Abstract
The rate of genetic gain in yield, quality, input use efficiency and adaptability of crops to biotic and abiotic stresses must be improved significantly to achieve global food and nutritional security by 2050. To achieve this goal, deciphering the physiological genetic basis and assembly of component traits through analytical breeding is necessary. The two pillars of analytical breeding are genotyping and phenotyping. Advances in genotyping technologies such as single-nucleotide polymorphism genotyping and genotyping by sequencing have made deep genotyping cheaper and quicker, while phenotyping has lagged behind and thus remains a rate limiting step. Recently, phenomics has emerged as a new way of accurately phenotyping large set of genotypes. Phenomics employ non-invasive sensors and advanced computational platforms for non-destructive and high-throughput phenotyping. The depth of component phenotypic traits and the spatio-temporal dynamic phenotypic data generated in phenomics are enormous and unparallel to the conventional phenotyping. The utility of phenomics in QTL mapping and genome-wide association studies has already been demonstrated in important food crops. Phenomics has high potential for phenome-wide association studies, genomics selection models for enhancing selection efficiency, and genetic-ecophysiological crop simulation models for prediction of genotype-phenotypes relationship, in silico phenotyping and ideotype design. With the advancement in the depth of phenome data acquisition and analyses capabilities of phenomics, phenome assisted breeding and phenomic selection is anticipated to be a reality in near future. Complementary use of conventional phenotyping and advanced phenomics is suggested to assist in fundamental discoveries and analytical crop breeding.
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References
Araus, J. L., & Cairns, J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science, 19, 52–61.
Bac-Molenaar, J. A., Vreugdenhil, D., Granier, C., & Keurentjes, J. J. B. (2015). Genome-wide association mapping of growth dynamics detects time-specific and general quantitative trait loci. Journal of Experimental Botany, 66, 5567–5580.
Bilder, R. M., Sabb, F. W., Cannon, T. D., London, E. D., Jentsch, J. D., Parker, D. S., et al. (2009). Phenomics: The systematic study of phenotypes on a genome-wide scale. Neuroscience, 164, 30–42.
Biskup, B., Scharr, H., Schurr, U., & Rascher, U. (2007). A stereo imaging system for measuring structural parameters of plant canopies. Plant, Cell and Environment, 30, 1299–1308.
Bogard, M., Ravel, C., Paux, E., Bordes, J., Balfourier, F., Chapman, S. C., et al. (2014). Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model. Journal of Experimental Botany, 65, 5849–5865.
Busemeyer, L., Ruckelshausen, A., Möller, K., Melchinger, A. E., Alheit, K. V., Maurer, H. P., et al. (2013). Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation. Scientific Reports, 3, 2442. doi:10.1038/srep02442.
Campbell, M. T., Knecht, A. C., Berger, B., Brien, C. J., Wang, D., & Walia, H. (2015). Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice. Plant Physiology, 168, 1476–1489.
Chen, D., Neumann, K., Friedel, S., Kilian, B., Chen, M., Altmann, T., et al. (2014). Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell, 26, 4636–4655.
Chinnusamy, V., Dalal, M., & Zhu, J. K. (2013). Epigenetic regulation of abiotic stress responses in plants. In M. A. Jenks & P. M. Hasegawa (Eds.), Plant abiotic stress (pp. 203–230). Hoboken: Wiley. doi:10.1002/9781118764374.ch8
Chinnusamy, V., Stevenson, B., Lee, B.-H., & Zhu, J. K. (2002). Screening for gene regulation mutants by bioluminescence imaging. Science’s STKE, 140, pl10.
Chinnusamy, V., & Zhu, J. K. (2009). RNA-directed DNA methylation and demethylation in plants. Science in China C Life Sciences, 52, 331–343.
Cobb, J. N., Declerck, G., Greenberg, A., Clark, R., & McCouch, S. (2013). Next-generation phenotyping: Requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics, 126, 867–887.
Das, B., Sahoo, R. N., Pargal, S., Krishna, G., Gupta, V. K., Verma, R., et al. (2016). Measuring leaf area index from colour digital image of wheat crop. Journal of Agrometeorology, 18, 22–28.
Denny, J. C., Bastarache, L., & Roden, D. M. (2016). Phenome-wide association studies as a tool to advance precision medicine. Annual Review of Genomics and Human Genetics, 17, 353–373.
Desta, Z. A., & Ortiz, R. (2014). Genomic selection: Genome-wide prediction in plant improvement. Trends in Plant Science, 19, 592–601.
Fahlgren, N., Feldman, M., Gehan, M. A., Wilson, M. S., Shyu, C., Bryant, D. W., et al. (2015a). A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Molecular Plant, 8, 1520–1535.
