Advertisement

Genetica

, Volume 145, Issue 6, pp 525–539 | Cite as

Genomic-based-breeding tools for tropical maize improvement

  • Thammineni Chakradhar
  • Vemuri Hindu
  • Palakolanu Sudhakar Reddy
Review

Abstract

Maize has traditionally been the main staple diet in the Southern Asia and Sub-Saharan Africa and widely grown by millions of resource poor small scale farmers. Approximately, 35.4 million hectares are sown to tropical maize, constituting around 59% of the developing worlds. Tropical maize encounters tremendous challenges besides poor agro-climatic situations with average yields recorded <3 tones/hectare that is far less than the average of developed countries. On the contrary to poor yields, the demand for maize as food, feed, and fuel is continuously increasing in these regions. Heterosis breeding introduced in early 90 s improved maize yields significantly, but genetic gains is still a mirage, particularly for crop growing under marginal environments. Application of molecular markers has accelerated the pace of maize breeding to some extent. The availability of array of sequencing and genotyping technologies offers unrivalled service to improve precision in maize-breeding programs through modern approaches such as genomic selection, genome-wide association studies, bulk segregant analysis-based sequencing approaches, etc. Superior alleles underlying complex traits can easily be identified and introgressed efficiently using these sequence-based approaches. Integration of genomic tools and techniques with advanced genetic resources such as nested association mapping and backcross nested association mapping could certainly address the genetic issues in maize improvement programs in developing countries. Huge diversity in tropical maize and its inherent capacity for doubled haploid technology offers advantage to apply the next generation genomic tools for accelerating production in marginal environments of tropical and subtropical world. Precision in phenotyping is the key for success of any molecular-breeding approach. This article reviews genomic technologies and their application to improve agronomic traits in tropical maize breeding has been reviewed in detail.

Keywords

Maize Next generation sequencing (NGS) Genome-wide association studies (GWAS) Genomic selection (GS) QTL-seq Phenotyping Informatics tools 

Notes

Acknowledgements

The authors wish to thank Dr. Suri M. Sehgal, founder of SM Sehgal foundation for his continuous support and encouragement for corn improvement in developing countries.

References

  1. Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H, Kanzaki H, Matsumura H, Yoshida K, Mitsuoka C, Tamiru M, Innan H, Cano L, Kamoun S, Terauchi R (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol 30:174–178PubMedCrossRefGoogle Scholar
  2. Ajmore-Marsan P, Gorni C, Chitto` A, Redaelli R, Van Vijk R, Stam P, Motto M (2001) Identification of QTLs for grain yield and grain-related traits of maize (Zea mays L.) using an AFLP map, different testers and cofator analysis. Theor Appl Genet 102:230–243CrossRefGoogle Scholar
  3. Atkinson JA, Wingen LU, Griffiths M, Pound MP, Gaju O, Foulkes MJ, Le Gouis J, Griffiths S, Bennett MJ, King J, Wells DM (2015) Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat. J Exp Bot 66:2283–2292PubMedPubMedCentralCrossRefGoogle Scholar
  4. Azmach G, Gedil M, Menkir A, Spillane C (2013) Marker-trait association analysis of functional gene markers for provitamin A levels across diverse tropical yellow maize inbred lines. BMC Plant Biol 13:227–231PubMedPubMedCentralCrossRefGoogle Scholar
  5. 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 111:888–897PubMedCrossRefGoogle Scholar
  6. Babu R, Nair SK, Vinayan MT, Zaidi PH, Vivek BS, Prasanna BM (2014) In: Paroda R et al (eds) Proceedings of 12th Asian Maize Conference and Expert Consultation on Maize for Food, Feed, Nutrition and Environmental Security. Bangkok, Thailand, pp 81–84Google Scholar
