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Whole-genome strategies for marker-assisted plant breeding

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An Erratum to this article was published on 14 March 2012

Abstract

Molecular breeding for complex traits in crop plants requires understanding and manipulation of many factors influencing plant growth, development and responses to an array of biotic and abiotic stresses. Molecular marker-assisted breeding procedures can be facilitated and revolutionized through whole-genome strategies, which utilize full genome sequencing and genome-wide molecular markers to effectively address various genomic and environmental factors through a representative or complete set of genetic resources and breeding materials. These strategies are now increasingly based on understanding of specific genomic regions, genes/alleles, haplotypes, linkage disequilibrium (LD) block(s), gene networks and their contribution to specific phenotypes. Large-scale and high-density genotyping and genome-wide selection are two important components of these strategies. As components of whole-genome strategies, molecular breeding platforms and methodologies should be backed up by high throughput and precision phenotyping and e-typing (environmental assay) with strong support systems such as breeding informatics and decision support tools. Some basic strategies are discussed in this article, including (1) seed DNA-based genotyping for simplifying marker-assisted selection (MAS), reducing breeding cost and increasing scale and efficiency, (2) selective genotyping and phenotyping, combined with pooled DNA analysis, for capturing the most important contributing factors, (3) flexible genotyping systems, such as genotyping by sequencing and arraying, refined for different selection methods including MAS, marker-assisted recurrent selection and genomic selection (GS), (4) marker-trait association analysis using joint linkage and LD mapping, and (5) sequence-based strategies for marker development, allele mining, gene discovery and molecular breeding.

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Abbreviations

CGIAR:

Consultative Group on International Agricultural Research

CIMMYT:

International Maize and Wheat Improvement Center

DH:

Doubled haploid

eQTL:

Expression quantitative trait locus/loci

GBS:

Genotyping-by-sequencing

GEBV:

Genomic estimated breeding value

GEI:

Genotype-by-environment interaction

GIS:

Geographic information system

GS:

Genomic selection

GWA:

Genome-wide association

HapMap:

Haplotype map

IPPN:

International Plant Phenomics Network

LYCE :

Lycopene epsilon cyclase

LD:

Linkage disequilibrium

MABC:

Marker-assisted backcrossing

MAGIC:

Multiparent advanced generation inter-cross

MARS:

Marker-assisted recurrent selection

MAS:

Marker-assisted selection

mQTL:

Metabolite quantitative trait locus/loci

NAM:

Nested association mapping

NGS:

Next-generation sequencing

QTL:

Quantitative trait locus/loci

pQTL:

Protein quantitative trait locus/loci

phQTL:

Phenotypic quantitative trait locus/loci

PoDA:

Pathways of distinction analysis

RE:

Restriction enzyme

RIL:

Recombinant inbred line

SLB:

Southern corn leaf blight

SNP:

Single nucleotide polymorphism

TILLING:

Targeting induced local lesions IN genomes

TP:

Training population

References

  • Atwell S, Huang YS, Vilhjálmsson BJ, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone AM, Hu TT, Jiang R, Muliyati NW, Zhang X, Amer MA, Baxter I, Brachi B, Chory J, Dean C, Debieu M, de Meaux J, Ecker JR, Faure N, Kniskern JM, Jones JD, Michael T, Nemri A, Roux F, Salt DE, Tang C, Todesco M, Traw MB, Weigel D, Marjoram P, Borevitz JO, Bergelson J, Nordborg M (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465:627–631

    Article  PubMed  CAS  Google Scholar 

  • Bansal V, Libiger O, Torkamani A, Schork NJ (2010) Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 11:773–785

    Article  PubMed  CAS  Google Scholar 

  • Beavis WD (1998) QTL analyses: power, precision and accuracy. In: Paterson AH (ed) Molecular dissection of complex traits. CRC Press, Boca Raton, pp 145–162

    Google Scholar 

  • Bergelson J, Roux F (2010) Towards identifying genes underlying ecologically relevant traits in Arabidopsis thaliana. Nat Rev Genet 11:867–879

