BREEDING VALUE ESTIMATION FOR YIELD AND QUALITY TRAITS IN WHEAT
USING BWGS PIPELINEGilles Charmet1*, Van Giang Tran1, Delphine Ly1 ,Jerome Auzanneau2
1INRA-Université Clermont II UMR1095 GDEC, Clermont-Ferrand, France2Auzanneau J, Agri-Obtentions, La Minière, France
- high yield- Lodging tolerance- Disease resistance
- Protein content- High test weight
- High bread making grade-
40000 hectares organic farming
Average yield: ~7.5 t/ha9 t/ha Pas de Calais
5 t/ha Gers
~5 millions hectares « conventionnal
farming »On average 6.3 pesticide Tilling (55%), No-till (45%)
# 165 kg/ha mineral N
WHEAT IN FRANCEWorldwide ranking 5th (1st in EU)
2015 highest harvest: 40.8 Mt on 5.1 Mha average yield 7.9 t/ha)
DURUM
Use of bread wheat in France
But wheat yields are stagnating in EU
Genetic progress must be speed up: needs for new methods
Angers-Nantes
Bordeaux
Clermont-Ferrand Theix
Toulouse
PACA
Versailles-Grignon
Angers-Nantes
Bordeaux
Clermont-Ferrand Theix
Toulouse
PACA
Versailles-Grignon
.05
Breeding for economically and environmentally sustainable wheat varieties: an integrated approach from
genomics to selection
www.breedwheat.fr
• Coordination by UMR GDEC
• 26 partners (11 private)• 124 permanent staff / 54
CDD• 9 years
• 34 M€ (9 M€ granted)
F1
F2
F3
F4
F5
F6
F7
F8
F9lines
10 y
ears
Typical wheat breeding scheme
Crosses: 10²
105
104
103
102
101
100
F2 bulks
F3 bulks
REGISTRATION
Typical wheat breeding scheme
Experiment /traits
Loc No remarks
Single plantsVisual trait
One Low h²# random selection
1-3 rowsVisual+diseases
1-2 Negative selection of worse rows/plants
Yield plots¨% protein
2-3, 1 rep
Low h²
Yield plot % protIndirect Q test
5-82-4 rep
Accurate yield evaluation + GxL
Yield plot % protBread making
8-104 reps
Accurate yield + BM tests + G x Y
Official registration trials
12-15 4 repsT NT,LI
2 year official trialsBM test on year 1 harvest
Crosses: 10²
105
104
103
102
101
100
F2 bulks
F3 bulks
REGISTRATION
Advantages of GS over phenotypic selection
DG = i h sG / L
Selection intensity: Can be increased if
Genotyping cost < phenotyping
Cycle length: can beShortenned by juvenil
Selection and intermating
h or prediction accuracyGenetic variability: can be
monitored by markers
Where to insert GS in a wheat breeding scheme ?
Crosses: 10²
105
104
103
102
101
100
F2 bulks
F3 bulks
REGISTRATION
DG = i h sG / L
BWGS pipeline V2.0: General structure
10
Dimentional reduction
Dimentional reduction
Imputation of genotypes
Dimentional reduction
Comparison of models
(cross-validation)
Optimal models GEBV
Training genotypic
data
Training phenotypic
data
Target phenotypic
data
Cor (y, GEBV)MSEP, SD
(yhat)Cor (y, GEBV)
bwgs.selgen.cv(…)
bwgs.predict(…)
quality indicators
BWGS pipeline V2.0: General structure
11
An application to INRA-AO real winter wheat breeding
programme:Preliminary results
Jérôme AUZANNEAUAGRI OBTENTIONS
13,670 validated SNPs
Genotyping: The BreedWheat 420K SNP Axiom chip
139,904 genic SNPs140,450 intergenic
SNPs
105,577 ISBP-SNPs9,570 candidate gene
SNPs
4,815 Axiom-validated SNPs
Illumina Infinium 90K chip
5,155 Axiom-validated SNPs
4,120 validated SNPs
124 major gene SNPs
423,385 SNPs
• 423385 SNP
QC+pol • 35 655 genic• 135768 InterG
MAF >0.01• 35 189 genic SNP• 120 957Inter Genic
Random sampling
Use of historical dataCrossa et al 2010, Dawson et al 2013, Rutkoski et al 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)
F7 issued in 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014year of trial
2002 F7: 1842003 F8: 64 F7: 1862004 F9: 4 F8: 72 F7: 2212005 F9: 6 F8: 93 1682006 F9: 11 72 1612007 8 65 1832008 5 77 1762009 7 66 1722010 8 54 1762011 4 56 1782012 6 66 1472013 8 73 1772014 9 88 1762015 ? ?
BLUE lmer(Y~geno+(1|year:site:trial:bloc)+(1|year:site:geno),data=…)BLUP lmer(Y~(1|year:site:trial:bloc)+(1|geno)+(1|year:site:geno),data=Y)
Cor (YieldBLUP, YieldBLUE)=0.94
Preliminary analyses: Influenceof marker no and training size (Yield, GBLUP)
Héritability and prediction accuracyGBLUP – 10 000 random markers
TRAIT h² = s2G/(s2G+s2
GE+s2
e) r = cor(GEBV, y) r/sqrt(h²)
Yield and protein %:Yield 0,307 0,558 1,007Protein 0,513 0,557 0,778Alveograph:dough strength W 0,705 0,536 0,638tenacity P 0,757 0,622 0,715extensibility L 0,564 0,574 0,764P / L 0,062 0,301 1,209Bread makingdough score 0,392 0,404 0,645crumb score 0,371 0,448 0,736bread score 0,275 0,405 0,772total score 0,433 0,452 0,687loaf volume 0,44 0,427 0,644Other:heading date 0,787 0,38 0,428plant height 0,296 0,353 0,649hagberg FN 0,505 0,427 0,601dietary fibre (visco) 0,908 0,68 0,714Fusarium HB score 0,563 0,63 0,84
Relationship h²- r(y,GEBV)
Comparing accuracy among methodsYield, N=760, 10 000 markers
METHOD r = cor(GEBV, y)
MKRKHS 0.5698 aRKHS 0.5688 aBayesian LASSO 0.5646 aRF regression 0.5628 aBayes B 0.5618 aEGBLUP 0.5606 aBayes A 0.5560 a bBayes C(p) 0.5514 a bBayesian RR 0.5508 a bGBLUP 0.5452 bLASSO 0.5316 bElastic net 0.5282 bSVM 0.2882 c
NK homogeneous groups
Comparing predictions among methodsYield, N=760, 10 000 markers
Cor (GEBV RKHS, GEBV GBLUP)= 0.92
Propose new schemes?
Crosses: 10²
105
104
103
102
101
100
F2 bulks
F3 bulks
REGISTRATION
DG = i h sG / L
Select parents
crosses
F2 or DH
Apply GS
2-3 years
Cycles GS
Adapted from J HickeyEUCARPIA Biometrics in Plant Breeding 2015
Use historical data for training
Select parents on GEBV per se of
expected progeny BV
Take home messages
• Important LD in breeding pop: few 1000s markers needed
• Historical data useful for training
• GEBV accurate enough to enable efficient GS
• Few differences among methods for accuracy and prediction
• Cost of genotyping: unafordable on 105 candidates
• New schemes to be explored
• Maintainance of accuracy across # germplasms?
• GxE and multitrait methods to be further developped (e.g. Jarquin et al 2014, Heslot et al 2014)
Aknowledgements
G CharmetINRA GDEC
ProgrammingDATA
ANALYSES
Advises, comments
23
Thank you for your attention
