BREEDING VALUE ESTIMATION
FOR YIELD AND QUALITY TRAITS
IN WHEAT USING BWGS PIPELINE
Gilles Charmet1*, Van Giang Tran1, Delphine Ly1 ,Jerome Auzanneau2
1INRA-Université
Clermont II UMR1095 GDEC, Clermont-Ferrand, France
J, Agri-Obtentions, La Minière, France
2Auzanneau
WHEAT IN FRANCE
Worldwide ranking 5th (1st in EU)
2015 highest harvest: 40.8 Mt on 5.1 Mha
average yield 7.9 t/ha)
Average yield: ~7.5 t/ha
~5 millions hectares
« conventionnal
farming »
9 t/ha Pas de Calais
5 t/ha Gers
On average 6.3 pesticide
Tilling (55%), No-till (45%)
# 165 kg/ha mineral N
- high yield
-Lodging tolerance
-Disease resistance
-Protein content
-High test weight
-High bread making grade
-
DURUM
40000 hectares organic
farming
Use of bread wheat in France
But wheat yields are stagnating in EU
Genetic progress must be speed up: needs for new methods
Breeding for economically and environmentally
sustainable wheat varieties: an integrated approach from
genomics to selection
Versailles-Grignon
AngersNantes
Clermont-Ferrand
Theix
PACA
Bordeaux
Toulouse
• Coordination by UMR GDEC
• 26 partners (11 private)
• 124 permanent staff / 54 CDD
• 9 years
• 34 M€ (9 M€ granted)
www.breedwheat.fr
.05
Typical wheat breeding scheme
Crosses: 10²
F1
F2
10 years
F3
F4
F5
F6
F7
F8
F9
lines
F2 bulks
F3 bulks
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REGISTRATION
Crosses: 10²
F2 bulks
Typical wheat breeding scheme
Experiment /
traits
Loc No
remarks
Single plants
Visual trait
One
Low h²
# random selection
1-3 rows
Visual+diseases
1-2
104
Negative selection of
worse rows/plants
103
Yield plots¨%
protein
2-3, 1
rep
Low h²
102
Yield plot % prot
Indirect Q test
5-8
2-4 rep
Accurate yield
evaluation + GxL
101
Yield plot % prot
Bread making
8-10
4 reps
Accurate yield + BM
tests + G x Y
Official
registration trials
12-15
4 reps
T NT,LI
2 year official trials
BM test on year 1
harvest
F3 bulks
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100
REGISTRATION
Advantages of GS over
phenotypic selection
h or prediction accuracy
Genetic variability: can be
monitored by markers
DG = i h sG / L
Selection intensity:
Can be increased if
Genotyping cost < phenotyping
Cycle length: can be
Shortenned by juvenil
Selection and intermating
Crosses: 10²
Where to insert GS in a wheat
breeding scheme ?
F2 bulks
F3 bulks
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REGISTRATION
DG = i h sG / L
BWGS pipeline V2.0: General structure
Training
phenotypic
data
Training
genotypic
data
quality indicators
bwgs.selgen.cv(…)
Dimentional
reduction
Imputation of
genotypes
Comparison
of models
(crossvalidation)
Cor (y, GEBV)
MSEP, SD
(yhat)
Cor (y, GEBV)
Optimal
models
GEBV
bwgs.predict(…)
Target
phenotypic
data
Dimentional
reduction
Dimentional
reduction
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BWGS pipeline V2.0: General structure
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An application to INRA-AO real
winter wheat breeding
programme:
Preliminary results
Jérôme AUZANNEAU
AGRI OBTENTIONS
Genotyping: The BreedWheat 420K SNP Axiom chip
Illumina Infinium 90K chip
124 major gene SNPs
105,577 ISBP-SNPs
9,570 candidate gene SNPs
13,670 validated
SNPs
423,385 SNPs
4,815 Axiom-validated
SNPs
139,904 genic SNPs
140,450 intergenic SNPs
4,120 validated SNPs
5,155 Axiom-validated
SNPs
• 423385 SNP
QC+pol
MAF >0.01
• 35 655 genic
• 135768 InterG
• 35 189 genic SNP
• 120 957Inter Genic
Random
sampling
Use of historical data
Crossa et al 2010, Dawson et al 2013, Rutkoski et al 2015)
F7 issued in
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
BLUE lmer(Y~geno+(1|year:site:trial:bloc)+(1|year:site:geno),data=…)
year of trial
BLUP
2002 F7: 184
lmer(Y~(1|year:site:trial:bloc)+(1|geno)+(1|year:site:geno),data=Y)
2003 F8: 64 F7: 186
2004 F9: 4
F8: 72 F7: 221 Cor (YieldBLUP, YieldBLUE)=0.94
2005
F9: 6
F8: 93
168
2006
F9: 11
72
161
2007
8
65
183
2008
5
77
176
2009
7
66
172
2010
8
54
176
2011
4
56
178
2012
6
66
147
2013
8
73
177
2014
9
88
176
2015
?
