Presentacion de 11th Asian Maize Conference which took place in Beijing, China from November 7 – 11, 2011.

1. 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

2. 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

3. Genomics-assisted breeding in maize
Setting the stage

4. Corn yield in the USA

5. © Chappatte - www.globecartoon.com

6. …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

7. Los Angeles Times April 13, 2008
Q T Ls
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.

8. Genomics-assisted breeding of quantitative traits
QTL characterization
QTL - QTL x E x M
discovery - Validation in different backgrounds
- Isogenization
QTL Genomics-assisted breeding
cloning - Cost-effectiveness
- High-throughput profiling
Perfect marker

9. Genomics-assisted breeding in maize
Implementing GAB
– Chasing genes and QTLs

10. QTL mapping and cloning strategies
Biparental Association mapping
linkage mapping Genetic (> 200 unrelated
(RIL, DH, BC, IL) resolution accessions)
QTL coarse 10-20 cM
mapping genome-wide
(high LD panel)
Near isogenic
lines (NIL)
Positional candidate gene
cloning (low LD panel)
1-100 kb
Candidate gene validation

11. To clone or not to clone QTLs?
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.
Salvi & Tuberosa (2005). Trends in Plant Science

12. QTL cloning:
A very tough nut
to crack!
QTL

13. Mapping and cloning QTLs for
drought tolerance at UNIBO
• flowering time (escape)
• root architecture (avoidance)

14. Vegetative to generative transition 1 (Vgt1)
Gaspé Flint N28E N28
Salvi et al., 2007. Proc. Nat. Acad. Sci. 104: 11376
N28E N28

15. Physical mapping and cloning of Vgt1
Vgt1
M12
M8
AFLP13 AFLP14
Genetic .38 .42 .08 .08 .34 cM
map
BAC ca. 70 kb
clone
Vgt1
M8 M12
N28
* ** ** ** ** * *
** ** * ** * *
**
C22-4
Rec 144-bp transposon Rec
(mite) insertion
* = SNP
ca. 2.7 kb
= INDEL
Salvi et al., 2007. PNAS 104: 11376-11381.

16. Flowering time in B73 and Gaspé Flint
20-day difference
13 days:
loci?
7 days:
Vgt1
B73 F1 Gaspé Flint

17. IL (BC5 B73 x Gaspe’) graphycal genotype
Maize chromosomes
1 2 3 4 5 6 7 8 9 10
Introgression line

18. Root phenotypic difference between B73
and Gaspé Flint
2
1 1
2 3
3 abscence of seminal
roots

19. Root analysis Root analysis
in paper rolls in pots

20. Chromosomes Seminal
roots-roll
1 2 3 4 5 6 7 8 9 10 - +
NA
IL lines
(Salvi et al., unpublished)

21. Chromosomes Seminal Seminal Crown
roots-roll roots- roots-
1 2 3 4 5 6 7 8 9 10 - + -pots + -pots
+ (vs. B73)
NA
IL lines
NA NA
(Salvi et al., unpublished)

22. Chromosomes Seminal Seminal Crown
roots-roll roots- roots-
1 2 3 4 5 6 7 8 9 10 - + -pots + -pots
+ (vs. B73)
NA
qSR1, bin 1.02
aroll = -1.30 (-45%)
apots = -1.11 (-39%)
qSR2, bin 3.05-7
aroll = -0.45 (-16%)
apots = -0.31 (-14%)
IL lines
qSR3, bin 7.01-2
aroll = -0.75 (-16%)
apots = -0.32 (-14%)
NA NA
qSR4, bin 8.04-5
aroll = -0.85 (-30%)
apots = -1.10 (-40%)
(Salvi et al., unpublished)

