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Challenges for crop improvement GWAS reveals genetic diversity Population projected to rise to ~10 billion by 2050 associated with variation in plant a architecture and leaf photosynthesis Two of the greatest challenges will be meeting increased demand for:
The results so far….! FOOD ⬆50% FRESHWATER ⬆55%
Sónia Negrão Nadia Al-Tamimi King Abdullah University of Science & Technology Saudi Arabia
Tester & Langridge (2010) Science 327: 818-22 Mekonnen & Hoekstra (2016). Science Advances 2: e1500323
The challenge and the opportunity Addressing the challenge with an opportunity
. Diversity panel to target natural genetic variation
. High-throughput phenotyping to target previously uncharacterized traits related to photosynthetic activity
. Field trials to target leaf anatomy and several photosynthetic parameters
Challenge: Need to increase food GWAS to identify loci contributing to photosynthesis supply using less resources
Source: http://www.yieldgap.org Opportunity: Increase ‘yield stability’ - e.g. stress tolerance photosynthetic activity Nadia Al-Tamimi Robert Coe PhD student (KAUST) Scientist (IRRI) Photo credit: Sónia Negrão
Unlocking salty water – using crop wild relatives Germplasm used for phenotyping
We use <1% of the world’s water. Unlock saline water: reduce cost of desalination (e.g. by partial desalination) and increase salinity tolerance of crops. High-density rice array- HDRA- Discover genes controlling salinity tolerance in crops – using forward genetics 700K SNPs Apply knowledge to increase crop yields in saline conditions McCouch et al. (2016) Nature Communications ~100 kb resolution Start with salt-tolerant crops – barley, tomato, quinoa 400K SNPs used for GWAS Use wild relatives for high salinity tolerance traits – 25 H. spontaneum NAM (Scientific Reports 6: 32586) – Galapagos tomatoes, S. pimpinellifolium And domesticate plants that are already salt tolerant – Chenopodium quinoa, C. hircinum
Phenomics of Rice Adaptation and Yield potential (PRAY) ~226 indica accessions representative of the genetic variation of 117,000 accessions at International Rice Gene Bank 30% increase in http://ricephenonetwork.irri.org/diversity-panels/pray-diversity-panel yield
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Phenotyping methods The Plant Accelerator
Under controlled conditions Under field conditions
. High throughput facility - 2,400 plants . Regular, automated non-destructive quantitative measurements of growth, some aspects of physiology (e.g. transpiration, senescence) . Automated data handling and processing Photosynthetic traits Structural traits Traits measured at high-throughput (e.g. rates of (e.g. stomatal density (e.g. transpiration, growth) photosynthesis) on the adaxial surface) The Plant Accelerator IRRI field trial Maligaya field trial
Seek correlations between photosynthesis and leaf anatomy data in field conditions with high- throughput data under control conditions
Furbank & Tester (2011) Trends in Plant Sciences 12, 635-644 Berger et al (2012) Methods in Molecular Biology 913: 399-413
Phenotyping at The Plant Accelerator Biomass is correlated with pixels
Images can also be analyzed for Side view 1 Side view 2 Top view other traits Y-axis (0,0)
The projected shoot area in pixels
= Σ pixels SV1+ SV2+ TV axis
- X
Daily data Key traits analyzed RGB imaging • Projected shoot area (biomass) Plant growth and architecture • Absolute growth rate Area ∆M • Relative growth rate Object.Extent.Y Water loss • Transpiration rate Object.Extent.X • Transpiration use efficiency Roundness Convexhull
Estimations of growth and GWAS reveals loci associated with transpiration and transpiration structural traits in controlled conditions (TPA)
600000 500000 0.1398x 400000 y = 82539e 300000 R² = 0.9909
[pixel] 200000 100000
Projected ShootAea 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time after salting [days]
Compactness Transpiration rate Transpiration use efficiency
For different: - time periods (e.g. 9-13 days) - sub-populations (e.g. Indica) - conditions (e.g. low salinity) - association models
Al-Tamimi et al. (2016) Nature Communications 7:13342 Building on Al-Tamimi et al. (2016) Nature Communications 7:13342
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Indica accessions reveal genetic Phenotyping in the field diversity for transpiration traits TUE C Photosynthesis measurements Leaf anatomy sampling i at IRRI, Philippines at PhilRice, Maligaya, Philippines
