the cimmyt global maize program: progress and challenges

The CIMMYT Global Maize Program: Progress and Challenges

Gary Atlin and the GMP team

El Batan22 June 2012

Outline

1. The role of GMP in the world’s maize seed system

2. How do our products compare to those of the multi-nationals?

3. Adaptation to mega-environments: implications for breeding

4. The role of managed stress testing in the breeding pipeline

5. Identifying donors and delivering markers for abiotic and biotic stress tolerance

6. Applying high density genotyping to maize breeding and managing the “data tsunami”

7. An “open-source” model for delivering the benefits of high-density genotyping and genomic selection to small breeding programs

8. Some things to watch out for

1. CIMMYT’s role in the world’s maize seed system

.

Only source of freely available maize parental lines

Our products support dozens of independent regional seed companies in Africa, Latin America, and Asia

Our products help local companies compete with multinationals

We provide direct support to seed companies in the commercialization of our hybrids (DTMA, IMIC)

We are a key source of donors of drought tolerance and disease resistance

CIMMYT’s maize breeding effort

• Africa: 5 line development breeders, 2 molecular breeders, 4 seed specialists, 1 physiologist, 2 biotic stress specialists

• Latin America: 4 line development breeders, 1 physiologist, 1 nutritional specialist, 1 molecular breeder, 1 seed specialist

• India: 1 line development breeder, 1 physiologist

• China1 molecular breeding lead, 1 pathologist/breeder , 1 bioinformaticist, 2 molecular geneticists

• ca. 10,000 lines genotyped with 500K SNPs via GBS

• ca. 5000 DH lines produced in 2011 in-house

• ca. 400,000 nursery and yield plots world-wide*

• At least 2 million phenotypic data points annually

• At least 25 billion genotypic data points annually

How do our products get to farmers?

• Hybrids are marketed mainly through regional seed companies

• OPVs are distributed mainly through national subsidy schemes

2. Where do we stand relative to the multinationals?

Latin American tropics: PCCMCA trial 2011 (21 locations)

Hybrid Pedigree

Grain Yield (t/ha)

Bad HuskCover(%) Ear rot (%)

MJ-9297 8.08 7.2 5.2

MH-9058 8.02 5.1 6.8

DK-357 Best Commercial Check 7.72 8.0 9.8

CIMMYT-2 CLRCW100/CLRCW96//CML494 7.68 5.2 9.5

CIMMYT-4 CML491/CLQ6316//CLRCWQ48 7.36 5.8 10.8

P4092W 6.93 3.3 6.5

P4063W 6.79 4.8 8.0

Heritability 0.91 0.89 0.91

LSD (0.05) 0.29 1.6 1.7

Pedigree Yield% ear

rot

%Root

lodging

%Stem

lodgingP-4082W 7.25 7.45 3.42 5.27DK-357 6.99 9.80 5.29 3.53(CML-264/CML-269)/CML494 6.64 8.67 4.11 6.00MG9051 6.56 11.44 3.60 3.57P-4081W 6.43 9.63 1.42 9.13(CLQ-RCWQ26/CLQ-RCWQ108) /CML-491 6.23 6.69 8.13 10.16NC 7218

6.20 12.84 1.84 1.24INIFAP check- Mexico 5.43 9.01 9.38 5.49

LSD.05 0.52 3.93 5.15 4.66Repeatability 0.94 0.67 0.53 0.47

Regional tropical hybrid trial (PCCMCA), 2009: 28 public and private sector hybrids, 18 locations in Mexico and Central America

Hybrid Yield

%bad husk cover % Ear rot

% Root lodging

% Stem lodging

(CML269/CML264)//CML494 7.033 11.70 5.48 3.49 5.52P4082W 6.299 10.55 6.37 3.78 2.59(CLG2312/CML495)//CML494 6.457 8.46 7.11 3.60 5.10H565 6.294 8.74 10.14 1.82 3.45H561 6.134 4.90 6.78 4.36 5.76H564C 5.899 4.94 11.71 1.74 5.17(LPS C7 F64-2-6-2-2-BBB / CML-495)//CML494 5.552 9.61 8.55 2.64 2.73H520 5.091 6.64 8.70 8.37 5.73

No. of locs. 18 11 13 13 13LSD 0.62 7.60 2.84 2.69 2.94Repeatability 0.86 0.00 0.78 0.80 0.41