    Article  PubMed  CAS  Google Scholar 

  • Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664

    Article  Google Scholar 

  • Bernardo R (2009) Genomewide selection for rapid introgression of exotic germplasm in maize. Crop Sci 49:419–425

    Article  Google Scholar 

  • Bernardo R (2010) Genomewide selection with minimal crossing in self-pollinated crops. Crop Sci 50:624–627

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090

    Article  Google Scholar 

  • Braun R, Buetow K (2011) Pathways of distinction analysis: a new technique for multi–SNP analysis of GWAS data. PLoS Genet 7(6):e1002101

    Article  PubMed  CAS  Google Scholar 

  • Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Guill K, Kroon DE, Larsson S, Lepak NK, Li H, Mitchell SE, Pressoir G, Peiffer JA, Rosas MO, Rocheford TR, Romay MC, Romero S, Salvo S, Villeda HS, Da Silva HS, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu J, Zhang Z, Kresovich S, McMullen MD (2009) The genetic architecture of maize flowering time. Science 325:714–718

    Article  PubMed  CAS  Google Scholar 

  • Charmet G, Robert N, Perretant MR, Gay G, Sourdille P, Groos C, Bernard S, Bernard M (1999) Marker-assisted recurrent selection for cumulating additive and interactive QTLs in recombinant inbred lines. Theor Appl Genet 99:1143–1148

    Article  Google Scholar 

  • Chaudhary RC (2000) Strategies for bridging the yield gap in rice: a regional perspective for Asia. Intl Rice Commun Newsl 49:22–31

    Google Scholar 

  • Chen Y, Lübberstedt T (2010) Molecular basis of trait correlations. Trends Plant Sci 15:454–461

    Article  PubMed  CAS  Google Scholar 

  • Clark AG (2004) The role of haplotypes in candidate gene studies. Genet Epidemiol 27:321–333

    Article  PubMed  Google Scholar 

  • Collins NC, Tardieu F, Tuberosa R (2008) Quantitative trait loci and crop performance under abiotic stress: where do we stand? Plant Physiol 147:469–486

    Article  PubMed  CAS  Google Scholar 

  • Crosbie TM, Eathington SR, Johnson GR, Edwards M, Reiter R, Stark S, Mohanty RG, Oyervides M, Buehler RE, Walker AK, Dobert R, Delannay X, Pershing JC, Hall MA, Lamkey KR (2006) Plant breeding: past, present, and future. In: Lamkey KR and Lee M (eds) Plant breeding: The Arnel R. Hallauer International Symposium. Blackwell, Ames, IA, pp. 3–50

  • Delannay X, McLaren G, Ribaut, JM (2012) Fostering molecular breeding in developing countries. Mol Breed (in press) doi:10.1007/s11032-011-9611-9

  • Den Herder G, Van Isterdael G, Beeckman T, De Smet I (2011) The roots of a new green revolution. Trends Plant Sci 15:600–607

    Article  CAS  Google Scholar 

  • Deschamps S, Campbell MA (2010) Utilization of next-generation sequencing platforms in plant genomics and genetic variant discovery. Mol Breed 25:553–570

    Article  CAS  Google Scholar 

  • Dwivedi SL, Crouch JH, Mackill DJ, Xu Y, Blair MW, Ragot M, Upadhyaya HD, Ortiz R (2007) The molecularization of public sector crop breeding: progress, problems and prospects. Adv Agron 95:163–318

    Article  CAS  Google Scholar 

  • Eathington SR, Crosbie TM, Edwards MD, Reiter RS, Bull JK (2007) Molecular markers in a commercial breeding program. Crop Sci 47(S3): S154–S163

    Google Scholar 

  • Edwards M, Johnson L (1994) RFLPs for rapid recurrent selection. In: Proceedings of symposium on analysis of molecular marker data. American Society of Horticultural Science and Crop Science Society of America, Corvallis, Oregon, pp. 33–40

  • Ehrenreich IM, Torabi N, Jia Y, Kent J, Martis S, Shapiro JA, Gresham D, Caudy AA, Kruglyak L (2010) Dissection of genetically complex traits with extremely large pools of yeast segregants. Nature 464:1039–1042

