advances in gene-based crop modeling
TRANSCRIPT
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C. Eduardo Vallejos
Tracing the Convoluted Path from a Genotype to its Phenotypic Spectrum
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X
P1
P2
F1
X
F2
Phenotype Genotype
le/le
Le/Le Le/le
Lester et al. (1997) Plant Cell 9, 1435; Martin et al. (1997) PNAS 94,8907.
Le/lele/le Le/LeLe/le
GLeAle
Phenotype to Genotype
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1. Quantitative Variation
2. Environmental Effects
G2P Challenges
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FR
EQ
UE
NC
Y
QUANTITATIVE TRAIT
1. Quantitative Variation
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M2
μM2
μM2
HA: μMn – μMn ≠ 0
M1
Ho: μMn – μMn = 0
μM1
μM1
μRI
1. G2P – QTL Analysis
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1PHENOTYPES
GENOTYPE 1
TEMPERATURE
2. Environmental Effects
2 3 n
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7Time
Ass
imil
ate
= {Xj [E (PG – RM)]} - SjdtdW
EM V1 R1 R3 R5 R7
2. G2P – Crop Simulation Models
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Cultivar X
E3E2E1 E7E4 E6E5
2. G2P – Crop Simulation Models
CROPGRO
GSP1X
GSPnX
Phenotypes(En)
Reverse Modeling
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E3E2E1 E7E4
CROPGRO
Cultivar X
E6E5
2. G2P – Crop Simulation Models
Phenotypes(En)
GSPsX
EnvironN
Modeling
Time
Biom
ass
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GENE-BASED CROP MODEL Mathematical Representation of
Growth and Development Responsive to Environmental Inputs Model Parameters = f (Genotype)
GENETICS Phenotype Genotype QTL Analysis
CROP MODEL Physiology Environment Genotype
G2P Solution = CSM + QTL
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Gene-based Crop Simulation Model
Central Hypothesis
GSPs capture genetic variation
GSPs Functions of the genotype (QTL)
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Simulation
Simulated Phenotypes
Evaluation
Gene-based Crop Simulation Model
Crop Simulation
Model
GSPs
QTL
Parameter Estimation
Input
Input
RILs (ij)Phenotype
QTL
Environ(j)
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Gene-based Crop Simulation Model
Crop Simulation
Model
GSPs
QTL
Parameter Estimation
Input
Input
Simulation
Simulated Phenotypes
EvaluationRILs (ij)Phenotype
QTL
Environ(j)
HYPOTHESIS
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Gene-based Crop Simulation Model
Env.2
Env.1
Env.3
Env.4
Env.j
Recombinant Inbred Family (1, 2, 3,…i)
GSP11, 2, 3…i
GSPn1, 2, 3…i
CROPGRO
Phenotypes(ij)
QTL Analysis
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Objective:Construct a Gene-Based Crop Simulation Model
Strategy:1. Segregating Progeny2. Genotype with Molecular Markers3. Multi-Environment Phenotyping4. Estimate Model Parameters (GSPs)5. Test Hypothesis
Gene-based Crop Simulation Model
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Mesoamerican Parent
Mapping PopulationGene-based Crop Simulation Model
AndeanParent
1. Segregating Progeny: Recombinant Inbred Family
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17Bhakta et al. | Plos One | Jan 2015
1 2 3 4 5 6 7 8 9 10 11
Gene-based Crop Simulation Model2. Genotyping: GBS-based Linkage Map of Phaseolus vulgaris
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North Dakota
Florida
Puerto Rico
Palmira
Popayán
27/13oC, 15:20 – 15:53 h
32/18oC, 12:30 – 13:30 h
29/19oC, 11:30 – 12:35 h
29/19oC, 11:56 – 11:58 h
23/13oC, 12:08 – 12:11 h
Gene-based Crop Simulation Model
3. Multienvironment Phenotyping (5 Sites)
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Phenotype Data Timing of Develop. Transitions: Em, V0, V1, Vn, R1, …R7, R8 Growth: LA, Organ DW, # of organs, length, LA, …
Gene-based Crop Simulation Model
3. Multienvironment Phenotyping (5 Sites)
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Gene-based Crop Simulation Model
4. Parameter Estimation of the RI Family
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Gene-based Crop Simulation Model
4. Parameter Estimation of the RI Family
Citra North Dakota Palmira Popayan Puerto Rico
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Gene-based Crop Simulation Model
4. Parameter Estimation of the RI Family
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Gene-based Crop Simulation Model
0
10
20
30
40
3.5
ND POP CIT PAL PR
0
2
4
6
8
10
3.5
Chrom1 Chrom3
Chrom4 Chrom6 Chrom7 Chrom11
5. Hypothesis Testing- Time to Flower QTLs:- PPSEN QTLs: - EM-FL QTLs:
LO
DL
OD
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Gene-based Crop Simulation Model
Crop Simulation
Model
GSPsRILs (ij)
Field Data
QTL QTL
Parameter Estimation
Simulation
Input
WeatherData(j)
Input
Simulated Phenotypes
Evaluation
TEST OF HYPOTHESIS
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Gene-based Crop Simulation Model
4. Parameter Estimation of the RI Family