Validation trials, 18 locations México, 2010

Regional on-farm trials in ESA (2010/11 season)

Name GY-Locations > 3 t/ha GY: Locs < 3 t/haDays to anthesis

CZH0616 5.96 2.37 64.4CZH0946 4.48 2.22 56.8CZH0837 5.62 2.08 62.7SC627 4.82 2.03 64.6SC403 4.61 2.03 57.8ZM627 4.74 1.90 65.0ZM309 4.09 1.74 55.1ZM521 4.27 1.73 59.6SC513 4.76 1.60 64.0Farmers Variety 4.64 1.54 64.7Pan53 5.39 1.51 64.5

Mean 4.87 1.80 62.30

n 30 19H 0.80 0.72

Optimal <3 t/ha

Optimal >3 t/ha

Managed drought+low N

CIMMYT hybrid mean, % of checks, 2005 102.3 104.3 86.9CIMMYT hybrid mean, % of checks, 2010 101.9 104.8 107.2

Mean of checks 2005 (t/ha) 1.73 4.63 1.30Mean of checks 2010 (t/ha) 2.11 6.24 2.09

No trials 2005 6 14 6No trials 2010 7 29 6

Mean yield of CIMMYT hybrids in the 2005 and 2010 Early and Intermediate Regional Hybrid Trials (EIHYB) for Southern Africa

Optimal >3 t/ha

Managed drought+low N

CIMMYT hybrid mean, % of checks, 2005 92.0 88.0CIMMYT hybrid mean, % of checks, 2010 94.5 101.8

Mean of checks 2005 6.08 1.57Mean of checks 2010 7.29 2.08

No trials 2005 15 6No trials 2010 24 7

Mean yield of CIMMYT hybrids in the 2005 and 2010 Intermediate and Late Regional Hybrid Trials (ILHYB) for Southern Africa.

So, overall, where do we stand?

1. In Latin America, our materials compete with the best multinational products, but we are not ahead• Low-cost three-way and double crosses are

competitive!

2. In ESA, our materials are superior in low-yield, short-duration locations. We are equivalent or ahead in high-yield locations

3. Investment by MNSCs is increasing in the tropics. We need to increase our rates of gain, especially in favorable rainfed

3. Adaptation to mega-environments: implications for breeding

1. Within and across huge regions, there is little local adaptation that is not explained by local diseases, elevation, and rainfall

- Breeding programs in Eastern and Southern Africa must be fully integrated

- Germplasm moves easily from one continent to another- We need efficient methods for transferring resistances to

adaptive diseases- This means we need markers linked to QTLs!- This means we need a marker-development pipeline!

Retrospective analysis in EIHYB and ILHYB

Years: 2001-2009 Genotypes: 448

(24-65/year) Maturity: early and late 513 trials with h² > 0.15 in

17 countries α-lattice design with 3

reps

Weber et al. (2012a, b), Crop Science

Subdivision strategies of the TPE

Subdivision Typical environmentClimate A: Mid altitude, humid warm

B: Mid altitude, humid hotC: Mid altitude, dryD: Lowland, tropical humidE: Lowland, tropical dry

Yield level low-yielding subregion, < 3 t ha-1

high-yielding subregion, ≥ 3 t ha-1

Geographic region

EastSouth

Bänziger et al., 2006

Variance components of maize grain yield in five different subdivision systems of the undivided target population of environments from 2001 to 2009: Southern Africa.

Early maturity group (n=219) †

VG VGS VGY(S) VGE(YS) VEClimate 0.18±0.10 0.01±0.01 0.06±0.08 0.32±0.09 0.56±0.09Altitude 0.15±0.09 0.01±0.01 0.07±0.10 0.33±0.09 0.56±0.09Yield level 0.09±0.04 0.05±0.05 0.08±0.12 0.30±0.09 0.56±0.10Geographic region

