S4.1 Genomics-assisted breeding for maize improvement

Genomics-assisted breeding for

maize improvement

Roberto Tuberosa

Dept. of Agroenvironmental Sciences & Technology

University of Bologna, Italy

11th Asian Maize Conference, 8-12 November 2011, Nanning, China

Outline

Setting the stage

Implementing genomics-assisted breeding (GAB)

Chasing genes and QTLs

Biparental (linkage) mapping

Association mapping

Nested associated mapping

Breeding applications

MAS, MABC and MARS

Genomewide selection

Conclusions and perspectives

Genomics-assisted breeding in maize

Setting the stage

Corn yield in the USA

© Chappatte - www.globecartoon.com

…more bad news…

Stocks of staples are low

Reduced funding for plant breeding and training

for several decades

Increase and sharp fluctuations in food prices

Decline in arable land

Higher protein consumption in China and India

7 billion people now and 9 billion people by 2050

Higher energy and water prices

Decrease in water available for irrigation

Q T L s

Los Angeles Times April 13, 2008

It follows that a given QTL can have positive,

null, or negative effects depending on the drought

scenario. This complication has slowed considerably

the utilization of QTL data for breeding.

Collins et al. (2008). Plant Physiol. 147: 469-486.

QTL

discovery

QTL characterization

- QTL x E x M

- Validation in different backgrounds

- Isogenization

Genomics-assisted breeding

- Cost-effectiveness

- High-throughput profiling

QTL

cloning

Perfect marker

TILLING, EcoTILLING

genetic engineering

Genomics-assisted breeding of quantitative traits


Implementing GAB – Chasing genes and QTLs

QTL mapping and cloning strategies

Biparental

linkage mapping

(RIL, DH, BC, IL)

genome-wide

(high LD panel)

Association mapping

(> 200 unrelated

accessions)

candidate gene

(low LD panel)

QTL coarse

mapping

Near isogenic

lines (NIL)

Candidate gene validation

Positional

cloning

Genetic

resolution

10-20 cM

1-100 kb

To clone or not to clone QTLs?

Salvi & Tuberosa (2005). Trends in Plant Science

QTL cloning as an essential step to:

• understand the functional basis of quantitatve traits

• unlock the allelic richness of germplasm by

direct haplotyping and sequencing of target loci

• identify the perfect marker for selection

• genetically engineer quantitative traits.

QTL cloning:

A very tough nut

to crack!

QTL

Mapping and cloning QTLs for

drought tolerance at UNIBO

• flowering time (escape)

• root architecture (avoidance)

N28E N28

N28E N28

Vegetative to generative transition 1 (Vgt1)

Salvi et al., 2007. Proc. Nat. Acad. Sci. 104: 11376

Gaspé Flint

AFLP13 AFLP14 Vgt1

M8

M12

.34 .08 .08 cM

Physical mapping and cloning of Vgt1

.38 Genetic

map

N28

C22-4

* * * * * * * * * * * * * * * * * * * * * *

144-bp transposon

(mite) insertion Rec Rec

ca. 2.7 kb * = SNP

= INDEL

BAC

clone

ca. 70 kb

.42

Vgt1

M8 M12

Salvi et al., 2007. PNAS 104: 11376-11381.

B73 Gaspé Flint F1

20-d

ay d

iffe

ren

ce

7 days:

Vgt1

13 days:

loci?

Flowering time in B73 and Gaspé Flint

Intr

ogre

ssio

n lin

es

1 2 3 4 5 6 7 8 9 10

Intr

og

ress

ion

lin

e

Maize chromosomes

IL (BC5 B73 x Gaspe’) graphycal genotype

Root phenotypic difference between B73

and Gaspé Flint

1

2

3

1 2

3

abscence of seminal

roots

Root analysis

in pots

Root analysis

in paper rolls

1 2 3 4 5 6 7 8 9 10

Seminal

roots-roll

- +

Chromosomes IL

lin

es

NA

(Salvi et al., unpublished)

