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Theoretical and Applied Genetics

, Volume 125, Issue 2, pp 255–271 | Cite as

Genetic dissection of grain yield and physical grain quality in bread wheat (Triticum aestivum L.) under water-limited environments

  • Dion Bennett
  • Ali Izanloo
  • Matthew Reynolds
  • Haydn Kuchel
  • Peter Langridge
  • Thorsten Schnurbusch
Original Paper

Abstract

In the water-limited bread wheat production environment of southern Australia, large advances in grain yield have previously been achieved through the introduction and improved understanding of agronomic traits controlled by major genes, such as the semi-dwarf plant stature and photoperiod insensitivity. However, more recent yield increases have been achieved through incremental genetic advances, of which, breeders and researchers do not fully understand the underlying mechanism(s). A doubled haploid population was utilised, derived from a cross between RAC875, a relatively drought-tolerant breeders’ line and Kukri, a locally adapted variety more intolerant of drought. Experiments were performed in 16 environments over four seasons in southern Australia, to physiologically dissect grain yield and to detect quantitative trait loci (QTL) for these traits. Two stage multi-environment trial analysis identified three main clusters of experiments (forming distinctive environments, ENVs), each with a distinctive growing season rainfall patterns. Kernels per square metre were positively correlated with grain yield and influenced by kernels per spikelet, a measure of fertility. QTL analysis detected nine loci for grain yield across these ENVs, individually accounting for between 3 and 18% of genetic variance within their respective ENVs, with the RAC875 allele conferring increased grain yield at seven of these loci. These loci were partially dissected by the detection of co-located QTL for other traits, namely kernels per square metre. While most loci for grain yield have previously been reported, their deployment and effect within local germplasm are now better understood. A number of novel loci can be further exploited to aid breeders’ efforts in improving grain yield in the southern Australian environment.

Keywords

Quantitative Trait Locus Flag Leaf Doubled Haploid Population Spikelet Fertility Water Soluble Carbohydrate 
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.

Notes

Acknowledgments

Help from James Edwards with some data collection and preliminary analysis was much appreciated. The assistance of the Australian Grain Technologies field teams at all nodes is gratefully acknowledged for their excellent trial management; particularly the Roseworthy team for assistance with some sample collection, the loan of equipment for data collection and facilities for sample storage. Thank you also to the team at the Minnipa Research Station for their assistance with data and sample collection and excellent trial management, in particular Leigh Davis and Willie Shoobridge. Funding from the Grains Research and Development Corporation, South Australian State Government, Adelaide University and the South Australian Grains Industry Trust made this project possible and is also gratefully acknowledged.

Supplementary material

122_2012_1831_MOESM1_ESM.doc (298 kb)
Supplementary material 1 (DOC 298 kb)

