2012. frank ordon. genomics based breeding research for improving resistance to biotic and abiotic stress in cereals

www.jki.bund.de

Genomics based breeding research for

improving resistance to biotic and abiotic

stress in cereals

Dragan Perovic, Albrecht Serfling, Katja Perner, Sandra Färber, Cristina Silvar,

Ilona Krämer, Antje Habekuß, Doris Kopahnke, Heike Lehnert, Thomas Vatter,

Gwendolin Wehner, Esther Mitterbauer, Andreas Graner, Nils Stein

and Frank Ordon

Institute for Resistance Research and Stress Tolerance

Acreage of cereals 2012

Wheat and barley growing area (ha)

and average yield (t/ha) in 2012

ha (Mio) t/ha

Wheat

World 215.49 3.11

India 29.86 3.18

Germany 3.06 7.33

Barley

World 49.52 2.68

India 0.77 2.10

Germany 1.68 6.19

http://faostat.fao.org

home.arcor.de http://www.nurbier.de/category/biergeschichte/ http://www.abzonline.de/praxis/kasten-weizenbrot,707243491.html


Challenges for plant production

Food security

Growing population

Bioenergy

Change in dietary habits

Climate change

Anstieg der weltweiten Mitteltemperatur für die

Zeitspanne 071 - 2100 relativ zu der Zeitspanne 1961 -

1990. © MPI Met


Climatechange

http://www.umweltdaten.de/publikationen/fpdf-l/GGTSPU-styx2.bba.de-6248-7152625-DAT/3133.pdf

www.digiklix.de

Beschreibende Sortenliste 2010

+1°C = 10% yield reduction in wheat

-27% predicted for 2050 compared to 2000 in some regions Wheat Initiative


http://www.transgen.de/pflanzenforschung/pflanzengesundheit/

Insects

Diseases

Weeds

Tota

l harv

est

Ric

e

Sorg

hum

Maiz

e

Oats

Wheat

Barley

Rye

Pota

to

Sugarc

ane

Average yield losses

Breeding for resistance to biotic and abiotic stress in cereals is of prime

importance to:

•avoid yield losses

•to ensure a consumer and environmental friendly production

Wheat (2012)

~140 million t

~$ 35 billion

FAOSTAT 2014


Mildew Leaf rust

No. Cultivars Yield

Year resistant susceptible resistant susceptible

1986 6 37 4.3* 5.6

1995 24 41 6.5 6.3

2005 52** 23 6.7 6.1

2011 55 9 6.9 6.4

Success of breeding for resistance in barley

BaMMV/BaYMV Ahlemeyer pers. comm.

1=minimum, 9=maximum


Asfaw Adugna , 2004. Alternate Approaches in Deploying Genes for Disease Resistance in Crop

Plants. Asian Journal of Plant Sciences, 3: 618-623.

The never ending story

P. hordei P. striiformis B. graminis

P. teres R. commune U. nuda

BaMMV/BaYMV BYDV

Barley Wheat


Marker type RFLPs Genomic SSRs AFLPs EST

SNPs/SSRs DArTs BOPAs/OPAs iSelect Genotyping by

sequencing

Throughput single marker

application single marker

application few marker application

single marker application 6K 1,5K 9K 50K

Multiplexing no mutiplexing few markers multiplexing

low multiplexing

few markers multiplexing

platform/ simultaneous

analysis


analysis


analysis


analysis

simultaneous multiplexing NGS/GBS

Amount of D N A Large amount low amount low amount low amount low amount low amount low amount low amount low amount

Quality of D N A very good average average average very good very good very good very good very good

Plant breeders toolbox


Marker based harnessing of genetic resources: B. graminis

7HS: 12.1 cM 5.3 cM 1.5cM

7HL: 41.9 cM 2.8 cM 1.3cM


Nested association mapping

NAM population HEB-25

• 25 wild accessions (H. spontaneum)

• 1 elite recipient (Barke)

• 1420 BC1S3 lines

TASSEL 4 (Q + K)

Significant differences (p <.0001; tukey-test) between and within families

Barke

incl. wildtype

Marker based harnessing of genetic resources: P. teres

Vatter et al. (unpublished)


