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Marker-Aided Breeding Revolutionizes Twenty-First Century Crop Improvement

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Seed Development: OMICS Technologies toward Improvement of Seed Quality and Crop Yield

Abstract

The ever increasing human population always needs more healthy and nutritious food, produced in environmentally sustainable ways. Marker-aided breeding significantly contributes towards this priority goal. Molecular markers are mainly identifiable DNA sequences present in the genome and follow the Mendelian inheritance. In present time, a broad range of molecular markers are available for various crops. Advances in crop genome sequencing, high resolution genetic mapping, and precise phenotyping largely help the discovery of functional alleles and allelic variation associated with traits of interest for plant breeding. This chapter provides a brief overview on DNA markers and their use in crop breeding with examples in rice (as the model for inbreeding species) and maize (as an out-crossing species). Molecular marker-aided breeding undoubtedly speeds the conventional breeding process and makes crop improvement more precise. Availability of physical maps, genomes sequences, and high-throughput technologies will also facilitate in developing new molecular breeding approaches in this twenty-first century.

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References

  • Akbari M, Wenzl P, Caig V, Carling J, Xia L, Yang S, Uszynski G, Mohler V, Lehmensiek A, Kuchel H, Hayden MJ, Howes N, Sharp P, Vanghan P, Rathmell B, Huttner E, Kilian A (2006) Diversity arrays technology (DArT) for high-throughput profiling of the hexaploid wheat genome. Theor Appl Genet 113:1409–1420

    Article  PubMed  CAS  Google Scholar 

  • Alpuerto VLEB, Norton GW, Alwang J, Ismail AM (2009) Economic impact analysis of marker-assisted breeding for tolerance to salinity and phosphorous deficiency in rice. Appl Econ Perspect Pol 31:779–792

    Google Scholar 

  • Angaji SA (2009) QTL mapping: a few key points. Intl J Appl Res Nat Prod 2:1–3

    Google Scholar 

  • Babu R, Nair SK, Prasanna BM, Gupta HS (2004) Integrating marker-assisted selection in crop breeding—prospects and challenges. Curr Sci 87:607–619

    CAS  Google Scholar 

  • Babu R, Nair SK, Kumar A, Venkatesh S, Sekhar JC, Singh NN, Srinivasan G, Gupta HS (2005) Two-generation marker-aided backcrossing for rapid conversion of normal maize lines to quality protein maize (QPM). Theor Appl Genet 111:888–897

    Article  PubMed  CAS  Google Scholar 

  • Bernacchi D, Beck-Bunn T, Emmatty D, Eshed Y, Inai S, Lopez J, Petiard V, Sayama H, Uhlig J, Zamir D, Tanksley S (1998a) Advanced backcross QTL analysis of tomato. II. Evaluation of near-isogenic lines carrying single-donor introgressions for desirable wild QTL-alleles derived from Lycopersicom hirsutum and L. pimpinellifolium. Theor Appl Genet 97:170–180

    Article  CAS  Google Scholar 

  • Bernacchi D, Beck-Bunn T, Eshed Y, Lopez J, Petiard V, Uhlig J, Zamir D, Tanksley S (1998b) Advanced backcross QTL analysis of tomato. I. Identification of QTLs for traits of agronomic importance from Lycopersicom hirsutum. Theor Appl Genet 97:381–397

    Article  CAS  Google Scholar 

  • Bernardo R (2004) What proportion of declared QTL in plants are false? Theor Appl Genet 109:419–424

    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 rapidintrogression of exotic germplasm in maize. Crop Sci 49:419–425

    Article  Google Scholar 

  • Bernardo R, Charcosset A (2004) Usefulness of gene information in marker-assisted recurrent selection: a simulation appraisal. Crop Sci 46:614–621

    Article  Google Scholar 

  • Bink MCAM, Boer MP, ter Braak CJF, Jansen J, Voorrips RE, van de Weg WE (2008) Bayesian analysis of complex traits in pedigreed plant populations. Euphytica 161:85–96

