doi:

DOI: 10.3724/SP.J.1006.2011.00191

Acta Agronomica Sinica (作物学报) 2011/37:2 PP.191-201

Molecular Design Breeding in Crops in China


Abstract:
Molecular design breeding is a highly integrated system built on multiple scientific disciplines and technological areas. It allows the simulation and optimization of the breeding procedure before breeders’ field experiments. Thus the best target genotypes to meet various breeding objectives in various ecological regions, and the most efficient and effective crossing and selection strategies approaching the best target genotypes can be identified. The design breeding greatly increases the predictability in con-ventional breeding, leading to the evolution from “phenotypic breeding by experience” to “genotypic breeding by prediction” and an increased breeding efficiency and effectiveness. Three major steps are involved in design breeding. The first step is to identify genes affecting breeding traits and to study gene and gene interactions, i.e., to seek for the original materials for producing the crop cultivars, which includes establishment of genetic populations, screening of polymorphism markers, construction of linkage maps, phenotypic evaluation and genetic analysis etc. The second step is to determine the target genotypes for various breeding objectives in various ecological regions, i.e., prototype of the final cultivar product, which includes the genotype-to-phenotype prediction based on identified and known gene information, i.e., locations of genes on chromosomes, biochemical pathways and expression networks from genes to traits, their genetic effects on breeding traits, and the interactions between genes. The third step is to identify the most efficient breeding strategies leading to the target genotypes determined in the second step, i.e., a detailed blue chart to produce the designed crop cultivars. Significant progresses have been made in crop molecular design breeding in China in recent years. This paper first summarized major progresses made in the development of novel genetic materials, genetic study of important breeding traits, development and application of breeding simulation tools, application of design breeding, and the platform research and development in molecular design breeding in crops in China. A perspective view of molecular design breeding was given for the near future after reviewing the current research both in China and worldwide. Finally, major research areas relevant to molecular design breeding in China were proposed, among which are prediction methods and tools of genetics and breeding, genetic mating designs and analysis, gene and environment interactions, functional genomics of crops, methods and tools of bioinformatics, technical systems and decision-supported tools. Professional development and education, and team build-ing are essential as well to China’s leading role in crop molecular design breeding in the world.

Key words:Crop,Molecular design breeding,Breeding simulation,Target genotype,Breeding strategy

ReleaseDate:2014-07-21 15:41:21



[1] Allard R W. Principles of Plant Breeding, 2nd edn. New York: John Wiley & Sons, 1999

[2] Bernardo R. Breeding for Quantitative Traits in Plants. Wood-bury, Minnesota: Stemma Press, 2002

[3] Cooper M, Hammer G L. Plant Adaptation and Crop Improve-ment. Wallingford, UK: CAB International, 1996

[4] Zhai H-Q(翟虎渠), Wang J-K(王建康). Applied Quantitative Genetics (应用数量遗传). Beijing: China Agricultural Science and Technology Press, 2007 (in Chinese)

[5] Wang J, Ginkel M, Podlich D, Ye G, Trethowan R, Pfeiffer W, DeLacy I H, Cooper M, Rajaram S. Comparison of two breeding strategies by computer simulation. Crop Sci, 2003, 43: 1764–1773

[6] Peleman J D, Voort J R. Breeding by design. Trends Plant Sci, 2003, 8: 330–334

[7] Wan J-M(万建民). Perspectives of molecular design breeding in crops. Acta Agron Sin (作物学报), 2006, 32(3): 455–462 (in Chinese with English abstract)

[8] Wang J, Wan X, Li H, Pfeiffer W, Crouch J, Wan J. Application of identified QTL-marker associations in rice quality improve-ment through a design breeding approach. Theor Appl Genet, 2007, 115: 87–100

[9] Wan J-M(万建民). Molecular design breeding in super rice. J Shenyang Agric Univ (沈阳农业大学学报), 2007, 38(5): 652–661 (in Chinese with English abstract)

[10] Zhou D-G(周德贵), Zhao Q-Y(赵琼一), Fu C-Y(付崇允), Li H(李宏), Cai X-F(蔡学飞), Luo D(罗达), Zhou S-C(周少川). The next generation sequencing and its effect on the rice molecu-lar design breeding. Mol Plant Breed (分子植物育种), 2008, 6(4): 619–630 (in Chinese with English abstract)