Fahlgren, N., Gehan, M. A., & Baxter, I. (2015b). Lights, camera, action: High-throughput plant phenotyping is ready for a close-up. Current Opinion in Plant Biology, 24, 93–99.
Fiorani, F., & Schurr, U. (2013). Future scenarios for plant phenotyping. Annual Review of Plant Biology, 64, 267–291.
Flood, P. J., Kruijer, W., Schnabel, S. K., van der Schoor, R., Jalink, H., Snel, J. F., et al. (2016). Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability. Plant Methods, 12, 14. doi:10.1186/s13007-016-0113-y.
Freimer, N., & Sabatti, C. (2003). The human phenome project. Nature Genetics, 34, 15–21.
Furbank, R. T., & Tester, M. (2011). Phenomics—Technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16, 635–644.
Golzarian, M. R., Frick, R. A., Rajendran, K., Berger, B., Roy, S., Tester, M., et al. (2011). Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods, 7, 2. doi:10.1186/1746-4811-7-2.
Grant, M., Brown, I., Adams, S., Knight, M., Ainslie, A., & Mansfield, J. (2000). The RPM1 plant disease resistance gene facilitates a rapid and sustained increase in cytosolic calcium that is necessary for the oxidative burst and hypersensitive cell death. Plant Journal, 23, 441–450.
Großkinsky, D. K., Svensgaard, J., Christensen, S., & Roitsch, T. (2015). Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. Journal of Experimental Botany, 66, 5429–5440.
Gu, J., Yin, X., Zhang, C., Wang, H., & Struik, P. C. (2014). Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress. Annals of Botany, 114, 499–511.
Honsdorf, N., March, T. J., Berger, B., Tester, M., & Pillen, K. (2014). High-throughput phenotyping to detect drought tolerance QTL in wild barley introgression lines. PLoS ONE, 9(e97047), 2014. doi:10.1371/journal.pone.0097047.eCollection.
Jin, K., Li, J., Vizeacoumar, F. S., Li, Z., Min, R., Zamparo, L., et al. (2012). PhenoM: a database of morphological phenotypes caused by mutation of essential genes in Saccharomyces cerevisiae. Nucleic Acids Research, 40, D687–D694.
Kjaer, K. H., & Ottosen, C. O. (2015). 3D Laser triangulation for plant phenotyping in challenging environments. Sensors (Basel), 15, 13533–13547.
Leister, D. (2012). Retrograde signaling in plants: from simple to complex scenarios. Frontiers in Plant Science, 3, 135. doi:10.3389/fpls.2012.00135.
Liu, W., Gowda, M., Reif, J. C., Hahn, V., Ruckelshausen, A., Weissmann, E. A., et al. (2014). Genetic dynamics underlying phenotypic development of biomass yield in triticale. BMC Genomics, 15, 458. doi:10.1186/1471-2164-15-458.
Lu, Y., Liu, Y., Niu, X., Yang, Q., Hu, X., Zhang, H. Y., et al. (2015). Systems genetic validation of the SNP-metabolite association in rice via metabolite-pathway-based phenome-wide association scans. Frontiers in Plant Science, 6, 1027. doi:10.3389/fpls.2015.01027.
Mahner, M., & Kary, M. (1997). What exactly are genomes, genotypes and phenotypes? And what about phenomes? Journal of Theoretical Biology, 186, 55–63.
Mervis, J. (2016). NSF director unveils big ideas. Science, 352, 755–756.
Mishra, A., Heyer, A. G., & Mishra, K. B. (2014). Chlorophyll fluorescence emission can screen cold tolerance of cold acclimated Arabidopsis thaliana accessions. Plant Methods, 10, 38. doi:10.1186/1746-4811-10-38.
Möller, M., Alchanatis, V., Cohen, Y., Meron, M., Tsipris, J., Naor, A., et al. (2007). Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. Journal of Experimental Botany, 58, 827–838.
Moore, C. R., Johnson, L. S., Kwak, I. Y., Livny, M., Broman, K. W., & Spalding, E. P. (2013). High-throughput computer vision introduces the time axis to a quantitative trait map of a plant growth response. Genetics, 195, 1077–1086.
Neilson, E. H., Edwards, A. M., Blomstedt, C. K., Berger, B., Møller, B. L., & Gleadow, R. M. (2015). Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time. Journal of Experimental Botany, 66, 1817–1832.
Parent, B., Shahinnia, F., Maphosa, L., Berger, B., Rabie, H., Chalmers, K., et al. (2015). Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat. Journal of Experimental Botany, 66, 5481–5492.
