applying molecular marker information and data mining to a ... · 1 applying molecular marker...

Applying Molecular Marker information and Data Mining

to a Commercial Breeding Pipeline

ISU Plant Breeding Lecture SeriesMay 19, 2006

David V. Butruille, Ph.D.Data Mining Lead - Breeding TechnologyMonsanto CompanyAnkeny, IA

OverviewBackground

Key processes and tools

Examples of data mining and marker applications

A company focused on agriculture

Monsanto 2005 Net Sales

Headquartered in St. Louis, Missouri

CEO Hugh Grant

More than 14,000 employees

More than 320 locations worldwide in four primary regions – Europe/Africa, Asia Pacific, Latin America and North America

$6.3 billion in annual sales

NYSE Symbol: MON

www.monsanto.com

A leader in the field of plant breeding, agricultural biotechnology and genomics.

Seeds &GenomicsAg Productivity

GENOMICS

CONVENTIONAL BREEDING

MOLECULAR BREEDING

CROP ANALYTICS

BIOTECHNOLOGY

Monsanto uses its scientific capabilities in a number of arenas to develop new products. These capabilities form a toolkit used throughout our product pipeline, as each tool can be used on its own or in combination to help study, test and refine new traits, germplasm and other characteristics in plants.

Genomics involves the mapping of the genes of plants to understand their structure and the role they play in how the plant functions.

Conventional breeding is the process of cross-pollinating plants with desirable qualities to develop improved plants in successive generations

Molecular breeding involves the use of DNA markers for genes in combination with physical measurement of traits to manage plant breeding programs. Molecular breeding significantly accelerates the efficiency in bringing new varieties to market.

Crop analytics involves the application of advanced analytical methods and technologies to identify the composition of food and feed traits.

Biotechnology is the application of scientific knowledge to transfer beneficial genetic traits to enhance plants’ growth or to provide nutritional or other benefits to farmers, food and feed processors, or consumers.

Our technology platforms

This talk’s focus

Average U.S. corn yields and kinds of corn, Civil War to 2004

Source: Troyer, AF. 2006. Adaptedness and Heterosis in Corn and Mule Hybrids. Crop Science 46:528-543

Technologies applied to breeding evolves over time

Statistics and Quantitative Genetics have been important for a long time, and that will not change any time soon

Technology 1950 1960 1970 1980 1990 2000 2010Statistics, Experimental Design Low Mod Mod High High High HighComputer Technologies None None Low Mod Mod High HighQuantitative Genetics Mod Mod High High High High HighPlot Mechanization None None Low Low Mod High HighWinter Nurseries None None Low Mod High High HighAnalytic Technologies Low Low Low Low Mod High HighDoubled Haploids Low Mod Mod Low Mod High HighMolecular Breeding None None None Low Mod High HighGenomics/Proteomics None None None None None Low HighBioinformatics None None None None None Low HighTransgenic Traits None None None None Mod High High

# Highs 0 0 1 2 3 9 11

Impact of Technology on Plant Breeding by Decade

Traditional breeding process

Choose parents based on data,

judgment Recombine genes

Advanced Trials, Commercialize

X6-to 8-year

process6-to 8- year

process

Year 1

Years 2-6

Success rate <.01%Success rate <.01%

Select best based on phenotype Make hybrids, Yield test

Select, Make HybridsAdvance Inbreeding Yield test

Select, Make HybridsAdvance Inbreeding

Advance Inbreeding

Advance InbreedingAdvance Inbreeding

Yield test

Select, Make Hybrids

Year 7

Ranges for key parameters of a global corn breeding program.

Breeding Populations

Segregating Lines

Finished Lines

Test Crosses

Finished Hybrids

Nursery Rows

Pipeline Step

Yield Trial Plots

2,000 – 5,000

200,000 – 500,000

10,000 – 30,000

200,000 – 500,000

20,000 – 50,000

1,000,000 – 2,000,000

Number of Items

2,000,000 – 4,000,000

Some of the key components:-Phenotype-Cycle time-Genotype-Information technology

Phenotypic dataQuality, accuracy, and throughput

State-of-the-art field plot equipment mimics best farming practices.