  7. Battistelli GM, Von Pinho RG, Justus A, Couto EGO, Balestre M (2013) Production and identification of doubled haploids in tropical maize. Genet Mol Res 12:4230–4242PubMedCrossRefGoogle Scholar
  8. Benchimol LL, de Souza CL, de Souza AP (2005) Microsatellite-assisted backcross selection in maize. Genet Mol Biol 28:789–797CrossRefGoogle Scholar
  9. Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47(3):1082–1090CrossRefGoogle Scholar
  10. Blanc G, Wolfe KH (2004) Widespread paleopolyploidy in model plant species inferred from age distributions of duplicate genes. Plant Cell 16:1667–1678PubMedPubMedCentralCrossRefGoogle Scholar
  11. Bouchet S, Servin B, Bertin P, Madur D, Combes V, Dumas F, Brunel D, Laborde J, Charcosset A, Nicolas S (2013) Adaptation of maize to temperate climates: mid- density genome-wide association genetics and diversity patterns reveal key genomic regions with a major contribution of the Vgt2 (ZCN8) locus. PLoS One 8:e71377PubMedPubMedCentralCrossRefGoogle Scholar
  12. Britt AB, Kuppu S (2016) Cenh3: an emerging player in haploid induction technology. Front Plant Sci 7:357–368PubMedPubMedCentralCrossRefGoogle Scholar
  13. Buckler ES, Stevens NM (2005) Maize origins, domestication, and selection. In: Motley TJ, Zerega N, Cross H (eds) Darwin’s harvest. Columbia University Press, New York, pp 67–90Google Scholar
  14. 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 HH, 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 JM, Zhang ZW, Kresovich S, McMullen MD (2009) The genetic architecture of maize flowering time. Science 325:714–718PubMedCrossRefGoogle Scholar
  15. Bybee SM, Bracken-Grissom H, Haynes BD, Hermansen RA, Byers RL, Clement MJ, Udall JA, Wilcox ER, Crandall KA (2011) Targeted amplicon sequencing (TAS): a scalable next-gen approach to multilocus, multitaxa phylogenetics. Genome Biol Evol 3:1312–1323PubMedPubMedCentralCrossRefGoogle Scholar
  16. Cairns JE, Sonder K, Zaidi PH, Verhulst N, Mahuku G, Babu R, Nair SK, Das B, Govaerts B, Vinayan MT, Rashid Z, Noor JJ, Devi P, Vicente FS, Prasanna BM (2012) Maize production in a changing climate: impacts, adaptation, and mitigation strategies. Adv Agron 114:1–58CrossRefGoogle Scholar
  17. Chaikam V, Nair SK, Babu R, Martinez L, Tejomurtula J, Boddupalli PM (2015) Analysis of effectiveness of R1-nj anthocyanin marker for in vivo haploid identification in maize and molecular markers for predicting the inhibition of R1-nj expression. Theor Appl Genet 128:159–171PubMedCrossRefGoogle Scholar
  18. Chen J, Shrestha R, Ding J, Zheng H, Mu C, Wu J, Mahuku G (2016) Genome-wide association study and QTL mapping reveal genomic loci associated with Fusarium ear rot resistance in tropical maize germplasm. G3 Genes Genom Genet 6(12):3803–3815Google Scholar
  19. Chia JM, Song C, Bradbury PJ, Costich D, de Leon N, Doebley J, Elshire RJ, Gaut B, Geller L, Glaubitz JC, Gore M, Guill KE, Holland J, Hufford MB, Lai JS, Li M, Liu X, Lu YL, McCombie R, Nelson R, Poland J, Prasanna BM, Pyhajarvi T, Rong TZ, Sekhon RS, Sun Q, Tenaillon MI, Tian F, Wang J, Xu X, Zhang ZW, Kaeppler SM, Ross- Ibarra J, McMullen MD, Buckler ES, Zhang GY, Xu YB, Ware D (2012) Maize HapMap2 identifies extant variation from a genome in flux. Nat Genet 44:803–807PubMedCrossRefGoogle Scholar
  20. Cook JP, McMullen MD, Holland JB, Tian F, Bradbury P, Ross-Ibarra J, Buckler ES, Flint- Garcia SA (2012) Genetic architecture of maize kernel composition in the nested association mapping and inbred association panels. Plant Physiol 158:824–834PubMedCrossRefGoogle Scholar
  21. Cooper M, Gho C, Leafgren R, Tang T, Messina C (2014) Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. J Exp Bot 65:6191–6204PubMedCrossRefGoogle Scholar
  22. Crossa J, de los Campos G, Perez P, Gianola D, Burgueno J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan JB, Arief V, Banziger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724PubMedPubMedCentralCrossRefGoogle Scholar
  23. Crossa J, Beyene Y, Kassa S, Perez P, Hickey JM, Chen C, de los Campos G, Burgueno J, Windhausen VS, Buckler E, Jannink JL, Cruz MAL, Babu R (2013) Genomic prediction in maize breeding populations with genotyping-by-sequencing. G3 Genes Genom Genet 3:1903–1926Google Scholar
