Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time

Abstract

Nested association mapping (NAM) offers power to dissect complex, quantitative traits. This study made use of a recently developed sorghum backcross (BC)-NAM population to dissect the genetic architecture of flowering time in sorghum; to compare the QTL identified with other genomic regions identified in previous sorghum and maize flowering time studies and to highlight the implications of our findings for plant breeding. A subset of the sorghum BC-NAM population consisting of over 1,300 individuals from 24 families was evaluated for flowering time across multiple environments. Two QTL analysis methodologies were used to identify 40 QTLs with predominately small, additive effects on flowering time; 24 of these co-located with previously identified QTL for flowering time in sorghum and 16 were novel in sorghum. Significant synteny was also detected with the QTL for flowering time detected in a comparable NAM resource recently developed for maize (Zea mays) by Buckler et al. (Science 325:714–718, 2009). The use of the sorghum BC-NAM population allowed us to catalogue allelic variants at a maximal number of QTL and understand their contribution to the flowering time phenotype and distribution across diverse germplasm. The successful demonstration of the power of the sorghum BC-NAM population is exemplified not only by correspondence of QTL previously identified in sorghum, but also by correspondence of QTL in different taxa, specifically maize in this case. The unification across taxa of the candidate genes influencing complex traits, such as flowering time can further facilitate the detailed dissection of the genetic control and causal genes.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Akhunov ED, Goodyear AW, Geng S, Qi LL, Echalier B, Gill BS, Miftahudin Gustafson JP, Lazo G, Chao SM, Anderson OD, Linkiewicz AM, Dubcovsky J, La Rota M, Sorrells ME, Zhang DS, Nguyen HT, Kalavacharla V, Hossain K, Kianian SF, Peng JH, Lapitan NLV, Gonzalez-Hernandeiz JL, Anderson JA, Choi DW, Close TJ, Dilbirligi M, Gill KS, Walker-Simmons MK, Steber C, McGuire PE, Qualset CO, Dvorak J (2003) The organization and rate of evolution of wheat genomes are correlated with recombination rates along chromosome arms. Genome Res 13:753–763

  2. Altschul S, Gish W, Miller WP, Myers E, Lipman D (1990) Basic local alignment search tool. J Mol Biol 215:403–410

  3. Amasino RM, Michaels SD (2010) The timing of flowering. Plant Physiol 154:516–520

  4. Beavis WD (1994) The power and deceit of QTL experiments: lessons from comparative QTL studies. In: Proceedings of 49th annual corn sorghum and research conference. American Seed Trade Association, Washington, DC, pp 250–266

  5. Brown PJ, Klein PE, Bortiri E, Acharya CB, Rooney WL, Kresovich S (2006) Inheritance of inflorescence architecture in sorghum. Theor Appl Genet 113:931–942

  6. Buckler ES et al (2009) The genetic architecture of maize flowering time. Science 325:714–718

  7. Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual release 3. Technical report, QLD Department of Primary Industries and Fisheries, Brisbane, QLD

  8. Chantereau J, Trouche G, Rami JF, Deu M, Barro C, Grivet L (2001) RFLP mapping of QTLs for photoperiod response in tropical sorghum. Euphytica 120:183–194

  9. Cockram J, Jones H, Leigh FJ, O’Sullivan D, Powell W, Laurie DA, Greenland AJ (2007) Control of flowering time in temperate cereals: genes, domestication and sustainable productivity. J Exp Bot 58:1231–1244

  10. Colasanti J, Coneva V (2009) Mechanisms of floral induction in grasses: something borrowed, something new. Plant Physiol 149:56–62

  11. Cook JP, McMullem 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 158:824–834

  12. Crasta OR, Xu WW, Nguyen HT, Rosenow DT, Mullet J (1999) Mapping of post flowering drought resistance traits in grain sorghum: association between QTLs influencing premature senescence and maturity. Mol Gen Genet 262:579–588

  13. Craufurd PQ, Wheeler TR (2009) Climate change and the flowering time of annual crops. J Exp Bot 60:2529–2539

  14. Cullis BR, Smith AR, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Envirn Stat 11:381–393

  15. Feltus FA, Hart GE, Schertz KF, Casa AM, Kresovich S, Abraham S, Klein PE, Brown PJ, Paterson AH (2006) Alignment of genetic maps and QTLs between inter- and intraspecific sorghum populations. Theor Appl Genet 112:1295–1305

  16. Fisher RA (1932) Statistical methods for research workers, 4th edn. Oliver and Boyd, Edinburgh

  17. Flowers JM, Hanzawa Y, Hall MC, Moore RC, Purugganan MD (2009) Population genomics of the Arabidopsis thaliana flowering time gene network. Mol Biol Evol 26:2475–2486

