SlideShare a Scribd company logo
1 of 30
Download to read offline
GCP Project Number: G7010.03.01:
Cloning, characterization and validation
of PUP1/P efficiency in maize
PI: Leon Kochian, USDA-ARS/Cornell University, USA
Co-Pi’s: Claudia Guimaraes, Sidney Parentoni, Jurandir
Magalhães, Vera Alves, Maria José Vasconcelos, Sylvia
Sousa, Roberto Noda, Embrapa Maize and Sorghum,
Sete Lagoas, Brazil
Lyza Maron, Miguel Pineros, Jiping Liu, Randy Clark, Ed
Buckler, Jon Shaff, USDA-ARS/Cornell, USA
Sam Gudu, Moi University/KARI, Eldoret, Kenya
Mathias Wissuwa, JIRCAS, Tsukuba, Ibaraki, Japan
• Identification of OsPSTOL1 (Pup-1)
orthologs in maize
• QTL/gene mapping for P use efficiency in
maize in the field and in hydroponics
• Inheritance studies on maize root
architecture under high and low P
• Validation of maize PSTOL1 candidate
genes and if necessary, novel P efficiency QTL
(if maize PSTOL1 homologues are not
functional in P efficiency)
Project Objectives
Gene Identification Physical position
Identity
(%)
Coverage (%) E-value
GRMZM2G451147 ZmPSTOL1 Chr8: 152.043.859 70 97 3.4e-131
GRMZM2G164612 ZmPSTOL2 Chr8: 152.100.275 70 97 2.3e-127
AC193632.2_FGP002 ZmPSTOL3 Chr4: 39.792.602 69 95 2.0e-105
GRMZM2G448672 ZmPSTOL4 Chr3: 206.918.421 66 97 4.7e-186
GRMZM2G412760 ZmPSTOL5 Chr3: 20.172.140 55 99 5.1e-104
GRMZM2G172396 ZmPSTOL6 Chr8: 13.267.001 55 99 9.6e-123
OsPSTOL1 Ortholog Identification in Maize
• Using OsPSTOL1 as a query, six predicted genes were found in the maize genome
sharing more than 55% of amino acid sequence identity with OsPSTOL1.
• These genes were located on chromosomes 3, 4 and 8, at physical positions
described in Table below.
• Genetic markers for the six ZmPSTOL genes were generated and mapped on a
linkage map for a L3 (P efficient) x L22 (P inefficient) RIL population.
Phylogeny of Maize PSTOL1 Orthologs
• Phylogenetic tree of OsPSTOL1
related sequences, including
predicted protein sequences
from maize, rice and
Arabidopsis.
• The predicted maize proteins
share more than 55% sequence
identity with OsPSTOL1.
• These predicted maize
proteins cluster together with
OsPSTOL1 and Arabidopsis SNC4
and PR5, suggesting like
OsPSTOL1 they are
serine/threonine receptor-like
kinases of the LRK10L-2
subfamily.
Maize Test Cross Hybrids Derived from Embrapa
Elite Diversity Panel Phenotyped for P efficiency
•321 testcross hybrids were evaluated in the field over two years under low and high P.
• High variability in yield under low & high P was observed as well as a significant
differences in grain yield under high vs. low P
• Phenotypic data will be used for association analysis and genomic selection
Low P High P
QTL Mapping of Maize P Efficiency in the Field
• RIL population derived from L3 (P efficient) x L53 (P inefficient) was
backcrossed to the parental lines and then phenotyped for P efficiency
traits in the field on low P and sufficient P field sites.
• P efficiency traits determined and mapped were P acquisition
efficiency (PAE; grain produced/amount soil available P), P use efficiency
(PUE; amount plant P/amount soil available P), and P utilization
efficiency (PUTIL; grain produced/amount plant P).
• Six QTLs were identified for PUE, six for PAE and five for PUTIL .
• Most of the QTLs mapped for PUE were coincident with the genomic
regions mapped for PAE. This agrees with the high correlation (0.89)
between these traits, which were also highly correlated with grain yield
under low P, 0.96 and 0.85, respectively.
• This result indicates that P use efficiency is mainly due to P acquisition
efficiency, as was also found by Parentoni et al. (Maydica 55:1; 2010).
• None of the QTLs for P utilization efficiency were coincident with the
other P efficiency indexes, suggesting that different genes are involved in
P utilization.
QTL Mapping of Maize P Efficiency in the Field (con’t)
QTL Mapping of Maize P Efficiency
and 2D Root Traits in Hydroponics
•The L3 x L22 maize RIL population was grown in paper pouches
moistened with low P and sufficient P nutrient solution and roots
were digitally imaged and root traits quantified using our
RootReader 2D platform (Clark et al, Plant Cell Envir 36: 454; 2013).
•Root and shoot dry weight and P accumulation were also
quantified and QTL mapping was conducted on P efficiency traits
(PUE, PAE, PUTIL) and root traits.
•Out of 32 root traits, four were selected for mapping analysis
based on De Sousa et al. (Functional Plant Biol; 2012): length (cm),
volume (cm3), volume of fine roots (1.0<d≤2.0 mm) (cm3) and root
surface area (cm2).
Root Phenotyping Tools
Growth:
Hydroponics (Al tolerance)
Agar Plates (Zn nutrition)
Pouches (P nutrition, Salinity stress)
Sand Pots (RSA validation, P nutrition)
Capture and Analysis:
Digital Photography (Single images)
RootReader2D Software calculates range of
root growth traits on both whole root system
and specific traits
 Visit: www.plantmineralnutrition.net
Overall Efficiency:
1000’s of plants per day
2D Phenotyping Platform 3D Phenotyping Platform
Clark et al., Plant Physiol2012
Growth:
Gel Cylinders (RSA - root system architecture)
*New: Hydroponics (RSA, Stress/functional studies)
Capture and Analysis:
Digital Photography (Image sequences)
RootReader3D Software reconstructs series of 2D
images into a 3D RSA and computes specific root traits
Overall Efficiency:
~100 plants per day
Clark et al., Plant, Cell & Envir2011
Root Imaging Pipeline – Imaging RSA in 2-D
Root Growth & Imaging
RootReader 2D Software
Clark et al. 2012. High-throughput
2D root system phenotyping platform
facilitates genetic analysis of root
growth and development. Plant Cell
Environ.
QTL Mapping of Maize P Efficiency
and 2D Root Traits in Hydroponics
•The L3 x L22 maize RIL population was grown in paper pouches
moistened with low P and sufficient P nutrient solution and roots
were digitally imaged and root traits quantified using our
RootReader 2D platform (Clark et al, Plant Cell Envir 36: 454; 2013).
•Root and shoot dry weight and P accumulation were also