    Article  PubMed  CAS  Google Scholar 

  • Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6(5):e19379

    Article  PubMed  CAS  Google Scholar 

  • Famoso AN, Zhao K, Clark RT, Tung C-W, Wright MH, Bustamante C, Kochian LV, McCouch SR (2011) Genetic architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS Genet 7(8):e1002221

    Article  PubMed  CAS  Google Scholar 

  • Feng S, Jacobsen SE (2011) Epigenetic modifications in plants: an evolutionary perspective. Curr Opin Plant Biol 14:179–186

    Article  PubMed  CAS  Google Scholar 

  • Ferrier T, Matus JT, Jin J, Riechmann JL (2011) Arabidopsis paves the way: genomic and network analyses in crops. Curr Opin Biotechnol 22:260–270

    Article  PubMed  CAS  Google Scholar 

  • Feuillet C, Leach JE, Rogers J, Schnable PS, Eversole K (2011) Crop genome sequencing: lessons and rationales. Trends Plant Sci 16:77–88

    Article  PubMed  CAS  Google Scholar 

  • Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374

    Article  PubMed  CAS  Google Scholar 

  • Frisch M (2004) Breeding strategies: optimum design of marker-assisted backcross programs. In: Lörz H, Wenzl G (eds) Biotechnology in agriculture and forestry, vol 55., Molecular marker systems in plant breeding and crop improvement. Springer-Verlag, Berlin, pp 319–334

    Google Scholar 

  • Gao S, Martinez C, Skinner DJ, Krivanek AF, Crouch JH, Xu Y (2008) Development of a seed DNA-based genotyping system for marker-assisted selection in maize. Mol Breed 22:477–494

    Article  CAS  Google Scholar 

  • Gao S, Babu R, Lu Y, Martinez C, Hao Z, Krivanek AF, Wang J, Rong T, Crouch J, Xu Y (2011) Revisiting the hetero-fertilization phenomenon in maize. PLoS ONE 6(1):e16101

    Article  PubMed  CAS  Google Scholar 

  • Giovannoni JJ, Wing RA, Ganal MW, Tanksley SD (1991) Isolation of molecular markers from specific chromosome intervals using DNA pools from existing populations. Nucleic Acids Res 19:6553–6558

    Article  PubMed  CAS  Google Scholar 

  • Goddard ME, Hayes BJ (2007) Genomic selection. J Anim Breed Genet 124:323–330

    Article  PubMed  CAS  Google Scholar 

  • Gore MA, Chia JM, Elshire RJ, Sun Q, Ersoz ES, Hurwitz BL, Peiffer JA, McMullen MD, Grills GS, Ross-Ibarra J, Ware DH, Buckler ES (2009) A first-generation haplotype map of maize. Science 326:1115–1117

    Article  PubMed  CAS  Google Scholar 

  • Gupta PK, Langridge P, Mir RR (2010) Marker-assisted wheat breeding: present status and future possibilities. Mol Breed 26:145–161

    Article  Google Scholar 

  • Harjes CE, Rocheford TR, Bai L, Brutnell TP, Kandianis CB, Sowinski SG, Stapleton AE, Vallabhaneni R, Williams M, Wurtzel ET, Yan J, Buckler ES (2008) Natural genetic variation in Lycopene Epsilon Cyclase tapped for maize biofortification. Science 319:330–333

    Article  PubMed  CAS  Google Scholar 

  • Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009) Genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443

    Article  PubMed  CAS  Google Scholar 

  • Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12

    Article  CAS  Google Scholar 

  • Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690

    Article  Google Scholar 

  • Holloway B, Li B (2010) Expression QTLs: applications for crop improvement. Mol Breed 26:381–391

    Article  Google Scholar 

  • Hospital F (2001) Size of donor chromosome segments around introgressed loci and reduction of linkage drag in marker-assisted backcross programs. Genetics 158:1363–1379

    PubMed  CAS  Google Scholar 

  • Hospital F, Charcosset A (1997) Marker-assisted introgression of quantitative trait loci. Genetics 147:1469–1485

    PubMed  CAS  Google Scholar 

  • Hospital F, Chevalet C, Mulsant P (1992) Using markers in gene introgression breeding programs. Genetics 231:1199–1210