Transpiration Use Efficiency Leaf intercellular CO2 concentration (mL H2O per plant per day per Kpixels produced) (ppm) TR Cond Photosynthetic traits: Structural traits: • Gas exchange traits • Leaf anatomy traits • Stomatal traits 48 traits 10 traits Transpiration Rate Stomatal conductance to water vapor -2 -1 (mL H2O per plant per day) (mmol H2O m s )
Overlap accessions between controlled GWAS reveals loci associated with and field conditions for transpiration transpiration and photosynthetic traits
Two of the best performers for Transpiration Use Efficiency were also the best performers for Leaf Intercellular CO2 Concentration
Very interesting overlap in results from controlled and field conditions
Stomatal conductance Leaf intercellular CO2 concentration
These accessions can be used as donors for breeding programs Re-analyzing using more complex models where run raw data and perform spatial correction in one go as a covariate in our association model
Comparing genetic analyses between TPA and Correlations between controlled field - no overlap of SNPs… so far! environment and field data
Photosynthetic traits
on ati (field) ntr Some correlation between stomatal ce SD= stomatal on 2.c p e te CO nt density o id e .Y .ra r. re te s.T s.S op id ss ic d la tu n density (field) and TPA traits: s s l.T .S a et n on llu ra .co ne ne ul ll .m th co ti ce e ll D D ct ct h hu of yn l. ira er th mp hy .S l_S a a e ex ex r. s ata p nt id te op al ia R mp mp um nv v te to m ns E f.i f.w f. or xi x G R UE o o ol o on en ho to ra U ea ea ea hl ba da - compactness R T T C C V C C C P S T W L L L c A A 1 RGR
(more compact plants with higher TR 0.8 stomatal density) TUE Compactness.Top - TUE 0.6 Compactness.Side
(lower stomatal density, higher TUE) Volume TPA traits 0.4 (controlled Convexhull.Top And note possible overlaps of SNPs Convexhull.Side 0.2 environment) Center.of.mass.Y
between TR and intercellular CO2 Photosynthetic.rate 0
Stomatal.cond
Transpiration -0.2
TPA may provide useful data on transpiration and WUE
structural traits which may have some relevance to the Leaf.intercellular.CO2.concentration -0.4 field Leaf.width -0.6 Leaf.temperature Transpiration rate Leaf intercellular CO2 concentration (ppm) chlorophyll.content -0.8 (controlled environment - Accelerator) (field experiment - IRRI) TPA also valuable when need to control or manipulate the Abaxial.SD Adaxial_SD environment (e.g. add salt) -1 Still some work to do……
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Conclusions Future perspectives
• Indicative associations for some traits from plants grown in controlled environment (TPA) • Explore multivariate models to integrate • Compactness, TR, TUE temporal and spatial responses • Indicative association for photosynthetic traits for field • Develop models to use raw data and performs data (IRRI) spatial correction and association in one go • Stomatal G, leaf intercellular CO2 concentration • Apply to most promising traits: Ci, Cond, TR and TUE • No overlap in associations between controlled and field • Incorporate both TPA and field datasets into one conditions... yet! single association analysis model • Perhaps transpiration rate & leaf intercellular CO2 conc • Need to improve statistical power with phenotypic • …..and help genomics be used to accelerate spatial correction and genotypic imputation breeding • This is just a progress report
Acknowledgements
KAUST TPA and Univ Adelaide IRRI Cornell University
Mark Tester Bettina Berger IRRI genebank (germplasm) Susan McCouch Nadia Al-Tamimi Helena Oakey GRiSP phenotyping network Stephanie Saade Chris Brien & GRiSP consortium Mitchell Morton Alex Garcia Michael Dingkuhn Sandra Schmӧckel Lidia Mischis Julie Mae Pasuquin The Salt Lab Trevor Garnett Robert Coe Guntur Tanjung W. Paul Quick Nicky Bond Fiona Groskreutz
The Salt Lab Nadia Al-Tamimi Robert Coe Paul Quick
https://saltlab.kaust.edu.sa/
شكرا
@sonicanegrao
Email: [email protected]
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We are recruiting…...
Full professors
Candidates will need to have a proven track record of excellence in plant science and a strong and original research program. Achievements in the following areas will be of particular interest: bioinformatics, genomics, genetics and breeding
Given the long-term emphasis of the plant science program at KAUST is to address issues related to increasing abiotic stress tolerance of crops, directing research towards this area of study is therefore desirable
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