[11] Li Y(黎裕), Wang J-K(王建康), Qiu L-J(邱丽娟), Ma Y-Z(马有志), Li X-H(李新海), Wan J-M(万建民). Crop molecular breed-ing in China: current status and perspectives. Acta Agron Sin (作物学报), 2010, 36(9): 1425–1430 (in Chinese with English abstract)

[12] Mackay T F C, Stone E A, Ayroles J F. The genetics of quantita-tive traits: challenges and prospects. Nat Rev Genet, 2009, 10: 565–577

[13] Wang J, Wan X, Crossa J, Crouch J, Weng J, Zhai H, Wan J. QTL mapping of grain length in rice (Oryza sativa L.) using chromosome segment substitution lines. Genet Res, 2006, 88: 93–104

[14] Li H, Ye G, Wang J. A modified algorithm for the improvement of composite interval mapping. Genetics, 2007, 175: 361–374

[15] Li H, Ribaut J M, Li Z, Wang J. Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in bi-parental populations. Theor Appl Genet, 2008, 116: 243–260

[16] Zhang L, Li H, Li Z, Wang J. Interactions between markers can be caused by the dominance effect of QTL. Genetics, 2008, 180: 1177–1190

[17] Phillips P C. Epistasis: the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet, 2008, 9: 855–867

[18] Wang J-K(王建康). Inclusive composite interval mapping of quantitative trait genes. Acta Agron Sin (作物学报), 2009, 35(2): 239–245 (in Chinese with English abstract)

[19] Kubo T, Aida Y, Nakamura K, Tsunematsu H, Doi K, Yoshimura A. Reciprocal chromosome segment substitution series derived from japonica and indica cross of rice (Oryza sativa L.). Breed Sci, 2002, 52: 319–325

[20] Cowley A W Jr, Roman R J, Jacob H J. Application of chromo-some substitution techniques in gene-function discovery. J Physiol, 2003, 554: 46–55

[21] Wan X Y, Wan J M, Su C C, Wang C M, Shen W B, Li J M, Wang H L, Jiang L, Liu S J, Chen L M, Yasui H, Yoshimura A. QTL detection for eating quality of cooked rice in a population of chromosome segment substitution lines. Theor Appl Genet, 2004, 110: 71–79

[22] Zeng R-Z(曾瑞珍), Shi J-Q(施军琼), Huang C-F(黄朝锋), Zhang Z-M(张泽民), Ding X-H(丁效华), Li W-T(李文涛), Zhang G-Q(张桂权). Development of a series of single segment substitution lines in indica background of rice (Oryza sativa L.). Acta Agron Sin (作物学报), 2006, 32(1): 88–95 (in Chinese with English abstract)

[23] Xu H-S(徐华山), Sun Y-J(孙永建), Zhou H-J(周红菊), Yu S-B(余四斌). Development and characterization of contiguous segment substitution lines with background of an elite restorer line. Acta Agron Sin (作物学报), 2007, 33(6): 979–986 (in Chinese with English abstract)

[24] Li X, Qian Q, Fu Z, Wang Y, Xiong G, Zeng D, Wang X, Liu X, Teng S, Hiroshi F, Yuan M, Luo D, Han B, Li J. Control of tiller-ing in rice. Nature, 2003, 422: 618–621

[25] Wan X Y, Wan J M, Weng J F, Jiang L, Bi J C, Wang C M, Zhai H Q. Stability of QTLs for rice grain dimension and endosperm chalkiness characteristics across eight environments. Theor Appl Genet, 2005, 110: 1334–1346

[26] Wan X Y, Wan J M, Jiang L, Wang J K, Zhai H Q, Weng J F, Wang H L, Lei C H, Wang J L, Zhang X, Cheng Z J, Guo X P. QTL analysis for rice grain length and fine mapping of an identi-fied QTL with stable and major effects. Theor Appl Genet, 2006, 112: 1258–1270

[27] Zhao F-M(赵芳明), Zhang G-Q(张桂权), Zeng R-Z(曾瑞珍), Yang Z-L(杨正林), Zhu H-T(朱海涛), Zhong B-Q(钟秉强), Ling Y-H(凌英华), He G-H(何光华). Additive effects and epistasis effects of QTL for plant height and its components using single segment substitution lines (SSSLs) in rice. Acta Agron Sin (作物学报), 2009, 35(1): 48–56 (in Chinese with Eng-lish abstract)