1 2 3 4 5 6 7 8 9 10

Seminal

roots-roll

Seminal

roots-

pots - +

Chromosomes IL

lin

es

Crown

roots-

pots (vs. B73)

NA NA

NA

- + - +


1 2 3 4 5 6 7 8 9 10

Seminal

roots-roll

Seminal

roots-

pots - +

Chromosomes IL

lin

es

Crown

roots-

pots (vs. B73)

NA NA

qSR1, bin 1.02

qSR2, bin 3.05-7

qSR3, bin 7.01-2

qSR4, bin 8.04-5

aroll = -1.30 (-45%)

apots = -1.11 (-39%)

aroll = -0.45 (-16%)

apots = -0.31 (-14%)

aroll = -0.75 (-16%)

apots = -0.32 (-14%)

aroll = -0.85 (-30%)

apots = -1.10 (-40%)

NA

- + - +


+ / +

ABA - / -

ABA

Lower yield Higher yield

Root-ABA1 (bin 2.04) (Landi et al. 2007, J. Exp. Bot. 58: 319)

Root-yield-1.06 (bin 1.06) (Landi et al, 2010, J. Exp. Bot. 61: 3553)

Lower yield Higher yield

QTL mapping and cloning strategies

Biparental

linkage mapping

(RIL, DH, BC, IL)

genome-wide

(high LD panel)

Association mapping

(> 200 unrelated

accessions)

candidate gene

(low LD panel)

QTL coarse

mapping

Near isogenic

lines (NIL)

Candidate gene validation

Positional

cloning

Genetic

resolution

10-20 cM

1-100 kb

QTL mapping/cloning by GWA

(Genome-Wide Association)

Belò et al. (2008) MGG

• 8,590 SNPs

• 553 maize

inbreds

• Phenotyped

for embryo

oleic acid

content

Fad2 (Fatty acid desaturase 2)

Science (2008), 319: 330-333

QTL mapping and cloning via linkage mapping and

GWAS

Krill et al. (2010). PLoS ONE 5, (4) e9958.

QTLs and candidate genes for Aluminum tolerance

Three F2s and a panel of 282 inbreds

Lu et al. (2010). PNAS 107: 19585–19590.

•QTLs and candidate genes for ASI and drought tolerance

•Three RIL populations + one panel of 305 inbreds

Li et al. (2011). Plos ONE 9, (6) e24699.

•QTL for palmitic acid (unsaturated/saturated ratio and oil content)

•One RIL + one BC population + one panel of 155 inbreds

CSA News, October 2011, 4-11.

What is NAM?

NAM is most powerful genetic resource for dissection of the

genetic bases of quantitative traits for any species.

Courtesy of Mike McMullen

Linkage Mapping Association Mapping

Recent recombination

High power

Low resolution

Analysis of 2 alleles

Moderate marker density

Genome scan

Historic recombination

Low power

High resolution

Analysis of many alleles

High marker density

Candidate gene testing

Nested Association Mapping

Recent and ancient recombination

High power

High resolution

Analysis of many alleles

Moderate genetic marker density

High projected marker density


Nested Association Analysis

Yu et al. (2008) Genetics 178: 539

25 DL

× B73

F1s

SSD

NAM

1

2

200

B97

CM

L10

3

CM

L22

8

CM

L24

7

CM

L27

7

CM

L32

2

CM

L33

3

CM

L52

CM

L69

Hp

30

1

Il1

4H

Ki1

1

Ki3

Ky2

1

M162W

M3

7W

Mo

18

W

MS

71

NC

350

NC

358

Oh

43

Oh

7B

P3

9

Tx

30

3

Tzi8


THE MAIZE DIVERSITY PROJECT

Courtesy of Jim Holland

Maize Phenomics:

Massively Parallel Phenotyping of the

Nested Association Mapping Population


Implementing GAB – MAS, MABC and MARS

Selection for mapped loci

MAS: MARKER-ASSISTED SELECTION

Plants are selected for one or more (up to 8-10) alleles

MABC: MARKER-ASSISTED BACKCROSS

One or more (up to 6-8) donor alleles are transferred to an elite line

MARS: MARKER-ASSISTED RECURRENT SELECTION

Selection for several (up to 20-30) mapped QTLs relies on index

(genetic) values computed for each individual based on its haplotype

at target QTLs.

