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16th - 17th November 2015

Clermont-Ferrand, FranceInternational

Wheat

Innovation

Workshop

Genomic selection in a real

wheat breeding programmes

Gilles Charmet – INRA UMR GDEC

Jérôme Auzanneau, Ari-Obtentions

International Wheat Innovation Workshop - 16th & 17th November 2015 - Clermont-Ferrand, France

Wheat in France: a strategic crop

Breeding goals-high yield

-Lodging tolerance

-Disease resistance

-Protein content

-High test weight

-High bread making grade

40000 hectares organic

farming

Average yield: ~7.5 t/ha

9 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

DURUM

Worldwide ranking 5th (1st in EU)2015 highest harvest: 40.8 Mt on 5.1 Mha

average yield 7.9 t/ha)


Typical wheat breeding scheme: long cycles

F1

F2

F3

F4

F5

F6

F7

F8

F9

lines

10

years

Crosses: 10²

105

104

103

102

101

100

F2 bulks

F3 bulks

REGISTRATION


Typical wheat breeding scheme:

Crosses: 10²

105

104

103

102

101

100

F2 bulks

F3 bulks

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

(Relatively) inefficient for complextraits in early generations


Genomic selection: how does it work

BWGS pipeline V2.0: General structure

Dimentional

reduction

Dimentional

reduction

Imputation of

genotypes

Imputation

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


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 juvenilSelection and intermating

h or prediction accuracyGenetic variability: can bemonitored by markers


Where to insert GS in a wheat breeding scheme ?

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


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 yearsCycles

GS

Adapted from J HickeyEUCARPIA Biometrics in Plant Breeding 2015

Use historicaldata for training

Select parents on GEBV per se of expected

progeny BV

Re-invent short cycle Recurrent selection

An application to INRA-AO real winterwheat breeding programme:

Preliminary results

Jérôme AUZANNEAU

AGRI OBTENTIONS


Genotyping: The BW420K SNP Axiom chip


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 2014

year of trial

2002 F7: 184

2003 F8: 64 F7: 186

2004 F9: 4 F8: 72 F7: 221

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 ? ?

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)=0.97


Heritability and prediction accuracy

(GBLUP – 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 1,007

Protein 0,513 0,557 0,778

Alveograph:

dough strength W 0,705 0,536 0,638

tenacity P 0,757 0,622 0,715

extensibility L 0,564 0,574 0,764

P / L 0,062 0,301 1,209

Bread making

dough score 0,392 0,404 0,645

crumb score 0,371 0,448 0,736

bread score 0,275 0,405 0,772

total score 0,433 0,452 0,687

loaf volume 0,44 0,427 0,644

Other:

heading date 0,787 0,38 0,428

plant height 0,296 0,353 0,649

hagberg FN 0,505 0,427 0,601

dietary fibre (visco) 0,908 0,68 0,714

Fusarium HB score 0,563 0,63 0,84

GS accuracy is good enough for most traits to enable genetic progress


Comparing accuracy among methods

Yield, N=760, 10 000 markersMETHOD r = cor(GEBV, y)

MKRKHS 0.5698 a

RKHS 0.5688 a

Bayesian LASSO 0.5646 a

RF regression 0.5628 a

Bayes B 0.5618 a

EGBLUP 0.5606 a

Bayes A 0.5560 a b

Bayes C(p) 0.5514 a b

Bayesian RR 0.5508 a b

GBLUP 0.5452 b

LASSO 0.5316 b

Elastic net 0.5282 b

SVM 0.2882 c

NK homogeneous groups

Various prediction methods giveconsistent results: GS is robust


Application to real breeding linesDoubled haploïde production from 14 crosses (1000 DH)Year 1

CTPS1

Genotyping (WP1)

2nd generation crosses

Genomic

Selection

DH production

Phenotypic

Selection

Genotyping WP1

Genomic

Selection

2

3

4

5

6

7

8

Yie

ld:

Tre

ate

d v

s

Un

tre

ate

d

Yield + quality test

(7 to 9 locations)

CTPS2

registration

?

CTPS1

Yie

ld:

Tre

ate

d v

s

Un

tre

ate

d

Yiled + quality test

(7 to 9 locations)

CTPS2

regisration

?Phenotypic

Selection

(Classic selection)

1st cylcle of GSY

iled

:

Tre

ate

d v

s

Un

tre

ate

d

Yield + quality test

(7 to 9 locations)

