Genomics-Based Breeding Technology

  • Fasong ZhouEmail author
  • Hang He
  • Haodong Chen
  • Huihui Yu
  • Mathias Lorieux
  • Yuqing He
Part of the Plant Genetics and Genomics: Crops and Models book series (PGG, volume 5)


The completed gnome sequences of rice subspecies, indica and japonica, as well as the characterization of a large number of important trait-related genes, laid a sound foundation for genomics-based breeding. Re-sequencing thousands of rice germplasm accessions by next-generation sequencing technologies provided breeders with enormous amount of sequences for genetic marker development. Additionally, high-throughput marker assays were recently made available to breeders, and subsequently large-scale genotyping became economically and timely feasible. All of these together speeded up the development and implementation of genomics-based breeding. In the foreseeable future, genomics-based breeding will potentially become a common practice in rice variety development. This chapter reviews the recent advances in high-throughput genetic marker development and assay technologies and also provides a few examples of strategies for practicing genomics-based breeding in rice.


Quantitative Trait Locus Quantitative Trait Locus Mapping Genomic Selection Bacterial Blight DArT Marker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Dr. Shunyuan Xiao for helpful comments and discussion. This research is partially supported by China 863 research program (2012AA10A304). We apologize for not being able to cite some of the related publications because of the scope.


  1. 1.
    Godfray HC, Beddington JR, Crute IR et al (2010) Food security: the challenge of feeding 9 billion people. Science 327(5967):812–818PubMedCrossRefGoogle Scholar
  2. 2.
    Jiang Y, Cai Z, Xie W, Long T, Yu H, Zhang Q (2011) Rice functional genomics research: progress and implications for crop genetic improvement. Biotechnol Adv 30(5):1059–1070PubMedCrossRefGoogle Scholar
  3. 3.
    Chen L, Ren J (2004) High-throughput DNA analysis by microchip electrophoresis. Comb Chem High Throughput Screen 7(1):29–43PubMedCrossRefGoogle Scholar
  4. 4.
    Coburn JR, Temnykh SV, Paul EM, McCouch SR (2002) Design and application of microsatellite marker panels for semiautomated genotyping of rice (Oryza sativa L.). Crop Sci 42(6):2092CrossRefGoogle Scholar
  5. 5.
    Blair MW, Hedetale V, McCouch SR (2002) Fluorescent-labeled microsatellite panels useful for detecting allelic diversity in cultivated rice (Oryza sativa L.). Theor Appl Genet 105(2–3):449–457PubMedCrossRefGoogle Scholar
  6. 6.
    Jain S, Jain RK, McCouch SR (2004) Genetic analysis of Indian aromatic and quality rice (Oryza sativa L.) germplasm using panels of fluorescently-labeled microsatellite markers. Theor Appl Genet 109(5):965–977PubMedCrossRefGoogle Scholar
  7. 7.
    Garris AJ, Tai TH, Coburn J, Kresovich S, McCouch S (2005) Genetic structure and diversity in Oryza sativa L. Genetics 169(3):1631–1638PubMedCrossRefGoogle Scholar
  8. 8.
    Pessoa-Filho M, Belo A, Alcochete AA, Rangel PH, Ferreira ME (2007) A set of multiplex panels of microsatellite markers for rapid molecular characterization of rice accessions. BMC Plant Biol 7:23PubMedCrossRefGoogle Scholar
  9. 9.
    Jaccoud D, Peng K, Feinstein D, Kilian A (2001) Diversity arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Res 29(4):E25PubMedCrossRefGoogle Scholar
  10. 10.
    Xie Y, McNally K, Li CY, Leung H, Zhu YY (2006) A high-throughput henomic tool: diversity array technology complementary for rice genotyping. J Integr Plant Biol 48(9):1069–1076CrossRefGoogle Scholar
  11. 11.
    Miller MR, Atwood TS, Eames BF et al (2007) RAD marker microarrays enable rapid mapping of zebrafish mutations. Genome Biol 8(6):R105PubMedCrossRefGoogle Scholar
  12. 12.
    Miller MR, Dunham JP, Amores A, Cresko WA, Johnson EA (2007) Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res 17(2):240–248PubMedCrossRefGoogle Scholar
  13. 13.
    Gupta PK, Rustgi S, Mir RR (2008) Array-based high-throughput DNA markers for crop improvement. Heredity 101(1):5–18PubMedCrossRefGoogle Scholar
  14. 14.