quantified and QTL mapping was conducted on P efficiency traits
(PUE, PAE, PUTIL) and root traits.
•Out of 32 root traits, four were selected for mapping analysis
based on De Sousa et al. (Functional Plant Biol; 2012): length (cm),
volume (cm3), volume of fine roots (1.0<d≤2.0 mm) (cm3) and root
surface area (cm2).
Co-localization of P Efficiency QTL from Field Studies with P
Efficiency and Root Trait QTL from Nutrient Solution Phenotyping
Hydroponics Hydroponics
Field Field
A B
Co-localization of P Efficiency QTL from Field Studies with P
Efficiency and Root Trait QTL from Nutrient Solution Phenotyping
Chr 7
Chr 7
Hydroponics Hydroponics
Chr 8
Field Field
•A region from 209 to 272 cM on chromosome 1: Co-localization
of QTLs controlling PUE, PAE and P utilization efficiency (PUTIL) in
the field with a multiple-trait QTL for root morphology and PAE in
nutrient solution
•A region spanning 82 - 95 cM on chromosome 3: Co-localization
of QTL controlling PUE and PAE in the field with a multiple-trait QTL
for root morphology and PAE in nutrient solution.
•A region from 77 to 83 cM on chromosome 7: Co-localization of
QTL controlling PUE and PAE in the field with a QTL for root
diameter.
•A region spanning 100 – 127 cM on chromosome 8: Co-
localization of QTL controlling PUE and PAE in the field with QTLs
for PAE, root length and root surface area in nutrient solution.
The Combined Analysis of QTL Mapping for Root Traits
and P Efficiency Indices in the Field Based On Grain
Yield Has Led Us To Focus on Four Genomic Regions
Colocalization of ZmPSTOL1 Orthologs with
Maize P Efficiency and/or Root Trait QTL
ZmPSTOL Expression in Roots & Shoots of
P Efficient (L3) and P Inefficient (L22) Maize
• ZmPSTOL1, 4 and 6 preferentially expressed in roots.
• ZmPSTOL1 and 4 expression increases in response to P deficiency.
• ZmPSTOL4 preferentially expressed in roots of P efficient L3 -
colocalizes with root traits and not P efficiency traits.
• ZmPSTOL1 only rice Pup1 homolog that colocalizes with PAE and PUE.
It’s expression is specific to roots and is induced by low P plant status.
• ZmPSTOL1 expression is exclusively in roots of P inefficient L22, but the
superior allele for this chr 8 PAE and PUE QTL donated by L22.
• ZmPSTOL1 is most similar in sequence of the maize orthologs to
OsPSTOL1.
Shallow Intermediate Deep
What Is the Ideal Root Architecture P Efficiency in Low P Soils?
[P]
[H+]
P Efficient Soybean Line
Dr. Hong Liao’s group, Root Biology Center, SCAU, Guangzhou
3D RSA Imaging System
• Stationary camera with fixed capture settings that is synchronized to a
turntable via a LabVIEW interface and digital controller
• 100 images captured per root system, 3.6° of rotation between images
• Capture time of approximately 10 minutes per root system
3D Reconstruction Process via RootReader 3D
Thresholded rotational image sequence
consisting of 40-100 2D images Perspective back projection of 2D root points from
each 2D image into a temporary 3D voxel volume
Transformation of each temporary
voxel into a final voxel volume
Adaptive thresholding of each horizontal cross section
through final voxel volume to generate 3D root model
Germplasm and Screening Mapping Results
Genetic Mapping of RSA
Peak SNP
-66kb -33kb 0 +33kb +66kb
“A” “B”
SNP Allele SNP Allele
n=39 n=118 QTL
QTL
Germplasm and Screening Mapping Results
Genetic Mapping of RSA
Peak SNP
-66kb -33kb 0 +33kb +66kb
“A” “B”
SNP Allele SNP Allele
n=44 n=107 QTL
QTL
Germplasm and Screening Mapping Results
Genetic Mapping of RSA
• Subpopulation SNPs selected from within 3kb of the
peak Indica SNP
Aus Indica
Temperate Japonica Tropical Japonica
All Subpops
n=211 n=360 n=9 n=80 n=44 n=107
n=10 n=169 n=7 n=162
QTL
QTL
The Gel-Based Root Growth
System Has Its Limitations
• Roots of some plant species such as maize &
sorghum as well as fine rooted species such as
canola don’t grow well in the gel cylinders
• Labor and cost intensive
• Can’t easily impose different nutrient regimes
• Limited to work with fairly small root systems
(young plants)
Transition From Gel to Hydroponics
M8 Rice line Nipponbare parent
12 day old rice
grown in low P
nutrient solution
3D Imaging/Analysis of
RSA in Hydroponics
•We can use the hydroponic systems for 3-D imaging because our
software subtracts out the mesh and reconstructs the images
•Are using this hydroponic system with 3D black plastic mesh to
screen sorghum populations (270 lines) and to correlate RSA traits
with physiological data.
•Can use much larger vessels than with the gel and still maintain
rapid throughput.
• Important design for longer growth periods, as crown roots may
be important for water acquisition and they don’t appear until
around 12 days.
Dr. Alexandre Falcão
computer wizard
3 D Printers
The growth system is created
from ABS plastic mesh circles
made with a 3-D printer.
The mesh system serves
to constrain the roots,
but not to impede their
growth.
Three-D RSA Reconstruction of 100 Two-D
Sorghum Root Images (15 Day Old Plant)
• Barbara Hufnagel from Jurandir Magalhaes’s lab is currently in our lab working with
our staff to phenotype and quantify RSA 3D traits for the sorghum association panel.
• We will be set up to phenotype maize RSA for this project in early 2014.
Products
•Due to the more upstream nature of this project, the products
are still in the pipeline and will be forthcoming starting later in
2014.
•Claudia is generating NILs for specific P efficiency QTL for
verification of QTL effects and as a breeding resource. Pyramiding
of multiple QTL in NILs will have greater potential for impact.
•Work ongoing to validate via association mapping analysis and
more in depth molecular physiological investigations of candidate
ZmPSTOL1 genes to identify OsPSTOL1 orthologs involved in maize
P efficiency.
•Ultimately will have breeding lines for improved P efficiency.
•Catalog of bi-parental and GWA QTL & markers for root system
architecture traits that may play a role in maize P efficiency.

