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QTL Mapping of Diffuse Reflectance Indices of Leaves in Hexaploid Bread Wheat (Triticum aestivum L.)

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Abstract

Quantitative trait loci (QTL) mapping of diffuse reflectance indices of laminas in bread wheat (Triticum aestivum L.) has been first performed under controlled conditions of a regulated agroecobiological testing ground in the presence or absence of nitrogen fertilizers. Indices chosen for the study determine a range of important characteristics, such as the content of chlorophylls and anthocyanins, carotenoid/chlorophyll ratio, photochemical activity of the photosynthetic apparatus, light scattering on a lamina, assimilating leaf surface area, and grain productivity. In total, 31 QTLs have been mapped. A significant correlation has been revealed between the introduction of a nitrogen fertilizer and the five of six optical characteristics of the photosynthetic apparatus activity in bread wheat. The only exception is the reflectance index for near-infrared radiation (800 nm), which depends on the structural features of leaf tissues. No statistically significant correlation has been revealed between the thousand-grain weight and spectral characteristics of the diffuse reflectance of the lamina measured at the booting stage. However, a significant correlation between the number of grains formed in the spike of the main stalk and the traits characterizing activity of the photosynthetic apparatus (reflectance indices, leaf area) has been observed. Results of the performed variance, correlation, and QTL analyses confirm each other indicating reliability of the revealed effect of nitrogen nutrition level on the manifestation of the studied reflectance indices in bread wheat under strictly controlled conditions of an agroecobiological testing ground. Application of noninvasive optical methods provides a high-throughput assessment of photosynthetic intensity in plants and, therefore, can be used for efficient selection of promising wheat genotypes with high grain productivity under both controlled and field conditions.

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REFERENCES

  1. Chesnokov, Yu.V., Molekulyarno-geneticheskie markery i ikh ispol’zovanie v predselektsionnykh issledovaniyakh (Molecular Genetic Markers and Their Use in Prebreeding Investigations), St. Petersburg: Agrophys. Res. Inst., 2013.

  2. Chesnokov, Yu.V., Mirskaya, G.V., Kanash, E.V., Kocherina, N.V., Lohwasser, U., and Börner, A., QTL mapping of bread wheat (Triticum aestivum L.) grown under controlled conditions of an agroecobiological testing ground, Russ. J. Plant Physiol., 2017, vol. 64, pp. 48–58.

    Article  CAS  Google Scholar 

  3. Chesnokov, Yu.V., Mirskaya, G.V., Kanash, E.V., Kocherina, N.V., Rusakov, D.V., Lohwasser, U., and Börner, A., QTL identification and mapping in soft spring wheat (Triticum aestivum L.) under controlled agroecological and biological testing area conditions with and without nitrogen fertilized, Russ. J. Plant Physiol., 2018, vol. 65, pp. 123–135. https://doi.org/10.1134/S102144371801003X

    Article  CAS  Google Scholar 

  4. Furbank, R.T. and Tester, M., Phenomics technologies to relieve the phenotyping bottleneck, Trends Plant Sci., 2011, vol. 16, pp. 635–644.

    Article  CAS  PubMed  Google Scholar 

  5. Walter, A., Studer, B., and Kolliker, R., Advanced phenotyping offers opportunities for improved breeding of forage and turf species, Ann. Bot., 2012, vol. 110, pp. 1271–1279.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kanash, E.V., Panova, G.G., and Blokhina, S.Yu., Optical criteria for assessment of efficiency and adaptogenic characteristics of biologically active preparations, Acta Hortic., 2013, vol. 1009, pp. 37–44.

  7. Graeff, S., Steffens, D., and Schubert, S., Use of reflectance measurements for the early detection of N, P, Mg, and Fe deficiencies in Zea mays L., J. Plant Nutr. Soil Sci., 2001, vol. 164, pp. 445–450.

