Speaker: Eduardo Vallejos Associate Professor, Molecular Biology & Physiology. The talk will cover overall perspective of both genetic and modeling and advanced methods for working with the genetic and phenotypic data with crop models and a perspective on promising future approaches.

1. C. Eduardo Vallejos
Tracing the Convoluted Path from a
Genotype to its Phenotypic Spectrum

2. X
P1
P2
F1
X
F2
Phenotype Genotype
le/le
Le/Le Le/le
2
Lester et al. (1997) Plant Cell 9, 1435; Martin et al. (1997) PNAS 94,8907.
Le/lele/le Le/LeLe/le
GLe
Ale
Phenotype to Genotype

3. 1. Quantitative Variation
2. Environmental Effects
G2P Challenges
3

4. 4
FREQUENCY
QUANTITATIVE TRAIT
1. Quantitative Variation

5. M2
μM2
μM2
HA: μMn – μMn ≠ 0
M1
Ho: μMn – μMn = 0
μM1
μM1
μRI
5
1. G2P – QTLAnalysis

6. 6
GENOTYPE 1
TEMPERATURE
2. Environmental Effects

7. Time
Assimilate
= {Xj [E  (PG – RM)]} - Sjdt
dW
EM V1 R1 R3 R5 R7
2. G2P – Crop Simulation Models
7

8. 8
Cultivar X
E3E2E1 E7E4 E6E5
2. G2P – Crop Simulation Models
CROPGRO
GSP1X
GSPnX
Phenotypes(En)
Reverse Modeling

9. E3E2E1 E7E4
CROPGRO 9
Cultivar X
E6E5
2. G2P – Crop Simulation Models
Phenotypes(En)
GSPsX
EnvironN
Modeling
Time
Biomass

10. GENE-BASED CROP MODEL
 Mathematical Representation of
Growth and Development
 Responsive to Environmental Inputs
 Model Parameters = f (Genotype)
10
GENETICS
 Phenotype
 Genotype
 QTLAnalysis
CROP MODEL
 Physiology
 Environment
 Genotype
G2P Solution = CSM + QTL

11. Gene-based Crop Simulation Model
Central Hypothesis
 GSPs capture genetic variation
 GSPs Functions of the genotype (QTL)
11

12. Simulated
Phenotypes
Evaluation
Gene-based Crop Simulation Model
Crop
Simulation
Model
GSPs
QTL
RILs (ij)
Phenotype
QTL
Environ(j)
12

13. Gene-based Crop Simulation Model
Crop
Simulation
Model
GSPs
QTL
Simulated
Phenotypes
EvaluationRILs (ij)
Phenotype
QTL
Environ(j)
HYPOTHESIS 13

14. 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)
QTLAnalysis
14

15. Objective:
Construct a Gene-Based Crop Simulation Model
Strategy:
1. Segregating Progeny
2. Genotype with Molecular Markers
3. Multi-Environment Phenotyping
4. Estimate Model Parameters (GSPs)
5. Test Hypothesis
Gene-based Crop Simulation Model
15

16. Mesoamerican
Parent
Mapping PopulationGene-based Crop Simulation Model
Andean
Parent
1. Segregating Progeny: Recombinant Inbred Family
16

17. Bhakta et al. | Plos One | Jan 2015
1 2 3 4 5 6 7 8 9 10 11
Gene-based Crop Simulation Model
2. Genotyping: GBS-based Linkage Map of Phaseolus vulgaris
17

18. North Dakota
Florida
Puerto Rico
Palmira
Popayán
27/13o
C, 15:20 – 15:53 h
32/18o
C, 12:30 – 13:30 h
29/19o
C, 11:30 – 12:35 h
29/19o
C, 11:56 – 11:58 h
23/13o
C, 12:08 – 12:11 h
Gene-based Crop Simulation Model
3. Multienvironment Phenotyping (5 Sites)

19. 19
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)

20. Gene-based Crop Simulation Model
4. Parameter Estimation of the RI Family
20

21. Gene-based Crop Simulation Model
4. Parameter Estimation of the RI Family
Citra North Dakota Palmira Popayan Puerto Rico
21

22. Gene-based Crop Simulation Model
4. Parameter Estimation of the RI Family
22

23. 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:
LODLOD
23

24. Gene-based Crop Simulation Model
Crop
Simulation
Model
GSPs
RILs (ij)
Field Data
QTL QTL
Weather
Data(j)
Simulated
Phenotypes
Evaluation
TEST OF HYPOTHESIS 24

25. Gene-based Crop Simulation Model
4. Parameter Estimation of the RI Family
Citra North Dakota Palmira Popayan Puerto Rico
25

26. Gene-based Crop Simulation Model
Crop
Simulation
Model
GSPs
RILs (ij)
Field Data
QTL QTL
Weather
Data(j)
Simulated
Phenotypes
Evaluation
TEST OF HYPOTHESIS
QTL
26

27. Palmira
Indeterminate
Determinate
C
J
Gene-based Crop Simulation Model
10
30
20
40
ThermalTime(oC-day)

28. Gene-based Crop Simulation Model
10
30
20
40
ThermalTime(oC-day)

29. Gene-based Crop Simulation Model
10
30
20
40
ThermalTime(oC-day)

30. Gene-based Crop Simulation Model
Results of Hypothesis Testing
 GSPs capture genetic variation
 GSPs are functions of the genotype (QTL)
30

31. 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 Expression
Gene-based Crop Simulation Model
31

32. 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 Expression
Gene-based Crop Simulation Model
32