2nd cycles of

GS


Phenotypic selection

DH

• 1600 haploid plantlets from 14 crosses

• Seed X: 990 DH with > 30 seeds

Nursery

• 990 rows, inoculation Yellow rust

• 140 lines selected

Yield

• Nursery + unreplicated plots x 3 loc

• 35 best lines selected for comparison


Genomic selection

DNA

• 990 DH lines genotyped with BW420K

• 188 000 polymorphic SNP

training

• Training set = 760 historical lines

• GEBV with GBLUP

GEBV

• Selection on GEBV for yield in H/L input

• 30/990 or 10 /140 for comparison


2014 – 2015 Comparison on trials

Qx / ha % Tem Qx / ha % Tem Qx / ha % Tem

Min 72,8 75,8 36,8 45,1 57,7 63,4

Max 98,6 103,6 85,5 104,7 92 103,6

Mean 86,3 89,7 70,1 85,9 78,2 87,8

Genomic selection

Treated Untreated Treated + Untreated

Qx / ha % Tem Qx / ha % Tem Qx / ha % Tem

Min 87,3 89,7 65,6 80,5 79 87,7

Max 106,6 109,5 89,2 109,5 97,9 109,5

Mean 94,4 97,3 75,9 93,1 85,2 95,2

Treated + Untreated

Phenotypic selection

Treated Untreated


PS vs GS comparison: top lines

PS only

PS x GS

GS only 30/990

GS only 10/140

line %Tem moy T+NT %Tem T % Tem NT line %Tem moy T+NT %Tem T % Tem NT line %Tem moy T+NT %Tem T % Tem NT

09BH040H047 109,5 109,5 109,5 AREZZO 96,3 97,8 94,9 09BH032H071 90,2 93,8 86,6

09BH025H051 106,1 104,6 107,6 09BH009H020 96,3 98,3 94,2 09BH019H026 89,9 93,2 86,6

RUBISKO 104,8 104,2 105,4 09BH032H013 96,2 96,0 96,5 09BH007H054 89,4 94,3 84,4

RUBISKO 104,4 103,2 105,7 09BH032H024 96,0 99,4 92,6 09BH032H066 89,3 97,3 81,3

09BH007H065 103,6 102,6 104,7 09BH019H048 95,4 101,8 89,0 09BH039H027 89,1 96,2 82,0

CELLULE 102,4 101,8 103,0 AREZZO 94,8 97,1 92,5 09BH009H032 89,1 90,9 87,2

CELLULE 101,8 100,3 103,2 09BH032H031 94,8 94,1 95,5 09BH009H001 88,7 88,5 88,8

09BH018H017 101,1 101,9 100,3 09BH032H006 94,8 94,8 94,7 09BH009H035 88,6 89,7 87,4

09BH040H076 100,9 100,5 101,3 09BH007H043 94,6 93,1 96,2 09BH006H074 88,1 90,7 85,5

09BH002H065 99,1 97,0 101,3 09BH025H030 94,4 91,9 97,0 09BH038H039 87,7 94,9 80,5

09BH037H013 99,0 99,6 98,4 09BH039H029 94,4 97,7 91,1 09BH007H023 86,7 84,8 88,6

09BH002H062 98,9 98,2 99,7 09BH032H043 94,0 91,3 96,7 09BH009H036 85,2 90,4 79,9

BERMUDE 98,7 98,4 98,9 09BH038H001 93,9 99,5 88,4 09BH009H075 84,9 86,1 83,7

09BH002H001 98,3 94,7 102,0 09BH032H039 92,7 95,9 89,5 09BH009H081 83,0 81,1 84,9

09BH040H026 98,3 99,3 97,3 09BH006H066 92,6 89,7 95,6 09BH025H015 82,2 82,0 82,4

09BH019H024 98,0 101,2 94,7 09BH007H074 92,0 102,8 81,3 09BH006H047 81,5 86,8 76,3

09BH009H079 96,9 96,9 97,0 09BH009H007 91,6 92,3 91,0 09BH009H063 79,8 79,3 80,3

09BH019H016 96,9 97,7 96,1 09BH002H047 91,4 92,9 89,9 09BH009H078 79,5 83,1 75,9

09BH019H034 96,9 99,0 94,8 09BH019H045 91,1 97,3 84,8 09BH009H028 76,9 75,8 78,0

BERMUDE 96,9 97,3 96,5 09BH018H050 90,9 94,9 86,8 09BH025H067 67,8 77,1 58,5

09BH032H004 96,8 98,1 95,4 09BH009H054 90,5 88,3 92,7 09BH002H071 63,4 81,7 45,1


Take home messages

• Marker coverage in large enough in Breeding pop

• Historical data are powerful for training

• GEBV are accurate enoughto enable efficient GS

• Variuos prediction modelsgive consistent results

• First field comparisons are encouraging

• Cost of genotyping: stillunafordable on 105

candidates

• New schemes to be explored

• Maintainance of accuracyacross # germplasms?

• Multiple traits MUST beconsidered

• GxE and multitrait methods to be further developped (e.g.

Jarquin et al 2014, Heslot et al 2014)

Aknowledgements

Van Giang Tran INRA for BWGS pipeline programmingJérôme Auzanneau AO for field experiments and data analyses

All partners in BW-WP4 for helpful discussion

THANK YOU FOR YOUR ATTENTION

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