    Zhu T, Salmeron J (2007) High-definition genome profiling for genetic marker discovery. Trends Plant Sci 12(5):196–202PubMedCrossRefGoogle Scholar
  15. 15.
    Winzeler EA, Richards DR, Conway AR et al (1998) Direct allelic variation scanning of the yeast genome. Science 281(5380):1194–1197PubMedCrossRefGoogle Scholar
  16. 16.
    Borevitz J (2006) Genotyping and mapping with high-density oligonucleotide arrays. Methods Mol Biol 323:137–145PubMedGoogle Scholar
  17. 17.
    Borevitz JO, Hazen SP, Michael TP et al (2007) Genome-wide patterns of single-feature polymorphism in Arabidopsis thaliana. Proc Natl Acad Sci U S A 104(29):12057–12062PubMedCrossRefGoogle Scholar
  18. 18.
    Borevitz JO, Liang D, Plouffe D et al (2003) Large-scale identification of single-feature polymorphisms in complex genomes. Genome Res 13(3):513–523PubMedCrossRefGoogle Scholar
  19. 19.
    Hazen SP, Borevitz JO, Harmon FG et al (2005) Rapid array mapping of circadian clock and developmental mutations in Arabidopsis. Plant Physiol 138(2):990–997PubMedCrossRefGoogle Scholar
  20. 20.
    Singer T, Fan Y, Chang HS, Zhu T, Hazen SP, Briggs SP (2006) A high-resolution map of Arabidopsis recombinant inbred lines by whole-genome exon array hybridization. PLoS Genet 2(9):e144PubMedCrossRefGoogle Scholar
  21. 21.
    West MAL, Leeuwen H, Kozik A et al (2006) High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis. Genome Res 16(6):787–795PubMedCrossRefGoogle Scholar
  22. 22.
    Kumar R, Qiu J, Joshi T, Valliyodan B, Xu D, Nguyen HT (2007) Single feature polymorphism discovery in rice. PLoS One 2(3):e284PubMedCrossRefGoogle Scholar
  23. 23.
    Wang J, Yu H, Xie W et al (2010) A global analysis of QTLs for expression variations in rice shoots at the early seedling stage. Plant J 63:1063–1074PubMedCrossRefGoogle Scholar
  24. 24.
    Xie W, Chen Y, Zhou G et al (2009) Single feature polymorphisms between two rice cultivars detected using a median polish method. Theor Appl Genet 119(1):151–164PubMedCrossRefGoogle Scholar
  25. 25.
    Cui XP, Xu J, Asghar R et al (2005) Detecting single-feature polymorphisms using oligonucleotide arrays and robustified projection pursuit. Bioinformatics 21(20):3852–3858PubMedCrossRefGoogle Scholar
  26. 26.
    Potokina E, Druka A, Luo Z, Wise R, Waugh R, Kearsey M (2008) Gene expression quantitative trait locus analysis of 16 000 barley genes reveals a complex pattern of genome-wide transcriptional regulation. Plant J 53(1):90–101PubMedCrossRefGoogle Scholar
  27. 27.
    Banks TWJ, Somers MC, Daryl J (2009) Single-feature polymorphism mapping in bread wheat. Plant Genome 2(2):167CrossRefGoogle Scholar
  28. 28.
    Coram TE, Settles ML, Wang M, Chen X (2008) Surveying expression level polymorphism and single-feature polymorphism in near-isogenic wheat lines differing for the Yr5 stripe rust resistance locus. Theor Appl Genet 117(3):401–411PubMedCrossRefGoogle Scholar
  29. 29.
    Sim SC, Robbins MD, Chilcott C, Zhu T, Francis DM (2009) Oligonucleotide array discovery of polymorphisms in cultivated tomato (Solanum lycopersicum L.) reveals patterns of SNP variation associated with breeding. BMC Genomics 10:466PubMedCrossRefGoogle Scholar
  30. 30.
    Brookes AJ (1999) The essence of SNPs. Gene 234(2):177–186PubMedCrossRefGoogle Scholar
  31. 31.
    Gupta PK, Roy JK, Prasad M (2001) Single nucleotide polymorphisms: a new paradigm for molecular marker technology and DNA polymorphism detection with emphasis on their use in plants. Curr Sci 80(4):524–535Google Scholar
  32. 32.