0.19±0.09 0.00±0.00 0.06±0.12 0.33±0.09 0.57±0.10

Country 0.21±0.11 0.01±0.01 0.06±0.07 0.30±0.09 0.57±0.11

2g 2gs 2

)(sgy 2)( ysge 2

2g 2gs 2

)(sgy2

)( ysge

2 2g 2gs 2

)(sgy

Rank changes over yield levels in the 2011 Southern African regional trial

All trials High yield trials Low yield trialsPEX 501 PEX 501 CZH1033SC535 X7A344W CZH0935AS113 AS113 CZH1036 X7A344W SC535 CZH0928AS115 AS115 CZH1031013WH63 CZH0923 CZH0946CZH0935 013WH63 CZH1030CZH0923 013WH29 AS115CZH1036 CZH0935 013WH63013WH29 CZH1036 CZH0831

Mean yield 4.81 6.51 2.17H 0.88 0.89 0.75

Top 10 of 54 entries in 14 high-yield trials and 9 low-yield trials

All trials High yield trials Low yield trialsPEX 501 PEX 501 CZH1033SC535 X7A344W CZH0935AS113 AS113 CZH1036 X7A344W SC535 CZH0928AS115 AS115 CZH1031013WH63 CZH0923 CZH0946CZH0935 013WH63 CZH1030CZH0923 013WH29 AS115CZH1036 CZH0935 013WH63013WH29 CZH1036 CZH0831

Mean yield 4.81 6.51 2.17H 0.88 0.89 0.75

Top 10 of 54 entries in 14 high-yield trials and 9 low-yield trials

All High

High 0.97

Low 0.57 0.36

Correlations among yield levels

Rank changes over yield levels in the 2011 Southern African regional trial

Some important points about maize hybrid adaptation:

2. Genotype x trial interaction and field “noise” are huge constraints on precision of screening

- Large multi-location testing networks drive gains- Genotype x trial interaction and plot-to-plot variability in

managed stress trials is greater than in optimally-managed trials

- Too much weight on low-H managed stress trials can reduce gains

Test environment

No. of trials

Grain yield (t ha-1)

VG VGE VE Predicted H for testing in:

5 trials 20 trials

Int-late trials

Optimal 175 6.26 22.2 22.4 55.3 0.68 0.92RAB 63 1.73 10.4 18.2 71.5 0.38 0.83MD 22 2.11 17.6 15.7 66.7 0.49 0.90

Low-N 34 1.82 15.7 15.3 68.9 0.49 0.89

2g 2ge2

Means, variances, and H for ESA regional trials conducted under optimal, managed drought (MD), low N, and random abiotic stress* (RAB) 2001-9

Managing field variation: developing comprehensive field maps

Kiboko HarareChiredzi

EM38 Penetrometer NDVI

Soil penetration resistance

(MPa)

4. The role of managed stress testing in the breeding pipeline

PH Zaidi, CIMMYT

60-80% yield reduction targeted for both low N and drought

Managed stress screening

Notable border effect indicates N depletion was successful

Managed stress screening over 30 years led to the development of the world’s most drought tolerant maize germplasm

Edmeades, Lafitte, Bolaños, Bänziger

Pedigree selection for drought tolerance by CIMMYT in eastern and southern Africa: Stage 1

evaluationManagement Season Sites Weight

Optimal Main 3-5 ?

Managed low N Main 1 ?

Managed drought Dry 1 ?

3000+ genotypes per year in Stage I testcross evaluation

Screens weighted based on their (assumed) importance in the target environment (= southern and eastern Africa)

Sa

me

ge

no t

y pe

s

TPESE

rG(SE-TPE)HSE

CR1(TPE-SE) = i rG HSE σP(SE)√

We select in selection environments (SE) to make gains in the target population of environments (TPE) via correlated response

Using managed-stress data to improve breeding gains is complicated!

TPE

Stress

Non-stress

rGSS

rGSN

rGNS

rGNN

Hstress

Hnonstress

SE

rG(SE)

Hnonstress > Hstress

All of the rG’s are positive

Selection environment Random abiotic stress*

Genetic correlationEarly maturity groupOptimal 0.80Managed drought 0.64Low-N 0.91

Late maturity groupOptimal 0.75Managed drought 0.76Low-N 0.90

Genetic correlations for yield between low-N and random abiotic stress (RAB) target environments and optimal, managed drought, and low-N selection environments: ESA 2001-9

5. Success in identifying donors for abiotic and biotic stress tolerance

• A massive effort has been undertaken by the breeders and physiologists to characterize AM sets to identify donors for drought, heat, and low N tolerance

• George has established a large hot-spot screening network to characterize donors for MSV, GLS, turcicum, tar spot, rust, ear rots

• Sudha and Babu have implemented a pipeline for developing breeder-ready markers.