References

  1. Bennett D, Izanloo A, Edwards J, Kuchel H, Chalmers K, Tester M, Reynolds M, Schnurbusch T, Langridge P (2011) Identification of novel quantitative trait loci for days to ear emergence and flag leaf glaucousness in a bread wheat (Triticum aestivum L.) population adapted to southern Australian conditions. Theor Appl Genet. doi: 10.1007/s00122-011-1740-3 (online ahead of print)
  2. Blum A, Sinmena B, Mayer J, Golan G, Shpiler L (1994) Stem reserve mobilization supports wheat grain filling under heat stress. Aust J Plant Physiol 21:771–781CrossRefGoogle Scholar
  3. Briggs KG, Kiplagat OK, Johnson-Flanagan AM (1999) Effects of pre-anthesis moisture stress on floret sterility in some semi-dwarf and conventional height spring wheat cultivars. Can J Plant Sci 79:515–520CrossRefGoogle Scholar
  4. Chenu K, Deihimfard R, Hammer G, Doherty A, Chapman S (2011) Characterisation of drought patterns across the Australian Wheat Belt. Wheat Breeding Assembly, Perth (24–26 August 2011)Google Scholar
  5. Clarke JM, McCaig TN, Depauw RM (1993) Relationship of glaucousness and epicuticular wax quantity of wheat. Can J Plant Sci 73:961–967CrossRefGoogle Scholar
  6. Condon A, Hall AE (1997) Adaptation to diverse environments: genotypic variation in water use efficiency within crop species. In: Jackson LE (ed) Agricultural ecology. Academic Press, San Diego, pp 79–116CrossRefGoogle Scholar
  7. Cuthbert JL, Somers DJ, Brule-Babel AL, Brown PD, Crow GH (2008) Molecular mapping of quantitative trait loci for yield and yield components in spring wheat (Triticum aestivum L.). Theor Appl Genet 117:595–608PubMedCrossRefGoogle Scholar
  8. Dreccer MF, van Herwaarden AF, Chapman SC (2009) Grain number and grain weight in wheat lines contrasting for stem water soluble carbohydrate concentration. Field Crop Res 112:43–54CrossRefGoogle Scholar
  9. Ehdaie B, Alloush GA, Madore MA, Waines JG (2006) Genotypic variation for stem reserves and mobilization in wheat: I. Postanthesis changes in internode dry matter. Crop Sci 46:735–746CrossRefGoogle Scholar
  10. Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269–293CrossRefGoogle Scholar
  11. Gonzalez A, Ayerbe L (2010) Effect of terminal water stress on leaf epicuticular wax load, residual transpiration and grain yield in barley. Euphytica 172:341–349CrossRefGoogle Scholar
  12. Groos C, Robert N, Bervas E, Charmet G (2003) Genetic analysis of grain protein-content, grain yield and thousand-kernel weight in bread wheat. Theor Appl Genet 106:1032–1040PubMedGoogle Scholar
  13. Huang XQ, Cloutier S, Lycar L, Radovanovic N, Humphreys DG, Noll JS, Somers DJ, Brown PD (2006) Molecular detection of QTLs for agronomic and quality traits in a doubled haploid population derived from two Canadian wheats (Triticum aestivum L.). Theor Appl Genet 113:753–766PubMedCrossRefGoogle Scholar
  14. Izanloo A, Condon AG, Langridge P, Tester M, Schnurbusch T (2008) Different mechanisms of adaptation to cyclic water stress in two South Australian bread wheat cultivars. J Exp Bot 59:3327–3346PubMedCrossRefGoogle Scholar
  15. Johnson DA, Richards RA, Turner NC (1983) Yield, water relations, gas-exchange and surface reflectances of near-isogenic wheat lines differing in glaucousness. Crop Sci 23:318–325CrossRefGoogle Scholar
  16. Kuchel H, Williams KJ, Langridge P, Eagles HA, Jefferies SP (2007) Genetic dissection of grain yield in bread wheat. I. QTL analysis. Theor Appl Genet 115:1029–1041PubMedCrossRefGoogle Scholar
  17. Kumar N, Kulwal PL, Balyan HS, Gupta PK (2007) QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat. Mol Breed 19:163–177CrossRefGoogle Scholar
  18. Maccaferri M, Sanguineti MC, Corneti S, Ortega JLA, Ben Salem M, Bort J, DeAmbrogio E, del Moral LFG, Demontis A, El-Ahmed A, Maalouf F, Machlab H, Martos V, Moragues M, Motawaj J, Nachit M, Nserallah N, Ouabbou H, Royo C, Slama A, Tuberosa R (2008) Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability. Genetics 178:489–511PubMedCrossRefGoogle Scholar
  19. Marza F, Bai GH, Carver BF, Zhou WC (2006) Quantitative trait loci for yield and related traits in the wheat population Ning7840 × Clark. Theor Appl Genet 112:688–698PubMedCrossRefGoogle Scholar
  20. Mason RE, Mondal S, Beecher FW, Pacheco A, Jampala B, Ibrahim AMH, Hays DB (2010) QTL associated with heat susceptibility index in wheat (Triticum aestivum L.) under short-term reproductive stage heat stress. Euphytica 174:423–436CrossRefGoogle Scholar
  21. Mathews KL, Malosetti M, Chapman S, McIntyre L, Reynolds M, Shorter R, van Eeuwijk F (2008) Multi-environment QTL mixed models for drought stress adaptation in wheat. Theor Appl Genet 117:1077–1091PubMedCrossRefGoogle Scholar
  22. McCartney CA, Somers DJ, Humphreys DG, Lukow O, Ames N, Noll J, Cloutier S, McCallum BD (2005) Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL4452  ×‘AC Domain’. Genome 48:870–883PubMedCrossRefGoogle Scholar
  23. McIntosh RA, Yamazaki Y, Devos KM, Dubcovsky J, Rogers WJ, Appels R (2003) Catalogue of gene symbols for wheat. Tenth International Wheat Genetics Symposium, PaestumGoogle Scholar