[28] Zhang Z-M(张泽民), Zhu H-T(朱海涛), Wang J(王江), Chen Z-G(陈兆贵), Liu F(刘芳), Wan X-S(宛新杉), Zhang J-L(张景六), Zhang G-Q(张桂权). Genetic analysis of a more-tiller mu-tant by T-DNA insertion in rice (Oryza sativa L.). Acta Agron Sin (作物学报), 2006, 32(11): 1737–1741 (in Chinese with English abstract)

[29] Wang J-K(王建康), Pfeiffer W H. Principle of simulation model-ing with applications in plant breeding. Sci Agric Sin (中国农业科学), 2007, 40(1): 1–12 (in Chinese with English ab-stract)

[30] Wang J, Ginkel M, Trethowan R, Ye G, DeLacy I H, Podlich D, Cooper M. Simulating the effects of dominance and epistasis on selecting response in the CIMMYT wheat breeding program us-ing QuCim. Crop Sci, 2004, 44: 2006–2018

[31] Wang J, Singh R P, Braun H J, Pfeiffer W H. Investigating the efficiency of the single backcrossing breeding strategy through computer simulation. Theor Appl Genet, 2009, 118: 683–694

[32] Wang J, Eagles H A, Trethowan R, Ginkel M. Using computer simulation of the selection process and known gene information to assist in parental selection in wheat quality breeding. Aust J Agric Res, 2005, 56: 465–473

[33] Wang J, Chapman S C, Bonnett D B, Rebetzke G J, Crouch J. Application of population genetic theory and simulation models to efficiently pyramid multiple genes via marker-assisted selection. Crop Sci, 2007, 47: 580–588

[34] Wang J, Chapman S C, Bonnett D G, Rebetzke G J. Simultane-ous selection of major and minor genes: use of QTL to increase selection efficiency of coleoptile length of wheat (Triticum aestivum L.). Theor Appl Genet, 2009, 119: 65–74

[35] Zhang Q. Strategies for developing Green Super Rice. Proc Natl Acad Sci USA, 2007, 104: 16402–16409

[36] Wei X, Liu L, Xu J, Jiang L, Zhang W, Wang J, Zhai J, Wan J. Breeding strategies for optimum heading date using genotypic information in rice. Mol Breed, 2009, 25: 287–298

[37] Chen L, Zhao Z, Liu X, Liu L, Jiang L, Liu S, Zhang W, Wang Y, Liu Y, Wan J. Marker-assisted breeding of a photoperiod-sensitive male sterile japonica rice with high cross-compatibility with indica rice. Mol Breed, 2010, DOI: 10.1007/s11032-010-9427-z (online published)

[38] Buckler E S, Holland J B, Bradbury P J, Acharya C B, Brown P J, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz J C, Goodman M M, Harjes C, Guill K, Kroon D E, Larsson S, Lepak N K, Li H, Mitchell S E, Pressoir G, Peiffer J A, Rosas M O, Rocheford T R, Romay M C, Romero S, Salvo S, Villeda, H S, Silva H S, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu J, Zhang Z, Kresovich S, McMullen M D. The genetic architecture of maize flowering time. Science, 2009, 325: 714–718

[39] McMullen M D, Kresovich S, Villeda H S, Bradbury P J, Li H, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms C, Brown P, Browne C, Eller M, Guill K, Harjes C, Kroon D, Lepak N, Mitchell S E, Peterson B, Pressoir G, Romero S, Rosas M O, Salvo S, Yates H, Hanson M, Jones E, Smith S, Glaubitz J C, Goodman M, Ware D, Holland J B, Buckler E S. Genetic proper-ties of the maize nested association mapping population. Science, 2009, 325: 737–740

[40] The Complex Trait Consortium. The collaborative cross, a com-munity resource for the genetic analysis of complex traits. Nat Genet, 2004, 36: 1133–1137

[41] Hopspital F, Chevalet C, Mulsant P. Using markers in gene intro-gression breeding programs. Genetics, 1992, 132: 1199–1210

[42] Frisch M, Bohn M, Melchinger A E. Comparison of selection strategies for marker-assisted backcrossing of a gene. Crop Sci, 1999, 39: 1295–1301

[43] Frisch M, Melchinger A E. Marker-assisted backcrossing for si-multaneous introgression of two genes. Crop Sci, 2001, 41: 1716–172

[44] Frisch M, Melchinger A E. Selection theory for marker-assisted backcrossing. Genetics, 2005, 170: 909–917