• MSV is in validation now

Grain yield (t ha-1)Pedigree Colour Texture Drought Drought +

heatWell-

wateredDTPWC9-F24-4-3-1 White Flint 3.10 1.43 6.97DTPYC9-F46-1-2-1-1-2 Yellow Flint 3.07 1.58 7.12La Posta Sequia C7-F64-2-6-2-2 White Flint 3.06 1.39 7.72

Check (CML442/CML444) 2.36 0.96 7.70

Number of locations 7 3 7H 0.64 0.50 0.84Trial mean 2.58 1.13 6.88

CIMMYT donors of drought and heat tolerance identified through screening in multiple environments in Mexico, Africa, and Asia

Finally on the DTMA website!…but these lines are at least 15 years old!

5. Success in identifying donors for abiotic and biotic stress tolerance

Best - bet sources of disease resistance (G. Mahuku)

Mean Disease rating (1-5)

Stock ID Pedigree GLS (6 locs)

MSV (3 locs)

NCLB(12 locs)

Rust (5) locs

DTMA-3 [(CML395/CML444)-B-4-1-3-1-B/CML395//DTPWC8F31-1-1-2-2]-5-1-2-2-BB 1.43 1.12 1.74 1.30

DTMA-10 CIMCALI8843/S9243-BB-#-B-5-1-BB-2-3-4 2.06 1.60 1.67 2.13

DTMA-11 CIMCALI8843/S9243-BB-#-B-5-1-BB-4-1-3 1.74 1.41 1.41 1.26

DTMA-12 CIMCALI8843/S9243-BB-#-B-5-1-BB-4-3-3 1.71 1.72 1.79 1.63DTMA-13 CIMCALI8843/S9243-BB-#-B-5-1-BB-4-3-4 1.93 1.60 1.70 1.38DTMA-17 [CML312/CML445//[TUXPSEQ]C1F2/P49-

SR]F2-45-3-2-1-BBB]-1-2-1-1-2-BBB-B 1.87 1.12 1.80 1.59

DTMA-90 CML311/MBR C3 Bc F112-1-1-1-B-B-B-B-B 2.24 2.37 2.50 1.59

DTMA-146 [CML-384 X CML-176]F3-107-3-1-1-B-B-B 2.25 2.45 1.94 1.71DTMA-268 La Posta Sequia C7-F33-1-2-1-B-B 2.25 2.23 1.99 1.58DTMA-293 La Posta Seq C7-F153-1-1-1-2-B-B-B 2.50 2.35 2.33 2.43DTMA-40 [CML144/[CML144/CML395]F2-8sx]-1-2-3-

2-B*5 2.01 2.03 1.70 1.52DTMA-19 [CML312/CML445//[TUXPSEQ]C1F2/P49-

SR]F2-45-3-2-1-BBB]-1-2-1-1-1-BBB-B 2.20 1.61 1.77 1.23DTMA-26 P502SRC0-F2-54-2-3-1-B 1.71 1.60 1.76 1.51

MSV – Harare 2010 data (Heritability = 0.79)

Association Mapping for Disease Resistance

GLS-combined analysis (Heritability = 0.6)

QTL mapping in three populations and identification of consensus interval Initial interval identified about 75-132Mb on chr1 for Msv1 Large F2 populations screened for the flanking markers of Msv1 and other

QTLs QTL isogenic recombinants identified

R R RS S S

Chr.1Msv1

Chr.3 Chr.4 Chr.8

PZ

E01

7569

8629

PZ

E01

1322

2093

6

PZ

A02

090_

1

PZ

A03

527_

1

PZ

A02

614_

2

PZ

A00

529_

4

PZ

A03

651_

1

PH

M1

4104

_23

Phenotyping of recombinants under artificial disease pressure in field conditions at Harare and IITA green house facilities

Association analysis in DTMA panel with 55K SNP chip and GBS genotypes identified SNP hits in the same interval

The SNP hits and other markers in the interval used in further linkage mapping on recombinants for fine-scale mapping

