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Rice root phenotyping of the OryzaSNP
panel: associated genomic regions and
environmental effects
Amelia Henry, Len Wade, ...
OryzaSNP panel
• 20 genotypes
• mapped for 160,000 SNP
markers
GCP Roots project:
• Develop and refine
screening tools and...
IR64 Dular N22 FR13
Root
phenotyping
systems
Total:
10 root study systems (field and containers)
19 grain yield environmen...
R. Shrestha, Z. Al-Shugeairy, F. Al-Ogaidi, M. Munasinghe, M. Radermacher, J. Vandenhirtz, A. H.
Price. 2013. Comparing si...
Recommendations after the experiments and data compilation:
1. Distribution of germplasm:
• choice of genotypes (ability t...
Recommendations
4. Have an understanding with the database
managers about formatting, especially regarding
curation of non...
GxE analysis
• Root dry weight
• maximum root depth
• % deep roots
• grain yield
Genotype grouping for root dry weight:
Ap...
OryzaSNP panel: Correlation of putative introgressions
with phenotypes
- Correlated introgression regions were identified
...
Chromosome 1: alignment of traits around 39.7 - 40.7 Mb
(root dry weight, yield % deep roots)
Chromosome 8: alignment of traits around 20.3-21.9 Mb
(yield and % deep roots)
Enrichment analysis: previously reported root QTLs from the regions
on chromosomes 1 and 8 that aligned in this study
Gene...
Hydraulics and aquaporin inhibition
Tr_Inh Tr_Inh Tr_Inh
YLD_D
Br_W
Br_D
Tr_W Br_W
Br_D
YLD_W
Chrom. 8
Chrom. 4
-0.6
-0.5
...
Phenotyping methods: Scaling up for association mapping
--> aus panel
3 field studies
1 lysimeter study
1 basket study
1 h...
Conclusions
 Refined root phenotyping systems  root methods manual
 Data uploaded to IRIS
 Better understanding of mos...
Acknowledgements
Ken McNally Rolly Torres Marinell Ramirez
Len Wade
CSU
Australia
Akira Yamauchi
Nagoya Univ.
Japan
Vinay ...
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GRM2013: Rice root phenotyping of the OryzaSNP panel: associated genomic regions and environmental effects -- A Henry

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GRM2013: Rice root phenotyping of the OryzaSNP panel: associated genomic regions and environmental effects -- A Henry