Development of markers for MAS

• Markers should be tightly-linked (< 5 cM) to target loci and

preferably within the sequences of interest

• Markers must be validated in different genetic backgrounds

• Original mapping markers should be converted to markers

more suitable for high-throughput profiling at the single locus

• Markers should preferably be codominant

• Success stories: QPM and pro-vitamin A, disease resistance

Marker-assisted backcrossing (MABC)

a) Select donor alleles at markers flanking target gene

b) Select recurrent parent alleles at other linked markers (to reduce

linkage drag around target gene)

c) Select for recurrent parent alleles in rest of genome (optional)

1 2 3 4

Target locus

1 2 3 4

‘RECOMBINANT’

SELECTION

1 2 3 4

„BACKGROUND’

SELECTION

‘TARGET

GENE/QTL’

SELECTION from: Collard and Mackill, 2006

a b c

Ribaut and Ragot (2007). J. Exp. Bot. 58: 351-360.

Under severe WS (ca. 60-80%

yield reduction), the best five

MABC-derived hybrids

outyielded by 50% the controls.

Under intermediate WS (< 50%

yield reduction), no difference

was observed between MABC-

derived hybrids and the controls.

No yield penalty of the MABC-

hybrids under WW conditions.

Outcome of MABC depends on:

• Number of genes/QTLs to transfer

• Genetic distance between genes and markers

• Nature of markers used

• Number of genotypes selected at each generation

• Genetic background

When much of the variation is controlled by minor QTLs, MABC has limited

applicability because estimates of QTL effects are inconsistent and

pyramiding becomes increasingly difficult as the number of QTLs increases.

A more effective strategy is to deploy MARS to increase the frequency of

favorable marker alleles in the population.

MARS involves (i) defining a selection index for F2 or F2-derived progenies

with desirable alleles at target QTLs, (ii) recombining selfed progenies of the

selected individuals and (iii) repeating the procedure for a number of cycles.

Marker-assisted recurrent selection (MARS)

Although the private sector has reported significant gains through MARS in

maize (Johnson, 2004; Eathington, 2005; Crosbie et al., 2006), fewer efforts

have been undertaken in the public sector.

Moreau et al. (2004) reported no advantage of MARS over phenotypic

selection for a multitrait performance index, probably due to the general high

heritability of traits and the limited (ca. 50%) σ2P accounted for by QTLs.

One shortcoming of MARS is caused by the inconsistency of QTL effects as

the genetic background changes during subsequent cycles of selection, a

problem which can be partially solved with the “Map as you go” (MAYGO)

approach suggested by Podlich et al. (2004).

Marker-assisted recurrent selection (MARS)


Implementing GAB – Genomic selection

• Requires low-cost, high-density molecular markers (LD level)

• Unlike in MARS, GS considers the effects of all markers together and

captures most of the additive variation

• Marker effects are first estimated based on a so-called

“training population” that needs to be sufficiently large (> 300)

• Breeding value is then predicted for each genotype in the

“testing population” using the estimated marker effects

Genomic selection

• GS focuses on the genetic improvement of quantitative traits rather than

on understanding their genetic basis

• Simulation studies have shown that across different numbers of QTLs

(20, 40 and 100) and levels of H, responses to GS were 18 to 43%

larger than MARS (Bernardo and Yu, 2007)