Marker based harnessing of genetic resources: P. triticina

0

0,25

0,5

0,75

1

1996 1998 2000 2002 2004 2005 2006 2007 2008

Sorten ohne Resistenzgen Sorten mit LR37Thatcher NIL-

Lr37

Thatcher without

resistance

Isolates Lr10 Lr11 Lr17 Lr18 Lr20 Lr28 Lr37 Lr49 T. monococcum T. boeticum

77WxR s s s s s s s s r s

167/176WxR s s s s s s s s r ps

Tommi 1 s s s s s s s s r s

13/20WxR s s s s s s s s r s

4136 ps s s s s r s s r s

s

ps

r

Analyzed isolates

virulent against all

known Lr-genes

located on the A

genome

The prehaustorial resistance of T. monococcum

0

20

40

60

80

12 24 48 72 96

HM

C/

Infe

ction

Time after inoculation (h)

Borenos wxr77

Pi272560 wxr77

Susceptible

accession

Resistant

accession

Su

sce

ptible

acce

ssio

n

Resis

tan

t

acce

ssio

n

24 hai 96 hai 168 hai

24 hai 96 hai 168 hai

HMC

Lr37: 2004 2013

Serfling et al. (in preparation)


Molecular characterization of the prehaustorial resistance by Massive

Analysis of cDNA (MACE)

Number of RNA samples: 12

Time after inoculation

0 to 8 hai 8 to 16 hai 16-24 hai

Resistant accession rust inoculated 1 1 1

Resistant accession mock inoculated 1 1 1

Susceptible accession rust inoculated 1 1 1

Susceptible accession mock inoculated 1 1 1

Number of differentially expressed tags after comparison of the inoculated resistant and susceptible accession 0-24 hai

Quantitativelly differentially expressed 6810 6780 4832 1648

Qualitativelly differentially expressed 4413 3592 3592 340

In silico map on the basis of SNP detection of annotated

tags

1A 2A 3A 4A 5A 6A 7A

Comprises 1136 genes in which

4358 SNPs were detected



Detailed analysis of peroxidases and chitinases

-6

-4

-2

0

2

4

6

0

-8 h

ai

8-1

6 h

ai

16

-24 h

ai

0

-8 h

ai

8-1

6 h

ai

16

-24 h

ai

0

-8 h

ai

8-1

6 h

ai

16

-24 h

ai

0

-8 h

ai

8-1

6 h

ai

16

-24 h

ai

0

-8 h

ai

8-1

6 h

ai

16

-24 h

ai

Pox6 Pox1 Prx113 Pox 54 Pox prec.

Lo

g2 o

f exp

ressio

n I

no

cu

late

d/

n

on

in

ocu

late

d

Resistant accession Susceptible accession

By Go terms identified Peroxidases

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0-8hai

8-16hai

16-24hai

0-8hai

8-16hai

16-24hai

Chitinase1 Chitinase 2

By Go terms identified Chitinases



Re

sis

tan

t

acce

sssio

n

Su

sce

ptible

acce

sssio

n

Resistant

accession

Susceptible

accession

0 6 12 24 48 72 96 168 hai

*

* *

*

µM

ol H

2O

2

Diaminobenzidine stain

Peroxidase activity

Re

sis

tan

t

acce

sssio

n

Su

sce

ptible

acce

sssio

n

0

15

30

45

60

Pe

rox

ida

se

ac

tivit

y

(Un

its

min

-1)

0 6 12 24 48 72 96 168 hai

* Ch

itin

as

e a

cti

vit

y

(Un

its

min

-1)

0

6

12

18

30

24

*

*

*

* *

Chitinase activity

Characterization of prehaustorial resistance

Calcofluor stain

? ?

0

0.05

0.1

0.15

0.2

0.25

0 50 100 150 200 250 300

Calibration curveµMol H2O2 l-1

Absorption

0 min ai

15 min ai

30 min ai



Chr cM Number of markers

1A 217.7 588

2A 260.1 771

3A 171.2 503

4A 111.5 339

5A 236.7 570

6A 232.6 620

7A 258.4 727

Total 1488.3 4118

LOD

3.15

24.4%

LOD

3.32

16.5%

LOD

3.84

13.0%

LOD

3.62

13.3%

Phenotyping:

Number of haustorial mother

cells 72 hai (F2/F3)

Identification of QTL for pre-haustorial resistance

Serfling et al. unpublished

Localization of

candidate

genes in QTLs

is ongoing


Gene isolation: BaYMV/BaYMV-2 resistance

distance marker 5H

‘HOR4224‘ (r) x ‘HOR10714‘ (s) Based on 3369 F2 - plants,

Resolution 0.015% rec.