    Google Scholar 

  • Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11:855–866

    Article  PubMed  CAS  Google Scholar 

  • Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, Guan J, Fan D, Wang Q, Huang T, Dong G, Sang T, Han B (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19:1068–1076

    Article  PubMed  CAS  Google Scholar 

  • Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967

    Article  PubMed  CAS  Google Scholar 

  • Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q, Li W, Guo Y, Deng L, Zhu C, Fan D, Lu Y, Weng Q, Liu K, Zhou T, Jing Y, Si L, Dong G, Huang T, Lu T, Feng Q, Qian Q, Li J, Bin Han B (2012) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44:32–39

    Article  CAS  Google Scholar 

  • Jansen M, Gilmer F, Biskup B, Nagel KA, Rascher U, Fischbach A, Briem S, Dreissen G, Tittmann S, Braun S, De Jaeger I, Metzlaff M, Schurr U, Scharr H, Walter A (2009a) Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Funct Plant Biol 11:902–914

    Article  CAS  Google Scholar 

  • Jansen RC, Tesson BM, Fu J, Yang Y, McIntyre LM (2009b) Defining gene and QTL networks. Curr Opin Plant Biol 12:241–246

    Article  PubMed  CAS  Google Scholar 

  • Jena KK, Mackill DJ (2008) Molecular markers and their use in marker-assisted selection in rice. Crop Sci 48:1266–1276

    Article  Google Scholar 

  • Johnson GC, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G, Ueda H, Cordell HJ, Eaves IA, Dudbridge F, Twells RC, Payne F, Hughes W, Nutland S, Stevens H, Carr P, Tuomilehto-Wolf E, Tuomilehto J, Gough SC, Clayton DG, Todd JA (2001) Haplotype tagging for the identification of common disease genes. Nat Genet 29:233–237

    Article  PubMed  CAS  Google Scholar 

  • Kim TY, Kim HU, Lee SY (2010a) Data integration and analysis of biological networks. Curr Opin Biotechnol 21:78–84

    Article  PubMed  CAS  Google Scholar 

  • Kim SY, Li Y, Guo Y, Li R, Holmkvist J, Hansen T, Pedersen O, Wang J, Nielsen R (2010b) Design of association studies with pooled or un-pooled next-generation sequencing data. Genet Epidemiol 34:479–491

    Article  PubMed  Google Scholar 

  • Knight J, Sham P (2006) Design and analysis of association studies using pooled DNA from large twin samples. Behav Genet 36:665–677

    Article  PubMed  Google Scholar 

  • Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD, Durrant C, Mott R (2009) A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet 5(7):e1000551

    Article  PubMed  CAS  Google Scholar 

  • Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, Zwonitzer JC, Kresovich S, McMullen MD, Ware D, Balint-Kurti PJ, Holland JB (2011) Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43:63–168

    Article  CAS  Google Scholar 

  • Lai J, Li R, Xu X, Jin W, Xu M et al (2010) Genome-wide patterns of genetic variation among elite maize inbreds. Nat Genet 42:1027–1030

    Article  PubMed  CAS  Google Scholar 

  • Lam HM, Xu X, Liu X, Chen W, Yang G, Wong FL, Li MW, He W, Qin N, Wang B, Li J, Jian M, Wang J, Shao G, Wang J, Sun SSM, Zhang G (2010) Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat Genet 12:1053–1059

    Article  CAS  Google Scholar 

  • Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756

    PubMed  CAS  Google Scholar 

  • Lander ES, Botstein D (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199

    PubMed  CAS  Google Scholar 

  • Lebowitz RL, Soller M, Beckmann JS (1987) Trait-based analysis for the detection of linkage between marker loci and quantitative trait loci in cross between inbred lines. Theor Appl Genet 73:556–562

    Article  Google Scholar 

  • Lee M (1995) DNA markers and plant breeding programs. Adv Agron 55:265–344

    Article  CAS  Google Scholar 

  • Li Y, Wang JK, Qiu LJ, Ma YZ, Li XH, Wan JM (2010) Crop molecular breeding in China: current status and perspectives. Acta Agron Sin 36:1425–1430