More Related Content

What's hot

GRM 2013: Asian Maize Drought Tolerance (AMDROUT) Project -- BS Vivek
GRM 2013: Asian Maize Drought Tolerance (AMDROUT) Project -- BS VivekGRM 2013: Asian Maize Drought Tolerance (AMDROUT) Project -- BS Vivek
GRM 2013: Asian Maize Drought Tolerance (AMDROUT) Project -- BS VivekCGIAR Generation Challenge Programme
 
GRM 2013: Implementing MARS Project for drought tolerance and the Cassava Bre...
GRM 2013: Implementing MARS Project for drought tolerance and the Cassava Bre...GRM 2013: Implementing MARS Project for drought tolerance and the Cassava Bre...
GRM 2013: Implementing MARS Project for drought tolerance and the Cassava Bre...CGIAR Generation Challenge Programme
 
GRM 2013: Improving sorghum productivity in semi-arid environments of Mali th...
GRM 2013: Improving sorghum productivity in semi-arid environments of Mali th...GRM 2013: Improving sorghum productivity in semi-arid environments of Mali th...
GRM 2013: Improving sorghum productivity in semi-arid environments of Mali th...CGIAR Generation Challenge Programme
 
GRM 2011: Development and evaluation of drought-adapted sorghum germplasm for...
GRM 2011: Development and evaluation of drought-adapted sorghum germplasm for...GRM 2011: Development and evaluation of drought-adapted sorghum germplasm for...
GRM 2011: Development and evaluation of drought-adapted sorghum germplasm for...CGIAR Generation Challenge Programme
 
GRM 2013: Improve cowpea productivity for marginal environments in sub-Sahar...
GRM 2013: Improve cowpea productivity for marginal  environments in sub-Sahar...GRM 2013: Improve cowpea productivity for marginal  environments in sub-Sahar...
GRM 2013: Improve cowpea productivity for marginal environments in sub-Sahar...CGIAR Generation Challenge Programme
 
GRM 2013: MAGIC Rice Multi-parent Advanced Generation Inter-Cross -- H Leung
GRM 2013: MAGIC Rice Multi-parent Advanced Generation Inter-Cross -- H LeungGRM 2013: MAGIC Rice Multi-parent Advanced Generation Inter-Cross -- H Leung
GRM 2013: MAGIC Rice Multi-parent Advanced Generation Inter-Cross -- H LeungCGIAR Generation Challenge Programme
 
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...Integrated Breeding Platform
 
TLM III: Improve cowpea productivity for marginal environments in sub-Sahara...
TLM III: Improve cowpea productivity for marginal  environments in sub-Sahara...TLM III: Improve cowpea productivity for marginal  environments in sub-Sahara...
TLM III: Improve cowpea productivity for marginal environments in sub-Sahara...CGIAR Generation Challenge Programme
 
MARKER ASSISTED SELECTION IN CROP IMPROVEMENT
MARKER ASSISTED SELECTION IN CROP IMPROVEMENTMARKER ASSISTED SELECTION IN CROP IMPROVEMENT
MARKER ASSISTED SELECTION IN CROP IMPROVEMENTVinod Pawar
 
MAGIC population and its application in crop improvement
MAGIC population and its application in crop improvementMAGIC population and its application in crop improvement
MAGIC population and its application in crop improvementSanghaviBoddu
 
Th1_New approaches, resources and tools for gene discovery
Th1_New approaches, resources and tools for gene discovery Th1_New approaches, resources and tools for gene discovery
Th1_New approaches, resources and tools for gene discovery Africa Rice Center (AfricaRice)
 
Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...
Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...
Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...CIAT
 
Tropical maize genome: what do we know so far and how to use that information
Tropical maize genome: what do we know so far and how to use that informationTropical maize genome: what do we know so far and how to use that information
Tropical maize genome: what do we know so far and how to use that informationCIMMYT
 

What's hot (20)

GRM 2013: Asian Maize Drought Tolerance (AMDROUT) Project -- BS Vivek
GRM 2013: Asian Maize Drought Tolerance (AMDROUT) Project -- BS VivekGRM 2013: Asian Maize Drought Tolerance (AMDROUT) Project -- BS Vivek
GRM 2013: Asian Maize Drought Tolerance (AMDROUT) Project -- BS Vivek
 
GRM 2013: Implementing MARS Project for drought tolerance and the Cassava Bre...
GRM 2013: Implementing MARS Project for drought tolerance and the Cassava Bre...GRM 2013: Implementing MARS Project for drought tolerance and the Cassava Bre...
GRM 2013: Implementing MARS Project for drought tolerance and the Cassava Bre...
 
GRM 2013: Improving sorghum productivity in semi-arid environments of Mali th...
GRM 2013: Improving sorghum productivity in semi-arid environments of Mali th...GRM 2013: Improving sorghum productivity in semi-arid environments of Mali th...
GRM 2013: Improving sorghum productivity in semi-arid environments of Mali th...
 
TLI 2012: Bean breeding - Ethiopia
TLI 2012: Bean breeding - EthiopiaTLI 2012: Bean breeding - Ethiopia
TLI 2012: Bean breeding - Ethiopia
 
GRM 2011: Development and evaluation of drought-adapted sorghum germplasm for...
GRM 2011: Development and evaluation of drought-adapted sorghum germplasm for...GRM 2011: Development and evaluation of drought-adapted sorghum germplasm for...
GRM 2011: Development and evaluation of drought-adapted sorghum germplasm for...
 
GRM 2013: Improve cowpea productivity for marginal environments in sub-Sahar...
GRM 2013: Improve cowpea productivity for marginal  environments in sub-Sahar...GRM 2013: Improve cowpea productivity for marginal  environments in sub-Sahar...
GRM 2013: Improve cowpea productivity for marginal environments in sub-Sahar...
 
GRM 2011: Sorghum Research Initiative progress report
GRM 2011: Sorghum Research Initiative progress reportGRM 2011: Sorghum Research Initiative progress report
GRM 2011: Sorghum Research Initiative progress report
 
Magic population
Magic populationMagic population
Magic population
 
GRM 2013: MAGIC Rice Multi-parent Advanced Generation Inter-Cross -- H Leung
GRM 2013: MAGIC Rice Multi-parent Advanced Generation Inter-Cross -- H LeungGRM 2013: MAGIC Rice Multi-parent Advanced Generation Inter-Cross -- H Leung
GRM 2013: MAGIC Rice Multi-parent Advanced Generation Inter-Cross -- H Leung
 
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
 
TLM III: Improve cowpea productivity for marginal environments in sub-Sahara...
TLM III: Improve cowpea productivity for marginal  environments in sub-Sahara...TLM III: Improve cowpea productivity for marginal  environments in sub-Sahara...
TLM III: Improve cowpea productivity for marginal environments in sub-Sahara...
 
MARKER ASSISTED SELECTION IN CROP IMPROVEMENT
MARKER ASSISTED SELECTION IN CROP IMPROVEMENTMARKER ASSISTED SELECTION IN CROP IMPROVEMENT
MARKER ASSISTED SELECTION IN CROP IMPROVEMENT
 
33 Pooran Gaur Objective5 Work Plan
33 Pooran Gaur Objective5 Work Plan33 Pooran Gaur Objective5 Work Plan
33 Pooran Gaur Objective5 Work Plan
 
TL III_Genetic gains_ICRISAT
TL III_Genetic gains_ICRISATTL III_Genetic gains_ICRISAT
TL III_Genetic gains_ICRISAT
 
MAGIC population and its application in crop improvement
MAGIC population and its application in crop improvementMAGIC population and its application in crop improvement
MAGIC population and its application in crop improvement
 
Th1_New approaches, resources and tools for gene discovery
Th1_New approaches, resources and tools for gene discovery Th1_New approaches, resources and tools for gene discovery
Th1_New approaches, resources and tools for gene discovery
 
MENDEL; 150 years on
MENDEL; 150 years onMENDEL; 150 years on
MENDEL; 150 years on
 
Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...
Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...
Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...
 