33. 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
Genotype
RIL001 0 0 1 0 1 1 0 0 1 1 1
RIL002 1 1 0 1 1 1 0 1 0 0 1
.
.
.
RIL 188 1 1 1 1 0 0 0 1 1 1 0
Site
Citra
North Dakota
Palmira
Popayan
Puerto Rico
G*E
Site x TF-2
Site x TF-3
Site x TF-4
Site x TF-6
Site x TF-11
G E G*E
Linear Mixed-Effects Statistical Model
33

34. Predicted
Observed
Days to First Flower from Planting
R2 = 0.92
Time to Flower Model: PredictionGene-based Crop Simulation Model
34

35. 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
Genotype
RIL001 0 0 1 0 1 1 0 0 1 1 1
RIL002 1 1 0 1 1 1 0 1 0 0 1
.
.
.
RIL 188 1 1 1 1 0 0 0 1 1 1 0
Site
Tmin
Tmax
Solar Rad
Day Length
G*E
Tmin x TF-2
Tmin x TF-3
Day L x TF-3
Tmax x TF-4
Day L x TF-6
Day L x TF-11
S Rad x TF-11
G E G*E
Linear Mixed-Effects Statistical Model
35

36. + +
Genetic Effect
QTL Jamapa (Days) Calima (Days)
TF-1 1.3 -1.3
TF-2 2.2 -2.2
TF-3 -1.5 1.5
TF-4 -0.1 0.1
TF-5 0.9 -0.9
TF-6 -0.9 0.9
TF-7 -0.4 0.4
TF-8 -0.7 0.7
TF-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

37. Environ. Effect on µ
Factor 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

38. 38
Time to Flower = µ + Genotype (G) + Environment (E) + ( G x E )
TF3 x Day-Length
30
35
40
45
50
55
60
65
70
10 11 12 13 14 15 16 17
DaystoFlower
Day Length (hrs)
Jamapa Calima
Slope = 5.76
Slope = 2.41
Gene-based Crop Simulation Model

39. R2 = 0.87
Site Model QTL-EC Model
R2 = 0.92
Predicted
Observed
Days to First Flower from Planting
Gene-based Crop Simulation Model
39

40. R2 = 0.87
Calima
Jamapa
Predicted
Observed
Days to Flower - Parental Lines
Gene-based Crop Simulation Model
Model Validation
40

41. 41
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40
NodeAdditionRate,#/d
Temperature, C
Jamapa (-1) Calima (+1)
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40
NodeAdditionRate,#/d
Temperature, C
Jamapa (-1) Jamapa with Calima FIN
Calima (+1) Calima with Jamapa FIN
(a) (b)
Gene-based Crop Simulation Model
IN SILICO GENE REPLACEMENT

42. 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
42

43. PvQTL Chr A. thaliana Gene
TF-1 1 miR156, CDF2
TF-2 1 TFL1a, SPY
TF-3 1 PIF3, PHYA, MYB, GAI, miR172, ELF4
TF-4 3 -
TF-5 3 miR156
TF-6 4 TFL1b
TF-7 6 PHYB
TF-8 7 -
TF-9 7 FLD , FLC
TF-10 11 CYP-450
TF-11 11 FBH-1
Gene-based Crop Simulation Model
What are the Identities of the PvQTL?
43

44. Linkage vs Physical Map
HOT
SPOTS
COLD SPOT
Chromosome 1
Mb
cM
cM/Mb
Gene-based Crop Simulation Model
TF-2
TF-3
TF-1
44

45. 0
10
20
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 28
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–SDDays
Gene-based Crop Simulation Model
45

46. 0.00
0.50
1.00
1.50
2.00
2.50
9:00
AM
12:00
PM
3:00
PM
6:00
PM
9:00
PM
12:00
AM
3:00
AM
6:00
AM
CALIMA LD
0.00
0.50
1.00
1.50
2.00
2.50
9:00
AM
12:00
PM
3:00
PM
6:00
PM
9:00
PM
12:00
AM
3:00
AM
6:00
AM
JAMAPA LD
0.00
0.50
1.00
1.50
2.00
2.50
9:00
AM
12:00
PM
3:00
PM
6:00
PM
9:00
PM
12:00
AM
3:00
AM
6:00
AM
CAL SD
0.00
0.50
1.00
1.50
2.00
2.50
9:00
AM
12:00
PM
3:00
PM
6:00
PM
9:00
PM
12:00
AM
3:00
AM
6:00
AM
SD
RelativeExpressionRelativeExpression
CALIMAJAMAPA
FTPHYA CO
Gene-based Crop Simulation Model
46

47. 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
47

48. G2P – Future Direction
Gene-Based
Crop Model
GenDB
GenDB PhenDB
PhenDB
EnvDB
EnvDB
Expert
System
Prediction
Ideotype
Species D
Gene-Based
Crop Model
GenDB
GenDB PhenDB
PhenDB
EnvDB
EnvDB
Expert
System
Prediction
Ideotype
Species C
Gene-Based
Crop Model
GenDB
GenDB PhenDB
PhenDB
EnvDB
EnvDB
Expert
System
Prediction
Ideotype
Species B
G
MGene-Based
Crop Model
GenDB
GenDB PhenDB
PhenDB
EnvDB
EnvDB
Expert
System
Prediction
Ideotype
Species A
48

49. 49
IOS-0923975
C. Eduardo Vallejos
Jim Jones
Ken Boote
Melanie Correll
Salvador Gezan
Melissa Carvalho
Subodh Acharya
Jose Clavijo
Mehul Bhakta
Li Zhang
Tara Bongiovani
Chrisropher Hwang
Jim Beaver
Elvin Roman
Abiezer Gonzalez
Steve Beebe
Idupulapati Rao
Jaumer Ricaurte
Martin Otero
Wei Hou
Juan Osorno
Raphael Colbert
Rongling Wu
Yaquan Wang
Ningtao Wang
Acknowledgements