    Article  CAS  Google Scholar 

  8. Yakushev, V., Kanash, E., Rusakov, D., and Blokhina, S., Specific and non-specific changes in optical characteristics of spring wheat leaves under nitrogen and water deficiency, Proc. 11th Eur. Conf. on Advances in Animal Biosciences: Precision Agriculture (ECPA), Edinburgh, July 16–20, 2017, The Animal Consortium, 2017, vol. 8, special issue 2, pp. 229–232. https://doi.org/10.1017/S204047001700053X

  9. Kanash, E.V. and Osipov, Yu.A., Optical signals of oxidative stress in crops physiological state diagnostics, Proc. 7th Eur. Conf. on Precision Agriculture, Wageningen, July 6–8, 2009, Wageningen: Academic, 2009, pp. 81–89.

  10. Reynolds, M. and Tuberosa, R., Translational research impacting on crop productivity in drought-prone environments, Curr. Opin. Plant Biol., 2008, vol. 11, pp. 171–179.

    Article  PubMed  Google Scholar 

  11. Sims, D.A. and Gamon, J.A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages, Remote Sens. Environ., 2002, vol. 81, pp. 337–354.

    Article  Google Scholar 

  12. Peñuelas, J., Barret, F., and Fitella, I., Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance, Photosynthetica, 1995, vol. 31, pp. 221–230.

  13. Merzlyak, M.N., Gitel’son, A.A., Chivkunova, O.B., Solovchenko, A.E., and Pogosyan, S.I., Application of reflectance spectroscopy for analysis of higher plant pigments, Russ. J. Plant Physiol., 2003, vol. 50, pp. 704–710.

    Article  CAS  Google Scholar 

  14. Haldane, J.B.S., The recombination of linkage values and the calculation of distance between the loci of linkage factors, J. Genet., 1919, vol. 8, pp. 299–309.

    Article  Google Scholar 

  15. Lander, E.S., Green, P., Abrahamson, J., Barlow, A., Daly, M.J., Lincoln, S.E., and Newburg, L., M-APMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations, Genomics, 1987, vol. 1, pp. 174–181.

    Article  CAS  PubMed  Google Scholar 

  16. Ganal, M.W. and Röder, M.S., Microsattelite and SNP markers in wheat breeding, in Genomics Assisted Crop Improvement: Genomics Applications in Crops, Varshney, R.K. and Tuberosa, R., Eds., Springer, 2007, vol. 2, pp. 1–24.

    Google Scholar 

  17. Kosambi, D.D., The estimation of map distances from recombination values, Ann. Eugen., 1944, vol. 12, pp. 172–175.

    Article  Google Scholar 

  18. Kocherina, N.V., Artemyeva, A.M., and Chesnokov, Yu.V., Use of LOD-score technology in mapping quantitative trait loci in plants, Russ. Agric. Sci., 2011, vol. 37, pp. 201–204.

    Article  Google Scholar 

  19. Lakin, G.F., Biometriya (Biometrics), Moscow: Vyssh. Shk., 1990.

    Google Scholar 

  20. Babar, M.A., Reynolds, M.P., van Ginkel, M., Klatt, A.R., Raun, W.R., and Stone, M.L., Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat, Crop Sci., 2006, vol. 46, pp. 1046–1057.

    Article  Google Scholar 

  21. Peñuelas, J., Munné-Bosch, S., Llusià, J., and Filella, I., Leaf reflectance and photo- and antioxidant protection in field-grown summer-stressed Phillyrea angustifolia. Optical signals of oxidative stress? New Phytol., 2004, vol. 162, pp. 115–124. https://doi.org/10.1111/j.1469-8137.2004.01007.x

  22. Gamon, J.A., Serrano, L., and Surfus, J.S., The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels, Oecologia, 1997, vol. 112, pp. 492–501.