  24. Dao A, Sanou J, Mitchell SE, Gracen V, Danquah EY (2014) Genetic diversity among INERA maize inbred lines with single nucleotide polymorphism (SNP) markers and their relationship with CIMMYT, IITA, and temperate lines. BMC Genet 15:127–131PubMedPubMedCentralCrossRefGoogle Scholar
  25. Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601PubMedCrossRefGoogle Scholar
  26. Dong X, Xu X, Miao J, Li L, Zhang D, Mi X, Liu C, Tian X, Melchinger AE, Chen S (2013) Fine mapping of qhir1 influencing in vivo haploid induction in maize. Theor Appl Genet 126:1713–1720PubMedCrossRefGoogle Scholar
  27. Dubreuil P, Warburton M, Chastanet M, Hoisington D, Charcosset A (2006) More on the introduction of temperate maize into Europe: large-scale bulk SSR genotyping and new historical elements. Maydica 51:281–291Google Scholar
  28. Ducrocq S, Giauffret C, Madur D, Combes V, Dumas F, Jouanne S, Coubriche D, Jamin P, Moreau L, Charcosset A (2009) Fine mapping and haplotype structure analysis of a major flowering time quantitative trait locus on maize chromosome 10. Genetics 183:1555–1563PubMedPubMedCentralCrossRefGoogle Scholar
  29. Duvick DN (1977) Genetic rates of gain in hybrid maize yields during the past 40 years. Maydica 22:187–196Google Scholar
  30. Duvick DN, Smith JSC, Cooper M (2004) Long-term selection in a commercial hybrid maize breeding program. In: Janick J (ed) Plant breeding reviews part 2. Wiley, New York, pp 109–151Google Scholar
  31. Dwivedi SL, Britt AB, Tripathi L, Sharma S, Upadhyaya HD, Ortiz R (2015) Haploids: constraints and opportunities in plant breeding. Biotechnol Adv 33:812–829PubMedCrossRefGoogle Scholar
  32. Edmeades GO, Bolanos J, Chapman SC, Lafitte HR, Banziger M (1999) Selection improves drought tolerance in tropical maize populations: I. Gains in biomass, grain yield, and harvest index. Crop Sci 39:1306–1315CrossRefGoogle Scholar
  33. 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:e19379PubMedPubMedCentralCrossRefGoogle Scholar
  34. Fraley RT (2009) Molecular genetic approaches to maize improvement—an introduction. In: Krizetal (eds) Molecular genetic approaches to maize improvement. Springer, Berlin, pp 3–6CrossRefGoogle Scholar
  35. Frova C, Krajewski P, di Fonzo N, Villa M, Sari-Gorla M (1999) Genetic analysis of drought tolerance in maize by molecular markers I. Yield components. Theor Appl Genet 99:280–288CrossRefGoogle Scholar
  36. Gaffney J, Schussler J, Löffler C, Cai W, Paszkiewicz S, Messina C, Cooper M (2015) Industry-scale evaluation of maize hybrids selected for increased yield in drought-stress conditions of the US Corn Belt. Crop Sci 55:1608–1618CrossRefGoogle Scholar
  37. Geiger HH, Gordillo GA (2009) Doubled haploids in hybrid maize breeding. Maydica 54:485–499Google Scholar
  38. Giraud H, Lehermeier C, Bauer E, Falque M, Segura V, Bauland C, Camisan C, Campo L, Meyer N, Ranc N, Schipprack W, Flament P, Melchinger AE, Menz M, Moreno- Gonzalez J, Ouzunova M, Charcosset A, Schon CC, Moreau L (2014) Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize. Genetics 198:1717–1734PubMedPubMedCentralCrossRefGoogle Scholar
  39. Gonza´lez-Camacho JM, de los Campos G, Pe´rez P, Gianola D, Cairns JE, Mahuku G, Babu R, Crossa J (2012) Genome-enabled prediction of genetic values using radial basis function neural networks. Theor Appl Genet 125:759–771CrossRefGoogle Scholar
  40. 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 326:1115–1117PubMedCrossRefGoogle Scholar
  41. Gorjanc G, Jenko J, Hearne SJ, Hickey JM (2016) Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations. BMC Genom 17(1):30CrossRefGoogle Scholar
  42. Guo M, Cooper M (2015) Future maize hybrid development: breeding with assistance of molecular and genomics technologies and transgenics. In: Wusirika R, Bohn M, Lai J, Kole C (eds) Genetics, genomics and breeding of maize. CRC Press, Boca Raton, pp 89–119Google Scholar