    Rafalski A (2002) Applications of single nucleotide polymorphisms in crop genetics. Curr Opin Plant Biol 5(2):94–100PubMedCrossRefGoogle Scholar
  33. 33.
    Batley J, Barker G, O’Sullivan H, Edwards KJ, Edwards D (2003) Mining for single nucleotide polymorphisms and insertions/deletions in maize expressed sequence tag data. Plant Physiol 132(1):84–91PubMedCrossRefGoogle Scholar
  34. 34.
    Clark RM, Schweikert G, Toomajian C et al (2007) Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science 317(5836):338–342PubMedCrossRefGoogle Scholar
  35. 35.
    Huang X, Wei X, Sang T et al (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42(11):961–967PubMedCrossRefGoogle Scholar
  36. 36.
    Vignal A, Milan D, SanCristobal M, Eggen A (2002) A review on SNP and other types of molecular markers and their use in animal genetics. Genet Sel Evol 34(3):275–306PubMedCrossRefGoogle Scholar
  37. 37.
    Newton CR, Graham A, Heptinstall LE et al (1989) Analysis of any point mutation in DNA. The amplification refractory mutation system (ARMS). Nucleic Acids Res 17(7):2503–2516PubMedCrossRefGoogle Scholar
  38. 38.
    Neff MM, Turk E, Kalishman M (2002) Web-based primer design for single nucleotide polymorphism analysis. Trends Genet 18(12):613–615PubMedCrossRefGoogle Scholar
  39. 39.
    Tung CW, Zhao K, Wright MH et al (2010) Development of a research platform for dissecting phenotype–genotype associations in rice (Oryza spp.). Rice 3(4):205–217Google Scholar
  40. 40.
    McCouch SR, Zhao K, Wright M et al (2010) Development of genome-wide SNP assays for rice. Breed Sci 60(5):524–535CrossRefGoogle Scholar
  41. 41.
    Deulvot C, Charrel H, Marty A et al (2010) Highly-multiplexed SNP genotyping for genetic mapping and germplasm diversity studies in pea. BMC Genomics 11(1):468PubMedCrossRefGoogle Scholar
  42. 42.
    Kwon SJ, Truco MJ, Hu J (2011) LSGermOPA, a custom OPA of 384 EST-derived SNPs for high-throughput lettuce (Lactuca sativa L.) germplasm fingerprinting. Mol Breed 29(4):887–901Google Scholar
  43. 43.
    Zhao K, Wright M, Kimball J et al (2010) Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome. PLoS One 5(5):e10780PubMedCrossRefGoogle Scholar
  44. 44.
    Yamamoto T, Nagasaki H, Yonemaru J et al (2010) Fine definition of the pedigree haplotypes of closely related rice cultivars by means of genome-wide discovery of single-nucleotide polymorphisms. BMC Genomics 11:267PubMedCrossRefGoogle Scholar
  45. 45.
    Zhou F, Xie W, Yu H, Li J, Zhang Q, Inventors (2012) A rice whole genome-wide SNP array and its application. China Patent 201210055775.X, 5 Mar 2012Google Scholar
  46. 46.
    Bell PA, Chaturvedi S, Gelfand CA et al (2002) SNPstream UHT: ultra-high throughput SNP genotyping for pharmacogenomics and drug discovery. Biotechniques suppl 70–72, 74, 76–77Google Scholar
  47. 47.
    Denomme GA, Van Oene M (2005) High-throughput multiplex single-nucleotide polymorphism analysis for red cell and platelet antigen genotypes. Transfusion 45(5):660–666PubMedCrossRefGoogle Scholar
  48. 48.
    Montpetit A, Phillips MS, Mongrain I, Lemieux R, St-Louis M (2006) High-throughput molecular profiling of blood donors for minor red blood cell and platelet antigens. Transfusion 46(5):841–848PubMedCrossRefGoogle Scholar
  49. 49.
    Meirmans PG, Lamothe M, Perinet P, Isabel N (2007) Species-specific single nucleotide polymorphism markers for detecting hybridization and introgression in poplar. Can J Bot 85(11):1082–1091CrossRefGoogle Scholar
  50. 50.
    De la Vega FM, Lazaruk KD, Rhodes MD, Wenz MH (2005) Assessment of two flexible and compatible SNP genotyping platforms: TaqMan SNP genotyping assays and the SNPlex genotyping system. Mutat Res 573(1–2):111–135PubMedGoogle Scholar
  51. 51.