    Article  Google Scholar 

  • Breseghello F, Sorrells ME (2006) Association analysis as a strategy for improvement of quantitative traits in plants. Crop Sci 46:1323–1330

    Article  Google Scholar 

  • Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC, Goodman MM, Harjes C, Guill K, Kroon DE, Larsson S, Lepak NK, Li H, Mitchell SE, Pressoir G, Peiffer JA, Oropeza Rosas M, Rocheford TR, Cinta Romay M, Romero S, Salvo S, Sanchez Villeda H, 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 

  • Campos H, Cooper M, Habben JE, Edmeades GO, Schussler JR (2004) Improving drought tolerance in maize: a view from industry. Field Crops Res 90:19–34

    Google Scholar 

  • Cattivelli C, Rizza F, Badeck FW, Mazzucotelli E, Mastrangelo AM, Francia E, Marè C, Tondelli A, Stanca AM (2008) Drought tolerance improvement in crop plants: an integrated viewfrom breeding to genomics. Field Crops Res 105:1–14

    Google Scholar 

  • Collard BCY, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Phil Trans R Soc B 363:557–572

    Article  PubMed  CAS  Google Scholar 

  • 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 142:169–196

    Article  CAS  Google Scholar 

  • Comai L, Henikoff S (2006) TILLING: practical single-nucleotide mutation discovery. Plant J 45:684–694

    Article  PubMed  CAS  Google Scholar 

  • Comai L, Young K, Till BJ, Reynolds SH, Greene EA, Codomo CA, Enns LC, Johnson JE, Burtner C, Odden AR, Henikoff S (2004) Efficient discovery of DNA polymorphisms in natural populations by Ecotilling. Plant J 37:778–786

    Article  PubMed  CAS  Google Scholar 

  • Cooper M, Smith OS, Graham G, Arthur L, Feng L, Podlich DW (2004) Genomics, genetics, and plant breeding: a private sector perspective. Crop Sci 44:1907–1913

    Article  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, Lee M (eds) Plant breeding: the Arnel R. Hallauer international symposium Blackwell Publishing, Ames, IA

    Google Scholar 

  • Crossa J, Burgueño J, Dreisigacker S, Vargas M, Herrera-Foessel SA, Lillemo M, Singh RP, Trethowan R, Warburton M, Franco J, Reynolds M, Crouch JH, Ortiz R (2007) Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics 177:1889–1913

    Article  PubMed  CAS  Google Scholar 

  • Crossa J, de los Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724

    Article  PubMed  CAS  Google Scholar 

  • Danson JW, Mbogori M, Kimani M, Lagat M, Kuria A, Diallo A (2006) Marker assisted introgression of opaque2 gene intoherbicide resistant elite maize inbred lines. Afr J Biotechnol 5:2417–2422

    CAS  Google Scholar 

  • Dayteg C, Tuvesson S, Merker A, Jahoor A, Kolodinska-Brantestam A (2007) Automation of DNA marker analysis for molecular breeding in crops: practical experience of a plant breeding company. Plant Breed 126:410–415

    Article  Google Scholar 

  • Dreher K, Khairallah M, Ribaut JM, Morris M (2003) Money matters (I): costs of field and laboratory procedures associated with conventional and marker-assisted maize breeding at CIMMYT. Mol Breed 11:221–234

    Article  Google Scholar 

  • Dwivedi SL, Crouch JH, Mackill DJ, Xu Y, Blair MW, Ragot M, Upadhyaya HD, Ortiz R (2007) The molecularizationof public sector crop breeding: progress, problems and prospects. Adv Agron 95:163–319

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Edwards JD, Janda J, Sweeney MT, Gaikwad AB, Liu B, Leung H, Galbraith DW (2008) Development and evaluation of a high-throughput, low-cost genotyping platform based on oligonucleotide microarrays in rice. Plant Methods 4:13