Exome capture

Perner et al. (in preparation)


Gene isolation: BaMMV resistance

GBS

Färber et al. (in preparation)


rym4/rym5

Isolation of resistance genes - allele mining

Hofinger et al. 2011. Molecular Ecology 20, 3653-3668

A. Graner

1000 accessions selected

27 resistant haplotypes

40 novel exon haplotypes

known haplotypes

13 susceptible

non allelic genes

identification of

8 novel eIF4E alleles

resequencing

resistance tests

test crosses,

resistance tests

year1

year2/3

year3

eIF4E allele mining

Hv-eIF4E

HvPDIL5-1

rym11

Yang et al. 2014. www.pnas.org/cgi/doi/10.1073/pnas.1320362111

Yang et al. 2014. Theor. Appl. Genet

Kanyuka et al.


Allele Editing: Directed mutagenesis using endonucleases

Puchta and Fauser (2014) The Plant Journal

ZFNs Zinc-Finger Nucleases

TALENs Transcription Activator-Like Effector Nucleases

CRISPR Clustered Regularly Interspaced Short Palindromic Repeats

Cas CRISPR-associated, RNA-guided endonuclase

Meganucleases

A. Graner


Wheat – powdery mildew resistance

A. Graner

Allele Editing: Directed mutagenesis using endonucleases


Drought stress in the juvenile stage

EQTL

156 BARLEY GENOTYPES

PHENOTYPING Biomass yield

Chlorophyll content Chlorophyll fluorescence

Osmotic adjustment Content of free proline

Total content of soluble sugars

DROUGHT STRESS Stress application starts 7das BBCH11 – BBCH33 4 weeks stress period Stress 20% water capacity of soil 3 replicates per genotype 3 years trials

CONTROL STRESS

CONTROL STRESS

GWAS

QTL

GENE EXPRESSION

PROTEIN DETECTION

Illumina 9k iSelect SNP Chip Consensus Map of markers

SNP Scoring LD and Population structure

Significant SNPs Chomosome 5H + 2H

NCBI BlastX Protein function UniProt

Genetic map

GENOTYPING

DR

OU

GH

T S

TR

ES

S

LE

AF S

EN

ES

CE

NC

E

qPCR Fluidigm Chip array Drought stress genes Genes for leaf senescence Genes out of GWAS

Tassel 3.0 Detection of QTL

Localisation of QTL


Correlations (Pearson) and Heritability:

Treatment BY SPAD ETR OA CFP CSS

h² Control 0.80 0.64 0.08 0.00 0.13 0.13

Stress 0.58 0.61 0.50 0.27 0.29 0.30

BY Control 0.395 *** 0.091 -0.127 -0.328 *** -0.220 **

Stress 0.361 *** -0.087 -0.124 0.307 *** 0.367 ***

SPAD Control 0.160 * -0.185 * -0.239 ** -0.192 *

Stress -0.105 0.034 0.425 *** 0.418 ***

ANOVA: significant effects (p <0.001) of genotype and treatment; significant GxT effect for BY, CFP and CSS

Significance level: P≤0.05 *. P≤0.01 **. P≤0.001 ***

Wehner et al. (submitted)



Trait Number of genomic regions associated with the traits on the seven linkage groups (barley chromosomes)

*highest R² 1H 2H 3H 4H 5H 6H 7H Total QTL

BY 81.7 cM (3 SNP) 2 cM (3 SNP) 76.2 cM (1 SNP) 99.1 cM (1 SNP) 46.7 cM (8 SNP) 48.3 cM (1 SNP) 19 (32 SNPs) 0.20%

92.2 cM (1 SNP) 5.5 cM (1 SNP) 135.5 cM (1 SNP)* 59.7 cM (1 SNP) 70.2 cM (1 SNP)

12.1 cM (1 SNP) 80.3 cM (1 SNP) 133.9 cM (1 SNP)

90.2 cM (3 SNP) 110.1 cM (1 SNP)

139.1 cM (1 SNP)

152.4 cM (1 SNP)

167.7 cM (1 SNP)

SPAD 49.2 cM (1 SNP)* 44.2 cM (4 SNP) 128.3 cM (1 SNP) 3 (6 SNPs) 3.80%

ETR 59.4 cM (1 SNP) 2.1 cM (1 SNP)* 2 (2 SNPs) 5.50%

OA 116.8 cM (1 SNP) 51.8 cM (1 SNP) 2.4 cM (1 SNP) 52.3 cM (1 SNP) 46.5 cM (1 SNP) 10.3 cM (1 SNP) 106.5 cM (1 SNP) 22 (29 SNPs) 3.50%

60.8 cM (2 SNP) 36.8 cM (2 SNP) 110.2 cM (1 SNP) 55.7 cM (1 SNP) 47.5 cM (1 SNP)

81.5 cM (4 SNP)* 51.8 cM (1 SNP) 95 cM (1 SNP) 51 cM (2 SNP)