  1. 1. Rice root phenotyping of the OryzaSNP panel: associated genomic regions and environmental effects Amelia Henry, Len Wade, Adam Price, Akira Yamauchi, R. Chandra Babu, V. Shenoy, S. Mande, V. Bartolome, R. Mauleon , Kenneth McNally
  2. 2. OryzaSNP panel • 20 genotypes • mapped for 160,000 SNP markers GCP Roots project: • Develop and refine screening tools and protocols for high- throughput phenotyping of dehydration-avoidance root traits. • Characterize rice germplasm and genetic diversity for plant water use, dehydration avoidance root traits and yield under drought. McNally et al. 2009
  3. 3. IR64 Dular N22 FR13 Root phenotyping systems Total: 10 root study systems (field and containers) 19 grain yield environments(field)  38 datasets
  4. 4. R. Shrestha, Z. Al-Shugeairy, F. Al-Ogaidi, M. Munasinghe, M. Radermacher, J. Vandenhirtz, A. H. Price. 2013. Comparing simple root phenotyping methods on a core set of rice genotypes. Plant Biology. DOI: 10.1111/plb.12096 Gowda, V.R.P., Henry A, V. Vadez, H.E. Shashidhar and R. Serraj 2012. Water uptake dynamics under progressive drought stress drought stress in OryzaSNP panel rice accessions. Functional Plant Biology 39: 402-411. Henry, A., V.R.P. Gowda, R. Torres, K. McNally, R. Serraj. 2011. Genetic variation in root architecture and drought response in Oryza sativa: Rainfed lowland field studies of the Oryza SNP panel. Field Crops Res. 120: 205-214.
  5. 5. Recommendations after the experiments and data compilation: 1. Distribution of germplasm: • choice of genotypes (ability to germinate) • check seed quality before distribution • account for quarantines/regulations in project timeline 2. Determine how GIDs will be identified 3. Have a plan to include at least 1 common measurements among project partners
  6. 6. Recommendations 4. Have an understanding with the database managers about formatting, especially regarding curation of non-traditional traits 5. Distribute a data formatting template, and ask partners to enter all data into this template as it is generated
  7. 7. GxE analysis • Root dry weight • maximum root depth • % deep roots • grain yield Genotype grouping for root dry weight: Appears to be soil type related Maximum root depth: most affected by method L. WadeV. Bartolome
  8. 8. OryzaSNP panel: Correlation of putative introgressions with phenotypes - Correlated introgression regions were identified - used a cutoff P-value of 0.001  183 regions to map (almost all japonica introgressions) Looked for alignment of 5+ traits/environments R. Mauleon McNally et al. 2009
  9. 9. Chromosome 1: alignment of traits around 39.7 - 40.7 Mb (root dry weight, yield % deep roots)
  10. 10. Chromosome 8: alignment of traits around 20.3-21.9 Mb (yield and % deep roots)
  11. 11. Enrichment analysis: previously reported root QTLs from the regions on chromosomes 1 and 8 that aligned in this study Gene Category List Hits List Total Population Hits Population Total Probability Reference Chr 1 Vigor|root number|DQC3|Chr. 1 11 11 28 3680 5.19E-25 Price et al 2000 Vigor|root number|AQC003|Chr. 1 11 11 28 3680 5.19E-25 Price et al 2000 Vigor|root number|AQO077|Chr. 1 11 11 28 3680 5.19E-25 Price et al 2002b Vigor|root dry weight|AQGI070|Chr. 1 11 11 42 3680 1.03E-22 Lian et al 2005 Abiotic stress|root dry weight to tiller number ratio|CQQ13|Chr. 1 9 11 56 3680 1.21E-15 Yadav et al 1997 Abiotic stress|root weight|CQQ6|Chr. 1 9 11 56 3680 1.21E-15 Yadav et al 1997 Abiotic stress|root weight|CQQ32|Chr. 1 9 11 56 3680 1.21E-15 Shen et al 2001 Abiotic stress|penetrated root thickness|DQF9|Chr. 1 9 11 56 3680 1.21E-15 Zheng et al 2000 Abiotic stress|relative root length|CQL2|Chr. 1 2 11 2 3680 8.12E-06 Wu et al 2000 Abiotic stress|relative root length|CQL1|Chr. 1 2 11 2 3680 8.12E-06 Wu et al 2000 Anatomy|seminal root length|CQS3|Chr. 1 2 11 2 3680 8.12E-06 Zhang et al 2001 Abiotic stress|penetrated root length|AQGC035|Chr. 1 3 11 60 3680 0.00061969 Ali et al 2000 Abiotic stress|penetrated root thickness|AQGC022|Chr. 1 3 11 60 3680 0.00061969 Ali et al 2000 Chr 8 Vigor|root to shoot ratio|AQO017|Chr. 8 13 17 22 3680 3.29E-28 Price et al 2002a and b Vigor|root to shoot ratio|AQO025|Chr. 8 13 17 22 3680 3.29E-28 Price et al 2002a and b Vigor|root number|CQAW26|Chr. 8 17 17 97 3680 3.35E-28 Ray et al 1996 Abiotic stress|relative root length|CQL9|Chr. 8 14 17 42 3680 3.76E-26 Wu et al 2000 Abiotic stress|relative root length|CQL8|Chr. 8 14 17 42 3680 3.76E-26 Wu et al 2000 Anatomy|root length|AQZ004|Chr. 8 4 17 13 3680 2.17E-07 Nguyen et al 2003 Abiotic stress|relative root length|AQZ008|Chr. 8 4 17 13 3680 2.17E-07 Nguyen et al 2003 Anatomy|root thickness|AQAL029|Chr. 8 4 17 120 3680 0.00184121 Kamoshita et al 2002
  12. 12. Hydraulics and aquaporin inhibition Tr_Inh Tr_Inh Tr_Inh YLD_D Br_W Br_D Tr_W Br_W Br_D YLD_W Chrom. 8 Chrom. 4 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 Inhibitionoftranspiration Transpiration VPD curves at ICRISAT Xylem sap bleeding rate A. Grondin • cutoff P-value of 0.01  33 introgression regions to map
  13. 13. Phenotyping methods: Scaling up for association mapping --> aus panel 3 field studies 1 lysimeter study 1 basket study 1 herbicide at depth study Univ. Aberdeen – A. Price IRRI
  14. 14. Conclusions  Refined root phenotyping systems  root methods manual  Data uploaded to IRIS  Better understanding of most effective parameters (RDW vs MRL)  Chromosome regions correlated with root architecture and grain yield phenotypes from multiple environments and study systems Next:  Scaling up to larger genotype sets (aus panel) to identify genes associated with drought resistance  Better understanding of hydraulics/ root function for drought resistance in rice
  15. 15. Acknowledgements Ken McNally Rolly Torres Marinell Ramirez Len Wade CSU Australia Akira Yamauchi Nagoya Univ. Japan Vinay Shenoy Barwale Foundation India Adam Price Univ. Aberdeen UK R. Chandrababu TNAU India M. Semon AfricaRice Nigeria

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