Tropical maize genome: what do we know so far and how to use that information
Tropical maize genome: what do we know so far and how to use that informationTropical maize genome: what do we know so far and how to use that information
Tropical maize genome: what do we know so far and how to use that information
 
15 pascal
15 pascal15 pascal
15 pascal
 

Similar to GRM 2013: Cloning, characterization and validation of PUP1/P efficiency in maize -- L Kochian

MAGIC population in Vegetables
MAGIC population in VegetablesMAGIC population in Vegetables
MAGIC population in VegetablesAnusha K R
 
Molecular characterization of rice (Oryza sativa L.) genotypes using target r...
Molecular characterization of rice (Oryza sativa L.) genotypes using target r...Molecular characterization of rice (Oryza sativa L.) genotypes using target r...
Molecular characterization of rice (Oryza sativa L.) genotypes using target r...Innspub Net
 
Development of SSR markers in mungbean
Development of SSR markers in mungbeanDevelopment of SSR markers in mungbean
Development of SSR markers in mungbeanNidhi Singh
 
GRM 2013: Molecular Breeding and Selection Strategies to Combine and Validate...
GRM 2013: Molecular Breeding and Selection Strategies to Combine and Validate...GRM 2013: Molecular Breeding and Selection Strategies to Combine and Validate...
GRM 2013: Molecular Breeding and Selection Strategies to Combine and Validate...CGIAR Generation Challenge Programme
 
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...ICRISAT
 
2013 GRM: Improve chickpea productivity for marginal environments in sub-Sah...
2013 GRM: Improve chickpea productivity for marginal environments in  sub-Sah...2013 GRM: Improve chickpea productivity for marginal environments in  sub-Sah...
2013 GRM: Improve chickpea productivity for marginal environments in sub-Sah...CGIAR Generation Challenge Programme
 
Genotyping by sequencing provides new insights into the molecular genetic div...
Genotyping by sequencing provides new insights into the molecular genetic div...Genotyping by sequencing provides new insights into the molecular genetic div...
Genotyping by sequencing provides new insights into the molecular genetic div...ILRI
 
Principal Component Analysis for Evaluation of Guinea grass (Panicum maximum...
Principal Component Analysis for Evaluation of Guinea grass  (Panicum maximum...Principal Component Analysis for Evaluation of Guinea grass  (Panicum maximum...
Principal Component Analysis for Evaluation of Guinea grass (Panicum maximum...Agriculture Journal IJOEAR
 
Msc. synopsis OAT Genetic diversity and molecular markers
Msc. synopsis OAT Genetic diversity and molecular markersMsc. synopsis OAT Genetic diversity and molecular markers
Msc. synopsis OAT Genetic diversity and molecular markersArushi Arora
 
Sorghum early vigor affects grain size, striga resistance and might be linked...
Sorghum early vigor affects grain size, striga resistance and might be linked...Sorghum early vigor affects grain size, striga resistance and might be linked...
Sorghum early vigor affects grain size, striga resistance and might be linked...ICRISAT
 
Research Program Genetic Gains (RPGG) Review Meeting 2021: Groundnut genomic ...
Research Program Genetic Gains (RPGG) Review Meeting 2021: Groundnut genomic ...Research Program Genetic Gains (RPGG) Review Meeting 2021: Groundnut genomic ...
Research Program Genetic Gains (RPGG) Review Meeting 2021: Groundnut genomic ...ICRISAT
 
Evaluating fodder quality in sorghum RIL population under contrasting water r...
Evaluating fodder quality in sorghum RIL population under contrasting water r...Evaluating fodder quality in sorghum RIL population under contrasting water r...
Evaluating fodder quality in sorghum RIL population under contrasting water r...ICRISAT
 
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...FOODCROPS
 
Getting to the root of domestication traits in carrot (Daucus carota L.)
Getting to the root of domestication traits in carrot (Daucus carota L.) Getting to the root of domestication traits in carrot (Daucus carota L.)
Getting to the root of domestication traits in carrot (Daucus carota L.) CIAT
 
Rice Root physiology work at CIAT: Identification of ideal root system to imp...
Rice Root physiology work at CIAT: Identification of ideal root system to imp...Rice Root physiology work at CIAT: Identification of ideal root system to imp...
Rice Root physiology work at CIAT: Identification of ideal root system to imp...CIAT
 
Credit seminar on rice genomics crrected
Credit seminar on rice genomics crrectedCredit seminar on rice genomics crrected
Credit seminar on rice genomics crrectedVarsha Gayatonde
 

Similar to GRM 2013: Cloning, characterization and validation of PUP1/P efficiency in maize -- L Kochian (20)

MAGIC population in Vegetables
MAGIC population in VegetablesMAGIC population in Vegetables
MAGIC population in Vegetables
 
Molecular characterization of rice (Oryza sativa L.) genotypes using target r...
Molecular characterization of rice (Oryza sativa L.) genotypes using target r...Molecular characterization of rice (Oryza sativa L.) genotypes using target r...
Molecular characterization of rice (Oryza sativa L.) genotypes using target r...
 
Development of SSR markers in mungbean
Development of SSR markers in mungbeanDevelopment of SSR markers in mungbean
Development of SSR markers in mungbean
 
GRM 2013: Molecular Breeding and Selection Strategies to Combine and Validate...
GRM 2013: Molecular Breeding and Selection Strategies to Combine and Validate...GRM 2013: Molecular Breeding and Selection Strategies to Combine and Validate...
GRM 2013: Molecular Breeding and Selection Strategies to Combine and Validate...
 
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
 
Biotechnological interventions for crop improvement in fruit crops.pptx
Biotechnological interventions for crop improvement in fruit crops.pptxBiotechnological interventions for crop improvement in fruit crops.pptx
Biotechnological interventions for crop improvement in fruit crops.pptx
 
2013 GRM: Improve chickpea productivity for marginal environments in sub-Sah...
2013 GRM: Improve chickpea productivity for marginal environments in  sub-Sah...2013 GRM: Improve chickpea productivity for marginal environments in  sub-Sah...
2013 GRM: Improve chickpea productivity for marginal environments in sub-Sah...
 
Genotyping by sequencing provides new insights into the molecular genetic div...
Genotyping by sequencing provides new insights into the molecular genetic div...Genotyping by sequencing provides new insights into the molecular genetic div...
Genotyping by sequencing provides new insights into the molecular genetic div...
 