Citra North Dakota Palmira Popayan Puerto Rico
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Gene-based Crop Simulation Model
Crop Simulation
Model
GSPsRILs (ij)
Field Data
QTL QTL
Parameter Estimation
Simulation
Input
WeatherData(j)
Input
Simulated Phenotypes
Evaluation
TEST OF HYPOTHESIS
QTL
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Palmira
Indeterminate
Determinate
C
J
Gene-based Crop Simulation Model
10
30
20
40
Ther
mal
Tim
e (o C
-day
)
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Gene-based Crop Simulation Model
10
30
20
40
Ther
mal
Tim
e (o C
-day
)
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Gene-based Crop Simulation Model
10
30
20
40
Ther
mal
Tim
e (o C
-day
)
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Gene-based Crop Simulation Model
Results of Hypothesis Testing
GSPs capture genetic variation
GSPs are functions of the genotype (QTL)
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SUMMARY Characterization of RI family
GBS-Genotyped Phenotyped in ME
• QTL analysis of phenotype Model Parameterization, GSPs
• QTL analysis of GSPs Testing of Central Hypothesis
Partially Correct New Direction
Diurnal Gene ExpressionGene-based Crop Simulation Model
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NEW DIRECTION Develop Modular GBCSM
Modules of Simple Processes• Growth and Development
Mixed-Effects Statistical Models• Effects: G, E, GxE
Central Model Integration of Modules
Diurnal Gene ExpressionGene-based Crop Simulation Model
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Time to Flower = µ + Gij + Ej + (G*E)ij + εij
All Terms Significant at p < 0.01
Time to Flower Model: Site-ModelGene-based Crop Simulation Model
GenotypeRIL001 0 0 1 0 1 1 0 0 1 1 1RIL002 1 1 0 1 1 1 0 1 0 0 1...RIL 188 1 1 1 1 0 0 0 1 1 1 0
TF1
TF2
TF3
TF4
TF5
TF6
TF7
TF8
TF9
TF10
TF11 Site
CitraNorth DakotaPalmiraPopayanPuerto Rico
G*ESite x TF-2Site x TF-3Site x TF-4Site x TF-6Site x TF-11
G E G*E
Linear Mixed-Effects Statistical Model
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Pre
dict
ed
Observed
Days to First Flower from Planting
R2 = 0.92
Time to Flower Model: PredictionGene-based Crop Simulation Model
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Time to Flower = µ + Gij + Ej + (G*E)ij + εij
All Terms Significant at p < 0.01
Time to Flower Model: Site-ModelGene-based Crop Simulation Model
GenotypeRIL001 0 0 1 0 1 1 0 0 1 1 1RIL002 1 1 0 1 1 1 0 1 0 0 1...RIL 188 1 1 1 1 0 0 0 1 1 1 0
TF1
TF2
TF3
TF4
TF5
TF6
TF7
TF8
TF9
TF10
TF11 Site
TminTmaxSolar RadDay Length
G*ETmin x TF-2Tmin x TF-3Day L x TF-3Tmax x TF-4Day L x TF-6Day L x TF-11S Rad x TF-11
G E G*E
Linear Mixed-Effects Statistical Model
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+ +
Genetic EffectQTL Jamapa (Days) Calima (Days)
TF-1 1.3 -1.3TF-2 2.2 -2.2TF-3 -1.5 1.5TF-4 -0.1 0.1TF-5 0.9 -0.9TF-6 -0.9 0.9TF-7 -0.4 0.4TF-8 -0.7 0.7TF-9 -0.4 0.4
TF-10 0.6 -0.6 TF-11 -0.3 0.3
Time to Flower = µ + Genotype (G) + Environment (E) + ( G x E )
Gene-based Crop Simulation Model
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Environ. Effect on µFactor Days
Day (hrs) 3.9
Tmin (˚C) -0.6
Tmax (˚C) -1.4
Srad (W/m2) 0.2
Time to Flower = µ + Genotype (G) + Environment (E) + ( G x E )
Gene-based Crop Simulation Model
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Time to Flower = µ + Genotype (G) + Environment (E) + ( G x E )
TF3 x Day-Length
10 11 12 13 14 15 16 1730
35
40
45
50
55
60
65
70Jamapa Calima
Day Length (hrs)
Da
ys
to F
low
er
Slope = 5.76
Slope = 2.41
Gene-based Crop Simulation Model
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R2 = 0.87
Site Model QTL-EC Model
R2 = 0.92
Pre
dict
ed
Observed
Days to First Flower from Planting
Gene-based Crop Simulation Model
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R2 = 0.87
CalimaJamapa
Pre
dict
ed
Observed
Days to Flower - Parental Lines
Gene-based Crop Simulation Model
Model Validation
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0 5 10 15 20 25 30 350
0.1
0.2
0.3
0.4
0.5
0.6
Jamapa (-1) Calima (+1)
Temperature, C
Nod
e A
dditi
on R
ate,
#/d
0 5 10 15 20 25 30 350
0.1
0.2
0.3
0.4
0.5
0.6
Jamapa (-1) Jamapa with Calima FIN
Calima (+1) Calima with Jamapa FIN
Temperature, C
Nod
e A
dditi
on R
ate,
#/d
(a) (b)
Gene-based Crop Simulation Model
IN SILICO GENE REPLACEMENT
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Gene-based Crop Simulation Model
Environmental Data(Temp, SRad, DayL, Other)
Genotype(QTL1, QTL2,…)
Linear Model (G, E, G*E)
Calendar (Timer)
y = f (x|p)
Process Module
Gene-Based Model
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PvQTL Chr A. thaliana GeneTF-1 1 miR156, CDF2TF-2 1 TFL1a, SPYTF-3 1 PIF3, PHYA, MYB, GAI, miR172, ELF4TF-4 3 -TF-5 3 miR156TF-6 4 TFL1bTF-7 6 PHYBTF-8 7 -TF-9 7 FLD , FLC
TF-10 11 CYP-450TF-11 11 FBH-1
Gene-based Crop Simulation Model
What are the Identities of the PvQTL?