    Article  PubMed  Google Scholar 

  • Fjellstrom R, McClung AM, Shank AR (2006) SSR markers closely linked to the Pi-z locus are useful for selection of blast resistance in a broad array of rice germplasm. Mol Breed 17:149–157

    Article  CAS  Google Scholar 

  • Frisch M, Melchinger AE (2001) Marker-assisted backcrossing for simultaneous introgression of two genes.Crop Sci 41:1716–1725

    Google Scholar 

  • Fukao T, Bailey-Serres J (2008) Submergence tolerance conferred by Sub1A is mediated by SLR1 and SLRL1 restriction of gibberellin responses in rice. Proc Natl Acad Sci U S A 105:16814–16819

    Article  PubMed  CAS  Google Scholar 

  • Fukao T, Xu K, Ronald PC, Bailey-Serres J (2006) A variable cluster of ethylene response factor-like genes regulates metabolic and developmental acclimation responses to submergence in rice. Plant Cell 18:2021–2034

    Article  PubMed  CAS  Google Scholar 

  • Fulton TM, Grandillo S, Beck-Bunn T, Fridman E, Frampton A, López J, Pétiard V, Uhlig J., Zamir D, Tanksley SD (2000) Advanced backcross QTL analysis of a Lycopersicon esculentum × Lycopersicon parviflorum cross. Theor Appl Genet 100:1025–1042

    Article  CAS  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 

  • Gianola D, van Kaam JBCHM (2008) Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178:2289–2303

    Article  PubMed  Google Scholar 

  • Gore MA, Chia J-M, 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, Rustgi S, Mir RR (2008) Array-based high-throughput DNA markers for crop improvement. Heredity 101:5–18

    Article  PubMed  CAS  Google Scholar 

  • Gupta HS, Agrawal PK, Mahajan V, Bisht GS, Kumar A, Verma P, Srivastava A, Saha S, Babu R, Pant MC, Mani VP (2009) Quality protein maize for nutritional security:rapid development of short duration hybridsthrough molecular marker assisted breeding. Curr Sci 96:230–237

    Google Scholar 

  • Gupta PK, Kumar, J, Mir RR, Kumar A (2010) Marker-assisted selection as a component of conventional plant breeding. Plant Breed Rev 33:145–217

    Article  Google Scholar 

  • Han B, Zhang Q (2008) Rice genome research: current status and future perspectives. Plant Genome 1:71–76

    Article  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  • Harushima Y, Yano M, Shomura A, Sato M, Shimano T, Kuboki Y, Yamamoto T, Lin SY, Antonio BA, Parco A, Kajiya H, Huang N, Yamamoto K, Nagamura Y, Kurata N, Khush GS, Sasaki T (1998) A high-density rice genetic linkage map with 2275 markers using a single F2 population. Genetics 148:479–494

    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 

  • Hippolyte I, Bakry F, Seguin M, Gardes L, Rivallan R, Risterucci A-M, Jenny C, Perrier X, Carreel F, Argout X, Piffanelli P, Khan IA, Miller RNG, Pappas JG, Mbéguié-A-Mbéguié D, Matsumoto T, De Bernardinis V, Huttner E, Kilian A, Baurens F-C, D’Hont A, Cote F, Courtois B, Glaszmann JC (2010) Asaturated SSR/DArT linkage map of Musa acuminata addressing genome rearrangements among bananas. BMC Plant Biol 10:65

    Article  PubMed  Google Scholar 

  • International Rice Genome Sequencing Project (2005) The map-based sequence of the rice genome. Nature 436:793–800

    Google Scholar 

  • Ismail AM, Heuer S, Thomson MJ, Wissuwa M (2007) Genetic and genomic approaches to develop rice germplasm for problem soils. Plant Mol Biol 65:547–570