Pauli, D., Chapman, S. C., Bart, R., Topp, C. N., Lawrence-Dill, C. J., Poland, J., et al. (2016). The quest for understanding phenotypic variation via integrated approaches in the field environment. Plant Physiology, 172, 622–634.
Plant Science Research Summit. (2013). Unleashing a decade of innovation in plant science: A vision for 2015–2025. http://plantsummit.wordpress.com/.
Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PLoS ONE, 8, e66428.
Romer, C., Wahabzada, M., Ballvora, A., Pinto, F., Rossini, M., Panigada, C., et al. (2012). Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. Functional Plant Biology, 39, 878–890.
Rötter, R. P., Tao, F., Höhn, J. G., & Palosuo, T. (2015). Use of crop simulation modelling to aid ideotype design of future cereal cultivars. Journal of Experimental Botany, 66, 3463–3476.
Sahoo, R. N., Ray, S. S., & Manjunath, K. R. (2015). Hyperspectral remote sensing of agriculture. Current Science, 108, 848–859.
Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21, 110–124.
Sun, D. W. (Ed.). (2009). Infrared spectroscopy for food quality analysis and control. London: Academic press.
Sunkar, R., Li, Y. F., & Jagadeeswaran, G. (2012). Functions of microRNAs in plant stress responses. Trends in Plant Science, 17, 196–203.
Talamond, P., Verdeil, J. L., & Conéjéro, G. (2015). Secondary metabolite localization by autofluorescence in living plant cells. Molecules, 20, 5024–5037.
Topp, C. N., Iyer-Pascuzzi, A. S., Anderson, J. T., Lee, C. R., Zurek, P. R., Symonova, O., et al. (2013). 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. Proceedings of National Academy of Sciences USA, 110, E1695–E1704.
Tuberosa, R. (2012). Phenotyping for drought tolerance of crops in the genomics era. Frontiers in Physiology, 3, 347. doi:10.3389/fphys.2012.00347.
Vadez, V., Kholová, J., Hummel, G., Zhockhavets, U., Gupta, S. K., & Tom Hash, C. (2015). LeasyScan: A novel concept combining 3D imaging and lysimetry for high-troughput phenotyping of traits of traits controlling plant water budget. Journal of Experimental Botany, 66, 5581–5593.
Van Oosten, M. J., Bressan, R. A., Zhu, J. K., Bohnert, H. J., & Chinnusamy, V. (2014). The role of the epigenome in gene expression control and the epimark changes in response to the environment. Critical Reviews in Plant Sciences, 33, 64–87.
Wahabzada, M., Mahlein, A. K., Bauckhage, C., Steiner, U., Oerke, E. C., & Kersting, K. (2016). Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Scientific Reports, 6, 22482. doi:10.1038/srep22482.
Wang, J., Zhu, J., Huang, R., & Yang, Y. (2012). Investigation of cell wall composition related to stem lodging resistance in wheat (Triticum aestivum L.) by FTIR spectroscopy. Plant Signaling & Behaviour, 7, 856–863.
Würschum, T., Liu, W., Busemeyer, L., Tucker, M. R., Reif, J. C., Weissmann, E. A., et al. (2014). Mapping dynamic QTL for plant height in triticale. BMC Genetics, 15, 59. doi:10.1186/1471-2156-15-59.
Xu, Y. (2016). Envirotyping for deciphering environmental impacts on crop plants. Theoretical and Applied Genetics, 129, 653–673.
Yang, W., Guo, Z., Huang, C., Duan, L., Chen, G., Jiang, N., et al. (2014). Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nature Communications, 5, 5087. doi:10.1038/ncomms6087.
Yang, D. L., Zhang, G., Tang, K., Li, J., Yang, L., Huang, H., et al. (2016). Dicer-independent RNA-directed DNA methylation in Arabidopsis. Cell Research, 26, 66–82.
Yin, X., Struik, P. C., van Eeuwijk, F. A., Stam, P., & Tang, J. (2005). QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley. Journal of Experimental Botany, 56, 967–976.
Zhang, X., Hause, R. J., & Borevitz, J. O. (2012). Natural genetic variation for growth and development revealed by high-throughput phenotyping in Arabidopsis thaliana. G3 Bethesda, 2, 29–34.
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The work was supported by the funding from National Agricultural Science Fund, Indian Council of Agricultural Research, and Indian Agricultural Research Institute, New Delhi.
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Sudhir Kumar and Dhandapani Raju contributed equally.
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Kumar, S., Raju, D., Sahoo, R.N. et al. Phenomics: unlocking the hidden genetic variation for breaking the barriers in yield and stress tolerance. Ind J Plant Physiol. 21, 409–419 (2016). https://doi.org/10.1007/s40502-016-0261-0
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DOI: https://doi.org/10.1007/s40502-016-0261-0