    CAS  Google Scholar 

  • Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161

    Article  PubMed  Google Scholar 

  • Lu Y, Zhang SH, Shah T, Xie C, Hao Z, Li X, Farkhari M, Ribaut JM, Cao M, Rong T, Xu Y (2010) Joint linkage–linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize. Proc Natl Acad Sci USA 107:19585–19590

    Article  PubMed  CAS  Google Scholar 

  • Lu Y, Hao Z, Xie C, Crossa J, Araus JL, Gao S, Vivek BS, Magorokosho C, Mugo S, Makumbi D, Taba S, Pan G, Li X, Rong T, Zhang S, Xu Y (2011) Large-scale screening for maize drought resistance using multiple selection criteria evaluated under water-stressed and well-watered environments. Field Crops Res 124:37–45

    Article  Google Scholar 

  • MacGregor S, Zhao ZZ, Henders A, Nicholas MG, Montgomery GW, Visscher PM (2008) Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays. Nucleic Acids Res 36(6):e35

    Article  PubMed  CAS  Google Scholar 

  • Massonnet C, Vile D, Fabre J, Hannah MA, Caldana C, Lisec J, Beemster GTS, Meyer RC, Messerli G, Gronlund JT, Perkovic J, Wigmore E, May S, Bevan MW, Meyer C, Rubio-Díaz S, Weigel D, Micol JL, Buchanan-Wollaston V, Fiorani F, Walsh S, Rinn B, Gruissem W, Hilson P, Hennig L, Willmitzer L, Granier C (2010) Probing the reproducibility of leaf growth and molecular phenotypes: a comparison of three Arabidopsis accessions cultivated in ten laboratories. Plant Physiol 152:2142–2157

    Article  PubMed  CAS  Google Scholar 

  • McMullen MM, Kresovich S, Villeda HS, Bradbury P, 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 SE, Peterson B, Pressoir G, Romero S, Rosas MO, Salvo S, Yates H, Hanson M, Jones E, Smith S, Glaubitz JC, Goodman M, Ware D, Holland JB, Buckler ES (2009) Genetic properties of the maize nested association mapping population. Science 32:737–740

    Article  CAS  Google Scholar 

  • Metzker ML (2010) Sequencing technologies: the next generation. Nat Rev Genet 11:31–46

    Article  PubMed  CAS  Google Scholar 

  • Meuwissen TH (2009) Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genet Sel Evol 41:35

    Article  PubMed  CAS  Google Scholar 

  • Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genomewide dense marker maps. Genetics 157:1819–1829

    PubMed  CAS  Google Scholar 

  • Michelmore RW, Paran I, Kesselli RV (1991) Identification of markers linked to disease resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genome regions using segregating populations. Proc Natl Acad Sci USA 88:9828–9832

    Article  PubMed  CAS  Google Scholar 

  • Mirouze M, Paszkowski J (2011) Epigenetic contribution to stress adaptation in plants. Curr Opin Plant Biol 14:267–274

    Article  PubMed  CAS  Google Scholar 

  • Miura K, Ashikari M, Matsuoka M (2011) The role of QTLs in the breeding of high-yielding rice. Trend Plant Sci 16:319–326

    Article  CAS  Google Scholar 

  • Montes JM, Technow F, Dhillon BS, Mauch F, Melchinger AE (2011) High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res 121:268–273

    Article  Google Scholar 

  • Moreno-Risueno MA, Busch W, Benfey PN (2010) Omics meet networks: using systems approaches to infer regulatory networks in plants. Curr Opin Plant Biol 13:126–131

    Article  PubMed  Google Scholar 

  • Myles S, Peiffer J, Brown PJ, Ersoz ES, Zhang Z, Costich DE, Buckler ES (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21:2194–2202

    Article  PubMed  CAS  Google Scholar 

  • Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87–98

    Article  PubMed  CAS  Google Scholar 

  • Peleman JD, van der Voort JR (2003) Breeding by design. Trends Plant Sci 8:330–334

    Article  PubMed  CAS  Google Scholar 

  • Podlich DW, Winkler CR, Cooper M (2004) Mapping as you go: an effective approach for marker-assisted selection of complex traits. Crop Sci 44:560–1571