The mapping confidence interval reduced to 7Mb 8 SNPs in this interval tested for validation in breeders’ populations Initial results are encouraging! Further reduction in interval to a probable gene-based marker

expected with the recombinants in this interval

Msv1 –Case Study

Genotypic data tsunami (25 billion data points annually)

maize breeder

6. Applying high density genotyping to maize breeding and managing the “data tsunami”

http://wallpapers.free-review.net/42__Big_wave.htm

Reduced representation sequencing for rapidly genotyping highly diverse species

RJ Elshire, JC Glaubitz, Q Sun, JA Poland, K Kawamoto,ES Buckler, and SE Mitchell

Institute for Genomic Diversity http://www.maizegenetics.net/

Genomes

Genome representation

s

SNP:Polymorphism

within the fragments

ATGACATATCAG

ATGAAATATCAG

SNP

Genotyping-by-sequencing (GBS)

Main genotyping options used by CIMMYTLow density: KasPar uniplex assays through KBiosciences

• KBio uniplex SNP assays: cost $20 to develop

• CIMMYT has about 3000, can share

• KBio SNPs are used for low-density QTL mapping, tracking specific (“forward breeding”) @ ca. $.10 per data point ($20/DNA sample for 200 markers)- Heterozygote calls are easily made

• Genotyping x sequencing for GWAS, genomic selection, and soon forward breeding @ $20/DNA sample for 500K+ markers

• - ca 50% missing data that must be imputed

- Heterozygotes are not easily called, but heterozygote calls probably don’t matter for GS applications

Status of our breeding informatics effort• All breeders, but not all phenotypers, are routinely generating

pedigrees in the IMIS database

• All lines have Genotype Identification Number (GID) to link pedigree, phenotypic data, and genotypic data

• We have no high-density genotype database. Relational databases do not work with more than 100K data points per element. Flat files are searched with custom scripts. New database systems are being developed by Cornell

• We have mixed-model software for combined analysis available via SAS and R scripts in Fieldbook, in routine use by breeders.

• Plan is for all lines entering replicated testing to be genotyped at high density next year

• Statistical support is excellent, informatics support is inadequate

Current status of high-density genotyping application in CIMMYT GMP

• All new CIMMYT lines have GID and are in IMIS pedigree database

• Over 10000 breeding lines have been GBS’d by the Cornell IGD

• Past phenotypic data are poorly linked to pedigree and genotype data

• No database capable of storing and searching 500+K allele calls in place

• GS pipeline is conceptualized but not in place; models are developed de novo for each GS experiment

Where should we be in two years?

• Over half of breeding lines should be DH• All lines entering replicated field trials should

be genotyped at high density• All phenotypic data should be linked through

the GID to pedigree and genotype• Imputation, allele calling, and prediction

pipeline should be delivering predictions to breeders

• SAGA should be operational

Lessons from our experience with high-density genotypic data

• As a rule of thumb, 25% of the PYs in a modern maize breeding program in a MNSC are devoted to breeding informatics

• Breeding informatics and breeding pipeline teams must be closely linked

• If you have no database, you have no molecular breeding program

• Pedigree and phenotypic databases must be linked and in very good condition

• Development teams are led by breeders or other agricultural scientists, preferably with programming skills.

• Development scientists are the interface between breeders and programmers

• These scientists do not manage breeding programs but are devoted full-time to application development

• Support must be available in real time.

At Pioneer, molecular breeding scientists support the adoption and use of new tools

MB scientist

Line breeder

3

Line breeder

2

Line breeder

1

App team 1 App team 2 App team 3

What is genomic selection?

• Much research shows that the inheritance of quantitative traits like yield in maize is controlled by many genes with small effects. QTL-based breeding approaches do not work well for such traits

• Genomic selection (GS) is the selection of genotypes for advancement or use as parents based on a high-density marker genotype, rather than phenotype

• GS differs from older QTL-based breeding approaches in that it uses all markers in a prediction of performance (genomic estimated breeding value) GEBV

• Low-cost genotyping systems make selection based on high-density markers feasible

• Bioinformatics requirements and breeding methods are complex

• Being used by multinational companies

• Networked approaches needed for small companies

Genomic selection systems can be used to:

- Discard unpromising lines based on genotype for disease resistance, abiotic stress tolerance

- Predict the best lines within a full-sib family for advancement of lines that have not been phenotyped

- Drastically reduce breeding cycle time through the use of recurrent selection schemes with selection based on genotype rather than phenotype