Biotechnological interventions for fruit crops improvement
Biotechnological interventions for fruit crops improvementBiotechnological interventions for fruit crops improvement
Biotechnological interventions for fruit crops improvement
 
Principal Component Analysis for Evaluation of Guinea grass (Panicum maximum...
Principal Component Analysis for Evaluation of Guinea grass  (Panicum maximum...Principal Component Analysis for Evaluation of Guinea grass  (Panicum maximum...
Principal Component Analysis for Evaluation of Guinea grass (Panicum maximum...
 
Msc. synopsis OAT Genetic diversity and molecular markers
Msc. synopsis OAT Genetic diversity and molecular markersMsc. synopsis OAT Genetic diversity and molecular markers
Msc. synopsis OAT Genetic diversity and molecular markers
 
Sorghum early vigor affects grain size, striga resistance and might be linked...
Sorghum early vigor affects grain size, striga resistance and might be linked...Sorghum early vigor affects grain size, striga resistance and might be linked...
Sorghum early vigor affects grain size, striga resistance and might be linked...
 
Research Program Genetic Gains (RPGG) Review Meeting 2021: Groundnut genomic ...
Research Program Genetic Gains (RPGG) Review Meeting 2021: Groundnut genomic ...Research Program Genetic Gains (RPGG) Review Meeting 2021: Groundnut genomic ...
Research Program Genetic Gains (RPGG) Review Meeting 2021: Groundnut genomic ...
 
Evaluating fodder quality in sorghum RIL population under contrasting water r...
Evaluating fodder quality in sorghum RIL population under contrasting water r...Evaluating fodder quality in sorghum RIL population under contrasting water r...
Evaluating fodder quality in sorghum RIL population under contrasting water r...
 
Striving for excellence in yam breeding using genomics tools
Striving for excellence in yam breeding using genomics toolsStriving for excellence in yam breeding using genomics tools
Striving for excellence in yam breeding using genomics tools
 
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
 
20140710 1 day1_nist_ercc2.0workshop
20140710 1 day1_nist_ercc2.0workshop20140710 1 day1_nist_ercc2.0workshop
20140710 1 day1_nist_ercc2.0workshop
 
Getting to the root of domestication traits in carrot (Daucus carota L.)
Getting to the root of domestication traits in carrot (Daucus carota L.) Getting to the root of domestication traits in carrot (Daucus carota L.)
Getting to the root of domestication traits in carrot (Daucus carota L.)
 
Rice Root physiology work at CIAT: Identification of ideal root system to imp...
Rice Root physiology work at CIAT: Identification of ideal root system to imp...Rice Root physiology work at CIAT: Identification of ideal root system to imp...
Rice Root physiology work at CIAT: Identification of ideal root system to imp...
 
Credit seminar on rice genomics crrected
Credit seminar on rice genomics crrectedCredit seminar on rice genomics crrected
Credit seminar on rice genomics crrected
 

More from CGIAR Generation Challenge Programme

ARM 2008: Dissection, characterisation and utilisation of disease QTL -- R Ne...
ARM 2008: Dissection, characterisation and utilisation of disease QTL -- R Ne...ARM 2008: Dissection, characterisation and utilisation of disease QTL -- R Ne...
ARM 2008: Dissection, characterisation and utilisation of disease QTL -- R Ne...CGIAR Generation Challenge Programme
 
ARM 2007: Dissection, characterisation and utilisation of disease QTL -- R Ne...
ARM 2007: Dissection, characterisation and utilisation of disease QTL -- R Ne...ARM 2007: Dissection, characterisation and utilisation of disease QTL -- R Ne...
ARM 2007: Dissection, characterisation and utilisation of disease QTL -- R Ne...CGIAR Generation Challenge Programme
 
The Generation Challenge Programme: Lessons learnt relevant to CRPs, and the ...
The Generation Challenge Programme: Lessons learnt relevant to CRPs, and the ...The Generation Challenge Programme: Lessons learnt relevant to CRPs, and the ...
The Generation Challenge Programme: Lessons learnt relevant to CRPs, and the ...CGIAR Generation Challenge Programme
 
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...CGIAR Generation Challenge Programme
 
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...CGIAR Generation Challenge Programme
 
TLM III: : Improve common bean productivity for marginal environments in su...
TLM III: :   Improve common bean productivity for marginal environments in su...TLM III: :   Improve common bean productivity for marginal environments in su...
TLM III: : Improve common bean productivity for marginal environments in su...CGIAR Generation Challenge Programme
 
TLM III: Improve groundnut productivity for marginal environments from sub-Sa...
TLM III: Improve groundnut productivity for marginal environments from sub-Sa...TLM III: Improve groundnut productivity for marginal environments from sub-Sa...
TLM III: Improve groundnut productivity for marginal environments from sub-Sa...CGIAR Generation Challenge Programme
 
TLIII: Overview of TLII achievements, lessons and challenges for Phase III – ...
TLIII: Overview of TLII achievements, lessons and challenges for Phase III – ...TLIII: Overview of TLII achievements, lessons and challenges for Phase III – ...
TLIII: Overview of TLII achievements, lessons and challenges for Phase III – ...CGIAR Generation Challenge Programme
 
TLIII: Tropical Legumes I – Improving Tropical Legume Productivity for Margin...
TLIII: Tropical Legumes I – Improving Tropical Legume Productivity for Margin...TLIII: Tropical Legumes I – Improving Tropical Legume Productivity for Margin...
TLIII: Tropical Legumes I – Improving Tropical Legume Productivity for Margin...CGIAR Generation Challenge Programme
 
Adoption of modern breeding tools in developing countries: challenges and opp...
Adoption of modern breeding tools in developing countries: challenges and opp...Adoption of modern breeding tools in developing countries: challenges and opp...
Adoption of modern breeding tools in developing countries: challenges and opp...CGIAR Generation Challenge Programme
 
PAG XXII 2014 – The Breeding Management System (BMS) of the Integrated Breedi...
PAG XXII 2014 – The Breeding Management System (BMS) of the Integrated Breedi...PAG XXII 2014 – The Breeding Management System (BMS) of the Integrated Breedi...
PAG XXII 2014 – The Breeding Management System (BMS) of the Integrated Breedi...CGIAR Generation Challenge Programme
 
PAG XXII 2014 – The Crop Ontology: A resource for enabling access to breeders...
PAG XXII 2014 – The Crop Ontology: A resource for enabling access to breeders...PAG XXII 2014 – The Crop Ontology: A resource for enabling access to breeders...
PAG XXII 2014 – The Crop Ontology: A resource for enabling access to breeders...CGIAR Generation Challenge Programme
 
PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowp...
PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowp...PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowp...
PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowp...CGIAR Generation Challenge Programme
 