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Linkage vs Physical Map
HOT SPOTS
COLD SPOT
Chromosome 1
Mb
cMcM
/ Mb
Gene-based Crop Simulation Model
TF-2
TF-3
TF-1
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 280
10
20
1 2 3 4 5 6 7 8 C 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 J
AL 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
PHYA 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1
Myby 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
AR 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3b1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3b2 1 1 1 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3b3 1 1 1 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3b4 1 1 1 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Myb-E4 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1
3b5 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1
3b6 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1
3b7 2 2 2 2 2 2 2 2 2 1 1 1 1 3 1 1 3 1 1 1 1 1 1 1 1 1 1 1
3b8 2 2 2 2 2 2 2 2 2 1 1 1 1 3 1 1 3 1 1 1 1 1 1 1 1 1 1 1
E4 2 2 2 2 2 2 2 2 2 1 1 1 1 3 1 1 3 1 1 1 1 1 1 1 1 1 1 1
- 10M
- 20M
- 30M
- 40M
- 50M
- 0M
-- 48M
-- 49M
-- 50M
-
-
-
-
-
-
-
-
-
--
-
-
-
-
-
-
-
-
-
LD –
SD D
ays
Gene-based Crop Simulation Model
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9:00 AM
12:00 PM
3:00 PM
6:00 PM
9:00 PM
12:00 AM
3:00 AM
6:00 AM
0.00
0.50
1.00
1.50
2.00
2.50Cal ima LD
9:00 AM
12:00 PM
3:00 PM
6:00 PM
9:00 PM
12:00 AM
3:00 AM
6:00 AM
0.00
0.50
1.00
1.50
2.00
2.50Jamapa LD
9:00 AM
12:00 PM
3:00 PM
6:00 PM
9:00 PM
12:00 AM
3:00 AM
6:00 AM
0.00
0.50
1.00
1.50
2.00
2.50CAL SD
9:00 AM
12:00 PM
3:00 PM
6:00 PM
9:00 PM
12:00 AM
3:00 AM
6:00 AM
0.00
0.50
1.00
1.50
2.00
2.50SD
Rel
ativ
e E
xpre
ssio
nR
elat
ive
Exp
ress
ion
CA
LIM
AJA
MA
PA
FTPHYA CO
Gene-based Crop Simulation Model
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Gene-based Crop Simulation Model
The PhyA is a Strong Candidate for PvTF-3
Evidence Advanced Backcross Families
Cal allele has strong photoperiod response Gene expression
Diurnal pattern of expression is different• PhyA mRNA Hi in early morning in LD
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G2P – Future Direction
Gene-BasedCrop Model
GenDBGenDB PhenDB
PhenDB
EnvDBEnvDB
Expert System
Prediction
Ideotype
Species D
Gene-BasedCrop Model
GenDBGenDB PhenDB
PhenDB
EnvDBEnvDB
Expert System
Prediction
Ideotype
Species C
Gene-BasedCrop Model
GenDBGenDB PhenDB
PhenDB
EnvDBEnvDB
Expert System
Prediction
Ideotype
Species B
GM
Gene-BasedCrop Model
GenDBGenDB PhenDB
PhenDB
EnvDBEnvDB
Expert System
Prediction
Ideotype
Species A
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IOS-0923975
C. Eduardo VallejosJim JonesKen BooteMelanie CorrellSalvador GezanMelissa CarvalhoSubodh AcharyaJose ClavijoMehul BhaktaLi ZhangTara BongiovaniChrisropher Hwang
Jim BeaverElvin RomanAbiezer Gonzalez
Steve BeebeIdupulapati RaoJaumer RicaurteMartin Otero
Wei HouJuan OsornoRaphael Colbert
Rongling WuYaquan WangNingtao Wang
Acknowledgements