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Quantitative trait loci for carbon isotope discrimination are repeatable across environments and wheat mapping populations

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Abstract

Wheat productivity is commonly limited by a lack of water essential for growth. Carbon isotope discrimination (Δ), through its negative relationship with transpiration efficiency, has been used in selection of higher wheat yields in breeding for rainfed environments. The potential also exists for selection of increased Δ for improved adaptation to irrigated and high rainfall environments. Selection efficiency of Δ would be enhanced with a better understanding of its genetic control. Three wheat mapping populations (Cranbrook/Halberd, Sunco/Tasman and CD87/Katepwa) containing between 161 and 190 F1-derived, doubled-haploid progeny were phenotyped for Δ and agronomic traits in 3–5 well-watered environments. The range for Δ was large among progeny (c. 1.2–2.3‰), contributing to moderate-to-high single environment (h 2 = 0.37–0.91) and line-mean (0.63–0.86) heritabilities. Transgressive segregation was large and genetic control complex with between 9 and 13 Δ quantitative trait loci (QTL) identified in each cross. The Δ QTL effects were commonly small, accounting for a modest 1–10% of the total additive genetic variance, while a number of chromosomal regions appeared in two or more populations (e.g. 1BL, 2BS, 3BS, 4AS, 4BS, 5AS, 7AS and 7BS). Some of the Δ genomic regions were associated with variation in heading date (e.g. 2DS, 4AS and 7AL) and/or plant height (e.g. 1BL, 4BS and 4DS) to confound genotypic associations between Δ and grain yield. As a group, high Δ progeny were significantly (P < 0.10–0.01) taller and flowered earlier but produced more biomass and grain yield in favorable environments. After removing the effect of height and heading date, strong genotypic correlations were observed for Δ and both yield and biomass across populations (r g = 0.29–0.57, P < 0.05) as might be expected for the favorable experimental conditions. Thus selection for Δ appears beneficial in increasing grain yield and biomass in favorable environments. However, care must be taken to avoid confounding genotypic differences in Δ with stature and development time when selecting for improved biomass and yield especially in environments experiencing terminal droughts. Polygenic control and small size of individual QTL for Δ may reduce the potential for QTL in marker-assisted selection for improved yield of wheat.

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References

  • Araus JL, Villegas D, Aparicio N, García del Moral LF, El Hani S, Rharrabti Y, Ferrio JP, Royo C (2003) Environmental factors determining carbon isotope discrimination and yield in durum wheat under Mediterranean conditions. Crop Sci 43:170–180

    Google Scholar 

  • Austin RB (1980) Physiological limitations to cereal yields and ways of reducing them by breeding. In: Hurd RG, Biscoe PV, Dennis C (eds) Opportunities for increasing crop yields. Pitman, London, pp 3–19

    Google Scholar 

  • Beavis WD (1998) QTL analyses: power, precision, and accuracy. In: Paterson AH (ed) Molecular dissection of complex traits. CRC Press, Boca Raton, pp 145–162

    Google Scholar 

  • Ceccarelli S, Acevedo E, Grando S (1991) Breeding for yield stability in unpredictable environments: single traits interaction between traits, and architecture of genotypes. Euphytica 56:169–185

    Article  Google Scholar 

  • Churchill GA, Doerge RW (1994) Emperical threshold values for quantitative trait mapping. Genetics 138:963–971

    PubMed  CAS  Google Scholar 

  • Condon AG, Richards RA (1992) Broad sense heritability and genotype × environment interaction for carbon isotope discrimination in field-grown wheat. Aust J Agric Res 43:921–934

    Article  Google Scholar 

  • Condon AG, Richards RA, Farquhar GD (1987) Carbon isotope discrimination is positively correlated with grain yield and dry matter production in field-grown wheat. Crop Sci 27:996–1001

    Google Scholar 

  • Condon AG, Farquhar GD, Richards RA (1990) Genotypic variation in carbon isotope discrimination and transpiration efficiency in wheat. Aust J Plant Phys 17:9–22

    Google Scholar 

  • Condon AG, Richards RA, Farquhar GD (1992) The effect of variation in soil water availability, vapour pressure deficit and nitrogen nutrition on carbon isotope discrimination in wheat. Aust J Agric Res 43:935–947