2011: Introduction to the CGIAR Generation Challenge Programme (GCP)
2011: Introduction to the CGIAR Generation Challenge Programme (GCP)2011: Introduction to the CGIAR Generation Challenge Programme (GCP)
2011: Introduction to the CGIAR Generation Challenge Programme (GCP)CGIAR Generation Challenge Programme
 
Working with diversity in international partnerships -- The GCP experience --...
Working with diversity in international partnerships -- The GCP experience --...Working with diversity in international partnerships -- The GCP experience --...
Working with diversity in international partnerships -- The GCP experience --...CGIAR Generation Challenge Programme
 
GRM 2013: Improving rice productivity in lowland ecosystems of Burkina Faso, ...
GRM 2013: Improving rice productivity in lowland ecosystems of Burkina Faso, ...GRM 2013: Improving rice productivity in lowland ecosystems of Burkina Faso, ...
GRM 2013: Improving rice productivity in lowland ecosystems of Burkina Faso, ...CGIAR Generation Challenge Programme
 
GRM 2013: Wheat product catalogue and project status -- Projects ongoing, com...
GRM 2013: Wheat product catalogue and project status -- Projects ongoing, com...GRM 2013: Wheat product catalogue and project status -- Projects ongoing, com...
GRM 2013: Wheat product catalogue and project status -- Projects ongoing, com...CGIAR Generation Challenge Programme
 
GRM 2013: Rice product catalogue and project status -- Projects ongoing, comp...
GRM 2013: Rice product catalogue and project status -- Projects ongoing, comp...GRM 2013: Rice product catalogue and project status -- Projects ongoing, comp...
GRM 2013: Rice product catalogue and project status -- Projects ongoing, comp...CGIAR Generation Challenge Programme
 

More from CGIAR Generation Challenge Programme (20)

Capacity Building: Gain or Drain? J-M Ribaut, F Okono and NN Diop
Capacity Building: Gain or Drain? J-M Ribaut, F Okono and NN DiopCapacity Building: Gain or Drain? J-M Ribaut, F Okono and NN Diop
Capacity Building: Gain or Drain? J-M Ribaut, F Okono and NN Diop
 
ARM 2008: Dissection, characterisation and utilisation of disease QTL -- R Ne...
ARM 2008: Dissection, characterisation and utilisation of disease QTL -- R Ne...ARM 2008: Dissection, characterisation and utilisation of disease QTL -- R Ne...
ARM 2008: Dissection, characterisation and utilisation of disease QTL -- R Ne...
 
ARM 2007: Dissection, characterisation and utilisation of disease QTL -- R Ne...
ARM 2007: Dissection, characterisation and utilisation of disease QTL -- R Ne...ARM 2007: Dissection, characterisation and utilisation of disease QTL -- R Ne...
ARM 2007: Dissection, characterisation and utilisation of disease QTL -- R Ne...
 
The Generation Challenge Programme: Lessons learnt relevant to CRPs, and the ...
The Generation Challenge Programme: Lessons learnt relevant to CRPs, and the ...The Generation Challenge Programme: Lessons learnt relevant to CRPs, and the ...
The Generation Challenge Programme: Lessons learnt relevant to CRPs, and the ...
 
Lessons learnt from the GCP experience – J-M Ribaut
Lessons learnt from the GCP experience – J-M RibautLessons learnt from the GCP experience – J-M Ribaut
Lessons learnt from the GCP experience – J-M Ribaut
 
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
 
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
Integrated Breeding Platform (IBP): A user-friendly platform to implement the...
 
TLM III: : Improve common bean productivity for marginal environments in su...
TLM III: :   Improve common bean productivity for marginal environments in su...TLM III: :   Improve common bean productivity for marginal environments in su...
TLM III: : Improve common bean productivity for marginal environments in su...
 
TLM III: Improve groundnut productivity for marginal environments from sub-Sa...
TLM III: Improve groundnut productivity for marginal environments from sub-Sa...TLM III: Improve groundnut productivity for marginal environments from sub-Sa...
TLM III: Improve groundnut productivity for marginal environments from sub-Sa...
 
TLIII: Overview of TLII achievements, lessons and challenges for Phase III – ...
TLIII: Overview of TLII achievements, lessons and challenges for Phase III – ...TLIII: Overview of TLII achievements, lessons and challenges for Phase III – ...
TLIII: Overview of TLII achievements, lessons and challenges for Phase III – ...
 
TLIII: Tropical Legumes I – Improving Tropical Legume Productivity for Margin...
TLIII: Tropical Legumes I – Improving Tropical Legume Productivity for Margin...TLIII: Tropical Legumes I – Improving Tropical Legume Productivity for Margin...
TLIII: Tropical Legumes I – Improving Tropical Legume Productivity for Margin...
 
Adoption of modern breeding tools in developing countries: challenges and opp...
Adoption of modern breeding tools in developing countries: challenges and opp...Adoption of modern breeding tools in developing countries: challenges and opp...
Adoption of modern breeding tools in developing countries: challenges and opp...
 
PAG XXII 2014 – The Breeding Management System (BMS) of the Integrated Breedi...
PAG XXII 2014 – The Breeding Management System (BMS) of the Integrated Breedi...PAG XXII 2014 – The Breeding Management System (BMS) of the Integrated Breedi...
PAG XXII 2014 – The Breeding Management System (BMS) of the Integrated Breedi...
 
PAG XXII 2014 – The Crop Ontology: A resource for enabling access to breeders...
PAG XXII 2014 – The Crop Ontology: A resource for enabling access to breeders...PAG XXII 2014 – The Crop Ontology: A resource for enabling access to breeders...
PAG XXII 2014 – The Crop Ontology: A resource for enabling access to breeders...
 
PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowp...
PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowp...PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowp...
PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowp...
 
2011: Introduction to the CGIAR Generation Challenge Programme (GCP)
2011: Introduction to the CGIAR Generation Challenge Programme (GCP)2011: Introduction to the CGIAR Generation Challenge Programme (GCP)
2011: Introduction to the CGIAR Generation Challenge Programme (GCP)
 
Working with diversity in international partnerships -- The GCP experience --...
Working with diversity in international partnerships -- The GCP experience --...Working with diversity in international partnerships -- The GCP experience --...
Working with diversity in international partnerships -- The GCP experience --...
 
GRM 2013: Improving rice productivity in lowland ecosystems of Burkina Faso, ...
GRM 2013: Improving rice productivity in lowland ecosystems of Burkina Faso, ...GRM 2013: Improving rice productivity in lowland ecosystems of Burkina Faso, ...
GRM 2013: Improving rice productivity in lowland ecosystems of Burkina Faso, ...
 
GRM 2013: Wheat product catalogue and project status -- Projects ongoing, com...
GRM 2013: Wheat product catalogue and project status -- Projects ongoing, com...GRM 2013: Wheat product catalogue and project status -- Projects ongoing, com...
GRM 2013: Wheat product catalogue and project status -- Projects ongoing, com...
 