  24. McIntyre CL, Mathews KL, Rattey A, Chapman SC, Drenth J, Ghaderi M, Reynolds M, Shorter R (2010) Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions. Theor Appl Genet 120:527–541PubMedCrossRefGoogle Scholar
  25. Passioura JB (1977) Grain yield, harvest index and water use efficiency of wheat. J Aust Inst Agric Sci 43:117–120Google Scholar
  26. Payne RW, Harding SA, Murray DA, Soutar DM, Baird DB, Welham SJ, Kane AF, Gilmour AR, Thompson R, Webster R, Tunnicliffe WG (2005) GenStat® Release 8.2 reference manual. VSN International, OxfordGoogle Scholar
  27. Peleg Z, Fahima T, Krugman T, Abbo S, Yakir D, Korol AB, Saranga Y (2009) Genomic dissection of drought resistance in durum wheat × wild emmer wheat recombinant inbreed line population. Plant Cell Environ 32:758–779PubMedCrossRefGoogle Scholar
  28. Pinto RS, Reynolds MP, Mathews KL, McIntyre CL, Olivares-Villegas JJ, Chapman SC (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor Appl Genet 121:1001–1021PubMedCrossRefGoogle Scholar
  29. R Development Core Team (2005) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  30. Rattey A, Shorter R, Chapman S, Dreccer F, van Herwaarden A (2009) Variation for and relationships among biomass and grain yield component traits conferring improved yield and grain weight in an elite wheat population grown in variable yield environments. Crop Pasture Sci 60:717–729CrossRefGoogle Scholar
  31. Rebetzke G, Condon A, Farquhar G, Appels R, Richards R (2008a) Quantitative trait loci for carbon isotope discrimination are repeatable across environments and wheat mapping populations. Theor Appl Genet 118:123–137PubMedCrossRefGoogle Scholar
  32. Rebetzke GJ, van Herwaarden AF, Jenkins C, Weiss M, Lewis D, Ruuska S, Tabe L, Fettell NA, Richards RA (2008b) Quantitative trait loci for water-soluble carbohydrates and associations with agronomic traits in wheat. Aust J Agric Res 59:891–905CrossRefGoogle Scholar
  33. Reynolds MP, Condon AG (2007) Quantifying potential genetic gains in wheat yield using a conceptual model of drought adaptation. In: Buck HT, Nisi JE, Salomon N (eds) Wheat production in stressed environments. Springer, Dordrecht, pp 331–340CrossRefGoogle Scholar
  34. Reynolds M, Manes Y, Izanloo A, Langridge P (2009) Phenotyping approaches for physiological breeding and gene discovery in wheat. Ann Appl Biol 155:309–320CrossRefGoogle Scholar
  35. Richards RA (1991) Crop improvement for temperate Australia—future opportunities. Field Crop Res 26:141–169CrossRefGoogle Scholar
  36. Richards RA, Rawson HM, Johnson DA (1986) Glaucousness in wheat—its development and effect on water use efficiency, gas exchange and photosynthetic tissue temperatures. Aust J Plant Physiol 13:465–473Google Scholar
  37. Schon 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 167:485–498PubMedCrossRefGoogle Scholar
  38. Somers DJ, Isaac P, Edwards K (2004) A high-density microsatellite consensus map for bread wheat (Triticum aestivum L.). Theor Appl Genet 109:1105–1114PubMedCrossRefGoogle Scholar
  39. Sun XY, Wu K, Zhao Y, Kong FM, Han GZ, Jiang HM, Huang XJ, Li RJ, Wang HG, Li SS (2009) QTL analysis of kernel shape and weight using recombinant inbred lines in wheat. Euphytica 165:615–624CrossRefGoogle Scholar
  40. Sun XC, Marza F, Ma HX, Carver BF, Bai GH (2010) Mapping quantitative trait loci for quality factors in an inter-class cross of US and Chinese wheat. Theor Appl Genet 120:1041–1051PubMedCrossRefGoogle Scholar
  41. Verma V, Foulkes MJ, Worland AJ, Sylvester-Bradley R, Caligari PDS, Snape JW (2004) Mapping quantitative trait loci for flag leaf senescence as a yield determinant in winter wheat under optimal and drought-stressed environments. Euphytica 135:255–263CrossRefGoogle Scholar
  42. Wardlaw IF, Willenbrink J (2000) Mobilization of fructan reserves and changes in enzyme activities in wheat stems correlate with water stress during kernel filling. New Phytol 148:413–422CrossRefGoogle Scholar
  43. Yang DL, Jing RL, Chang XP, Li W (2007) Identification of quantitative trait loci and environmental interactions for accumulation and remobilization of water-soluble carbohydrates in wheat (Triticum aestivum L.) stems. Genetics 176:571–584PubMedCrossRefGoogle Scholar
  44. Zadoks JC, Chang TT, Konzak CF (1974) Decimal code for growth stages of cereals. Weed Res 14:415–421CrossRefGoogle Scholar
  45. Zhang LY, Liu DC, Guo XL, Yang WL, Sun JZ, Wang DW, Zhang AM (2010) Genomic distribution of quantitative trait loci for yield and yield-related traits in common wheat. J Integr Plant Biol 52:996–1007PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Dion Bennett
    • 1
    • 2
  • Ali Izanloo
    • 1
    • 4
  • Matthew Reynolds
    • 3
  • Haydn Kuchel
    • 2
  • Peter Langridge
    • 1
  • Thorsten Schnurbusch
    • 1
    • 5
  1. 1.Australian Centre for Plant Functional Genomics, Waite CampusUniversity of AdelaideGlen OsmondAustralia
  2. 2.Australian Grain TechnologiesRoseworthyAustralia
  3. 3.International Maize and Wheat Improvement Center (CIMMYT)México, D.F.Mexico
  4. 4.Department of Agronomy and Plant Breeding, Faculty of AgricultureUniversity of BirjandBirjandIran
  5. 5.Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany

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