Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T06:25:31.092Z Has data issue: false hasContentIssue false
  • English
  • Français

Unravelling the complex trait of seed quality: using natural variation through a combination of physiology, genetics and -omics technologies

Published online by Cambridge University Press:  05 January 2012

Wilco Ligterink ,
Ronny V.L. Joosen  and
Henk W.M. Hilhorst
Wilco Ligterink*
Affiliation:
Wageningen Seed Lab, Lab of Plant Physiology, Wageningen University, Wageningen, The Netherlands
Ronny V.L. Joosen
Affiliation:
Wageningen Seed Lab, Lab of Plant Physiology, Wageningen University, Wageningen, The Netherlands
Henk W.M. Hilhorst
Affiliation:
Wageningen Seed Lab, Lab of Plant Physiology, Wageningen University, Wageningen, The Netherlands
*
*Correspondence Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Seed quality is a complex trait that is the result of a large variety of developmental processes. The molecular-genetic dissection of these seed processes and their relationship with seed and seedling phenotypes will allow the identification of the regulatory genes and signalling pathways involved and, thus, provide the means to predict and enhance seed quality. Natural variation for seed-quality aspects found in recombinant inbred line (RIL) populations is a great resource to help unravel the complex networks involved in the acquisition of seed quality. Besides extensive phenotyping, RILs can also be profiled by -omics technologies, such as transcriptomics, proteomics and metabolomics in a sophisticated so-called generalized genetical genomics approach. This combined use of physiology, genetics and several -omics technologies, followed by advanced data analysis, allows the construction of regulatory networks involved in the various attributes of seed and seedling quality. This type of analysis of the genetic variation in RIL populations in combination with genome-wide association (GWA) studies will allow a relatively rapid identification of genes that are responsible for quality-related traits of seeds and seedlings. New developments in several -omics technologies, especially the fast-evolving next-generation sequencing techniques, will make a similar system-wide approach more applicable to non-model species in the near future and this will be a huge boost for the potential to breed for seed quality.

Keywords

genetical genomicsgenome-wide association studieshigh-throughput phenotyping-omics techno-logiesquantitative trait locus analysisseed quality
Type
Review Article
Information
Seed Science Research , Volume 22 , Supplement S1: Seed Science in the 21st Century , February 2012 , pp. S45 - S52
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alonso-Blanco, C. and Koornneef, M. (2000) Naturally occurring variation in Arabidopsis: An underexploited resource for plant genetics. Trends in Plant Science 5, 22–29.CrossRefGoogle ScholarPubMed
Argyris, J., Truco, M., Ochoa, O., Knapp, S., Still, D., Lenssen, G., Schut, J., Michelmore, R. and Bradford, K. (2005) Quantitative trait loci associated with seed and seedling traits in Lactuca. TAG Theoretical and Applied Genetics 111, 1365–1376.CrossRefGoogle ScholarPubMed
Argyris, J., Dahal, P., Hayashi, E., Still, D.W. and Bradford, K.J. (2008) Genetic variation for lettuce seed thermoinhibition is associated with temperature-sensitive expression of abscisic acid, gibberellin, and ethylene biosynthesis, metabolism, and response genes. Plant Physiology 148, 926–947.CrossRefGoogle ScholarPubMed
Armengaud, P., Zambaux, K., Hills, A., Sulpice, R., Pattison, R.J., Blatt, M.R. and Amtmann, A. (2009) EZ-Rhizo: integrated software for fast and accurate measurement of root system architecture. Plant Journal 57, 945–956.CrossRefGoogle ScholarPubMed
Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M. and Sherlock, G. (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nature Genetics 25, 25–29.CrossRefGoogle ScholarPubMed
Atwell, S., Huang, Y.S., Vilhjalmsson, B.J., Willems, G., Horton, M., Li, Y., Meng, D., Platt, A., Tarone, A.M., Hu, T.T., Jiang, R., Muliyati, N.W., Zhang, X., Amer, M.A., Baxter, I., Brachi, B., Chory, J., Dean, C., Debieu, M., de Meaux, J., Ecker, J.R., Faure, N., Kniskern, J.M., Jones, J.D.G., Michael, T., Nemri, A., Roux, F., Salt, D.E., Tang, C., Todesco, M., Traw, M.B., Weigel, D., Marjoram, P., Borevitz, J.O., Bergelson, J. and Nordborg, M. (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631.CrossRefGoogle ScholarPubMed
Bassel, G.W., Lan, H., Glaab, E., Gibbs, D.J., Gerjets, T., Krasnogor, N., Bonner, A.J., Holdsworth, M.J. and Provart, N.J. (2011) Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions. Proceedings of the National Academy of Sciences, USA 108, 9709–9714.CrossRefGoogle ScholarPubMed
Baxter, I., Brazelton, J.N., Yu, D., Huang, Y.S., Lahner, B., Yakubova, E., Li, Y., Bergelson, J., Borevitz, J.O., Nordborg, M., Vitek, O. and Salt, D.E. (2010) A coastal cline in sodium accumulation in Arabidopsis thaliana is driven by natural variation of the sodium transporter athkt1;1. PLoS Genetics 6, e1001193.CrossRefGoogle ScholarPubMed
Bentsink, L., Hanson, J., Hanhart, C.J., Blankestijn-de Vries, H., Coltrane, C., Keizer, P., El-Lithy, M., Alonso-Blanco, C., de Andres, M.T., Reymond, M., van Eeuwijk, F., Smeekens, S. and Koornneef, M. (2010) Natural variation for seed dormancy in Arabidopsis is regulated by additive genetic and molecular pathways. Proceedings of the National Academy of Sciences, USA 107, 4264–4269.CrossRefGoogle ScholarPubMed
Borevitz, J.O., Liang, D., Plouffe, D., Chang, H.S., Zhu, T., Weigel, D., Berry, C.C., Winzeler, E. and Chory, J. (2003) Large-scale identification of single-feature polymorphisms in complex genomes. Genome Research 13, 513–523.CrossRefGoogle ScholarPubMed
Bourgeois, M., Jacquin, F., Cassecuelle, F., Savois, V., Belghazi, M., Aubert, G., Quillien, L., Huart, M., Marget, P. and Burstin, J. (2011) A PQL (protein quantity loci) analysis of mature pea seed proteins identifies loci determining seed protein composition. Proteomics 11, 1581–1594.CrossRefGoogle ScholarPubMed
Brachi, B., Faure, N., Horton, M., Flahauw, E., Vazquez, A., Nordborg, M., Bergelson, J., Cuguen, J. and Roux, F. (2010) Linkage and association mapping of Arabidopsis thaliana flowering time in nature. PLoS Genetics 6, e1000940.CrossRefGoogle ScholarPubMed
Buckler, E. and Gore, M. (2007) An Arabidopsis haplotype map takes root. Nature Genetics 39, 1056–1057.CrossRefGoogle ScholarPubMed
Clerkx, E.J.M., El-Lithy, M.E., Vierling, E., Ruys, G.J., Blankestijn-De Vries, H., Groot, S.P.C., Vreugdenhil, D. and Koornneef, M. (2004) Analysis of natural allelic variation of Arabidopsis seed germination and seed longevity traits between the accessions Landsberg erecta and Shakdara, using a new recombinant inbred line population. Plant Physiology 135, 432–443.CrossRefGoogle ScholarPubMed
Dell'Aquila, A. (2009) Digital imaging information technology applied to seed germination testing. A review. Agronomy for Sustainable Development 29, 213–221.CrossRefGoogle Scholar
Dias, P., Brunel-Muguet, S., Dürr, C., Huguet, T., Demilly, D., Wagner, M.-H. and Teulat-Merah, B. (2011) QTL analysis of seed germination and pre-emergence growth at extreme temperatures in Medicago truncatula. Theoretical and Applied Genetics 122, 429–444.CrossRefGoogle ScholarPubMed
Dickson, M.H. (1980) Genetic-aspects of seed quality. Hortscience 15, 771–774.CrossRefGoogle Scholar