    Article  PubMed  CAS  Google Scholar 

  • Jaccoud D, Peng K, Feinstein D, Kilian A (2001) Diversity arrays: a solid state technology for sequence information independent genotyping. Nucl Acids Res 29:e25

    Google Scholar 

  • Jackson MB, Ram PC (2003) Physiological and molecular basis of susceptibility and tolerance of rice plants to complete submergence. Ann Bot 91:227–241

    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 

  • Johal GS, Balint-Kurti P, Weil CF (2008) Mining and harnessing naturalvariation: a little magic. Crop Sci 48:2066–2073

    Article  Google Scholar 

  • Keurentjes JJB, Koornneef M, Vreugdenhi D (2008) Quantitative genetics in the age of omics. Curr Opin Plant Biol 11:123–128

    Article  PubMed  CAS  Google Scholar 

  • Kraakman ATW, Niks RE, Van Den Berg PMMM, StamP, Van Eeuwijk FA (2004) Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars. Genetics 168:435–446

    Article  PubMed  CAS  Google Scholar 

  • Lu Y, Yan J, Guimarães CT, Taba S, Hao Z, Gao S, Chen S, Li J, Zhang S, Vivek BS, Magorokosho C, Mugo S, Makumbi D, Parentoni SN, Shah T, Reng T, Crouch JH, Xu Y (2009) Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms. Theor Appl Genet 120:93–115

    Article  PubMed  CAS  Google Scholar 

  • Mace ES, Xia L, Jordan DR, Halloran K, Parh DK, Huttner E, Wenzl P, Kilian A (2008) DArT markers: diversity analyses and mapping in Sorghum bicolor. BMC Genomics 9:26

    Article  PubMed  Google Scholar 

  • Mackay I, Powell W (2007) Methods for linkage disequilibrium mapping in crops. Trends Plant Sci 12:57–63

    Article  PubMed  CAS  Google Scholar 

  • Malosetti M, Ribaut JM, Vargas M, Crossa J, van Eeuwijk FA (2008) A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica 161:241–257

    Article  Google Scholar 

  • McCallum CM, Comai L, Greene EA, Henikoff S (2000) Targeting induced local lesions in genomes (TILLING) forplant functional genomics. Plant Physiol 123:439–442

    Article  PubMed  CAS  Google Scholar 

  • McMullen MD, 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, Oropeza Rosas M, 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 325:737–740

    Article  PubMed  CAS  Google Scholar 

  • McNally KL, Childs KL, Bohnert R, Davidson RM, Zhao K, Ulat VJ, Zeller G, Clark RM, Hoen DR, Bureau TE, Stokowski R, Ballinger DG, Frazer KA, Cox DR, Padhukasahasram B, Bustamante CD, Weigel D, Mackill DJ, Bruskiewich RM, Rätsch G, Buell CR, Leung H, Leach JE (2009) Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proc Natl Acad Sci U S A 106:12273–12278

    Article  PubMed  CAS  Google Scholar 

  • Moncada P, Martínez CP, Borrero J, Chatel M, Gauch Jr H, Guimaraes E, Tohme J, McCouch SR (2001) Quantitative trait loci for yield and yield components in an Oryza sativa× Oriza rufipogon BC2F2 population evaluated in an upland environment. Theor Appl Genet 102:41–52

    Article  CAS  Google Scholar 

  • Moose SP, Munn RH (2008) Molecular plant breeding as the foundation for 21st century crop improvement. Plant Physiol 147:969–977

    Article  PubMed  CAS  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 

  • Ortiz R, Taba S, Chávez Tovar VH, Mezzalama M, Xu Y, Yan J, Crouch JH (2010) Conserving and enhancing maize genetic resources as global public goods—a perspective from CIMMYT. Crop Sci 50:13–28

    Article  Google Scholar 

  • Pérez P, de los Campos G, Crossa J, Gianola D (2010) Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. Plant Genome 3:106–116

    Article  PubMed  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 44:1560–1571