    Article  Google Scholar 

  • Prasanna BM, Pixley K, Warburton ML, Xie CX (2010) Molecular marker-assisted breeding options for maize improvement in Asia. Mol Breed 26:339–356

    Article  CAS  Google Scholar 

  • Qiu LJ, Guo Y, Li Y, Wang XB, Zhou GA, Liu ZX, Zhou SR, Li XH, Ma YZ, Wang JK, Wan JM (2011) Novel gene discovery of crops in China: status, challenging, and perspective. Acta Agron Sin 37:1–17

    Article  CAS  Google Scholar 

  • Rafalski JA (2010) Association genetics in crop improvement. Curr Opin Plant Biol 13:1–7

    Article  CAS  Google Scholar 

  • Ragot M, Lee M (2007) Marker-assisted selection in maize: current status, potential, limitations and perspectives from the private and public sectors. In: Guimarães EP et al (eds) Marker-assisted selection, current status and future perspectives in crops, livestock, forestry, and fish. FAO, Rome, pp 117–150

    Google Scholar 

  • Ribaut JM, de Vicente MC, Delannay X (2010) Molecular breeding in developing countries: challenges and perspectives. Curr Opin Plant Biol 13:213–218

    Article  PubMed  Google Scholar 

  • Roy SJ, Tucker EJ, Tester M (2011) Genetic analysis of abiotic stress tolerance in crops. Curr Opin Plant Biol 14:1–8

    Article  CAS  Google Scholar 

  • Rutkoski JE, Heffner EL, Sorrells ME (2011) Genomic selection for durable stem rust resistance in wheat. Euphytica 179:161–173

    Article  Google Scholar 

  • Schmitz RJ, Zhang X (2011) High-throughput approaches for plant epigenomic studies. Curr Opin Plant Biol 14:130–136

    Article  PubMed  CAS  Google Scholar 

  • Schneeberger K, Weigel D (2011) Fast-forward genetics enabled by new sequencing technologies. Trends Plant Sci 16:282–288

    Article  PubMed  CAS  Google Scholar 

  • Stam P (1995) Marker-assisted breeding. In: Van Ooijen JW, Jansen J (eds) Biometrics in plant breeding: Applications of molecular markers. Proceedings of the 9th meeting of EUCARPIA section on biometrics in plant breeding (1994) Centre for plant breeding and reproduction research, Wageningen, Netherlands, pp. 32–44

  • Stam P (2003) Marker-assisted introgression: speed at any cost? In: van Hintum Th. JL, Lebeda A, Pink D, Schut JW (eds) Proceedings of the Eucarpia meeting on leafy vegetables genetics and breeding, 19–21 March 2003, Noordwijkerhout, Netherlands. Centre for Genetic Resources (CGN), Wageningen, Netherlands, pp. 117–124

  • Stuber CW, Moll RH, Goodman MM, Schaffer HE, Weir BS (1980) Allozyme frequency changes associated with selection for increased grain yield in maize (Zea mays). Genetics 95:225–336

    PubMed  CAS  Google Scholar 

  • Sun Y, Wang J, Crouch JH, Xu Y (2010) Efficiency of selective genotyping for genetic analysis of complex traits and potential applications in crop improvement. Mol Breed 26:493–511

    Article  Google Scholar 

  • Tester M, Langridge P (2011) Breeding technologies to increase crop production in a changing world. Science 327:818–822

    Article  CAS  Google Scholar 

  • Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162

    Article  PubMed  CAS  Google Scholar 

  • Till BJ, Comai L, Henikoff S (2007) TILLING and EcoTILLING for crop improvement. In: Varshney RK, Tuberosa R (eds) Genomics-assisted crop improvement, vol 1., Genomic approaches and platforms. Springer, Dordrecht, pp 333–350

    Chapter  Google Scholar 

  • van Berloo R, Stam P (1998) Marker-assisted selection in autogamous RIL populations: a simulation study. Theor Appl Genet 96:147–154

    Article  Google Scholar 

  • van Berloo R, Stam P (2001) Simultaneous marker-assisted selection for multiple traits in autogamous crops. Theor Appl Genet 102:1107–1112