[45] Prigge V, Melchinger A E, Dhillon B S, Frisch M. Efficiency gain of marker-assisted backcrossing by sequentially increasing marker densities over generations. Theor Appl Genet, 2009, 119: 23–32

[46] Bernardo R, Charcosset A. Usefulness of gene information in marker-assisted recurrent selection: a simulation appraisal. Crop Sci, 2006, 46: 614–621

[47] Bernardo R, Moreau L, Charcosset A. Number and fitness of se-lected individuals in marker-assisted and phenotypic recurrent selection. Crop Sci, 2006, 46: 1972–1980

[48] Lorenzana R E, Bernardo R. Accuracy of genotypic value predic-tions for marker-based selection in biparental plant populations. Theor Appl Genet, 2009, 120: 151–161

[49] Mayor P J, Bernardo R. Genomewide selection and marker-assisted recurrent selection in doubled haploid versus F2 populations. Crop Sci, 2009, 49: 1719–1725

[50] Bernardo R, Yu J. Prospects for genomewide selection for quan-titative traits in maize. Crop Sci, 2007, 47: 1082–1090

[51] Wong C K, Bernardo R. Genomewide selection in oil palm: in-creasing selection gain per unit time and cost with small popula-tions. Theor Appl Genet, 2008, 116: 815–824

[52] Bernardo R. Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci, 2008, 48: 1649–1664

[53] Heffner E L, Sorrells M E, Jannink J L. Genomic selection for crop improvement. Crop Sci, 2009, 49: 1–12

[54] Meuwissen T H E, Hayes B J, Goddard M E. Prediction of total genetic value using genome-wide dense marker maps. Ge-netics, 2001, 157: 1819–1829

[55] Servin B, Martin O C, Mezard M, Hospital F. Toward a theory of marker-assisted pyramiding. Genetics, 2004, 168: 513–523

[56] Schaeffer L R. Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet, 2006, 123: 218–223

[57] Piyasatian N, Fernando R L, Dekkers J C M. Genomic selection for marker-assisted improvement in line crosses. Theor Appl Genet, 2007, 115: 665–674

[58] de Roos A P, Schrooten C, Mullaart E, Calus M P, Veerkamp R F. Breeding value estimation for fat percentage using dense markers on Bos taurus autosome 14. J Dairy Sci, 2007, 90: 4821–4829

[59] Solberg T R, Sonesson A K, Woolliams J A, Meuwissen T H E. Genomic selection using different marker types and densities. J Anim Sci, 2008, 86: 2447–2454

[60] Habier D, Fernando R L, Dekkers J C M. Genomic selection us-ing low-density marker panels. Genetics, 2009, 182: 343–353

[61] Bernardo R. Genomewide selection for rapid introgression of ex-otic germplasm in maize. Crop Sci, 2009, 49: 419–425

[62] Hayes B J, Bowman P J, Chamberlain A J, Goddard M E. Ge-nomic selection in dairy cattle: progress and challenges. J Dairy Sci, 2009, 92: 433–443

[63] Muir W M. Comparison of genomic and traditional BLUP-esti-mated breeding value accuracy and selection response under al-ternative trait and genomic parameters. J Anim Breed Genet, 2007, 124: 342–355

[64] Goddard M E. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica, 2008, 136: 245–257

[65] Zhong S, Dekkers J C M, Fernando R L, Jannink J L. Factors af-fecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics, 2009, 182: 355–364

[66] Metzker M L. Sequencing technologies: the next generation. Nat Rev Genet, 2010, 11: 31–46

[67] Salathia N, Lee H N, Sangster T A, Morneau K, Landry C R, Schellenberg K, Behere A S, Gunderson K L, Cavalieri D, Jander G, Queitsch C. Indel arrays: an affordable alternative for geno-typing. Plant J, 2007, 51: 727–737

[68] Hawkins R D, Hon G C, Ren B. Next-generation genomics: an integrative approach. Nat Rev Genet, 2010, 11: 476–486

[69] Thomas D. Gene-environment-wide association studies: emerg-ing approaches. Nat Rev Genet, 2010, 11: 259–272

[70] Pastinen T. Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet, 2010, 11: 533–538

[71] Cooper M, Podlich D W, Smith O S. Gene-to-phenotype and complex trait genetics. Aust J Agric Res, 2005, 56: 895–918

[72] Houle D, Govindaraju D R, Omholt S. Phenomics: the next chal-lenge. Nat Rev Genet, 2010, 11: 855–866

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