• GS more effective with complex traits, low H and haplotypes rather than

single markers

• GS and QTL discovery are not mutually exclusive

• Application of GS as a function of objectives, resources of breeding

programs and the genetic architecture of traits

• Yield per se: difficult to identify major QTLs, particularly in elite x elite

Genomic selection

• Current maize inbreds have very little exotic germplasm

• Prebreeding via recurrent selection is usually required

• 10 cycles of testcross phenotypic selection require 20 years vs. 4 for GS

• The outcome of long-term (5-10 cycles) GS is unknown

Genomic selection for introgression of exotic germplasm

Response to 15 cycles of GS for

introgression of exotic germplasm

Bernardo, 2009

Crop Sci., 49: 419

F2 is preferable to BC1 and BC2

6-7 cycles of GS appear to be sufficient

After 7th cycle, reestimate of marker-

based selection index

Drought-tolerant corn by MAB; marketed by Pioneer in 2011

Accelerated Yield Technology (AYT™)

2009, 19, 10


Perspectives and

conclusions

DROPS

EU-funded

Euro 8.7 M

15 Partners

5 companies

Plant Accelerator, ACPFG, Adelaide, Australia

Existence of a breeding program

Breeders familiar with molecular procedures, potential and shortcomings

Capacity to run 2-3 generations/year and produce DH

Capacity to automate DNA extraction

Access to high-throughput genotyping

Maintain a healthy pipeline between gene/QTL discovery and MAS

Access to an informatics platform to handle samples and data

Accurate and relevant phenotyping

Critical factors for the success of GAB

• Crop modeling will increasingly allow us to:

• Dissect complex traits into simpler components

• Help resolving G x E x M

• Support MAB with a breeding-by-design approach

Future opportunities for GAB

• Comparative genomics and other “omics” data will accelerate the

identification of candidate genes

• “Omics” platforms should be used in a very focused way

• Sequencing and novel bionformatic tools will facilitate collecting and

exploiting “omics” data

• Resequencing of target loci in mini-core collections for allele mining and

haplotype definition

Tying it all together

• On a case-by-case basis, develop appropriate breeding

strategies for the improvement of multiple traits and/or complex

traits.

• Delivering new cultivars via GAB will require a close collaboration

among molecular geneticists, breeders, physiologists, pathologists,

agronomists and other relevant stakeholders.

• Only an appropriate multi-disciplinary effort engagement will allow

us to effectively harness the potential of GAB while advancing our

quest to dissect the genetic make-up of agronomic traits.

G. Taramino et al., Pioneer Dupont, USA

M. Ouzunova et al., KWS, Germany

Many thanks to:

• Marco Maccaferri

• Silvio Salvi

• Maria C. Sanguineti

• Pierangelo Landi

• Silvia Giuliani

• Simona Corneti

• Sandra Stefanelli

• Marta Graziani

Funds: European Union, Pioneer-DuPont, KWS

INTERDROUGHT-IV 6-9 September 2013

Burswood Entertainment Complex

Perth, Western Australia

Congress Chair: Roberto Tuberosa, Italy Program Committee Chair: Graeme Hammer, Australia Local Organizing Committee Chair: Mehmet Cakir, Australia

www.interdrought4.com

Questionnaire on marker-assisted breeding

(sent to 5 seed companies)

What % of financial resources will be devoted to MAB in next 5 years?

Company A: 10-15%

Company B: MAB will be exploited in all our corn breeding projects

As to the resources devoted to MAB, what % is devoted to:

- MAS for simple traits

- MARS for complex traits

- GS for complex traits

Selection for complex traits is increasing, as is selection for both

simple and complex traits within the same breeding project

to a large extent

to a low extent

moderate with increasing importance

Questionnaire on marker-assisted breeding

Is GS fulfilling the potential expected from published simulations?

To a large extent Moderately

To what extent has AM allowed you to dissect complex traits?

Moderately Moderately

What are the 3 main factors limiting a more widespread use of MAB?

1: Cost; 2: Reluctance to change well-established breeding programs

3: Standardization

1: Experience; 2: Logistics; 3: Standardization

To what extent has GBS changed your perspective on MAB?

Moderately Moderately

Education

S4.1 Genomics-assisted breeding for maize improvement