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