?
• Yield, protein: 35 298 records/ 1589 lines (760
Genotyped)
• Fusarium HB: 27 135 records, 1705 lines (672 G)
• Bread-making traits: 5887records / 526 lines (357 G)
Preliminary analyses: Influence
of marker no and training size (Yield, GBLUP)
Héritability and prediction accuracy
GBLUP – 10 000 random markers
TRAIT
Yield and protein %:
Yield
Protein
Alveograph:
dough strength W
tenacity P
extensibility L
P/L
Bread making
dough score
crumb score
bread score
total score
loaf volume
Other:
heading date
plant height
hagberg FN
dietary fibre (visco)
Fusarium HB score
h² = s 2G/(s2G+s 2GE+s 2e)
r = cor(GEBV, y)
r/sqrt(h²)
0,307
0,513
0,558
0,557
1,007
0,778
0,705
0,757
0,564
0,062
0,536
0,622
0,574
0,301
0,638
0,715
0,764
1,209
0,392
0,371
0,275
0,433
0,44
0,404
0,448
0,405
0,452
0,427
0,645
0,736
0,772
0,687
0,644
0,787
0,296
0,505
0,908
0,563
0,38
0,353
0,427
0,68
0,63
0,428
0,649
0,601
0,714
0,84
Relationship h²- r(y,GEBV)
Comparing accuracy among methods
Yield, N=760, 10 000 markers
METHOD
MKRKHS
RKHS
Bayesian LASSO
RF regression
Bayes B
EGBLUP
Bayes A
Bayes C(p)
Bayesian RR
GBLUP
LASSO
Elastic net
SVM
r = cor(GEBV, y)
0.5698
0.5688
0.5646
0.5628
0.5618
0.5606
0.5560
0.5514
0.5508
0.5452
0.5316
0.5282
0.2882
NK homogeneous groups
a
a
a
a
a
a
a
a
a
b
b
b
b
b
b
c
Comparing predictions among methods
Yield, N=760, 10 000 markers
Cor (GEBV RKHS, GEBV GBLUP)= 0.92
Propose new schemes?
Select parents on
GEBV per se of
expected progeny BV
2-3
years
Cycles
GS
Crosses: 10²
Select
parents
Apply GS
F2 bulks
crosses
F3 bulks
105
F2 or DH
Use historical
data for
training
DG = i h sG / L
Adapted from J Hickey
EUCARPIA Biometrics in Plant Breeding 2015
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REGISTRATION
Take home messages
•
•
•
•
• Cost of genotyping:
Important LD in
unafordable on 105
breeding pop: few
candidates
1000s markers needed
• New schemes to be
Historical data useful
explored
for training
• Maintainance of accuracy
GEBV accurate enough across # germplasms?
to enable efficient GS
• GxE and multitrait
Few differences among methods to be further
methods for accuracy
developped (e.g. Jarquin et al
and prediction
2014, Heslot et al 2014)
Aknowledgements
Programming
DATA
ANALYSES
Advises,
comments
G Charmet
INRA GDEC
Thank you for your attention
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