    Pomeroy R, Duncan G, Sunar-Reeder B et al (2009) A low-cost, high-throughput, automated single nucleotide polymorphism assay for forensic human DNA applications. Anal Biochem 395(1):61–67PubMedCrossRefGoogle Scholar
  52. 52.
    Paux E, Sourdille P, Mackay I, Feuillet C (2011) Sequence-based marker development in wheat: advances and applications to breeding. Biotechnol Adv 30(5):1071–1088PubMedCrossRefGoogle Scholar
  53. 53.
    Martinez-Cruz B, Ziegle J, Sanz P et al (2011) Multiplex single-nucleotide polymorphism typing of the human Y chromosome using TaqMan probes. Investig Genet 2:13PubMedCrossRefGoogle Scholar
  54. 54.
    Jiang Y, Shen H, Liu X et al (2011) Genetic variants at 1p11.2 and breast cancer risk: a two-stage study in Chinese women. PLoS One 6(6):e21563PubMedCrossRefGoogle Scholar
  55. 55.
    Hopp K, Weber K, Bellissimo D, Johnson ST, Pietz B (2010) High-throughput red blood cell antigen genotyping using a nanofluidic real-time polymerase chain reaction platform. Transfusion 50(1):40–46PubMedCrossRefGoogle Scholar
  56. 56.
    Zhao K, Tung CW, Eizenga GC et al (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467PubMedCrossRefGoogle Scholar
  57. 57.
    Ganal MW, Durstewitz G, Polley A et al (2011) A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS One 6(12):e28334PubMedCrossRefGoogle Scholar
  58. 58.
    Mitsui J, Fukuda Y, Azuma K et al (2010) Multiplexed resequencing analysis to identify rare variants in pooled DNA with barcode indexing using next-generation sequencer. J Hum Genet 55(7):448–455PubMedCrossRefGoogle Scholar
  59. 59.
    Craig DW, Pearson JV, Szelinger S et al (2008) Identification of genetic variants using bar-coded multiplexed sequencing. Nat Methods 5(10):887–893PubMedCrossRefGoogle Scholar
  60. 60.
    Cronn R, Liston A, Parks M, Gernandt DS, Shen R, Mockler T (2008) Multiplex sequencing of plant chloroplast genomes using Solexa sequencing-by-synthesis technology. Nucleic Acids Res 36(19):e122PubMedCrossRefGoogle Scholar
  61. 61.
    Huang X, Feng Q, Qian Q et al (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19(6):1068–1076PubMedCrossRefGoogle Scholar
  62. 62.
    Huang X, Zhao Y, Wei X et al (2011) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44(1):32–39PubMedCrossRefGoogle Scholar
  63. 63.
    Ku CS, Loy EY, Salim A, Pawitan Y, Chia KS (2010) The discovery of human genetic variations and their use as disease markers: past, present and future. J Hum Genet 55(7):403–415PubMedCrossRefGoogle Scholar
  64. 64.
    Hattori Y, Nagai K, Furukawa S et al (2009) The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water. Nature 460(7258):1026–1030PubMedCrossRefGoogle Scholar
  65. 65.
    Ammiraju JSS, Luo M, Goicoechea JL et al (2006) The Oryza bacterial artificial chromosome library resource: construction and analysis of 12 deep-coverage large-insert BAC libraries that represent the 10 genome types of the genus Oryza. Genome Res 16(1):140–147PubMedCrossRefGoogle Scholar
  66. 66.
    Ammiraju JSS, Song X, Luo M et al (2010) The Oryza BAC resource: a genus-wide and genome scale tool for exploring rice genome evolution and leveraging useful genetic diversity from wild relatives. Breed Sci 60(5):536–543CrossRefGoogle Scholar
  67. 67.
    Wing R, Kim HR, Goicoechea J et al (2007) The Oryza Map Alignment Project (OMAP): a new resource for comparative genome studies within Oryza [computer program]. Springer, New YorkGoogle Scholar
  68. 68.
    Edwards D, Batley J (2010) Plant genome sequencing: applications for crop improvement. Plant Biotechnol J 8(1):2–9PubMedCrossRefGoogle Scholar
  69. 69.