GRM 2013: Rice product catalogue and project status -- Projects ongoing, comp...
GRM 2013: Rice product catalogue and project status -- Projects ongoing, comp...GRM 2013: Rice product catalogue and project status -- Projects ongoing, comp...
GRM 2013: Rice product catalogue and project status -- Projects ongoing, comp...
 

Recently uploaded

TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
What is Artificial Intelligence?????????
What is Artificial Intelligence?????????What is Artificial Intelligence?????????
What is Artificial Intelligence?????????blackmambaettijean
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsNathaniel Shimoni
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionDilum Bandara
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 

Recently uploaded (20)

TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
What is Artificial Intelligence?????????
What is Artificial Intelligence?????????What is Artificial Intelligence?????????
What is Artificial Intelligence?????????
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directions
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An Introduction
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 

GRM 2013: Cloning, characterization and validation of PUP1/P efficiency in maize -- L Kochian

  • 1. GCP Project Number: G7010.03.01: Cloning, characterization and validation of PUP1/P efficiency in maize PI: Leon Kochian, USDA-ARS/Cornell University, USA Co-Pi’s: Claudia Guimaraes, Sidney Parentoni, Jurandir Magalhães, Vera Alves, Maria José Vasconcelos, Sylvia Sousa, Roberto Noda, Embrapa Maize and Sorghum, Sete Lagoas, Brazil Lyza Maron, Miguel Pineros, Jiping Liu, Randy Clark, Ed Buckler, Jon Shaff, USDA-ARS/Cornell, USA Sam Gudu, Moi University/KARI, Eldoret, Kenya Mathias Wissuwa, JIRCAS, Tsukuba, Ibaraki, Japan
  • 2. • Identification of OsPSTOL1 (Pup-1) orthologs in maize • QTL/gene mapping for P use efficiency in maize in the field and in hydroponics • Inheritance studies on maize root architecture under high and low P • Validation of maize PSTOL1 candidate genes and if necessary, novel P efficiency QTL (if maize PSTOL1 homologues are not functional in P efficiency) Project Objectives
  • 3. Gene Identification Physical position Identity (%) Coverage (%) E-value GRMZM2G451147 ZmPSTOL1 Chr8: 152.043.859 70 97 3.4e-131 GRMZM2G164612 ZmPSTOL2 Chr8: 152.100.275 70 97 2.3e-127 AC193632.2_FGP002 ZmPSTOL3 Chr4: 39.792.602 69 95 2.0e-105 GRMZM2G448672 ZmPSTOL4 Chr3: 206.918.421 66 97 4.7e-186 GRMZM2G412760 ZmPSTOL5 Chr3: 20.172.140 55 99 5.1e-104 GRMZM2G172396 ZmPSTOL6 Chr8: 13.267.001 55 99 9.6e-123 OsPSTOL1 Ortholog Identification in Maize • Using OsPSTOL1 as a query, six predicted genes were found in the maize genome sharing more than 55% of amino acid sequence identity with OsPSTOL1. • These genes were located on chromosomes 3, 4 and 8, at physical positions described in Table below. • Genetic markers for the six ZmPSTOL genes were generated and mapped on a linkage map for a L3 (P efficient) x L22 (P inefficient) RIL population.
  • 4. Phylogeny of Maize PSTOL1 Orthologs • Phylogenetic tree of OsPSTOL1 related sequences, including predicted protein sequences from maize, rice and Arabidopsis. • The predicted maize proteins share more than 55% sequence identity with OsPSTOL1. • These predicted maize proteins cluster together with OsPSTOL1 and Arabidopsis SNC4 and PR5, suggesting like OsPSTOL1 they are serine/threonine receptor-like kinases of the LRK10L-2 subfamily.
  • 5. Maize Test Cross Hybrids Derived from Embrapa Elite Diversity Panel Phenotyped for P efficiency •321 testcross hybrids were evaluated in the field over two years under low and high P. • High variability in yield under low & high P was observed as well as a significant differences in grain yield under high vs. low P • Phenotypic data will be used for association analysis and genomic selection Low P High P
  • 6. QTL Mapping of Maize P Efficiency in the Field • RIL population derived from L3 (P efficient) x L53 (P inefficient) was backcrossed to the parental lines and then phenotyped for P efficiency traits in the field on low P and sufficient P field sites. • P efficiency traits determined and mapped were P acquisition efficiency (PAE; grain produced/amount soil available P), P use efficiency (PUE; amount plant P/amount soil available P), and P utilization efficiency (PUTIL; grain produced/amount plant P). • Six QTLs were identified for PUE, six for PAE and five for PUTIL . • Most of the QTLs mapped for PUE were coincident with the genomic regions mapped for PAE. This agrees with the high correlation (0.89) between these traits, which were also highly correlated with grain yield under low P, 0.96 and 0.85, respectively. • This result indicates that P use efficiency is mainly due to P acquisition efficiency, as was also found by Parentoni et al. (Maydica 55:1; 2010). • None of the QTLs for P utilization efficiency were coincident with the other P efficiency indexes, suggesting that different genes are involved in P utilization.
  • 7. QTL Mapping of Maize P Efficiency in the Field (con’t)
  • 8. QTL Mapping of Maize P Efficiency and 2D Root Traits in Hydroponics •The L3 x L22 maize RIL population was grown in paper pouches moistened with low P and sufficient P nutrient solution and roots were digitally imaged and root traits quantified using our RootReader 2D platform (Clark et al, Plant Cell Envir 36: 454; 2013). •Root and shoot dry weight and P accumulation were also quantified and QTL mapping was conducted on P efficiency traits (PUE, PAE, PUTIL) and root traits. •Out of 32 root traits, four were selected for mapping analysis based on De Sousa et al. (Functional Plant Biol; 2012): length (cm), volume (cm3), volume of fine roots (1.0<d≤2.0 mm) (cm3) and root surface area (cm2).
  • 9. Root Phenotyping Tools Growth: Hydroponics (Al tolerance) Agar Plates (Zn nutrition) Pouches (P nutrition, Salinity stress) Sand Pots (RSA validation, P nutrition) Capture and Analysis: Digital Photography (Single images) RootReader2D Software calculates range of root growth traits on both whole root system and specific traits  Visit: www.plantmineralnutrition.net Overall Efficiency: 1000’s of plants per day 2D Phenotyping Platform 3D Phenotyping Platform Clark et al., Plant Physiol2012 Growth: Gel Cylinders (RSA - root system architecture) *New: Hydroponics (RSA, Stress/functional studies) Capture and Analysis: Digital Photography (Image sequences) RootReader3D Software reconstructs series of 2D images into a 3D RSA and computes specific root traits Overall Efficiency: ~100 plants per day Clark et al., Plant, Cell & Envir2011