    Article  Google Scholar 

  • Raghuvanshi S, Kapoor M, Tyagi S, Kapoor S, Khurana P, Khurana J, Tyagi A (2010) Rice genomics moves ahead. Mol Breed 26:257–273

    Article  CAS  Google Scholar 

  • Ram PC, Singh BB, Singh AK, Ram P, Singh PN, Singh HP, Boamfa I, Harren F, Santosa E, Jackson MB, Setter TL, Reuss J, Wade LJ, Singh VP, Singh RK (2002) Submergence tolerance in rainfed lowland rice: physiological basis and prospects for cultivar improvement through marker-aided breeding. Field Crops Res 76:131–152

    Article  Google Scholar 

  • Reinke R (2006) Evaluating diversity array technology (DArT) for the NSW rice breeding program. Rural Industries Research and Development Corporation, Canberra

    Google Scholar 

  • Ribaut JM, Ragot M (2007) Marker-assisted selection to improve drought adaptation inmaize: the backcross approach, perspectives, limitations, and alternatives. J Exp Bot 58:351–360

    Article  PubMed  CAS  Google Scholar 

  • Risterucci AM, Hippolyte I, Perrier X, Xia L, Caig V, Evers M, Huttner E, Kilian A, Glaszmann JC (2009) Development and assessment of diversity arrays technology for high-throughput DNA analyses in Musa. Theor Appl Genet 119:1093–1103

    Article  PubMed  CAS  Google Scholar 

  • Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, Minx P, Reily AD, Courtney L, Kruchowski SS, Tomlinson C, Strong C, Delehaunty K, Fronick C, Courtney B, Rock SM, Belter E, Du F, Kim K, Abbott RM, Cotton M, Levy A, Marchetto P, Ochoa K, Jackson SM, Gillam B, Chen W, Yan L, Higginbotham J, Cardenas M, Waligorski J, Applebaum E, Phelps L, Falcone J, Kanchi K, Thane T, Scimone A, Thane N, Henke J, Wang T, Ruppert J, Shah N, Rotter K, Hodges J, Ingenthron E, Cordes M, Kohlberg S, Sgro J, Delgado B, Mead K, Chinwalla A, Leonard S, Crouse K, Collura K, Kudrna D, Currie J, He R, Angelova A, Rajasekar S, Mueller T, Lomeli R, Scara G, Ko A, Delaney K, Wissotski M, Lopez G, Campos D, Braidotti M, Ashley E, Golser W, Kim H, Lee S, Lin J, Dujmic Z, Kim W, Talag J, Zuccolo A, Fan C, Sebastian A, Kramer M, Spiegel L, Nascimento L, Zutavern T, Miller B, Ambroise C, Muller S, Spooner W, Narechania A, Ren L, Wei S, Kumari S, Faga B, Levy MJ, McMahan L, Van Buren P, Vaughn MW, Ying K, Yeh CT, Emrich SJ, Jia Y, Kalyanaraman A, Hsia AP, Barbazuk WB, Baucom RS, Brutnell TP, Carpita NC, Chaparro C, Chia JM, Deragon JM, Estill JC, Fu Y, Jeddeloh JA, Han Y, Lee H, Li P, Lisch DR, Liu S, Liu Z, Nagel DH, McCann MC, SanMiguel P, Myers AM, Nettleton D, Nguyen J, Penning BW, Ponnala L, Schneider KL, Schwartz DC, Sharma A, Soderlund C, Springer NM, Sun Q, Wang H, Waterman M, Westerman R, Wolfgruber TK, Yang L, Yu Y, Zhang L, Zhou S, Zhu Q, Bennetzen JL, Dawe RK, Jiang J, Jiang N, Presting GG, Wessler SR, Aluru S, Martienssen RA, Clifton SW, McCombie WR, Wing RA, Wilson RK (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115