    Imelfort M, Duran C, Batley J, Edwards D (2009) Discovering genetic polymorphisms in next-generation sequencing data. Plant Biotechnol J 7(4):312–317PubMedCrossRefGoogle Scholar
  70. 70.
    Pop M, Salzberg SL (2008) Bioinformatics challenges of new sequencing technology. Trends Genet 24(3):142–149PubMedCrossRefGoogle Scholar
  71. 71.
    Laitinen RAE, Schneeberger K, Jelly NS, Ossowski S, Weigel D (2010) Identification of a spontaneous frame shift mutation in a nonreference Arabidopsis accession using whole genome sequencing. Plant Physiol 153(2):652–654PubMedCrossRefGoogle Scholar
  72. 72.
    Schneeberger K, Ossowski S, Lanz C et al (2009) SHOREmap: simultaneous mapping and mutation identification by deep sequencing. Nat Methods 6(8):550–551PubMedCrossRefGoogle Scholar
  73. 73.
    Abe A, Kosugi S, Yoshida K et al (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol 30(2):174–178PubMedCrossRefGoogle Scholar
  74. 74.
    Xie W, Feng Q, Yu H et al (2010) Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing. Proc Natl Acad Sci U S A 107:10578–10583PubMedCrossRefGoogle Scholar
  75. 75.
    Yu H, Xie W, Wang J et al (2011) Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers. PLoS One 6:e17595PubMedCrossRefGoogle Scholar
  76. 76.
    Van Tassell CP, Smith TP, Matukumalli LK et al (2008) SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods 5(3):247–252PubMedCrossRefGoogle Scholar
  77. 77.
    Baird NA, Etter PD, Atwood TS et al (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One 3(10):e3376PubMedCrossRefGoogle Scholar
  78. 78.
    Trebbi D, Maccaferri M, de Heer P et al (2011) High-throughput SNP discovery and genotyping in durum wheat (Triticum durum Desf.). Theor Appl Genet 123(4):555–569PubMedCrossRefGoogle Scholar
  79. 79.
    Gore MA, Chia JM, Elshire RJ et al (2009) A first-generation haplotype map of maize. Science 326(5956):1115–1117PubMedCrossRefGoogle Scholar
  80. 80.
    Hyten DL, Cannon SB, Song Q et al (2010) High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence. BMC Genomics 11(1):38PubMedCrossRefGoogle Scholar
  81. 81.
    Seeb JE, Carvalho G, Hauser L, Naish K, Roberts S, Seeb LW (2011) Single-nucleotide polymorphism (SNP) discovery and applications of SNP genotyping in nonmodel organisms. Mol Ecol Resour 11(suppl 1):1–8PubMedCrossRefGoogle Scholar
  82. 82.
    Davey JL, Blaxter MW (2010) RADSeq: next-generation population genetics. Brief Funct Genomics 9(5–6):416–423PubMedGoogle Scholar
  83. 83.
    Emrich SJ, Barbazuk WB, Li L, Schnable PS (2007) Gene discovery and annotation using LCM-454 transcriptome sequencing. Genome Res 17(1):69–73PubMedCrossRefGoogle Scholar
  84. 84.
    Severin AJ, Peiffer GA, Xu WW et al (2010) An integrative approach to genomic introgression mapping. Plant Physiol 154(1):3–12PubMedCrossRefGoogle Scholar
  85. 85.
    Trick M, Long Y, Meng J, Bancroft I (2009) Single nucleotide polymorphism (SNP) discovery in the polyploid Brassica napus using Solexa transcriptome sequencing. Plant Biotechnol J 7(4):334–346PubMedCrossRefGoogle Scholar
  86. 86.
    Ebana K, Yonemaru J, Fukuoka S et al (2010) Genetic structure revealed by a whole-genome single-nucleotide polymorphism survey of diverse accessions of cultivated Asian rice (Oryza sativa L.). Breed Sci 60(4):390–397CrossRefGoogle Scholar
  87. 87.
    Masouleh AK, Waters DLE, Reinke RF, Henry RJ (2009) A high-throughput assay for rapid and simultaneous analysis of perfect markers for important quality and agronomic traits in rice using multiplexed MALDI-TOF mass spectrometry. Plant Biotechnol J 7(4):355–363PubMedCrossRefGoogle Scholar
  88. 88.
    Bai SL, Zhong X, Ma L et al (2007) A simple and reliable assay for detecting specific nucleotide sequences in plants using optical thin-film biosensor chips. Plant J 49(2):354–366PubMedCrossRefGoogle Scholar
  89. 89.