Doganlar, S., Frary, A. and Tanksley, S.D. (2000) The genetic basis of seed-weight variation: tomato as a model system. Theoretical and Applied Genetics 100, 1267–1273.CrossRefGoogle Scholar
Foolad, M.R., Zhang, L.P. and Subbiah, P. (2003) Genetics of drought tolerance during seed germination in tomato: inheritance and QTL mapping. Genome 46, 536–545.CrossRefGoogle ScholarPubMed
Foolad, M.R., Subbiah, P. and Zhang, L. (2007) Common QTL affect the rate of tomato seed germination under different stress and nonstress conditions. International Journal of Plant Genomics 2007, 97386.CrossRefGoogle ScholarPubMed
French, A., Ubeda-Tomas, S., Holman, T.J., Bennett, M.J. and Pridmore, T. (2009) High-throughput quantification of root growth using a novel image-analysis tool. Plant Physiology 150, 1784–1795.CrossRefGoogle ScholarPubMed
Galpaz, N. and Reymond, M. (2010) Natural variation in Arabidopsis thaliana revealed a genetic network controlling germination under salt stress. PLoS ONE 5, e15198.CrossRefGoogle ScholarPubMed
Gupta, V., Mathur, S., Solanke, A.U., Sharma, M.K., Kumar, R., Vyas, S., Khurana, P., Khurana, J.P., Tyagi, A.K. and Sharma, A.K. (2009) Genome analysis and genetic enhancement of tomato. Critical Reviews in Biotechnology 29, 152–181.CrossRefGoogle ScholarPubMed
Hamblin, M.T., Buckler, E.S. and Jannink, J.-L. (2011) Population genetics of genomics-based crop improvement methods. Trends in Genetics 27, 98–106.CrossRefGoogle ScholarPubMed
Harada, J.J. (1997) Seed maturation and control of germination. pp. 545–592 in Larkins, B.; Vasil, I. (Eds) Cellular and molecular biology of plant seed development. Dordrecht, Kluwer Academic Publishers.CrossRefGoogle Scholar
Hilhorst, H.W.M. and Toorop, P.E. (1997) Review on dormancy, germinability, and germination in crop and weed seeds. Advances in Agronomy 61, 111–165.CrossRefGoogle Scholar
Huang, X., Schmitt, J., Dorn, L., Griffith, C., Effgen, S., Takao, S., Koornneef, M. and Donohue, K. (2010a) The earliest stages of adaptation in an experimental plant population: Strong selection on QTLs for seed dormancy. Molecular Ecology 19, 1335–1351.CrossRefGoogle Scholar
Huang, X., Wei, X., Sang, T., Zhao, Q., Feng, Q., Zhao, Y., Li, C., Zhu, C., Lu, T., Zhang, Z., Li, M., Fan, D., Guo, Y., Wang, A., Wang, L., Deng, L., Li, W., Lu, Y., Weng, Q., Liu, K., Huang, T., Zhou, T., Jing, Y., Lin, Z., Buckler, E.S., Qian, Q., Zhang, Q.F., Li, J. and Han, B. (2010b) Genome-wide association studies of 14 agronomic traits in rice landraces. Nature Genetics 42, 961–967.CrossRefGoogle ScholarPubMed
Ingvarsson, P.K. and Street, N.R. (2011) Association genetics of complex traits in plants. New Phytologist 189, 909–922.CrossRefGoogle ScholarPubMed
Jansen, R.C. and Nap, J.P. (2001) Genetical genomics: the added value from segregation. Trends in Genetics 17, 388–391.CrossRefGoogle ScholarPubMed
Jiménez-Gómez, J.M., Wallace, A.D. and Maloof, J.N. (2010) Network analysis identifies ELF3 as a QTL for the shade avoidance response in Arabidopsis. PLoS Genetics 6, e1001100.CrossRefGoogle ScholarPubMed
Joosen, R.V., Ligterink, W., Hilhorst, H.W. and Keurentjes, J.J. (2009) Advances in genetical genomics of plants. Current Genomics 10, 540–549.CrossRefGoogle ScholarPubMed
Joosen, R.V., Kodde, J., Willems, L.A., Ligterink, W., van der Plas, L.H. and Hilhorst, H.W. (2010) Germinator: a software package for high-throughput scoring and curve fitting of arabidopsis seed germination. Plant Journal 62, 148–159.CrossRefGoogle ScholarPubMed
Jordan, M.C., Somers, D.J. and Banks, T.W. (2007) Identifying regions of the wheat genome controlling seed development by mapping expression quantitative trait loci. Plant Biotechnology Journal 5, 442–453.CrossRefGoogle ScholarPubMed
Keurentjes, J.J.B. (2009) Genetical metabolomics: closing in on phenotypes. Current Opinion in Plant Biology 12, 223–230.CrossRefGoogle Scholar