  18. Greenup A, Peacock WJ, Dennis ES, Trevaskis B (2009) The molecular biology of seasonal flowering-responses in Arabidopsis and the cereals. Ann Bot 103:1165–1172

  19. Hammer GL, Sinclair TR, Chapman SC, van Oosterom EJ (2004) On systems thinking, systems biology and in the in silico plant. Plant Physiol 134:909–911

  20. Hammer GL, van Oosterom E, McLean G, Chapman SC, Broad I, Harland P, Muchow RC (2010) Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J Exp Bot 61:2185–2202

  21. Hart GE, Schertz KF, Peng Y, Syed NH (2001) Genetic mapping of Sorghum bicolor (L.) Moench QTLs that control variation in tillering and other morphological characters. Theor Appl Genet 103:1232–1242

  22. Higgins JA, Bailey PC, Laurie DA (2010) Comparative genomics of flowering time pathways using Brachypodium distachyon as a model for the temperate grasses. PLoS ONE 5:e10065

  23. Imaizumi T (2010) Arabidopsis circadian clock and photoperiodism: time to think about location. Curr Opin Plant Biol 13:83–89

  24. Jordan DR, Mace ES, Cruickshank AW, Hunt CH, Henzell RG (2011) Exploring and exploiting genetic variation from unadapted sorghum germplasm in a breeding program. Crop Sci 51:1444–1457

  25. Jung C, Muller AE (2009) Flowering time control and applications in plant breeding. Trends Plant Sci 14:563–573

  26. Kebede H, Subadhi PK, Rosenow DT, Nguyen HT (2001) Quantitative trait loci influencing drought tolerance in grain sorghum (Sorghum bicolor L. Moench). Theor Appl Genet 103:266–276

  27. Kim J (2003) Genomic analysis of sorghum by fluorescence in situ hybridization. PhD thesis. Texas A&M University, Texas

  28. Kirby JS, Atkins RE (1968) Heterotic response for vegetative and mature plant characters in grain sorghum, Sorghum bicolour (L.) Moench. Crop Sci 8:335–339

  29. Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, Zwonitzer JC, Kresovich S, McMullen MD, Ware D, Balint-Kurti PJ, Holland JB (2011) Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43:163–168

  30. Lagercrantz U (2009) At the end of the day: a common molecular mechanism for photoperiod response in plants? J Exp Bot 60:2501–2515

  31. Liang GHL, Walter TL (1968) Heritability estimates and gene effects for agronomic traits in grain sorghum, Sorghum vulgare Pers. Crop Sci 8:77–81

  32. Lin Y-R, Schertz KF, Paterson AH (1995) Comparative analysis of QTLs affecting plant height and maturity across the Poaceae, in reference to an interspecific sorghum population. Genetics 141:391–411

  33. Mace ES, Jordan DR (2011) Integrating sorghum whole genome sequence information with a compendium of sorghum QTL studies reveals non-random distribution of QTL and of gene rich regions with significant implications for crop improvement. Theor Appl Genet 123:169–191

  34. 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

  35. Mace ES, Rami JF, Bouchet S, Klein PE, Klein RR, Kilian A, Wenzl P, Xia L, Halloran K, Jordan DR (2009a) A consensus genetic map of sorghum that integrates multiple component maps and high-throughput diversity array technology (DArT) markers. BMC Plant Biol 9:13

  36. Mace ES, Hunt C, Rodgers D, Jordan DR (2009b) Multi-population QTL analysis in sorghum. SABRAO J Breed Genet (41), special supplement. A paper presented at the 14th Australasian plant breeding conference, 10–14 Aug 2009, Cairns, Australia

  37. Mannai YE, Shehzad T, Okuno K (2011) variation in flowering time in sorghum core collection and mapping of QTLs controlling flowering time by association analysis. Genet Resour Crop Evol 58:983–989

  38. McMullen MD et al (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740

  39. Messing J (2009) The polyploidy origin of maize. Handbook of maize, pp 221–238. doi:10.1007/978-0-387-77863-1_11

  40. Mouradov A, Cremer F, Coupland G (2002) Control of flowering time: interacting pathways as a basis for diversity. Plant Cell 14(supplement):S111–S130

  41. Murphy RL, Klein RR, Morishge DT, Brady JA, Rooney WL, Miller FR, Dugas DV, Klein PE, Mullet JE (2011) Coincident light and clock regulation of pseudoresponse regulator protein 37 (PRR37) controls photoperiodic flowering in sorghum. Proc Natl Acad Sci USA 108:16469–16474

  42. Murray SC, Rooney WL, Mitchell SE, Sharma A, Klein PE, Mullet JE, Kresovich S (2008) Genetic improvement of sorghum as a biofuel feedstock: II. QTL for stem and leaf structural carbohydrates. Crop Sci 48:2180–2193

