Session2 - A. AMELONG pdf

Predicting maize kernel number using
genetic information
Agustina Amelong, Brenda L. Gambín and Lucas Borrás
Universidad Nacional de Rosario - CONICET
Argentina
Montpellier
2015
Background
• Kernel number is responsible for most maize yield
variations.
• Kernel number is a quantitative trait highly
influenced by the environment.
• Crop physiology models can help link the
environment with the trait of interest, and predict GxE
interactions.
Kernel Number per Plant
Crop physiology model for kernel number
determination
Ear Biomass
Model based in:
Edmeades & Daynard, 1979; Tollenaar, 1991; Otegui & Bonhomme, 1998; Andrade
et al., 1999; Echarte et al., 2004; Borrás et al., 2007; Pagano & Maddonni, 2007;
D’Andrea et al., 2008; Borrás et. al, 2009.
Plant Growth Rate
Kernel Number per Plant
Ear Biomass
Crop physiology model for kernel number
determination
Ear Biomass
Model based in:
Edmeades & Daynard, 1979; Tollenaar, 1991; Otegui & Bonhomme, 1998; Andrade
et al., 1999; Echarte et al., 2004; Borrás et al., 2007; Pagano & Maddonni, 2007;
D’Andrea et al., 2008; Borrás et. al, 2009.
Crop physiology model for kernel number
determination
Plant Growth Rate
Kernel Set Efficiency
Kernel Number per Plant
Ear Biomass
Plant Biomass Partitioning
Ear Biomass
Model based in:
Edmeades & Daynard, 1979; Tollenaar, 1991; Otegui & Bonhomme, 1998; Andrade
et al., 1999; Echarte et al., 2004; Borrás et al., 2007; Pagano & Maddonni, 2007;
D’Andrea et al., 2008; Borrás et al., 2009.
Crop physiology model for kernel number
determination
Ear Biomass
A
Ceb
ISeb
PGRb
Plant Growth Rate
Kernel Set Efficiency
Kernel Number per Plant
Plant Biomass Partitioning
B
Ckn
ISkn
EBb
Ear Biomass
Model based in:
Edmeades & Daynard, 1979; Tollenaar, 1991; Otegui & Bonhomme, 1998; Andrade
et al., 1999; Echarte et al., 2004; Borrás et al., 2007; Pagano & Maddonni, 2007;
D’Andrea et al., 2008; Borrás et. al, 2009.
Ear Biomass
Differences in plant biomass partitioning among
genotypes
35
35
30
30
25
25
20
20
15
15
10
10
5
5
0
0
0
1
2
3
4
5
6
7
35
35
30
30
25
25
20
20
15
15
10
10
5
5
0
0
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Plant Growth Rate
Borrás et al. Crop Science 2009.
We phenotyped model parameters for a RIL
population (B73 x Mo17).
Kernel number per plant
(kernenls pl-1)
We phenotyped model parameters for a RIL
population (B73 x Mo17).
Ear Biomass (g pl-1)
30
ISEB: 16
CEB: 0.39
20
PGRb:1.2
10
0
800
CKN: 0.09
ISKN: 87
600
EBb:2.1
400
200
0
0
2
4
Plant growth rate
(g pl-1 d-1)
6
0
10
Ear Biomass (g
Example: Curve parameters for RIL #157
Amelong et al. Field Crops Research 2015.
20
pl-1)
30
Genetic analysis
Phenotypic
information
+
Genetic
information
www.maizegdb.org
First, we did a QTL analysis.
Second, we did a Genomic Prediction analysis.
Objective
Compare kernel number and ear biomass
predictions using QTL information vs.
genomic estimations (GBLUP).
Model validation
Exp
Location
Sowing Date
RILs
Stand Density
pl m-2
I
Zavalla
Sep 2009
42 + parents
5
II
Zavalla
Oct 2010
115 + parents
7.5
III
Zavalla
Oct 2011
26 + parents
4 y 10
IV
Pergamino
Sept 2009
20 + parents
3 y 12
V
Iowa
May 2007
parents
3y9
CEB
Ear Biomass
(g ear-1)
A
ISEB
PGRb
Plant growth rate
(g pl-1 d-1)
Kernel number per plant
(KN pl-1)
Prediction using our crop physiology model
B
CKN
ISKN
EBb
Ear Biomass
(g ear-1)
CEB
Ear Biomass
(g ear-1)
A
ISEB
PGRb
Plant growth rate
(g pl-1 d-1)
MEASURED
Plant
growth rate
Kernel number per plant
(KN pl-1)
Prediction using our crop physiology model
B
CKN
ISKN
EBb
Ear Biomass
(g ear-1)
Prediction using our crop physiology model
ESTIMATED
Kernel number
per plant
CEB
Ear Biomass
(g ear-1)
A
ISEB
PGRb
Plant growth rate
(g pl-1 d-1)
MEASURED
Plant
growth rate
Kernel number per plant
(KN pl-1)
ESTIMATED
Ear Biomass
B
CKN
ISKN
EBb
Ear Biomass
(g ear-1)
Results
Predicted values
Ear Biomass
(gr pl-1)
Kernel Number per Plant
(kernel pl-1)
r2: 0.18
p<0.001
Observed values
Amelong et al. Field Crops Research 2015.
r2: 0.14
p<0.001
QTL analysis
over model
parameters
Predicted values
Results
Ear Biomass
(gr pl-1)
Kernel Number per Plant
(kernel pl-1)
r2: 0.43
P<0.001
r2: 0.38
P<0.001
r2: 0.18
p<0.001
r2: 0.14
p<0.001
Observed values
Model
parameters
estimated
with GBLUP
QTL analysis
over model
parameters
Predicted values
Results
Ear Biomass
(gr pl-1)
Kernel Number per Plant
(kernel pl-1)
r2: 0.43
P<0.001
r2: 0.38
P<0.001
r2: 0.18
p<0.001
r2: 0.14
p<0.001
r2: 0.46
P<0.001
r2: 0.35
P<0.001
Observed values
Model
parameters
estimated
with GBLUP
QTL analysis
over model
parameters
Average
model
parameters
Conclusions
• Genetic information helped predict kernel number per
plant with different accuracies depending on the
approach, although overall accuracies were low.
Conclusions
• Genetic information helped predict kernel number per
plant with different accuracies depending on the
approach, although overall accuracies were low.
• Results indicate higher prediction accuracies when using
GBLUP compared to using QTL information.
Conclusions
• Genetic information helped predict kernel number per
plant with different accuracies depending on the
approach, although overall accuracies were low.
• Results indicate higher prediction accuracies when using
GBLUP compared to using QTL information.
• Predictions accuracy of ear biomass or kernel number
per plant was mostly related to accurate plant growth data
rather than genotype specific parameters describing
biomass partitioning and kernel set differences.
Thanks for your attention.