Basic steps in the GS process:

1. A set of lines (training population) is genotyped at high density.

- These lines can be unselected testcrosses in the breeding pipeline

2. Lines are phenotyped in testcross and/or per se.

3. Effects of markers or haplotype alleles are estimated.

4. Sum of marker effects in a line is the Genomic Estimated Breeding Value (GEBV)

5. GEBVs are calculated on the next cohort of unselected lines and used to predict their performance

6. GEBVs can be calculated for any trait for which the training population has been phenotyped

7. Accuracy of the GEBV is expressed as the correlation between the phenotype and the GEBV. Depends on population size, heritability, marker number

8. The accuracy of a GEBV doesn’t need to be 1. It just needs to be close to √H for the screening system

(see Heffner et al. 2009 Crop Sci. 49:1-12)

Factors that affect GS accuracy

1. Relatedness between training and selected populations

2. Training population size

3. Broad-sense heritability in the phenotyping system used for model training

4. Marker density

Advantages of GS for stress-prone environments

• GS allows programs to select for traits for which they cannot screen, if they can have access to haplotype effects from other programs

• Breeding cycle times could be reduced five-fold, greatly increasing gains

• Sharing haplotype effects permits novel and synergistic ways to network small breeding programs

• GS networks could make available to NARS and SME breeding programs tools, methods, and scale now only available to multinationals

1. Incorporate GEBVs into a conventional pedigree breeding pipeline to discard lines with weaknesses.

As number of DH lines increases, we will need to discard many lines without phenotyping, based on GEBV

First use will be for defensive traits, with slightly higher H than yield. Breeder will receive a two-way table of GEBVs for all traits, and discard lines

predicted to have a serious weakness. Breeders will assess the reliability of predictions by comparing validation r

with √H achieved in field testing. To achieve gains, many more lines must be genotyped than phenotyped

Entry GY-Opt GY-DT GLS Ear rot

CKL001 4.69 1.4 2.5 14.5

CKL002 5.24 4.2 4.0 3.8

CKL003 7.15 3.1 2.2 4.9

r between geno. and pheno. in training pop 0.34 0.22 0.62 0.58

√H 0.80 0.55 0.85 0.80

There are 3 main ways to use GS in cultivar development

Empirical results to date

Zhao et al Theor Appl Genet (2012) 124:769–776- For grain yield, r across half-sib pops summing to 788 lines: 0.54

Albrecht et al, 2011:- For grain yield, r=0.7 when prediction and validation sets contain

close relatives; 0.5 for prediction across distantly related families

- Crossa et al 2010- For yield and other traits, r up to 0.79

- These are all huge over-estimates of GS accuracy!!

GS prediction ability across breeding groups for grain yield (GY) and anthesis date (AD) on 55K markers.

GY AD

Breeding populations 0.12±0.28 0.02±0.25

• Cross-validation studies that use random lines with population structure overestimate GS accuracy

• Markers simply assign the lines to groups, and the means of the groups predict the phenotype

• Not relevant to real breeding situations

2. Use GEBVs to select unphenotyped DH lines within full-sib families for advancement from Stage 1 to Stage 2 .

As number of DH lines increases, we will need to discard many lines without phenotyping, based on GEBV

We know predictions are very poor across families, and only work for close relatives in high-LD populations

Models can be trained on part of a large full-sib family, then used to advance some ungenotyped lines to Stage 2

Example

A set of 200 DH lines is extracted from an elite cross All lines are genotyped 50 are phenotyped and used as a training set to build a GS model Best lines from training set are advanced based on phenotype Best lines from unphenotyped group are advanced based on GEBV Should result in modest gains from increased selection intensity

Correlation between GEBV and phenotype within full-sib families: mean of cross-validation in 6 bi-parental populations

Size of training popMean

accuracy

50 0.3870 0.40

90 0.41

√H 0.70No. of lines 236.5No. of markers 240.2No. of trials 4.33

3. Set up closed synthetic populations of key inbreds, and conduct recurrent selection

Advantages for GS are greatest with rapid-cycling Closed populations where a few elite parents contribute

equally ensure that marker allele effect estimates relate directly to the population under selection

High LD low marker density required Improved populations can be used directly or as sources

of new inbreds Most CIMMYT breeding programs have now set up these

populations in the A and B heterotic groups, and are beginning to phenotype

7. Implementing an open-source GS network

“Open-source” breeding networks can provide companies with proprietary lines, but allow haplotypes to be shared

Sharing haplotype effects allows phenotyping done by one program to benefit another, even if they don’t test the same lines.