    Wittwer CT, Reed GH, Gundry CN, Vandersteen JG, Pryor RJ (2003) High-resolution genotyping by amplicon melting analysis using LCGreen. Clin Chem 49(6):853PubMedCrossRefGoogle Scholar
  90. 90.
    Borras E, Jurado I, Hernan I et al (2011) Clinical pharmacogenomic testing of KRAS, BRAF and EGFR mutations by high resolution melting analysis and ultra-deep pyrosequencing. BMC Cancer 11:406PubMedCrossRefGoogle Scholar
  91. 91.
    Chen X, Kong F, Wang Q, Li C, Zhang J, Gilbert GL (2011) Rapid detection of isoniazid, rifampin, and ofloxacin resistance in Mycobacterium tuberculosis clinical isolates using high-resolution melting analysis. J Clin Microbiol 49(10):3450–3457PubMedCrossRefGoogle Scholar
  92. 92.
    Dagar V, Chow CW, Ashley DM, Algar EM (2009) Rapid detection of SMARCB1 sequence variation using high resolution melting. BMC Cancer 9:437PubMedCrossRefGoogle Scholar
  93. 93.
    Krypuy M, Newnham GM, Thomas DM, Conron M, Dobrovic A (2006) High resolution melting analysis for the rapid and sensitive detection of mutations in clinical samples: KRAS codon 12 and 13 mutations in non-small cell lung cancer. BMC Cancer 6:295PubMedCrossRefGoogle Scholar
  94. 94.
    Reed GH, Wittwer CT (2004) Sensitivity and specificity of single-nucleotide polymorphism scanning by high-resolution melting analysis. Clin Chem 50(10):1748–1754PubMedCrossRefGoogle Scholar
  95. 95.
    Hofinger BJ, Jing HC, Hammond-Kosack KE, Kanyuka K (2009) High-resolution melting analysis of cDNA-derived PCR amplicons for rapid and cost-effective identification of novel alleles in barley. Theor Appl Genet 119(5):851–865PubMedCrossRefGoogle Scholar
  96. 96.
    Lehmensiek A, Sutherland MW, McNamara RB (2008) The use of high resolution melting (HRM) to map single nucleotide polymorphism markers linked to a covered smut resistance gene in barley. Theor Appl Genet 117(5):721–728PubMedCrossRefGoogle Scholar
  97. 97.
    Dong C, Vincent K, Sharp P (2009) Simultaneous mutation detection of three homoeologous genes in wheat by high resolution melting analysis and mutation surveyor. BMC Plant Biol 9:143PubMedCrossRefGoogle Scholar
  98. 98.
    Li YD, Chu ZZ, Liu XG, Jing HC, Liu YG, Hao DY (2010) A cost-effective high-resolution melting approach using the EvaGreen dye for DNA polymorphism detection and genotyping in plants. J Integr Plant Biol 52(12):1036–1042PubMedCrossRefGoogle Scholar
  99. 99.
    Yu R, Shan X, Wang S et al (2011) A screening method for detecting simple sequence repeat (SSR) polymorphism of Zea mays using high-resolution melting-curve analysis. Afr J Biotechnol 10(73):16443–16447Google Scholar
  100. 100.
    Li J, Wang X, Dong R et al (2011) Evaluation of high-resolution melting for gene mapping in rice. Plant Mol Biol Rep 29(4):979–985Google Scholar
  101. 101.
    Croxford AE, Rogers T, Caligari PD, Wilkinson MJ (2008) High-resolution melt analysis to identify and map sequence-tagged site anchor points onto linkage maps: a white lupin (Lupinus albus) map as an exemplar. New Phytol 180(3):594–607PubMedCrossRefGoogle Scholar
  102. 102.
    Golding BGB, Hee-Jin Jeong HJJ, Jo YDJYD, Soung-Woo Park SWP, Byoung-Cheorl Kang BCK (2010) Identification of Capsicum species using SNP markers based on high resolution melting analysis. Genome 53(12):1029–1040CrossRefGoogle Scholar
  103. 103.
    Wu SB, Wirthensohn MG, Hunt P, Gibson JP, Sedgley M (2008) High resolution melting analysis of almond SNPs derived from ESTs. Theor Appl Genet 118(1):1–14PubMedCrossRefGoogle Scholar
  104. 104.
    Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142:169–196CrossRefGoogle Scholar
  105. 105.
    Steele KA, Edwards G, Zhu J, Witcombe JR (2004) Marker-evaluated selection in rice: shifts in allele frequency among bulks selected in contrasting agricultural environments identify genomic regions of importance to rice adaptation and breeding. Theor Appl Genet 109(6):1247–1260PubMedCrossRefGoogle Scholar
  106. 106.
    Hospital F (2001) Size of donor chromosome segments around introgressed loci and reduction of linkage drag in marker-assisted backcross programs. Genetics 158(3):1363–1379PubMedGoogle Scholar
  107. 107.
    Collard BC, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond B Biol Sci 363(1491):557–572PubMedCrossRefGoogle Scholar
  108. 108.
    Singh S, Sidhu J, Huang N et al (2001) Pyramiding three bacterial blight resistance genes (xa5, xa13 and Xa21) using marker-assisted selection into indica rice cultivar PR106. Theor Appl Genet 102(6):1011–1015CrossRefGoogle Scholar
  109. 109.
    Zhang Q (2007) Strategies for developing Green Super Rice. Proc Natl Acad Sci U S A 104(42):16402–16409PubMedCrossRefGoogle Scholar
  110. 110.
    Peleman JD, van der Voort JR (2003) Breeding by design. Trends Plant Sci 8(7):330–334PubMedCrossRefGoogle Scholar
  111. 111.
    Wang JK, Li HH, Zhang XC et al (2011) Molecular design breeding in crops in China. Acta Agron Sin 37(2):191–201 (In Chinese with English abstract)CrossRefGoogle Scholar
  112. 112.
    Wang J, Wan X, Li H, Pfeiffer WH, Crouch J, Wan J (2007) Application of identified QTL-marker associations in rice quality improvement through a design-breeding approach. Theor Appl Genet 115(1):87–100PubMedCrossRefGoogle Scholar
  113. 113.
    Wang J, Chapman SC, Bonnett DG, Rebetzke GJ, Crouch J (2007) Application of population genetic theory and simulation models to efficiently pyramid multiple genes via marker-assisted selection. Crop Sci 47(2):582–590CrossRefGoogle Scholar
  114. 114.
    Wei X, Liu L, Xu J et al (2010) Breeding strategies for optimum heading date using genotypic information in rice. Mol Breed 25(2):287–298CrossRefGoogle Scholar
  115. 115.
    Qu Z, Li L, Luo J et al (2012) QTL mapping of combining ability and heterosis of agronomic traits in rice backcross recombinant inbred lines and hybrid crosses. PLoS One 7(1):e28463PubMedCrossRefGoogle Scholar
  116. 116.
    Riedelsheimer C, Czedik-Eysenberg A, Grieder C et al (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat Genet 44(2):217–220PubMedCrossRefGoogle Scholar
  117. 117.
    Chen S, Lin XH, Xu CG, Zhang Q (2000) Improvement of bacterial blight resistance of ‘Minghui 63’, an elite restorer line of hybrid rice, by molecular marker-assisted selection. Crop Sci 40(1):239–244CrossRefGoogle Scholar
  118. 118.
    Liu SP, Li X, Wang CY, Li XH, He YQ (2003) Improvement of resistance to rice blast in Zhenshan 97 by molecular marker-aided selection. Acta Bot Sin 45(11):1346–1350Google Scholar
  119. 119.
    Joseph M, Gopalakrishnan S, Sharma RK et al (2004) Combining bacterial blight resistance and Basmati quality characteristics by phenotypic and molecular marker-assisted selection in rice. Mol Breed 13(4):377–387CrossRefGoogle Scholar
  120. 120.
    Zhang Q, Li J, Xue Y, Han B, Deng XW (2008) Rice 2020: a call for an international coordinated effort in rice functional genomics. Mol Plant 1(5):715–719PubMedCrossRefGoogle Scholar
  121. 121.
    Koide Y, Kawasaki A, Telebanco-Yanoria M et al (2010) Development of pyramided lines with two resistance genes, Pish and Pib, for blast disease (Magnaporthe oryzae B. Couch) in rice (Oryza sativa L.). Plant Breed 129(6):670–675CrossRefGoogle Scholar
  122. 122.