    Article  PubMed  CAS  Google Scholar 

  • Schön CC, Utz HF, Groh S, Truberg B, Openshaw S, Melchinger AE (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits. Genetics 167:485–498

    Article  PubMed  Google Scholar 

  • Semagn K, Bjørnstad Ã…, Ndjiondjop MN (2006a) An overview of molecular marker methods for plants. Afr J Biotechnol 5:2540–2568

    CAS  Google Scholar 

  • Semagn K, Bjørnstad Ã…, Ndjiondjop MN (2006b) Progress and prospects of marker assisted backcrossing as a tool in crop breeding programs. Afr J Biotechnol 5:2588–2603

    CAS  Google Scholar 

  • Septiningsih EM, Pamplona AM, Sanchez DL, Neeraja CN, Vergara GV, Heuer S, Ismail AM, Mackill DJ (2008) Development of submergence tolerant rice cultivars: the Sub1 locus and beyond. Ann Bot 103:151–160

    Article  PubMed  Google Scholar 

  • Servin B., Martin OC, Mézard M, Hospital F (2004) Toward a theory of marker-assisted gene pyramiding. Genetics 168:513–523

    Article  PubMed  CAS  Google Scholar 

  • Sneller CH, Mather DE, Crepieux S (2009) Analytical approaches and population types for finding and utilizing QTL in complex plant populations. Crop Sci 49:363–380

    Article  Google Scholar 

  • Sorkheh K, Malysheva-Otto LV, Wirthensohn MG, Tarkesh-Esfahani S, Martínez-Gómez P (2008) Linkage disequilibrium, genetic association mapping and gene localization in crop plants. Genet Mol Biol 31:805–814

    Article  Google Scholar 

  • Stich B, Utz HF, Piepho HP, Maurer HP, Melchinger AE (2010) Optimum allocation of resources for QTL detection using a nested association mapping strategy in maize. Theor Appl Genet 120:553–561

    Article  PubMed  Google Scholar 

  • Stinchcombe JR, Hoekstra HE (2008) Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits. Heredity 100:158–170

    Article  PubMed  CAS  Google Scholar 

  • Swanson-Wagner RA, DeCook R, Jia Y, Bancroft T, Ji T, Zhao X, Nettleton D, Schnable PS (2009) Paternal dominance of trans-eQTL influences gene expression patterns in maize hybrids. Science 326:1118–1119

    Google Scholar 

  • Tanksley SD, Nelson JC (1996) Advanced backcross QTL analysis: a method for simultaneously discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theor Appl Genet 92:191–203

    Article  Google Scholar 

  • Thomson MJ, Tai TH, McClung AM, Lai XM, Hinga ME, Lobos KB, Xu Y, Martinez CP, McCouch SR (2003) Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theor Appl Genet 107:479–493

    Article  PubMed  CAS  Google Scholar 

  • Toenniessen GH, O’Toole JC, DeVries J (2003) Advances in plant biotechnology and its adoption in developing countries. Curr Opin Plant Biol 6:191–198

    Article  PubMed  Google Scholar 

  • van Bueren ETL, Backes G, de Vriend H, ØstergÃ¥rd H (2010) The role of molecular markers and marker assisted selection in breeding for organic agriculture. Euphytica 175:51–64

    Article  Google Scholar 

  • Varshney RK, Graner A, Sorrells ME (2005) Genomics-assisted breeding for crop improvement. Trends Plant Sci 10:621–630

    Article  PubMed  CAS  Google Scholar 

  • Vielle-Calzada JP, Martínez de la Vega O, Hernández-Guzmán G, Ibarra-Laclette E, Alvarez-Mejía C, Vega-Arreguín JC, Jiménez-Moraila B, Fernández-Cortés A, Corona-Armenta G, Herrera-Estrella L, Herrera-Estrella A (2009) The Palomero genome suggests metal effects on domestication. Science 326:1078

    Article  PubMed  CAS  Google Scholar 

  • Walsh, B (2001) Quantitative genetics in the era of genomics. Theor Pop Biol 59:175–184