  • 10. Root Imaging Pipeline – Imaging RSA in 2-D Root Growth & Imaging RootReader 2D Software Clark et al. 2012. High-throughput 2D root system phenotyping platform facilitates genetic analysis of root growth and development. Plant Cell Environ.
  • 11. QTL Mapping of Maize P Efficiency and 2D Root Traits in Hydroponics •The L3 x L22 maize RIL population was grown in paper pouches moistened with low P and sufficient P nutrient solution and roots were digitally imaged and root traits quantified using our RootReader 2D platform (Clark et al, Plant Cell Envir 36: 454; 2013). •Root and shoot dry weight and P accumulation were also quantified and QTL mapping was conducted on P efficiency traits (PUE, PAE, PUTIL) and root traits. •Out of 32 root traits, four were selected for mapping analysis based on De Sousa et al. (Functional Plant Biol; 2012): length (cm), volume (cm3), volume of fine roots (1.0<d≤2.0 mm) (cm3) and root surface area (cm2).
  • 12. Co-localization of P Efficiency QTL from Field Studies with P Efficiency and Root Trait QTL from Nutrient Solution Phenotyping Hydroponics Hydroponics Field Field
  • 13. A B Co-localization of P Efficiency QTL from Field Studies with P Efficiency and Root Trait QTL from Nutrient Solution Phenotyping Chr 7 Chr 7 Hydroponics Hydroponics Chr 8 Field Field
  • 14. •A region from 209 to 272 cM on chromosome 1: Co-localization of QTLs controlling PUE, PAE and P utilization efficiency (PUTIL) in the field with a multiple-trait QTL for root morphology and PAE in nutrient solution •A region spanning 82 - 95 cM on chromosome 3: Co-localization of QTL controlling PUE and PAE in the field with a multiple-trait QTL for root morphology and PAE in nutrient solution. •A region from 77 to 83 cM on chromosome 7: Co-localization of QTL controlling PUE and PAE in the field with a QTL for root diameter. •A region spanning 100 – 127 cM on chromosome 8: Co- localization of QTL controlling PUE and PAE in the field with QTLs for PAE, root length and root surface area in nutrient solution. The Combined Analysis of QTL Mapping for Root Traits and P Efficiency Indices in the Field Based On Grain Yield Has Led Us To Focus on Four Genomic Regions
  • 15. Colocalization of ZmPSTOL1 Orthologs with Maize P Efficiency and/or Root Trait QTL
  • 16. ZmPSTOL Expression in Roots & Shoots of P Efficient (L3) and P Inefficient (L22) Maize • ZmPSTOL1, 4 and 6 preferentially expressed in roots. • ZmPSTOL1 and 4 expression increases in response to P deficiency. • ZmPSTOL4 preferentially expressed in roots of P efficient L3 - colocalizes with root traits and not P efficiency traits. • ZmPSTOL1 only rice Pup1 homolog that colocalizes with PAE and PUE. It’s expression is specific to roots and is induced by low P plant status. • ZmPSTOL1 expression is exclusively in roots of P inefficient L22, but the superior allele for this chr 8 PAE and PUE QTL donated by L22. • ZmPSTOL1 is most similar in sequence of the maize orthologs to OsPSTOL1.
  • 17. Shallow Intermediate Deep What Is the Ideal Root Architecture P Efficiency in Low P Soils? [P] [H+] P Efficient Soybean Line Dr. Hong Liao’s group, Root Biology Center, SCAU, Guangzhou
  • 18. 3D RSA Imaging System • Stationary camera with fixed capture settings that is synchronized to a turntable via a LabVIEW interface and digital controller • 100 images captured per root system, 3.6° of rotation between images • Capture time of approximately 10 minutes per root system
  • 19. 3D Reconstruction Process via RootReader 3D Thresholded rotational image sequence consisting of 40-100 2D images Perspective back projection of 2D root points from each 2D image into a temporary 3D voxel volume Transformation of each temporary voxel into a final voxel volume Adaptive thresholding of each horizontal cross section through final voxel volume to generate 3D root model
  • 20. Germplasm and Screening Mapping Results Genetic Mapping of RSA Peak SNP -66kb -33kb 0 +33kb +66kb “A” “B” SNP Allele SNP Allele n=39 n=118 QTL QTL
  • 21. Germplasm and Screening Mapping Results Genetic Mapping of RSA Peak SNP -66kb -33kb 0 +33kb +66kb “A” “B” SNP Allele SNP Allele n=44 n=107 QTL QTL
  • 22. Germplasm and Screening Mapping Results Genetic Mapping of RSA • Subpopulation SNPs selected from within 3kb of the peak Indica SNP Aus Indica Temperate Japonica Tropical Japonica All Subpops n=211 n=360 n=9 n=80 n=44 n=107 n=10 n=169 n=7 n=162 QTL QTL
  • 23. The Gel-Based Root Growth System Has Its Limitations • Roots of some plant species such as maize & sorghum as well as fine rooted species such as canola don’t grow well in the gel cylinders • Labor and cost intensive • Can’t easily impose different nutrient regimes • Limited to work with fairly small root systems (young plants)
  • 24. Transition From Gel to Hydroponics M8 Rice line Nipponbare parent 12 day old rice grown in low P nutrient solution
  • 25. 3D Imaging/Analysis of RSA in Hydroponics •We can use the hydroponic systems for 3-D imaging because our software subtracts out the mesh and reconstructs the images •Are using this hydroponic system with 3D black plastic mesh to screen sorghum populations (270 lines) and to correlate RSA traits with physiological data. •Can use much larger vessels than with the gel and still maintain rapid throughput. • Important design for longer growth periods, as crown roots may be important for water acquisition and they don’t appear until around 12 days. Dr. Alexandre Falcão computer wizard
  • 27.
  • 28. The growth system is created from ABS plastic mesh circles made with a 3-D printer. The mesh system serves to constrain the roots, but not to impede their growth.
  • 29. Three-D RSA Reconstruction of 100 Two-D Sorghum Root Images (15 Day Old Plant) • Barbara Hufnagel from Jurandir Magalhaes’s lab is currently in our lab working with our staff to phenotype and quantify RSA 3D traits for the sorghum association panel. • We will be set up to phenotype maize RSA for this project in early 2014.
  • 30. Products •Due to the more upstream nature of this project, the products are still in the pipeline and will be forthcoming starting later in 2014. •Claudia is generating NILs for specific P efficiency QTL for verification of QTL effects and as a breeding resource. Pyramiding of multiple QTL in NILs will have greater potential for impact. •Work ongoing to validate via association mapping analysis and more in depth molecular physiological investigations of candidate ZmPSTOL1 genes to identify OsPSTOL1 orthologs involved in maize P efficiency. •Ultimately will have breeding lines for improved P efficiency. •Catalog of bi-parental and GWA QTL & markers for root system architecture traits that may play a role in maize P efficiency.

Editor's Notes

  1. 2D-&gt; growth characteristic; 3D RSA and development; coupling hydroponics with 3D allows for controlled functional studies