Advances in gene-based crop modeling

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

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

3

1. Quantitative Variation

2. Environmental Effects

G2P Challenges

4

FR

EQ

UE

NC

Y

QUANTITATIVE TRAIT

1. Quantitative Variation

5

M2

μM2

μM2

HA: μMn – μMn ≠ 0

M1

Ho: μMn – μMn = 0

μM1

μM1

μRI

1. G2P – QTL Analysis

6

1PHENOTYPES

GENOTYPE 1

TEMPERATURE

2. Environmental Effects

2 3 n

7Time

Ass

imil

ate

= {Xj [E (PG – RM)]} - SjdtdW

EM V1 R1 R3 R5 R7

2. G2P – Crop Simulation Models

8

Cultivar X

E3E2E1 E7E4 E6E5


CROPGRO

GSP1X

GSPnX

Phenotypes(En)

Reverse Modeling

9

E3E2E1 E7E4

CROPGRO

Cultivar X

E6E5


Phenotypes(En)

GSPsX

EnvironN

Modeling

Time

Biom

ass

10

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

11

Gene-based Crop Simulation Model

Central Hypothesis

GSPs capture genetic variation

GSPs Functions of the genotype (QTL)

12

Simulation

Simulated Phenotypes

Evaluation


Crop Simulation

Model

GSPs

QTL

Parameter Estimation

Input

Input

RILs (ij)Phenotype

QTL

Environ(j)

13


Crop Simulation

Model

GSPs

QTL


Input

Input

Simulation


EvaluationRILs (ij)Phenotype

QTL

Environ(j)

HYPOTHESIS

14


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

15

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


16

Mesoamerican Parent

Mapping PopulationGene-based Crop Simulation Model

AndeanParent

1. Segregating Progeny: Recombinant Inbred Family

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

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


3. Multienvironment Phenotyping (5 Sites)

19

Phenotype Data Timing of Develop. Transitions: Em, V0, V1, Vn, R1, …R7, R8 Growth: LA, Organ DW, # of organs, length, LA, …


3. Multienvironment Phenotyping (5 Sites)

20


4. Parameter Estimation of the RI Family

21



Citra North Dakota Palmira Popayan Puerto Rico

22



23


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

24


Crop Simulation

Model

GSPsRILs (ij)

Field Data

QTL QTL


Simulation

Input

WeatherData(j)

Input


Evaluation

TEST OF HYPOTHESIS

25



Citra North Dakota Palmira Popayan Puerto Rico

26


Crop Simulation

Model

GSPsRILs (ij)

Field Data

QTL QTL


Simulation

Input

WeatherData(j)

Input


Evaluation

TEST OF HYPOTHESIS

QTL

Palmira

Indeterminate

Determinate

C

J


10

30

20

40

Ther

mal

Tim

e (o C

-day

)


10

30

20

40

Ther

mal

Tim

e (o C

-day

)


10

30

20

40

Ther

mal

Tim

e (o C

-day

)

30


Results of Hypothesis Testing

GSPs capture genetic variation

GSPs are functions of the genotype (QTL)

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 ExpressionGene-based Crop Simulation Model

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 ExpressionGene-based Crop Simulation Model

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

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

34

Pre

dict

ed

Observed

Days to First Flower from Planting

R2 = 0.92

Time to Flower Model: PredictionGene-based Crop Simulation Model

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

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

+ +

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 )


Environ. Effect on µFactor Days

Day (hrs) 3.9

Tmin (˚C) -0.6

Tmax (˚C) -1.4

Srad (W/m2) 0.2



38


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


39

R2 = 0.87

Site Model QTL-EC Model

R2 = 0.92

Pre

dict

ed

Observed

Days to First Flower from Planting


40

R2 = 0.87

CalimaJamapa

Pre

dict

ed

Observed

Days to Flower - Parental Lines


Model Validation

41

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)


IN SILICO GENE REPLACEMENT

42


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

43

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


What are the Identities of the PvQTL?

44

Linkage vs Physical Map

HOT SPOTS

COLD SPOT

Chromosome 1

Mb

cMcM

/ Mb


TF-2

TF-3

TF-1

45

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


46

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


47


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

48

G2P – Future Direction

Gene-BasedCrop Model

GenDBGenDB PhenDB

PhenDB

EnvDBEnvDB

Expert System

Prediction

Ideotype

Species D


GenDBGenDB PhenDB

PhenDB

EnvDBEnvDB

Expert System

Prediction

Ideotype

Species C


GenDBGenDB PhenDB

PhenDB

EnvDBEnvDB

Expert System

Prediction

Ideotype

Species B

GM


GenDBGenDB PhenDB

PhenDB

EnvDBEnvDB

Expert System

Prediction

Ideotype

Species A

49

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

Science

Advances in gene-based crop modeling