23. Lower yield Higher yield
+/+ -/-
ABA ABA
Root-ABA1 (bin 2.04)
(Landi et al. 2007, J. Exp. Bot. 58: 319)

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

25. QTL mapping and cloning strategies
Biparental Association mapping
linkage mapping Genetic (> 200 unrelated
(RIL, DH, BC, IL) resolution accessions)
QTL coarse 10-20 cM
mapping genome-wide
(high LD panel)
Near isogenic
lines (NIL)
Positional candidate gene
cloning (low LD panel)
1-100 kb
Candidate gene validation

26. QTL mapping/cloning by GWA
(Genome-Wide Association)
• 8,590 SNPs
• 553 maize
inbreds
• Phenotyped
for embryo
oleic acid
content
Fad2 (Fatty acid desaturase 2)
Belò et al. (2008) MGG

27. Science (2008), 319: 330-333

28. 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

29. CSA News, October 2011, 4-11.

30. 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

31. Linkage Mapping Association Mapping
Recent recombination Historic recombination
High power Low power
Low resolution High resolution
Analysis of 2 alleles Analysis of many alleles
Moderate marker density High marker density
Genome scan 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
Courtesy of Mike McMullen

32. Nested Association Analysis
25 DL
B97
CML52
Hp301
Il14H
Ky21
Oh7B
P39
Tx303
CML103
CML228
CML247
CML277
CML322
CML333
Ki11
M162W
MS71
NC350
NC358
Tzi8
CML69
Ki3
Oh43
Mo18W
M37W
×
B73
F1s
  
SSD
        
  
1
2
NAM 


200
Courtesy of Mike McMullen Yu et al. (2008) Genetics 178: 539

33. Maize Phenomics:
Massively Parallel Phenotyping of the
Nested Association Mapping Population
THE MAIZE DIVERSITY PROJECT
Courtesy of Jim Holland

34. Genomics-assisted breeding in maize
Implementing GAB
– MAS, MABC and MARS

35. 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.

36. 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
• Markers should preferably be codominant
• Original mapping markers should be converted to markers
more suitable for high-throughput profiling at the single locus
• Success stories: QPM and pro-vitamin A, disease resistance

37. 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)
a b c
1 2 3 4 1 2 3 4 1 2 3 4
Target locus
‘TARGET ‘RECOMBINANT’ BACKGROUND’
„
GENE/QTL’ SELECTION SELECTION
SELECTION
from: Collard and Mackill, 2006

38. 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.
Ribaut and Ragot (2007). J. Exp. Bot. 58: 351-360.

39. 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

40. Marker-assisted recurrent selection (MARS)
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.

41. 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).

42. Genomics-assisted breeding in maize
Implementing GAB
– Genomic selection

43. 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

44. 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

45. Genomic selection for introgression of exotic germplasm
• 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
Response to 15 cycles of GS for F2 is preferable to BC1 and BC2
introgression of exotic germplasm 6-7 cycles of GS appear to be sufficient
Bernardo, 2009 After 7th cycle, reestimate of marker-
Crop Sci., 49: 419 based selection index

46. Drought-tolerant corn by MAB; marketed by Pioneer in 2011
2009, 19, 10
Accelerated Yield Technology (AYT™)

47. Genomics-assisted breeding in maize
Perspectives and
conclusions

50. Plant Accelerator, ACPFG, Adelaide, Australia
DROPS
EU-funded
Euro 8.7 M
15 Partners
5 companies

51. Critical factors for the success of GAB
 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

52. 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
• 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

53. 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.

54. Many thanks to:
• Marco Maccaferri
• Silvio Salvi
• Maria C. Sanguineti
• Pierangelo Landi
• Silvia Giuliani
• Simona Corneti
• Sandra Stefanelli
• Marta Graziani
G. Taramino et al., Pioneer Dupont, USA
M. Ouzunova et al., KWS, Germany
Funds: European Union, Pioneer-DuPont, KWS

55. 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

56. 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 to a large extent
- MARS for complex traits to a low extent
- GS for complex traits moderate with increasing importance
Selection for complex traits is increasing, as is selection for both
simple and complex traits within the same breeding project

57. 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