  43. 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 hybrids through molecular marker assisted breeding. Curr Sci India 96:230–237Google Scholar
  44. Gupta PK, Kulwal PL, Jaiswal V (2014) Association mapping in crop plants: opportunities and challenges. In: Friedmann T et al (eds) Advances in genetics 85. Academic Press, Cambridge, pp 109–148Google Scholar
  45. Hainey C, Rafalski JA, Hanafey M, Zhang Y, Krespan W, Tingey S (2015) Genomic distribution of genetic diversity in elite maize germplasm. In: Ramakrishna et al (eds) Genetics, genomics and breeding of maize. CRC, New Yark, pp 51–63Google Scholar
  46. Halilu AD, Ado SG, Usman IS, Appiah-Kubi D (2013) Prospects of endosperm DNA in maize seed characterization. Maydica 58:288–290Google Scholar
  47. Hayes B, Goddard M (2010) Genome-wide association and genomic selection in animal breeding. Genome 53:876–883PubMedCrossRefGoogle Scholar
  48. Hillel D, Rosenzweig C (2005) The role of biodiversity in agronomy. Adv Agron 88:1–34CrossRefGoogle Scholar
  49. Ho J, McCouch S, Smith M (2002) Improvement of hybrid yield by advanced backcross QTL analysis in elite maize. Theor Appl Genet 105:440–448PubMedCrossRefGoogle Scholar
  50. Hu H, Schrag TA, Peis R, Unterseer S, Schipprack W, Chen S, Lai J, Yan J, Prasanna BM, Chaikam V (2016) The genetic basis of haploid induction in maize identified with a novel genome-wide association method. Genetics 202(4):1267–1276PubMedPubMedCentralCrossRefGoogle Scholar
  51. IFAD (2002) Assesment of Rural Poverty: Asia and the Pacific Asia and the Pacific Division, project management Department. International Fund for Agriculture Development. http://www.ifad.org/poverty/region/pi/PI_part1.pdf
  52. IPCC (2007) Climate change (2007) In: Solomon et al (eds) Assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  53. Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177PubMedCrossRefGoogle Scholar
  54. Kashiani P, Saleh G, Panandam JM, Abdullah NAP, Selamat A (2012) Molecular characterization of tropical sweet corn inbred lines using microsatellite markers. Maydica 57:154–163Google Scholar
  55. Kassa S, Beyene Y, Babu R, Nair S, Gowda M, Das B, Tarekegne A, Mugo NS, Mahuku G, Worku M, Warburton LM, Olseu SM, Prasanna BM (2015) QTL mapping and molecular breeding for developing stress resilient maize for sub-Saharan Africa. Crop Sci 5:1–11Google Scholar
  56. Kelliher T, Starr D, Wang W, McCuiston J, Zhong H, Nuccio ML, Martin B (2016) Maternal haploids are preferentially induced by CENH3-tailswap transgenic complementation in maize. Front Plant Sci 7:414PubMedPubMedCentralCrossRefGoogle Scholar
  57. Khakwani K, Dogar MR, Ahsan M, Hussain A, Asif M, Malhi AR, Altaf M (2015) Development of maize haploid inducer lines and doubled haploid lines in Pakistan. Br Biotechnol J 8:1–7CrossRefGoogle Scholar
  58. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink JL (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123CrossRefGoogle Scholar
  59. Marsan PA, Gorni C, Chitto A, Redaelli R, van Vijk R, Stam P, Motto M (2001) Identification of QTLs for grain yield and grain-related traits of maize (Zea mays L.) using an AFLP map, different testers, and cofactor analysis. Theor Appl Genet 102:230–243CrossRefGoogle Scholar
  60. Massman JM, Gordillo A, Lorenzana RE, Bernardo R (2013) Genomewide predictions from maize single-cross data. Theor Appl Genet 126:13–22PubMedCrossRefGoogle Scholar
  61. Masuka B, Atlin GN, Olsen M, Magorokosho C, Labuschagne M, Crossa J, Vivek BS, Macrobert J (2017) Gains in maize genetic improvement in eastern and southern Africa: I. CIMMYT hybrid breeding pipeline. Crop Sci 57:168–179CrossRefGoogle Scholar
  62. 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 E, 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 325:737–740PubMedCrossRefGoogle Scholar
  63. Mendes MP, de Souza CL (2016) Genomewide prediction of tropical maize single-crosses. Euphytica 209(3):651–663CrossRefGoogle Scholar
  64. Messina CD, Podlich D, Dong Z, Samples M, Cooper M (2011) Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance. J Exp Bot 62:855–868PubMedCrossRefGoogle Scholar