    Article  Google Scholar 

  • Wang CL, Zhang YD, Zhu Z, Chen T, Zhao L, Lin J, Zhou LH (2009) Development of a new japonica rice variety Nanjing 46 with good eating quality by marker assisted selection. Mol Plant Breed 7:1070–1076

    CAS  Google Scholar 

  • Wei X, Liu LL, Xu JF, Jiang L, Zhang WW, Wang JK, Zhai HQ, Wan JM (2009) Breeding strategies for optimum heading date using genotypic information in rice. Mol Breed 25:287–298

    Article  CAS  Google Scholar 

  • Wong CK, Bernardo R (2008) Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116:815–824

    Article  PubMed  CAS  Google Scholar 

  • Xie W, Feng Q, Yu H, Huang X, Zhao Q, Xing Y, Yu S, Han B, Zhang Q (2010) Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing. Proc Natl Acad Sci USA 107:10578–10583

    Article  PubMed  CAS  Google Scholar 

  • Xu Y (1997) Quantitative trait loci: separating, pyramiding, and cloning. Plant Breed Rev 15:85–139

    CAS  Google Scholar 

  • Xu Y (2002) Global view of QTL: rice as a model. In: Kang MS (ed) Quantitative genetics, genomics and plant breeding. Wallingford, UK, CABI Publishing, pp 109–134

    Google Scholar 

  • Xu Y (2003) Developing marker-assisted selection strategies for breeding hybrid rice. Plant Breed Rev 23:73–174

    CAS  Google Scholar 

  • Xu Y (2010) Molecular plant breeding. CAB International, Wallingford, p 734

    Book  Google Scholar 

  • Xu Y, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48:391–407

    Article  Google Scholar 

  • Xu Y, Lu Y, Yan J, Babu R, Hao Z, Gao S, Zhang S, Li J, Vivek BS, Magorokosho C, Mugo S, Makumbi D, Taba S, Palacios N, Guimarães CT, Araus JL, Wang J, Davenport GF, Crossa J, Crouch JH (2009) SNP-chip based genomewide scan for germplasm evaluation and marker-trait association analysis and development of a molecular breeding platform. Proceedings of 14th Australasian plant breeding & 11th Society for the Advancement in Breeding Research in Asia & Oceania Conference, 10 to 14 August 2009, Cairns, Tropical North Queensland, Australia. Distributed by CD RAM

  • Xu Y, Xie, C, Wan J, He Z, Prasanna BM (2012) Marker-assisted selection: strategies and examples from cereals. In: Gupta PK, Varshney RK (eds) Cereal Genomics II. Springer, (in press)

  • Yan J, Kandianis CB, Harjes CE, Bai L, Kim E, Yang X, Skinner D, Fu Z, Mitchell S, Li Q, Fernandez MGS, Zaharieva M, Babu R, Fu Y, Palacios N, Li J, DellaPenna D, Brutnell T, Buckler ES, Warburton ML, Rocheford T (2010) Rare genetic variation at Zea mays crtRB1 increases β-carotene in maize grain. Nat Genet 42:322–327

    Article  PubMed  CAS  Google Scholar 

  • Yu J, Hollan JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551

    Article  PubMed  Google Scholar 

  • Zhong SQ, Dekkers JCM, Fernando RL, Jannink JL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

Genomics and molecular breeding research at CIMMYT, Mexico, and China has been funded by the Rockefeller Foundation, the Bill and Melinda Gates Foundation, and the European Community, and through other attributed or unrestricted funds provided by the members of the Consultative Group on International Agricultural Research (CGIAR) and national governments of USA, Japan, and UK. Research at the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences is supported by the National High Technology Research and Development Program of China and International Collaboration Project, Ministry of Science and Technology of China (2011DFA31140).

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Correspondence to Yunbi Xu.

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An erratum to this article can be found at http://dx.doi.org/10.1007/s11032-012-9724-9.

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Xu, Y., Lu, Y., Xie, C. et al. Whole-genome strategies for marker-assisted plant breeding. Mol Breeding 29, 833–854 (2012). https://doi.org/10.1007/s11032-012-9699-6

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