Difference between revisions of "IC4R012-Metabolomics-2012-22229385"
(→The Background of This Project) |
(→The Background of This Project) |
||
| Line 5: | Line 5: | ||
==The Background of This Project== | ==The Background of This Project== | ||
*As metabolic state is modulated by heritable factors,the genetic control of metabolic traits has been a major focus in the field of plant metabolic research. However, the relationship between genotype and metabolomic traits(m-traits) is not straightforward, because m-traits are under complex influences of quantitative trait loci (QTL) (Fernie and Schauer, 2009; Kliebenstein, 2009; Peleg et al., 2009).Furthermore, the metabolic composition of plant tissues is dynamically affected by environmental factors through post-translational interactions involving entire metabolic networks (Chan et al., 2010; Kerwin et al., 2011). Recent progress in metabolomics and QTL mapping techniques has made it possible to investigate the effect of genetic background on m-trait levels for a wide variety of metabolites.Metabolome QTL (mQTL) analyses have been performed for Arabidopsis and tomato (Solanum lycopersicum) using metabolomic techniques such as GC–MS and LC–MS, from which a better understanding of the genetics of plant metabolism has emerged (Keurentjes et al., 2006; Schauer et al., 2006, 2008; Lisec et al., 2008, 2009; Rowe et al., 2008). | *As metabolic state is modulated by heritable factors,the genetic control of metabolic traits has been a major focus in the field of plant metabolic research. However, the relationship between genotype and metabolomic traits(m-traits) is not straightforward, because m-traits are under complex influences of quantitative trait loci (QTL) (Fernie and Schauer, 2009; Kliebenstein, 2009; Peleg et al., 2009).Furthermore, the metabolic composition of plant tissues is dynamically affected by environmental factors through post-translational interactions involving entire metabolic networks (Chan et al., 2010; Kerwin et al., 2011). Recent progress in metabolomics and QTL mapping techniques has made it possible to investigate the effect of genetic background on m-trait levels for a wide variety of metabolites.Metabolome QTL (mQTL) analyses have been performed for Arabidopsis and tomato (Solanum lycopersicum) using metabolomic techniques such as GC–MS and LC–MS, from which a better understanding of the genetics of plant metabolism has emerged (Keurentjes et al., 2006; Schauer et al., 2006, 2008; Lisec et al., 2008, 2009; Rowe et al., 2008). | ||
| + | <br> | ||
| + | *In this study, an mQTL analysis was performed for rice grains. An understanding of the genetic background of m-traits in rice (Oryza sativa) grain would provide a foundation for improvement of the nutritional quality of one of the world’s most important food crops.To address these issues, a large-scale metabolome dataset, including a wide variety of rice metabolites, was obtained using an analytical platform that covers primary and secondary metabolites. Based on this dataset, the genetic backgrounds representing natural variations in rice metabolism were investigated by analysis of broad-sense heritability, metabolite–metabolite correlation, QTL mapping and identification of putative causative genes from a candidate mQTL region. | ||
<br> | <br> | ||
Revision as of 07:34, 22 July 2016
Contents
Project Title
- Dissection of genotype–phenotype associations in rice grains using metabolome quantitative trait loci analysis
The Background of This Project
- As metabolic state is modulated by heritable factors,the genetic control of metabolic traits has been a major focus in the field of plant metabolic research. However, the relationship between genotype and metabolomic traits(m-traits) is not straightforward, because m-traits are under complex influences of quantitative trait loci (QTL) (Fernie and Schauer, 2009; Kliebenstein, 2009; Peleg et al., 2009).Furthermore, the metabolic composition of plant tissues is dynamically affected by environmental factors through post-translational interactions involving entire metabolic networks (Chan et al., 2010; Kerwin et al., 2011). Recent progress in metabolomics and QTL mapping techniques has made it possible to investigate the effect of genetic background on m-trait levels for a wide variety of metabolites.Metabolome QTL (mQTL) analyses have been performed for Arabidopsis and tomato (Solanum lycopersicum) using metabolomic techniques such as GC–MS and LC–MS, from which a better understanding of the genetics of plant metabolism has emerged (Keurentjes et al., 2006; Schauer et al., 2006, 2008; Lisec et al., 2008, 2009; Rowe et al., 2008).
- In this study, an mQTL analysis was performed for rice grains. An understanding of the genetic background of m-traits in rice (Oryza sativa) grain would provide a foundation for improvement of the nutritional quality of one of the world’s most important food crops.To address these issues, a large-scale metabolome dataset, including a wide variety of rice metabolites, was obtained using an analytical platform that covers primary and secondary metabolites. Based on this dataset, the genetic backgrounds representing natural variations in rice metabolism were investigated by analysis of broad-sense heritability, metabolite–metabolite correlation, QTL mapping and identification of putative causative genes from a candidate mQTL region.
Plant Culture & Treatment
Research Findings
Labs working on this Project
- RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Japan,
- QTL Genomics Research Center, National Institute of Agrobiological Sciences, Kannondai 2-1-2, Tsukuba, Ibaraki, Japan, and
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
Corresponding Author
- Kazuki Saito：ksaito@psc.riken.jp