    Article  CAS  PubMed  Google Scholar 

  23. Evain, S., Flexas, J., and Moya, I., A new instrument for passive remote sensing. 2. Measurement of leaf and canopy reflectance changes at 531 nm and their relationship with photosynthesis and chlorophyll fluorescence, Remote Sens. Environ., 2004, vol. 91, pp. 175–185. https://doi.org/10.1016/j.rse.2004.03.012

    Article  Google Scholar 

  24. Gitelson, A.A., Gamon, J.A., and Solovchenko, A., Multiple drivers of seasonal change in PRI: implications for photosynthesis. 1. Leaf level, Remote Sens. Environ., 2017, vol. 191, pp. 110–116.

    Article  Google Scholar 

  25. Grant, L., Diffuse and specular characteristics of leaf reflectance, Remote Sens. Environ., 1987, vol. 22, pp. 309–322.

    Article  Google Scholar 

  26. Slaton, M.R., Hunt, E.R., and Smith, W.K., Estimating near-infrared leaf reflectance from structural characteristics, Am. J. Bot., 2001, vol. 88, pp. 278–284.

    Article  CAS  PubMed  Google Scholar 

  27. Leng, P., Itamura, H., Yamamura, H., and Deng, X.M., Anthocyanin accumulation in apple and peach shoots during cold acclimation, Sci. Hortic., 2000, vol. 83, pp. 43–50.

    Article  CAS  Google Scholar 

  28. Cobbina, J. and Miller, M.H., Purpling in maize hybrids as influenced by temperature and soil phosphorus, Agron. J., 1987, vol. 79, pp. 576–582.

    Article  CAS  Google Scholar 

  29. Nozzolillo, C., Isabelle, P., and Das, G., Seasonal changes in the phenolic constituents of jack pine seedling (Pinus banksiana), Can. J. Bot., 1990, vol. 68, pp. 2010–2017.

    Article  CAS  Google Scholar 

  30. Gould, K.S., McKelvie, J., and Markham, K.R., Do anthocyanins function as antioxidants in leaves? Imaging of H2O2 in red and green leaves after mechanical injury, Plant Cell Environ., 2002, vol. 25, pp. 1261–1269.

    Article  CAS  Google Scholar 

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ACKNOWLEDGMENTS

The study was partially supported by the Russian Foundation for Basic Research (project no. 16-04-00311а).

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Authors and Affiliations

  1. Agrophysical Research Institute, 195220, St. Petersburg, Russia

    Yu. V. Chesnokov, E. V. Kanash, G. V. Mirskaya, N. V. Kocherina & D. V. Rusakov

  2. Leibniz Institute of Plant Genetics and Crop Plant Research, D-06466, Gatersleben, Germany

    U. Lohwasser & A. Börner

Authors
  1. Yu. V. Chesnokov
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  2. E. V. Kanash
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  3. G. V. Mirskaya
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  4. N. V. Kocherina
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  5. D. V. Rusakov
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  6. U. Lohwasser
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  7. A. Börner
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Correspondence to Yu. V. Chesnokov.

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The authors declare that they have no conflict of interest. This article does not contain any studies involving animals or human participants performed by any of the authors.

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Translated by N. Statsyuk

Abbreviations: ALSA—assimilating leaf surface area; ARI—anthocyanin reflectance index characterizing the content of anthocyanins in leaves; ChlRI—chlorophyll reflectance index; cM—centimorgan; ITMI—International Triticeae Mapping Initiative; LOD—logarithm of odds; NSeSp—number of grains per spike; PRI—photochemical reflectance index; QTL—quantitative trait loci; R800—light scattering index dependent on a leaf structure; RIL—recombinant inbred lines; SIPI—structure insensitive pigment index characterizing the carotenoid/chlorophyll ratio; TGW—thousand grain weight; VIF—vegetation irradiation facility.

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Chesnokov, Y.V., Kanash, E.V., Mirskaya, G.V. et al. QTL Mapping of Diffuse Reflectance Indices of Leaves in Hexaploid Bread Wheat (Triticum aestivum L.). Russ J Plant Physiol 66, 77–86 (2019). https://doi.org/10.1134/S1021443719010047

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