  65. Messmer R, Fracheboud Y, Banziger M, Vargas M, Stamp P, Ribaut JM (2009) Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits. Theor Appl Genet 119:913–930PubMedCrossRefGoogle Scholar
  66. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  67. Nair SK, Babu R, Magorokosho C, Mahuku G, Semagn K, Beyene Y, Das B, Mukambi D, Kumar PL, Boddupalli PM (2015) Fine mapping of Msv1, a major QTL for resistance to Maize Streak Virus leads to development of production markers for breeding pipelines. Theor Appl Genet 128:1839–1854PubMedCrossRefGoogle Scholar
  68. Nepolean T, Hossain F, Shiriga K, Mittal S, Arora K, Rathore A, Mohan S, Shah T, Sharma R, Namratha PM, Mithra ASV, Mohaptara T, Gupta HS (2013) Unravelling the genetic architecture of subtropical maize (Zea mays L.) lines and their utility in breeding programs. BMC Genom 14:877–890CrossRefGoogle Scholar
  69. Osman KA, Tang B, Wang YP, Chen JH, Yu F, Li L, Han XS, Zhang ZX, Yan JB, Zheng YL, Yue B, Qiu FZ (2013) Dynamic QTL analysis and candidate gene mapping for waterlogging tolerance at maize seedling stage. PLoS One 8:e79305PubMedPubMedCentralCrossRefGoogle Scholar
  70. Owens BF, Lipka AE, Magallanes-Lundback M, Tiede T, Diepenbrock CH, Kandianis CB, Kim E, Cephala J, Buell CR, Buckler ES (2014) A foundation for provitamin A biofortification of maize: genome-wide association and genomic prediction models of carotenoid levels. Genetics 198(4):1699–1716PubMedPubMedCentralCrossRefGoogle Scholar
  71. Parts L, Cubillos FA, Warringer J, Jain K, Salinas F, Bumpstead SJ, Molin M, Zia A, Simpson JT, Quail MA, Moses A, Louis EJ, Durbin R, Liti G (2011) Revealing the genetic structure of a trait by sequencing a population under selection. Genome Res 21:1131–1138PubMedPubMedCentralCrossRefGoogle Scholar
  72. Perez-de-Castro AM, Vilanova S, Canizares J, Pascual L, Blanca JM, Diez MJ, Prohens J, Pico B (2012) Application of genomic tools in plant breeding. Curr Genom 13:179–195CrossRefGoogle Scholar
  73. Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE (2012) Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS One 7:e37135PubMedPubMedCentralCrossRefGoogle Scholar
  74. Poland JA, Bradbury PJ, Buckler ES, Nelson RJ (2011) Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci USA 108:6893–6898PubMedPubMedCentralCrossRefGoogle Scholar
  75. Prasanna BM (2012a) Diversity in global maize germplasm: characterization and utilization. J Biosciences 37:843–855CrossRefGoogle Scholar
  76. Prasanna BM (2012b) Molecular breeding and biotechnology for maize improvement in the developing world: challenges and opportunities. In: Proceedings of the 3rd National Maize Workshop of Ethiopia, pp 87–93Google Scholar
  77. Prasanna BM, Hoisington DA (2003) Molecular breeding for maize improvement: an overview. Indian J Biotechnol 2:85–98Google Scholar
  78. Prasanna BM, Pixley K, Warburton ML, Xie CX (2010) Molecular marker-assisted breeding options for maize improvement in Asia. Mol Breed 26:339–356CrossRefGoogle Scholar
  79. Prasanna B, Babu R, Nair S, Semagn K, Chikam V, Cairns J (2014) Molecular marker-assisted breeding for tropical maize improvement. In: Ramakrishna et al (eds) Genetics, genomics and breeding of maize. CRC, New York, pp 89–119Google Scholar
  80. Prigge V, Sanchez C, Dhillon BS, Schipprack W, Araus JL, Banziger M, Melchinger AE (2011) Doubled haploids in tropical maize: I. Effects of inducers and source germplasm on in vivo haploid induction rates. Crop Sci 51:1498–1506CrossRefGoogle Scholar
  81. Prigge V, Schipprack W, Mahuku G, Atlin GN, Melchinger AE (2012a) Development of in vivo haploid inducers for tropical maize breeding programs. Euphytica 185:481–490CrossRefGoogle Scholar
  82. Prigge V, Xu XW, Li L, Babu R, Chen SJ, Atlin GN, Melchinger AE (2012b) New insights into the genetics of in vivo induction of maternal haploids, the backbone of doubled haploid technology in maize. Genetics 190:781–793PubMedPubMedCentralCrossRefGoogle Scholar
