings of 7th World Congress on Genetics Applied to Livestock Production.Montpellier,France.2002,CD-ROM communication no 28-20.
[36]Woolliams JA,Berg P,Dagnachew BS,Meuwissen TH.Genetic contributions and their optimization.J Anim Breed Genet,2015,132(2):89-99.
[37]Pong-Wong R,Woolliams JA.Optimisation of contribution of candidate parents to maximise genetic gain and restricting inbreeding using semidefinite programming.Genet Sel Evol,2007,39(1):3-25.
[38]Ahlinder J,Mullin TJ,Yamashita M.Using semidefinite programming to optimize unequal deployment of genotypes to a clonal seed orchard.Tree Genet Genomes,2014,10(1):27-34.
[39]Carvalheiro R,Queiroz SAD,Kinghorn B.Optimum contribution selection using differential evolution.R Bras Zootec,2010,39(7):1429-1436.
[40]Storn R,Price K.Differential evolution a simple and efficient heuristic for global optimization over continuous spaces.J Global Optim,1997,11(4):341-359.
[41]Kinghorn BP.An algorithm for efficient constrained mate selection.Genet Sel Evol,2011,43(1):4.
[42]Mullin TJ,Belotti P.Using branch-and-bound algorithms to optimize selection of a fixed-size breeding population under a relatedness constraint.Tree Genet Genomes,2016,12(1):4.
[43]Goddard M.Genomic selection:prediction of accuracy and maximisation of long term response.Genetica,2009,136(2):245-257.
[44]Sun C,VanRaden PM,O’Connell JR,Weigel KA,Gianola D.Mating programs including genomic relationships and dominance effects.J Dairy Sci,2013,96(12):8014-8023.
[45]Liu H,Henryon M,S?rensen AC.Mating strategies with genomic information reduce rates of inbreeding in animal breeding schemes without compromising genetic gain.Animal,2017,11(4):547-555.
[46]Liu AYH,Woolliams JA.Continuous approximations for optimizing allele trajectories.Genet Res(Camb),2010,92:157-166.
[47]Li B,Leal SM.Methods for detecting associations with rare variants for common diseases:application to analysis of sequence data.Am J Hum Genet,2008,83(3):311-321.
[48]Sargolzaei M,Schenkel FS.QMSim:a large-scale genome simulator for livestock.Bioinformatics,2009,25(5):680-681.
[49]Brito FV,Neto JB,Sargolzaei M,Cobuci JA,Schenkel FS.Accuracy of genomic selection in simulated populations mimicking the extent of linkage disequilibrium in beef cattle.BMC Genet,2011,12(1):80.
[50]Wellmann R.Optimum contribution selection for animal breeding and conservation:the R package optiSel.BMCBioinformatics,2019,20(1):25.