Th1_Identifying Core Regions of the O. sativa Genome Controlling
•Transferir como PPT, PDF•
1 gostou•743 visualizações
3rd Africa Rice Congress Theme 1. Climate resilient rice Mini symposium: towards improved resistance to abiotic stresses Author: Sparks
Recomendados
Recomendados
Mais conteúdo relacionado
Semelhante a Th1_Identifying Core Regions of the O. sativa Genome Controlling
Semelhante a Th1_Identifying Core Regions of the O. sativa Genome Controlling (8)
Mais de Africa Rice Center (AfricaRice)
Mais de Africa Rice Center (AfricaRice) (20)
Último
Último (20)
Th1_Identifying Core Regions of the O. sativa Genome Controlling
- 1. Identifying Core Regions of the O. sativa Genome Controlling Root Architecture Erin E. Sparks PhD Philip Benfey Laboratory Duke University Durham, NC, USA Tuesday, 22nd October 2013
- 2. Root System Architecture (RSA) Influences Plant Function in Changing Environmental Conditions RSA the Spatial Organization of Roots in the Soil
- 3. How can we modify roots to improve agriculture?
- 4. Oryza sativa Cultivars have Diverse RSAs Moroberekan Lemont IR64 Nipponbare Basmati 217 Iyer-Pascuzzi et al., Plant Physiology 2010 Jefferson Teqing Caiapo Carolina Gold Iyer-Pascuzzi et al. 2010
- 5. Identify Core Regions of the Genome Controlling RSA in Rice High-throughput Root Imaging System Imaging time: 5 minutes / plant
- 6. Digital Phenotyping automatic 2-D and 3-D trait extraction GiA-Roots – Galkovskyi et al. BMC Plant Biology 2012; Iyer-Pascuzzi, Symonova, et al. Plant Physiology 2010, & unpublished
- 7. Identifying Core Regions of the Genome Controlling RSA in Rice Bala x Azucena Recombinant Inbred Lines (RILs) were phenotyped at d12, d14 and d16 Bala upland Indica Azucena tropical Japonica Adam Price, Aberdeen, UK
- 8. Root Traits are Segregating in the Bala x Azucena RIL Population Bala RIL 001 RIL 002 RIL 003 Azucena
- 9. Successful Identification of Core Regions of the Genome Controlling RSA in Rice Positive allele = Azucena Network_Solidity_2D_d16 Network_Solidity_2D_d16 RM234 C507 a123714 a123612 RG351 Network_Solidity_2D_d14 Network_Solidity_2D_d14 RG650 144.0 158.5 173.0 175.4 179.2 Network_Solidity_2D_d12 Network_Solidity_2D_d12 125.2 Network_Solidity_2D_d16 Network_Solidity_2D_d16 R1357 Network_Solidity_2D_d14 Network_Solidity_2D_d14 106.1 Network_Solidity_2D_d12 Network_Solidity_2D_d12 C1057 RM1353 L538T7 RM3484 L09 G338 C39 PIP R1440 G20 C451 Network_Convex_Area_2D_d16 0.0 10.5 20.4 23.1 28.0 42.2 46.5 51.5 56.5 59.0 83.3 7 Topp et al., PNAS, 2013 Positive allele = Bala 89 Univariate QTLs Clustered at 11 Loci 0.0 12.4 18.8 23.8 43.3 68.3 86.7 89.8 96.1 103.4 111.2 115.6 133.2 9
- 10. Multivariate QTLs Give Rise to Distinctive RSA based on 9 traits over 3 time points Bala allele Azucena allele Thomas Mitchell-Olds, Jill Anderson, Cheng-Ruei Lee Topp et al., PNAS, 2013
- 11. Current & Future Directions -Generation of additional genetic markers by Multiplexed Shotgun Genotyping (Andolfatto, et al., 2011) -Fine mapping and RNA-sequencing to identify differentially expressed genes underlying the QTL intervals -Introgression the genomic regions into additional cultivars to assay yield under specific nutrient-limited conditions
- 12. Genetic Control of RSA Changes in Response to Limited Nutrients 100% N 1% N high Nitrogen low Nitrogen Azucena
- 13. Core Regions Controlling Root System Architecture are Candidates Targets for Crop Improvement
- 14. Acknowledgements Benfey Lab 2013 Collaborators • Tom Mitchell-Olds (Duke, USA) • Joshua Weitz (Georgia Tech, USA) • Herbert Edelsbrunner (IST, Austria) • Leon Kochian (Cornell, USA) Chris Topp (Donald Danforth Plant Science Center, USA) Anjali Iyer-Pascuzzi (Purdue University, USA) Funding: NSF AT2010, NIH R01, NIH P50, GBMF/HHMI