    Article  CAS  Google Scholar 

  • Condon AG, Richards RA, Farquhar GD (1993) Relationships between carbon isotope discrimination, water use efficiency and transpiration efficiency for dryland wheat. Aust J Agric Res 44:1693–1711

    Article  CAS  Google Scholar 

  • Condon AG, Richards RA, Rebetzke GJ, Farquhar GD (2004) Breeding for high water use efficiency. J Exp Bot 55:2447–2460

    Article  PubMed  CAS  Google Scholar 

  • Condon AG, Reynolds MP, Rebetzke GJ, van Ginkel M, Richards RA, Farquhar GD (2007) Using stomatal aperture traits to select for high yield potential in bread wheat. In: Buck HT, Nisi JE, Salomón N (eds) Wheat production in stressed environments. Proceedings of the 7th international wheat conference, Mar del Plata, Argentina. Springer, Netherlands, pp 617–624

  • Ehdaie B, Waines JG (1994) Growth and transpiration efficiency of near isogenic lines for height in a spring wheat. Crop Sci 34:1443–1451

    Google Scholar 

  • Ehdaie B, Hall AE, Farquhar GD, Nguyen HT, Waines JG (1991) Water-use efficiency and carbon isotope discrimination in wheat. Crop Sci 31:1282–1288

    Google Scholar 

  • Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman, Essex, p 464

    Google Scholar 

  • Farquhar GD, Richards RA (1984) Isotopic composition of plant carbon correlates with water-use efficiency of wheat genotypes. Aust J Plant Phys 11:539–552

    CAS  Google Scholar 

  • Fischer RA, Wood JT (1979) Drought resistance in spring wheat cultivars. III Yield associations with morpho-physiological traits. Aust J Agric Res 30:1001–1020

    Article  Google Scholar 

  • Fischer RA, Rees D, Sayre KD, Lu Z, Condon AG, Saavendra AL (1998) Wheat yield progress is associated with higher stomatal conductance, higher photosynthetic rate and cooler canopies. Crop Sci 38:1467–1475

    Google Scholar 

  • Forster BP, Ellis RP, Moir J, Talamè V, Sanguineti MC, Tuberosa R, This D, Teulat-Merah B, Ahmed I, Mariy SAEE, Bahri H, El Ouahabi M, Zoumarou-Wallis N, El-Fellah M, Ben Salem M (2004) Genotype and phenotype associations with drought tolerance in barley tested in North Africa. Ann Appl Biol 144:157–168

    Article  Google Scholar 

  • Foulkes MJ, Snape JW, Shearman VJ, Reynolds MP, Gaju O, Sylvester-Bradley R (2007) Genetic progress in yield potential in wheat: recent advances and future prospects. J Agric Sci (Camb) 145:17–29

    Article  CAS  Google Scholar 

  • Hall AE, Richards RA, Condon AG, Wright GC, Farquhar GD (1994) Carbon isotope discrimination and plant breeding. Plant Breed Rev 12:81–113

    Google Scholar 

  • Hausmann N, Juenger TE, Sen S, Stowe KA, Dawson TE, Simms EL (2005) Quantitative trait loci affecting δ13C and response to differential water availability in Arabidopsis thaliana. Evolution 59:81–96

    PubMed  CAS  Google Scholar 

  • Hervé D, Fabre F, Berrios EF, Leroux N, Chaarani GA, Planchon C, Sarrafi A, Gentzbittel L (2001) Regulation of growth, development and whole organism physiology: QTL analysis of photosynthesis and water status traits in sunflower (Helianthus annus L.) under green house conditions. J Exp Bot 52:1857–1864

    Article  PubMed  Google Scholar 

  • Johnson DA, Asay KH, Tieszen LL, Ehleringer JR, Jefferson PG (1990) Carbon isotope discrimination: potential in screening cool-season grasses for water-limited environments. Crop Sci 30:338–343

    Google Scholar 

  • Kammholz SJ, Campbell AW, Sutherland MW, Hollamby G, Martin P, Eastwood R, Barclay I, Wilson R, Brennan P, Sheppard J (2001) Establishment and characterisation of wheat genetic mapping populations. Aust J Agric Res 52:1079–1088