135.8 cM (1 SNP) 61.9 cM (1 SNP) 137.9 cM (1 SNP)

146.5 cM (1 SNP) 89.4 cM (1 SNP)

100.7 cM (2 SNP)

CSS 95.8 cM (1 SNP)* 1 (1 SNP) 1.60%

Total QTL 4 (6 SNPs) 10 (18 SNPs) 8 (10 SNPs) 3 (3 SNPs) 12 (22 SNPs) 4 (5 SNPs) 6 (6 SNPs) 47 (70 SNPs)




FTSH3_BY_0.2%

PME49_CSS_1.6%

1H

SUS4_SPAD_3.8%

YSL2_OA_0.7%YSL15_OA_0.7%GDH2_OA_1.4%AMP1_OA_2.3%GPX1_BY_0.2%

2H

FBL21_OA_2.8%

ACO1_OA_2.3%

3H

PYL5_OA_2.5%

4H

AVP1_SPAD_BY_0.2%ATM_SPAD_BY_2.6%TRIUR3_SPAD_BY_3.1%SAPK9_SPAD_BY_3.1%

DREB1A_SPAD_OA_2.4%

EGY1_OA_1.4%

5H 6H

CHX_ETR_5.5%

ERF062_BY_0.2%

7H

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

Genetic map of QTLs including the significant associated SNP marker positions for

significant blasted proteins (BlastX) linked to drought stress or leaf senescence,

related traits for drought stress treatment and percentage of phenotypic variance

(explained R² in %) of the SNPs for all linkage groups (barley chromosomes).

cM




New breeding goals: Mycorrhization and drought stress

Experimental design:

• 94 Genotypes, 2 Years, 3 Replications

• Treatments

– Mycorrhization (Myco, N-Myco)

– Irrigation (25% and 75% maximal water capacity, MWC)

Quantification root colonization:

Ink vinegar staining (Vierheilig et al., 1998)

Magnified intersection method (McGonigle et al., 1990)

N-myco Myco Myco Myco Myco

Myco 25% MWC Myco 75% MWC

Lehnert et al. (in preparation)


Trait: Root colonization (%)

Myco 25% MWC Myco 75% MWC

-lo

g10(p

)

-lo

g10(p

)

Chromosome Chromosome




• Trait: Biomass (g), Yield (g), Ears per plant, 1000 grain weight (g),

Grains per ear

•




amb: 400 ppm

eCO2: 700 ppm

New breeding goals: CO2


Mitterbauer et al. (in preparation)

Yie

ld

Ea

rs/p

lan

t T

KW

Ke

rne

l/E

ar

(2-r

ow

ed

) K

ern

el/E

ar

(6-r

ow

ed

) P

rote

in



• Genome wide association

analyses

• (QK mixed model

approach;

• minor allele frequency

>5%)

• 3886 marker

Yield response (E/A)

Biomass response (E/A)

Kernel #/ear(E/A)

Stem weight (E/A)

Mitterbauer et al. (in preparation)



Summary and future prospects

•Genomic tools facilitate an enhanced marker development and isolation of major

genes and QTL for resistance to biotoc and abiotic stress leading to a deeper

understanding of trait development and the transfer of marker based selection to the

allele level.

•This will lead to a more directed and faster use of genetic variation.

•High throughput marker systems also offer the opportunity to implement new

breeding goals efficiently into applied breeding procedures.

•Knowledge on gene sequences will facilitate the targeted editing of respective alleles

in the future by endonucleases.

•Genomic tools will speed up breeding for resistance to biotic

and abiotic stress

http://www.nature.com/mtna/journal/v1/n1/full/mtna20115a.html

Fernie, A.R., N. Schauer, 2008: Trends in Genetics 25, 39-48


Thanks Dr. Dragan Perovic

Dr. Ilona Krämer

Dr. Antje Habekuß

Dr. Christiane Balko

Dr. Esther Mitterbauer

Dr. Nadine Knöchel

Gwendolin Wehner

Katja Perner

Sandra Färber

Dr. Doris Kopahnke

Thomas Vatter

Dr. Albrecht Serfling

Prof. Dr. Wolfgang Friedt

Prof. Dr. Andreas Graner

Dr. Nils Stein

Dr. Ping Yang

Martin Mascher

Prof. Dr. Klaus Pillen

Dr. Ernesto Igartua

Dr. Ana Casas

Dr. Cristina Silvar

Dr. Brian Steffenson

Dr. Kostya Kanyuka

Prof. Dr. Olga Afanasenko