Small programs could receive unique, unphenotyped DH lines (say, 500 ) from a “hub” program, with a GEBV predicting their performance

Lines would then be testcrossed Company would phenotype the testcrossed set, and contribute the

phenotypes to the “training population” for the next cycle Company advances the lines with the best performance into product

testing.

Rapid-cycle marker-only selection

“Open-source” genomic selection breeding plan



Line extracted, genotyped: untested, proprietary DH lines provided to companies based on GEBVs


Phenotyping: company 3Phenotyping: company 1 Phenotyping: company 2





Pheno

typic

data

retu

rned

for

haplo

type

effec

t esti

mat

ion


Phenotypic data returned for

haplotype effect estimation




Pheno

typic

data

retu

rned

for

haplo

type

effec

t esti

mat

ion


Phenotypic data returned for

haplotype effect estimation


Commercialization: company 3

Commercialization:company 1 Commercialization: company 2

Distribution of roles in an open-source breeding network

Hub program

• Manages rapid-cycle source pops

• Extracts DH lines

• Genotypes DH lines at high density

• Coordinates managed stress screening

• Estimates GEBVs

• Updates model with new phenotypic data from partners

• Maintains database

Distribution of roles in an open-source breeding network

Partner (spoke?) programs

• Receive and own proprietary DH lines with GEBV

• Phenotype, and contribute phenotypes to model

• Commercialize and deliver to farmers the best lines on the basis of their own phenotyping

• Form new pedigree breeding populations, provide to hub for DH line extraction, genotyping

Does this model make sense for pre-breeding in China?

Advantages of open-source network model

• Small programs can access haplotype effect estimates for stresses, environments, and traits for which they cannot do evaluation

• Partners benefit from the phenotyping done by other network members, without having to share germplasm

• The small partner program accesses DH lines without the cost of setting up a DH facility

• Lines are proprietary- only haplotype (marker) effects are shared

• The hub program provides partners with efficient DH, genotyping, and informatics pipeline services, with economies of scale

• Low-cost out-sourced genotyping allows breeding programs to focus on screening, selection, seed production, and marketing

The open-source GS network model can provide SMEs and NARS with powerful breeding technologies now only available to multinationals

Things to watch out for:

• Projects vs pipelines• Over-weighting and inappropriate use of managed

stress data• Failure to deliver the products of molecular breeding

to the product development pipeline• Failure to exploit synergies and economies of scale

across regions• Failure to exploit synergies and economies of scale

across maize and wheat• Failure to come to grips with our data and breeding

informatics needs• Thinking small about our science

The CIMMYT biparental populations: the world’s largest resource for GS, GWAS in tropical maize

• 28 biparental populations from DTMA and WEMA MARS pops

• >200 lines/pop, over 5000 lines in total• All elite Africa-adapted parents or drought donors• Several linked half-sib families• All genotyped with ca. 200 SNPs• 100 lines per family GBS’d• Imputation will permit assignment of genotypes for

>500K SNPs to each of the >5000 lines• Phenotyped in 3-4 drought and 3-4 optimal

environments• We will find genes for drought tolerance and disease

resistance, and pilot GS methods that work

Conclusions

1. GMP is the world’s most important source of elite and stress-resistant germplasm, and the only large “open” public breeding program

2. Our germplasm is competitive with MNSC hybrids in most of our target regions, and usually superior in low-yield environments

3. Gains in favorable conditions are inadequate. We must remain competitive in commercial systems to interest seed company partners

4. We need to think hard about how to use managed stress data

5. Our drought and heat-tolerant germplasm is well-characterized and unequalled: it needs to be used.

6. Using our stress-tolerant germplasm requires development of breeder-ready markers

7. We have made no gains on maximum DT since the end of the physiology breeding program

8. We have unparalled resources for genetic and breeding research for development. Are we up to the task?

the cimmyt global maize program: progress and challenges

Business

seed specialists

molecular breeders

worlds maize seed system2

line development breeders

seed specialist india

breeding pipeline5

molecular geneticists

cimmyt global maize