Keurentjes, J.J., Fu, J., Terpstra, I.R., Garcia, J.M., van den Ackerveken, G., Snoek, L.B., Peeters, A.J., Vreugdenhil, D., Koornneef, M. and Jansen, R.C. (2007) Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci. Proceedings of the National Academy of Sciences, USA 104, 1708–1713.CrossRefGoogle ScholarPubMed
Kliebenstein, D. (2009) Quantitative genomics: analyzing intraspecific variation using global gene expression polymorphisms or eQTLs. Annual Review of Plant Biology 60, 93–114.CrossRefGoogle ScholarPubMed
Laubinger, S., Zeller, G., Henz, S., Sachsenberg, T., Widmer, C., Naouar, N., Vuylsteke, M., Scholkopf, B., Ratsch, G. and Weigel, D. (2008) At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana. Genome Biology 9, R112.CrossRefGoogle ScholarPubMed
Li, Y., Breitling, R. and Jansen, R.C. (2008) Generalizing genetical genomics: getting added value from environmental perturbation. Trends in Genetics 24, 518–524.CrossRefGoogle ScholarPubMed
Li, Y., Breitling, R., Snoek, L.B., van der Velde, K.J., Swertz, M.A., Riksen, J., Jansen, R.C. and Kammenga, J.E. (2010a) Global genetic robustness of the alternative splicing machinery in Caenorhabditis elegans. Genetics 186, 405–410.CrossRefGoogle ScholarPubMed
Li, Y., Huang, Y., Bergelson, J., Nordborg, M. and Borevitz, J.O. (2010b) Association mapping of local climate-sensitive quantitative trait loci in Arabidopsis thaliana. Proceedings of the National Academy of Sciences, USA 107, 21199–21204.CrossRefGoogle ScholarPubMed
Matsui, A., Ishida, J., Morosawa, T., Mochizuki, Y., Kaminuma, E., Endo, T.A., Okamoto, M., Nambara, E., Nakajima, M., Kawashima, M., Satou, M., Kim, J.-M., Kobayashi, N., Toyoda, T., Shinozaki, K. and Seki, M. (2008) Arabidopsis transcriptome analysis under drought, cold, high-salinity and ABA treatment conditions using a tiling array. Plant and Cell Physiology 49, 1135–1149.CrossRefGoogle ScholarPubMed
McManus, C.J., Coolon, J.D., Duff, M.O., Eipper-Mains, J., Graveley, B.R. and Wittkopp, P.J. (2010) Regulatory divergence in Drosophila revealed by mRNA-seq. Genome Research 20, 816–825.CrossRefGoogle ScholarPubMed
Metzker, M.L. (2010) Sequencing technologies – the next generation. Nature Reviews Genetics 11, 31–46.CrossRefGoogle ScholarPubMed
Mockler, T.C. and Ecker, J.R. (2005) Applications of DNA tiling arrays for whole-genome analysis. Genomics 85, 1–15.CrossRefGoogle ScholarPubMed
Montgomery, S.B., Sammeth, M., Gutierrez-Arcelus, M., Lach, R.P., Ingle, C., Nisbett, J., Guigo, R. and Dermitzakis, E.T. (2010) Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464, 773–777.CrossRefGoogle Scholar
Nesi, N., Delourme, R., Brégeon, M., Falentin, C. and Renard, M. (2008) Genetic and molecular approaches to improve nutritional value of Brassica napus L. seed. Comptes Rendus Biologies 331, 763–771.CrossRefGoogle ScholarPubMed
Pickrell, J.K., Marioni, J.C., Pai, A.A., Degner, J.F., Engelhardt, B.E., Nkadori, E., Veyrieras, J.B., Stephens, M., Gilad, Y. and Pritchard, J.K. (2010) Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772.CrossRefGoogle ScholarPubMed
Potokina, E., Druka, A., Luo, Z., Wise, R., Waugh, R. and Kearsey, M. (2008) Gene expression quantitative trait locus analysis of 16,000 barley genes reveals a complex pattern of genome-wide transcriptional regulation. Plant Journal 53, 90–101.CrossRefGoogle ScholarPubMed
Potokina, E., Druka, A. and Kearsey, M.J. (2009) Transcript profiling and expression level mapping. pp. 81–92 in Somers, D.J.; Langridge, P.; Gustafson, J.P. (Eds) Plant genomics. New York, Humana Press.CrossRefGoogle Scholar
Powell, A.A. (2006) Seed vigor and its assessment. pp. 603–648 in Basra, A.S. (Ed.) Handbook of seed science and technology. Binghamton, USA, Food Products Press.Google Scholar