  83. Ribaut JM, Hoisington DA, Deutsch JA, Jiang C, Gonzalez-de-Leon D (1996) Identification of quantitative trait loci under drought conditions in tropical maize. 1. Flowering parameters and the anthesis-silking interval. Theor Appl Genet 92:905–914PubMedCrossRefGoogle Scholar
  84. Ribaut JM, Jiang C, GonzalezdeLeon D, Edmeades GO, Hoisington DA (1997) Identification of quantitative trait loci under drought conditions in tropical maize, Yield components and marker-assisted selection strategies. Theor Appl Genet 94:887–896CrossRefGoogle Scholar
  85. Richard C, Osiru DS, Mwala MS, Lubberstedt T (2016) Genetic diversity and heterotic grouping of the core set of southern African and temperate maize (Zea mays L.) Inbred lines using SNP markers. Maydica 61(1):M3Google Scholar
  86. Sabadin PK, de Souza CL, de Souza AP, Garcia AAF (2008) QTL mapping for yield components in a tropical maize population using microsatellite markers. Hereditas 145:194–203CrossRefGoogle Scholar
  87. Schnable PS, Ware D, Fulton RS, Stein JC, Wei FS, Pasternak S, Liang CZ, Zhang JW, 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 FY, Kim K, Abbott RM, Cotton M, Levy A, Marchetto P, Ochoa K, Jackson SM, Gillam B, Chen WZ, 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 RF, 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 JK, 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 LY, 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 YJ, Lee H, Li PH, Lisch DR, Liu SZ, Liu ZJ, 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 LX, Yu Y, Zhang LF, Zhou SG, Zhu Q, Bennetzen JL, Dawe RK, Jiang JM, 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 326:1112–1115PubMedCrossRefGoogle Scholar
  88. Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q, Nordborg M (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44(7):825–830PubMedPubMedCentralCrossRefGoogle Scholar
  89. Semagn K, bjørnstad Å, Xu Y (2010) The genetic dissection of quantitative traits in crops. Electron J Biotechnol 13:5CrossRefGoogle Scholar
  90. Semagn K, Babu R, Hearne S, Olsen M (2014) Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Mol Breed 33:1–14CrossRefGoogle Scholar
  91. Sharma L, Prasanna BM, Ramesh B (2010) Analysis of phenotypic and microsatellite-based diversity of maize landraces in India, especially from the North East Himalayan region. Genetica 138:619–631PubMedCrossRefGoogle Scholar
  92. Shehata AI, Al-Ghethar HA, Al-Homaidan AA (2009) Application of simple sequence repeat (SSR) markers for molecular diversity and heterozygosity analysis in maize inbred lines. Saudi J Biol Sci 16:57–62PubMedPubMedCentralCrossRefGoogle Scholar
  93. Sibov ST, De Souza CL, Garcia AAF, Garcia AF, Silva AR, Mangolin CA, Benchimol LL, De Souza AP (2003) Molecular mapping in tropical maize (Zea mays L.) using microsatellite markers. 1. Map construction and localization of loci showing distorted segregation. Hereditas 139:96–106PubMedCrossRefGoogle Scholar
  94. Sood S, Flint-Garcia S, Willcox MC, Holland JB (2014) Mining natural variation for maize improvement: Selection on phenotypes and genes. In: Tuberosa R et al (eds) Genomics of plant genetic resources. Springer, Netherlands, pp 615–649CrossRefGoogle Scholar
  95. Swarts K, Li H, Romero Navarro JA, An D, Romay MC, Hearne S, Buckler ES (2014) Novel methods to optimize genotypic imputation for low-coverage, next-generation sequence data in crop plants. Plant Genome 7(3).  https://doi.org/10.3835/plantgenome2014.05.0023
  96. Swinnen S, Schaerlaekens K, Pais T, Claesen J, Hubmann G, Yang YD, Demeke M, Foulquie-Moreno MR, Goovaerts A, Souvereyns K, Clement L, Dumortier F, Thevelein JM (2012) Identification of novel causative genes determining the complex trait of high ethanol tolerance in yeast using pooled-segregant whole-genome sequence analysis. Genome Res 22:975–984PubMedPubMedCentralCrossRefGoogle Scholar