    Article  CAS  Google Scholar 

  • Wang J, Chapman SC, Bonnett DG, Rebetzke GJ, Crouch J (2007a) Application of population genetic theoryand simulation models to efficiently pyramidmultiple genes via marker-assisted selection. Crop Sci 47:582–590

    Article  Google Scholar 

  • Wang J, Wan X, Li H, Pfeiffer WH, Crouch J, Wan J (2007b) Application of identified QTL-marker associations in rice quality improvement through a design-breeding approach. Theor Appl Genet 115:87–100

    Article  Google Scholar 

  • Wenzl P, Carling J, Kudrna D, Jaccoud D, Huttner E, Kleinhofs A, Kilian A (2004) Diversity arrays technology (DArT) for whole-genome profiling of barley. Proc Natl Acad Sci U S A 101:9915–9920

    Article  PubMed  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 

  • Xia L, Peng K, Yang S, Wenzl P, de Vicente MC, Fregene M, Kilian A (2005) DArT for high-throughput genotyping of cassava (Manihot esculenta) and its wild relatives. Theor Appl Genet 110:1092–1098

    Article  PubMed  CAS  Google Scholar 

  • Xu K, Mackill DJ (1996) A major locus for submergence tolerance mapped on rice chromosome 9. Mol Breed 2:219–224

    Article  CAS  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 K, Xu X, Ronald PC, Mackill DJ (2000) A high-resolution linkage map in the vicinity of the rice submergence tolerance locus Sub1. Mol Gen Genet 263:681–689

    Article  PubMed  CAS  Google Scholar 

  • Xu K, Deb R, Mackill DJ (2004) A microsatellite marker and a codominant PCR-based marker for marker-assisted selection of submergence tolerance in rice. Crop Sci 44:248–253

    Article  CAS  Google Scholar 

  • Xu K, Xu X, Fukao T, Canlas R, Maghirang-Rodriguez R, Heuer S, Ismail AM, Bailey-Serres J, Ronald PC, Mackill DJ (2006) Sub1 A is an ethylene-response-factor-like gene that confers submergence tolerance to rice. Nature 442:705–708

    Article  PubMed  CAS  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 P, Guimarães CT, Araus JL, Wang G, Davenport GF, Crossa J, Crouch JH (2009a) SNP chip-based genome wide scans for germplasm evaluation, marker-trait association analysis and development of a molecular breeding platform in maize. In: Proceedings of 14th Australasian plant breeding conference (APBC) & 11th Congress of the Society for the Advancement of Breeding Research in Asia and Oceania (SABRAO), Cairns Convention Centre, Cairns, 10–14 August 2009. http://open.irri.org/sabrao/images/stories/conference/site/…/apb09final00307.pdf

  • Xu Y, Skinner DJ, Wu H, Palacios-Rojas N, Araus JL, Yan J, Gao S, Warburton ML, Crouch JH (2009b) Advances in maize genomics and their value for enhancing genetic gains from breeding. Intl J Plant Genomics 2009:957602

    Google Scholar 

  • Ye G, Smith KF (2010) Marker-assisted gene pyramiding for cultivar development. Plant Breed Rev 33:219–256

    Article  Google Scholar 

  • Yu J, Zhang Z, Zhu C, Tabanao DA, Pressoir G, Tuinstra MR, Kresovich S, Todhunter RJ, Buckler ES (2009) Simulation appraisal of the adequacy of number of background markers for relationship estimation in association mapping. Plant Genome 2:63–77

    Article  Google Scholar 

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Correspondence to Rodomiro Ortiz .

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Ortiz, R. (2012). Marker-Aided Breeding Revolutionizes Twenty-First Century Crop Improvement. In: Agrawal, G., Rakwal, R. (eds) Seed Development: OMICS Technologies toward Improvement of Seed Quality and Crop Yield. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4749-4_21

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