Rockman, M.V. and Kruglyak, L. (2006) Genetics of global gene expression. Nature Reviews Genetics 7, 862–872.CrossRefGoogle ScholarPubMed
Salvi, S. and Tuberosa, R. (2005) To clone or not to clone plant QTLs: present and future challenges. Trends in Plant Science 10, 297–304.CrossRefGoogle ScholarPubMed
Shi, C., Uzarowska, A., Ouzunova, M., Landbeck, M., Wenzel, G. and Lübberstedt, T. (2007) Identification of candidate genes associated with cell wall digestibility and eQTL (expression quantitative trait loci) analysis in a Flint × Flint maize recombinant inbred line population. BMC Genomics 8, 22.CrossRefGoogle Scholar
Shriner, D., Vaughan, L.K., Padilla, M.A. and Tiwari, H.K. (2007) Problems with genome-wide association studies. Science 316, 1840–1842.CrossRefGoogle ScholarPubMed
Sorkheh, K., Malysheva-Otto, L.V., Wirthensohn, M.G., Tarkesh-Esfahani, S. and Martínez-Gómez, P. (2008) Linkage disequilibrium, genetic association mapping and gene localization in crop plants. Genetics and Molecular Biology 31, 805–814.CrossRefGoogle Scholar
Usadel, B., Obayashi, T., Mutwil, M., Giorgi, F.M., Bassel, G.W., Tanimoto, M., Chow, A., Steinhauser, D., Persson, S. and Provart, N.J. (2009) Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant, Cell & Environment 32, 1633–1651.CrossRefGoogle ScholarPubMed
Vandecasteele, C., Teulat-Merah, B., Morere-LePaven, M.C., Leprince, O., LyVu, B., Viau, L., Ledroit, L., Pelletier, S., Payet, N., Satour, P., Lebras, C., Gallardo, K., Huguet, T., Limami, A.M., Prosperi, J.M. and Buitink, J. (2011) QTL analysis reveals a correlation between the ratio of sucrose/raffinose family oligosaccharides and seed vigour in Medicago truncatula. Plant, Cell & Environment 34, 1473–1487.CrossRefGoogle ScholarPubMed
Varshney, R.K., Nayak, S.N., May, G.D. and Jackson, S.A. (2009) Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends in Biotechnology 27, 522–530.CrossRefGoogle ScholarPubMed
Vuylsteke, M., Van Den Daele, H., Vercauteren, A., Zabeau, M. and Kuiper, M. (2006) Genetic dissection of transcriptional regulation by cDNA-AFLP. Plant Journal 45, 439–446.CrossRefGoogle ScholarPubMed
West, M.A.L., Van Leeuwen, H., Kozik, A., Kliebenstein, D.J., Doerge, R.W., St. Clair, D.A. and Michelmore, R.W. (2006) High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis. Genome Research 16, 787–795.CrossRefGoogle ScholarPubMed
West, M.A.L., Kim, K., Kliebenstein, D.J., Van Leeuwen, H., Michelmore, R.W., Doerge, R.W. and St. Clair, D.A. (2007) Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics 175, 1441–1450.CrossRefGoogle ScholarPubMed
Winter, D., Vinegar, B., Nahal, H., Ammar, R., Wilson, G.V. and Provart, N.J. (2007) An ‘Electronic Fluorescent Pictograph’ browser for exploring and analyzing large-scale biological data sets. PLoS ONE 2, e718.CrossRefGoogle ScholarPubMed
Zeng, D.L., Guo, L.B., Xu, Y.B., Yasukumi, K., Zhu, L.H. and Qian, Q. (2006) QTL analysis of seed storability in rice. Plant Breeding 125, 57–60.CrossRefGoogle Scholar
Zhang, J., Chiodini, R., Badr, A. and Zhang, G. (2011) The impact of next-generation sequencing on genomics. Journal of Genetics and Genomics 38, 95–109.CrossRefGoogle ScholarPubMed
Zhang, X. and Borevitz, J.O. (2009) Global analysis of allele-specific expression in Arabidopsis thaliana. Genetics 182, 943–954.CrossRefGoogle ScholarPubMed
Zhang, X., Richards, E.J. and Borevitz, J.O. (2007) Genetic and epigenetic dissection of cis regulatory variation. Current Opinion in Plant Biology 10, 142–148.CrossRefGoogle ScholarPubMed
Zhang, X., Byrnes, J., Gal, T., Li, W.-H. and Borevitz, J. (2008) Whole genome transcriptome polymorphisms in Arabidopsis thaliana. Genome Biology 9, R165.CrossRefGoogle ScholarPubMed