  97. Takagi H, Uemura A, Yaegashi H, Tamiru M, Abe A, Mitsuoka C, Utsushi H, Natsume S, Kanzaki H, Matsumura H, Saitoh H, Yoshida K, Cano LM, Kamoun S, Terauchi R (2013) MutMap-Gap: whole-genome resequencing of mutant F2 progeny bulk combined with de novo assembly of gap regions identifies the rice blast resistance gene Pii. New Phytol 200:276–283PubMedCrossRefGoogle Scholar
  98. Tamilkumar P, Senthil N, Sureshkumar S, Thangavelu AU, Nagarajan P, Vellaikumar S, Raveendran M (2014) Introgression of low phytic acid locus (lpa2-2) into an elite Maize (Zea mays L.) inbred through marker assisted backcross breeding. Aust J Crop Sci 8:1224–1231Google Scholar
  99. Technow F, Schrag TA, Schipprack W, Bauer E, Simianer H, Melchinger AE (2014) Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize. Genetics 197:1343–1355PubMedPubMedCentralCrossRefGoogle Scholar
  100. Thirunavukkarasu N, Hossain F, Arora K, Sharma R, Shiriga K, Mittal S, Mohan S, Namratha PM, Dogga S, Rani TS, Katragadda S, Rathore A, Shah T, Mohapatra T, Gupta HS (2014) Functional mechanisms of drought tolerance in subtropical maize (Zea mays L.) identified using genome-wide association mapping. BMC Genom 15:1182CrossRefGoogle Scholar
  101. Thomson MJ (2014) High-throughput SNP genotyping to accelerate crop improvement. Plant Breed Biotechnol 2:195–212CrossRefGoogle Scholar
  102. 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 43:159–162PubMedCrossRefGoogle Scholar
  103. Tombuloglu H, Aydin M, Filiz E (2015) Comparative analysis of embryo surrounding region (Esr-6) genes in Turkish maize varieties: sequencing and modeling. Braz J Bot 38:10Google Scholar
  104. Tuberosa R (2012) Phenotyping for drought tolerance of crops in the genomics era. Front Physiol 3:347–382PubMedPubMedCentralCrossRefGoogle Scholar
  105. Tuberosa R, Salvi S (2006) Genomics-based approaches to improve drought tolerance of crops. Trends Plant Sci 11:405–412PubMedCrossRefGoogle Scholar
  106. Tuberosa R, Salvi S, Sanguineti MC, Landi P, Maccaferri M, Conti S (2002) Mapping QTLs regulating morpho-physiological traits and yield: Case studies, shortcomings and perspectives in drought-stressed maize. Ann Bot (Lond) 89:941–963CrossRefGoogle Scholar
  107. Unterseer S, Bauer E, Haberer G, Seidel M, Knaak C, Ouzunova M, Meitinger T, Strom TM, Fries R, Pausch H, Bertani C, Davassi A, Mayer KF, Schön CC (2014) A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array. BMC Genom 15:232–233CrossRefGoogle Scholar
  108. Vadez V, Kholova J, Hummel G, Zhokhavets U, Gupta SK, Hash CT (2015) LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget. J Exp Bot 66:5581–5593PubMedPubMedCentralCrossRefGoogle Scholar
  109. Van Inghelandt D, Melchinger AE, Martinant JP, Stich B (2012) Genome-wide association mapping of flowering time and northern corn leaf blight (Setosphaeria turcica) resistance in a vast commercial maize germplasm set. BMC Plant Biol 12:56–71PubMedPubMedCentralCrossRefGoogle Scholar
  110. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genom Biol 3:research/0034.11CrossRefGoogle Scholar
  111. Veldboom LR, Lee M (1996) Genetic mapping of quantitative trait loci in maize in stress and nonstress environments: I. Grain yield and yield components. Crop Sci 36:1310–1319CrossRefGoogle Scholar
  112. Vielle-Calzada JP, de la Vega OM, Hernandez-Guzman G, Ibarra-Laclette E, Alvarez-Mejia C, Vega-Arreguin JC, Jimenez-Moraila B, Fernandez-Cortes A, Corona-Armenta G, Herrera-Estrella L, Herrera-Estrella A (2009) The palomero genome suggests metal effects on domestication. Science 326:1078–1078PubMedCrossRefGoogle Scholar
  113. Vinayan MT, Babu R, Jyothsna T, Zaidi PH, Blummel M (2013) A note on potential candidate genomic regions with implications for maize stover fodder quality. Field Crop Res 153:102–106CrossRefGoogle Scholar
  114. Wallace JG, Larsson SJ, Buckler ES (2014) Entering the second century of maize quantitative genetics. Heredity 112:30–38PubMedCrossRefGoogle Scholar
  115. Warburton ML, Xianchun X, Franco J, Melchinger AE, Frisch M, Bohn M, Hoisington D (2002) Genetic characterization of CIMMYT inbred maize lines and open pollinated populations using large scale fingerprinting methods. Crop Sci 42:1832–1840CrossRefGoogle Scholar