    Article  CAS  Google Scholar 

  • Laza RM, Kondo M, Ideta O, Barlaan E, Imbe T (2006) Identification of quantitative trait loci for δ 13C and productivity in irrigated lowland rice. Crop Sci 46:763–773

    Article  Google Scholar 

  • Lehmensiek A, Eckermann PJ, Verbyla AP, Appels R, Sutherland MW, Daggard GE (2005) Curation of wheat maps to improve map accuracy and QTL detection. Aust J Agric Res 56:1347–1354

    Article  Google Scholar 

  • Littell RC, Milliken GA, Stroup WW, Wolfinger RD (1996) SAS system for mixed models. SAS Institute Inc., Cary

    Google Scholar 

  • Limin AE, Fowler DB (2001) Inheritance of cell size in wheat (Triticum aestivum L.) and its relationship to the vernalization loci. Theor Appl Genet 103:277–281

    Article  CAS  Google Scholar 

  • Malik TA, Wright D, Virk DS (1999) Inheritance of net photosynthesis and transpiration efficiency in spring wheat, Triticum aestivum L., under drought. Plant Breed 118:93–95

    Article  CAS  Google Scholar 

  • Masle J, Gilmore SR, Farquhar GD (2005) The ERECTA gene regulates plant transpiration efficiency in Arabidopsis. Nature 436:866–870

    Article  PubMed  CAS  Google Scholar 

  • Mather K, Jinks JL (1982) Biometrical genetics, 3rd edn. Chapman & Hall, London

    Google Scholar 

  • McKay JK, Richards JH, Mitchell-Olds T (2003) Genetics of drought adaptation in Arabidopsis thaliana: pleiotropy contributes to genetic correlations among ecological traits. Mol Ecol 12:1137–1151

    Article  PubMed  CAS  Google Scholar 

  • Morgan JA, LeCain DR, Wells R (1990) Semidwarfing genes concentrate photosynthetic machinery and affect leaf gas exchange of wheat. Crop Sci 30:602–608

    CAS  Google Scholar 

  • Morgan JA, LeCain DR, McCaig TN, Quick JS (1993) Gas exchange, carbon isotope discrimination, and productivity in winter wheat. Crop Sci 33:178–186

    CAS  Google Scholar 

  • Perry MW, D’Antuono MFD (1989) Yield improvement and associated characteristics of some Australian spring wheat cultivars introduced between 1860 and 1982. Aust J Agric Res 40:457–472

    Google Scholar 

  • Price AH, Cairns JE, Horton P, Jones HG, Griffiths H (2002) Linking drought-resistance mechanisms to drought avoidance in upland rice using a QTL approach: progress and new opportunities to integrate stomatal and mesophyll responses. J Exp Bot 53:989–1004

    Article  PubMed  CAS  Google Scholar 

  • Rawlings JO (1988) Applied regression analysis: a research tool. Wadsworth & Banks/Cole. Advanced Books & Software, Pacific Grove, CA

  • Rebetzke GJ, Condon AG, Richards RA, Farquhar GD (2002) Selection for reduced carbon-isotope discrimination increases aerial biomass and grain yield of rainfed bread wheat. Crop Sci 42:739–745

    Google Scholar 

  • Rebetzke GJ, Condon AG, Richards RA, Farquhar GD (2003) Gene action for leaf conductance in three wheat crosses. Aust J Agric Res 54:381–387

    Article  Google Scholar 

  • Rebetzke GJ, Richards RA, Condon AG, Farquhar GD (2006) Inheritance of reduced carbon isotope discrimination in bread wheat (Triticum aestivum L.). Euphytica 150:97–106

    Article  CAS  Google Scholar 

  • Richards RA (1992) The effect of dwarfing genes in spring wheat in dry environments. II. Growth, water use and water-use efficiency. Aust J Agric Res 43:529–539

    Article  Google Scholar 

  • Richards RA (2000) Selectable traits to increase crop photosynthesis and yield of grain crops. J Exp Bot 51:447–458

    Article  PubMed  CAS  Google Scholar 

  • Richards RA, Rebetzke GJ, Condon AG, van Herwaarden A (2002) Breeding for greater water use efficiency in wheat. Crop Sci 42:111–121