    Koide Y, Kobayashi N, Xu D, Fukuta Y (2009) Resistance genes and selection DNA markers for blast disease in rice (Oryza sativa L.). Jpn Agric Res Q 43:255–280CrossRefGoogle Scholar
  123. 123.
    Myint KKM, Fujita D, Matsumura M, Sonoda T, Yoshimura A, Yasui H (2011) Mapping and pyramiding of two major genes for resistance to the brown planthopper (Nilaparvata lugens [Stal]) in the rice cultivar ADR52. Theor Appl Genet 124(3):495–504Google Scholar
  124. 124.
    Wang C, Wen G, Lin X, Liu X, Zhang D (2009) Identification and fine mapping of the new bacterial blight resistance gene, Xa31 (t), in rice. Eur J Plant Pathol 123(2):235–240CrossRefGoogle Scholar
  125. 125.
    Jiang G, Xu C, Tu J, Li X, He Y, Zhang Q (2004) Pyramiding of insect- and disease-resistance genes into an elite indica, cytoplasm male sterile restorer line of rice, ‘Minghui 63’. Plant Breed 123(2):112–116CrossRefGoogle Scholar
  126. 126.
    Zhang J, Li X, Jiang G, Xu Y, He Y (2006) Pyramiding of Xa7 and Xa21 for the improvement of disease resistance to bacterial blight in hybrid rice. Plant Breed 125(6):600–605CrossRefGoogle Scholar
  127. 127.
    Hu J, Li X, Wu C et al (2012) Pyramiding and evaluation of the brown planthopper resistance genes Bph14 and Bph15 in hybrid rice. Mol Breed 29(1):61–69CrossRefGoogle Scholar
  128. 128.
    Liu SP, Li X, Wang ZY, Li XH, He YQ (2003) Gene pyramiding to increase the blast resistance in rice. Mol Plant Breed 1(1):22–26Google Scholar
  129. 129.
    Zhou P, Tan Y, He Y, Xu C, Zhang Q (2003) Simultaneous improvement for four quality traits of Zhenshan 97, an elite parent of hybrid rice, by molecular marker-assisted selection. Theor Appl Genet 106(2):326–331PubMedGoogle Scholar
  130. 130.
    Hu J, Yang CJ, Zhang QL, Gao GJ, He YQ (2011) Resistance of pyramided rice hybrids to brown planthoppers. Chinese Bull Entomol 48(5):1341–1347 (In Chinese with English abstract)Google Scholar
  131. 131.
    Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829PubMedGoogle Scholar
  132. 132.
    Kolbehdari D, Schaeffer LR, Robinson JA (2007) Estimation of genome-wide haplotype effects in half-sib designs. J Anim Breed Genet 124(6):356–361PubMedCrossRefGoogle Scholar
  133. 133.
    Hoerl AE, Kennard RW (2000) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 42(1):80–86CrossRefGoogle Scholar
  134. 134.
    Bennewitz J, Solberg T, Meuwissen T (2009) Genomic breeding value estimation using nonparametric additive regression models. Genet Sel Evol 41(1):20PubMedCrossRefGoogle Scholar
  135. 135.
    Long N, Gianola D, Rosa GJ, Weigel KA, Avendano S (2007) Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J Anim Breed Genet 124(6):377–389PubMedCrossRefGoogle Scholar
  136. 136.
    Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9(2):166–177PubMedCrossRefGoogle Scholar
  137. 137.
    Habier D, Fernando RL, Dekkers JC (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177(4):2389–2397PubMedGoogle Scholar
  138. 138.
    Zhong S, Dekkers JC, Fernando RL, Jannink JL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study. Genetics 182(1):355–364PubMedCrossRefGoogle Scholar
  139. 139.
    Ober U, Ayroles JF, Stone EA et al (2012) Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster. PLoS Genet 8(5):e1002685PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Fasong Zhou
    • 1
    Email author
  • Hang He
    • 2
  • Haodong Chen
    • 2
  • Huihui Yu
    • 1
  • Mathias Lorieux
    • 3
  • Yuqing He
    • 4
  1. 1.Genomic Breeding, Life Science and Technology Center, China National Seed Group Co. Ltd.WuhanChina
  2. 2.College of Life SciencesPeking UniversityBeijingChina
  3. 3.UMR DIADEInstitut de Recherche pour le DéveloppementMontpellierFrance
  4. 4.National Center of Molecular BreedingHuazhong Agricultural UniversityWuhanChina

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