  116. Wasala SK, Prasanna BM (2012) Microsatellite marker-based diversity and population genetic analysis of selected lowland and mid-altitude maize landrace accessions of India. J Plant Biochem Biotechnol 22:392–400CrossRefGoogle Scholar
  117. Weber VS, Araus JL, Cairns JE, Sanchez C, Melchinger AE, Orsini E (2012) Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crops Res 128:82–90CrossRefGoogle Scholar
  118. Wen W, Franco J, Chavez-Tovar VH, Yan J, Taba S (2012) Genetic characterization of a core set of a tropical maize race Tuxpeño for further use in maize improvement. PLoS One 7(3):e32626Google Scholar
  119. White WG, Moose SP, Weil CF, McCann MC, Carpita NC, Below FE (2011) Tropical maize: exploiting maize genetic diversity to develop a novel annual crop for lignocellulosic biomass and sugar production. In: Routes to cellulosic ethanol. Springer, New York, pp 167–179CrossRefGoogle Scholar
  120. Xu Y, Skinner DJ, Wu H, Palacios-Rojas N, Araus JL, Yan J, Gao S, Warburton ML, Crouch JH (2009) Advances in maize genomics and their value for enhancing genetic gains from breeding. Int J Plant Genom 2009:957602Google Scholar
  121. Xu J, Yuan YB, Xu YB, Zhang GY, Guo XS, Wu FK, Wang Q, Rong TZ, Pan GT, Cao MJ, Tang QL, Gao SB, Liu YX, Wang J, Lan H, Lu YL (2014) Identification of candidate genes for drought tolerance by whole-genome resequencing in maize. BMC Plant Biol 14:83PubMedPubMedCentralCrossRefGoogle Scholar
  122. Xu C, Ren Y, Jian Y, Guo Z, Zhang Y, Xie C, Fu J, Wang H, Li P (2017) Development of a maize 55 K SNP array with improved genome coverage for molecular breeding. Mol Breed 37:20–32PubMedPubMedCentralCrossRefGoogle Scholar
  123. Yan JB, Kandianis CB, Harjes CE, Bai L, Kim EH, Yang XH, Skinner DJ, Fu ZY, Mitchell S, Li Q, Fernandez MGS, Zaharieva M, Babu R, Fu Y, Palacios N, Li JS, DellaPenna D, Brutnell T, Buckler ES, Warburton ML, Rocheford T (2010a) Rare genetic variation at Zea mays crtRB1 increases beta-carotene in maize grain. Nat Genet 42:322–327PubMedCrossRefGoogle Scholar
  124. Yan JB, Yang XH, Shah T, Sanchez-Villeda H, Li JS, Warburton M, Zhou Y, Crouch JH, Xu YB (2010b) High-throughput SNP genotyping with the Golden Gate assay in maize. Mol Breed 25:441–451CrossRefGoogle Scholar
  125. Yang X, Gao S, Xu S, Zhang Z, Prasanna BM, Li L, Li J, Yan J (2010) Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize. Mol Breed 28:511–526CrossRefGoogle Scholar
  126. Yang N, Lu YL, Yang XH, Huang J, Zhou Y, Ali F, Wen WW, Liu J, Li JS, Yan JB (2014) Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet 10(9):e1004573Google Scholar
  127. Zaidi PH, Rashid Z, Vinayan MT, Almeida GD, Phagna RK, Babu R (2015) QTL mapping of agronomic waterlogging tolerance using recombinant inbred lines derived from tropical maize (Zea mays L) germplasm. PLoS One 10:e0124350PubMedPubMedCentralCrossRefGoogle Scholar
  128. Zaidi PH, Seetharam K, Krishna G, Krishnamurthy L, Gajanan S, Babu R, Zerka R, Vinayan MT, Vivek BS (2016) Genomic regions associated with root traits under drought stress in tropical maize (Zea mays L.). PloS one 11(10):e0164340PubMedPubMedCentralCrossRefGoogle Scholar
  129. Zhang X, Vicente SF, Beyene Y, Semagn K, Crossa J (2014) Genomic selection for tropical maize improvement. In: Proceedings of 12th Asian maize work shop, Bangkok, pp 81–84Google Scholar
  130. Zhang X, Perez-Rodriguez P, Semagn K, Beyene Y, Babu R, Lopez-Cruz MA, San Vicente F, Olsen M, Buckler E, Jannink JL, Prasanna BM, Crossa J (2015) Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity 114:291–299PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Thammineni Chakradhar
    • 1
  • Vemuri Hindu
    • 2
  • Palakolanu Sudhakar Reddy
    • 3
  1. 1.Sehgal FoundationC/o International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)HyderabadIndia
  2. 2.Department of BiotechnologySri Padmavati Mahila VisvavidyalayamTirupatiIndia
  3. 3.International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)HyderabadIndia

Personalised recommendations