    PubMed  Google Scholar 

  • Saranga Y, Menz M, Jiang C, Wright RJ, Yakir D, Paterson AH (2001) Genomic dissection of genotype ×  environment interactions conferring adaptation of cotton to arid conditions. Genom Res 11:1988–1995

    Article  CAS  Google Scholar 

  • Sayre KD, Rajaram S, Fischer RA (1997) Yield potential progress in short bread wheats in northwest Mexico. Crop Sci 37:36–42

    Google Scholar 

  • Sharma RC (1993) Selection for biomass yield in wheat. Euphytica 70:35–42

    Article  Google Scholar 

  • Shearman VJ, Sylvester-Bradley R, Scott RK, Foulkes MJ (2005) Physiological processes associated with wheat yield progress in the UK. Crop Sci 45:175–185

    Google Scholar 

  • Simόn MR (1994) Gene action and heritability for photosynthetic activity in two wheat crosses. Euphytica 76:235–238

    Article  Google Scholar 

  • Solomon KF, Labuschagne MT (2004) Inheritance of evapotranspiration and transpiration efficiencies in diallel F1 hybrids of durum wheat (Triticum turgidum L. var. durum). Euphytica 136:69–79

    Article  Google Scholar 

  • Snape JW, Butterworth K, Whitechurch E, Worland AJ (2001) Waiting for fine times: genetics of flowering time in wheat. Euphytica 119:185–190

    Article  CAS  Google Scholar 

  • Takai T, Matsuura S, Nishio T, Ohsumi A, Shiraiwa T, Horie T (2006) Rice yield potential is closely related to crop growth rate during late reproduction period. Field Crops Res 96:328–335

    Article  Google Scholar 

  • Teulat B, Merah O, Sirault X, Borries C, Waugh R, This D (2002) QTLs for grain carbon isotope discrimination in field-grown barley. Theor Appl Genet 106:118–126

    PubMed  CAS  Google Scholar 

  • Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93:77–78

    Article  PubMed  CAS  Google Scholar 

  • Wang J, Bonnett DG, Chapman SC, Rebetzke GJ (2007) Efficient use of marker-based selection in plant breeding. Crop Sci 47:580–588

    Google Scholar 

  • Yang J, Hu CC, Ye XZ, Zhu J (2005) QTLNetwork 2.0. Institute of Bioinformatics, Zhejiang University, Hangzhou, China. http://ibi.zju.edu.cn/software/qtlnetwork

  • Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14:415–421

    Article  Google Scholar 

  • Zhou Y, He ZH, Sui XX, Xia XC, Zhang XK, Zhang GS (2007) Genetic improvement of grain yield and associated traits in the Northern China winter wheat region from 1960 to 2000. Crop Sci 47:245–253

    Article  CAS  Google Scholar 

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Acknowledgments

We would like to thank B. Mickelson and M. Weiss for dedicated assistance with experimental aspects associated with this paper. We would also like to thank R. Phillips for ∆ analysis of wheat samples, Anke Lehmensiek of University of Southern Queensland for providing the genetic maps, and Tony Fischer, Thorsten Schnurbusch and Chris Lambrides for valuable comments on the manuscript. Thanks also to the anonymous reviewers for their helpful comments.

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Authors and Affiliations

  1. CSIRO Plant Industry, P.O. Box 1600, Canberra, ACT, 2601, Australia

    G. J. Rebetzke, A. G. Condon & R. A. Richards

  2. Australian National University, P.O. Box 475, Canberra, ACT, 2601, Australia

    G. D. Farquhar

  3. Centre for Comparative Genomics, Murdoch University, South St Perth, WA, 6150, Australia

    R. Appels

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  1. G. J. Rebetzke
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  2. A. G. Condon
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  3. G. D. Farquhar
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  5. R. A. Richards
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Correspondence to G. J. Rebetzke.

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Communicated by C. Schön.

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Rebetzke, G.J., Condon, A.G., Farquhar, G.D. et al. Quantitative trait loci for carbon isotope discrimination are repeatable across environments and wheat mapping populations. Theor Appl Genet 118, 123–137 (2008). https://doi.org/10.1007/s00122-008-0882-4

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  • DOI: https://doi.org/10.1007/s00122-008-0882-4

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