  43. Parh D (2005) DNA-based markers for ergot resistance in sorghum. PhD thesis. University of Queensland, Brisbane

  44. 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 108:6893–6898

  45. Putterill J, Laurie R, Macknight R (2004) It’s time to flower: the genetic control of flowering time. BioEssays 26:363–373

  46. Quinby JR (1967) The maturity genes of sorghum. In: Ag Norman (ed) Advances in Agronomy, vol 19. Academic Press, New York, pp 267–305

  47. R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0

  48. Ritter KB, Jordan DR, Chapman SC, Godwin ID, Mace ES, McIntyre CL (2008) Identification of QTL for sugar-related traits in a sweet x grain sorghum (Sorghum bicolor L. Moench) recombinant inbred population. Mol Breed 22:367–384

  49. Rooney WL, Aydin S (1999) Genetic control of a photoperiod-sensitive response in Sorghum bicolor (L.) Moench. Crop Sci 39:397–400

  50. Schuler GD (1998) Electronic PCR: bridging the gap between genome mapping and genome sequencing. Trends Biotechnol 16:456–459

  51. Shiringani AL, Frisch M, Friedt W (2010) Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench. Theor Appl Genet 121:323–336

  52. Simpson GG (2004) The autonomous pathway: epigenetic and post-transcriptional gene regulation in the control of Arabidopsis flowering time. Curr Opinion Pl Biol 7:1–5

  53. Smith A, Cullis B, Thompson R (2001) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57:1138–1147

  54. Srinivas G, Satish K, Madhusudhana R, Nagaraja Reddy R, Murali Mohan S, Seetharama N (2009) Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum. Theor Appl Genet 118:1439–1454

  55. Stephens JC, Miller FR, Rosenow DT (1967) Conversion of alien sorghums to early combine genotypes. Crop Sci 7:396

  56. Sun X, Peng T, Mumm RH (2011) The role and basics of computer simulation in support of critical decisions in plant breeding. Mol Breeding 28:421–436

  57. Taylor JS, Raes J (2004) Duplication and divergence: the evolution of new genes and old ideas. Ann Rev Genet 38:615–643

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

  59. Van Oosterom EJ, Hammer GL, Chapman SC, Doherty A, Mace ES, Jordan DR (2006) Predicting flowering time in sorghum using a simple gene network: functional physiology or fictional functionality? In: Proceedings of the 5th Australian sorghum conference. 30 Jan–2 Feb 2006, Gold Coast, Australia

  60. VSN International (2011) GenStat for Windows. 14th edn. VSN International, Hemel Hempstead, UK. http://GenStat.co.uk

  61. Wenzel WG (1988) Relationships between yield, base temperature and drought resistance in grain sorghum. S Afr J Pl Soil 5:150–154

  62. Woodhouse MR, Schnable JC, Pedersen BS, Lyons E, Lisch D, Subramanian S, Freeling M (2010) Following tetraploidy in maize, a short deletion mechanism removed genes preferentially from one of the two homologs. PLoS Biol 8:e1000409

Download references

Acknowledgments

The authors would like to thank Erik van Oosterum, Ian Godwin and Chris Lambrides for their critical review of the manuscript and to acknowledge the Queensland Government and the Grains Research and Development Corporation (GRDC) for providing funding for this research.

Author information

Correspondence to E. S. Mace.

Additional information

Communicated by R. Snowdon.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Figure S1. Allele effect graphs for all 40 DTF QTL detected in BC-NAM population (PPT 1117 kb)

Figure S2. Consensus genetic linkage map of sorghum showing location of previously identified sorghum flowering time QTL in relation to QTL identified in current study (PPT 415 kb)

Figure S3. Genetic linkage maps of sorghum location of NAM flowering time QTL in maize vs sorghum (PPT 252 kb)

Figure S4. QTL allele effect size comparison between ABC population and S. arundinaceum BC-NAM population (PPT 104 kb)

File S1. Details of the statistical methods used to analyse the phenotypic data, the single marker analysis and the mpQTL process (DOC 43 kb)

Table S1. The occurrence of the 24 BC-NAM populations across years (DOC 43 kb)

Table S2. Number of markers within each BC-NAM family after excluding monomorphic markers and those with frequency greater than 95% and less than 5% (DOC 33 kb)

Table S3. Details of co-locating QTL from previous sorghum and maize flowering time studies. Identifiers of QTL are those detailed in Buckler et al 2009 (for maize) and in Mace and Jordan (2011) for sorghum (XLSX 18 kb)

Table S4. Details of candidate genes for flowering time identified in the sorghum genome (XLSX 23 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Mace, E.S., Hunt, C.H. & Jordan, D.R. Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time. Theor Appl Genet 126, 1377–1395 (2013). https://doi.org/10.1007/s00122-013-2059-z

Download citation

Keywords

  • Sorghum
  • Flowering Time
  • Association Mapping
  • DArT Marker
  • Allele Effect