Increase in plot testing capacity High quality phenotypic data

Green Snap

Properly identify and screen for performance reducers

Gray Leaf Spot

Southern Leaf Blight

Northern Leaf Blight

Anthracnose Leaf Blight

Stewarts Wilt

Common Rust

Southern Rust

Anthracnose Stalk RotAnthracnose Stalk Rot

DiplodiaDiplodiaEar RotEar Rot

Quality Traits:Phenotype is not measured in the field only

MSPMulti Season

Program

MSP dramatically accelerates the product development

Winter

Historically breeding programs used MSP Sites to grow a second generation during the “winter” season decreasing development time by 50%

Breeding– Corn– Soybean– Sunflower– Sorghum

Major MSP sites are now focal points for many areas and conduct research year round

Breeding– Northern

Hemisphere,– Several Crops

Spring

Breeding– Southern

Hemisphere– Several Crops

Markers

Year Round Nurseries

Genomics

Multi GenBiotechnology

– Yieldguard CB– Yieldguard CRW– Roundup Ready– Etc.

Multi Gen/YrsMulti Gen

Winter

Trait Integration

Summer

Multi Gen Single Gen

Single Gen

DNA MarkersGetting to the

Genotype

We have identified and developed assays for thousands of corn SNPs

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45.9Q-NC0009755X-NC004001147.0X-NC019961747.5Q-NC0002285Q-NC003927548.2X-NC0173817Q-NC0173817X-NC019985948.6 Q-NC005466149.9Q-NC010451250.3Q-NC000304150.5Q-NC002271750.6Q-NC000320651.4X-NC017394151.5Q-NC000364051.8X-NC0199514X-NC0196069X-NC0145256X-NC014570452.3Q-NC0172480X-NC017248053.2Q-NC0153304Q-NC0188632Q-NC0199117Q-NC019914253.9X-NC005310854.0 Q-NC0111360X-NC0170312X-NC0171068Q-NC0170312Q-NC0009295Q-NC017106854.1Q-NC010909054.3X-NC0173432Q-NC017343254.6X-NC019939454.8 Q-NC008419656.5Q-NC001673056.6 Q-NC003135857.0Q-NC010794157.2 Q-NC011209057.7Q-NC0173818X-NC017381858.2X-NC001100259.5X-NC019935864.4Q-NC001839264.5Q-NC017139464.6X-NC017350265.5X-NC010877066.9Q-NC010877067.7Q-NC015270868.3Q-NC002744768.6Q-NC008177668.8 X-NC019432769.1Q-NC017389669.2 Q-NC019432769.7X-NC004840269.8 Q-NC001374572.0Q-NC003013472.3X-NC0174077Q-NC017407773.8 Q-NC001018374.0X-NC001018374.1X-NC003366475.0X-NC019947580.1 X-NC002196281.7X-NC005992581.9 Q-NC001111583.2X-NC003860485.3Q-NC007090585.5X-NC0038574X-NC0197151Q-NC019715185.8Q-NC003857485.9Q-NC017035386.2Q-NC015258289.6Q-NC004043190.3Q-NC006717391.7Q-NC005549891.8 Q-NC010674292.8Q-NC010640693.2X-NC019958893.7X-NC011314095.0Q-NC011314095.8Q-NC011234196.4

50.3Q-NC000304150.5Q-NC002271750.6Q-NC000320651.4X-NC017394151.5Q-NC000364051.8X-NC0199514X-NC0196069X-NC0145256X-NC014570452.3Q-NC0172480X-NC017248053.2Q-NC0153304Q-NC0188632Q-NC0199117Q-NC019914253.9X-NC005310854.0 Q-NC0111360X-NC0170312X-NC0171068Q-NC0170312Q-NC0009295Q-NC017106854.1Q-NC010909054.3X-NC0173432Q-NC017343254.6X-NC019939454.8 Q-NC008419656.5Q-NC001673056.6 Q-NC003135857.0Q-NC010794157.2 Q-NC011209057.7Q-NC0173818X-NC017381858.2X-NC001100259.5X-NC019935864.4Q-NC001839264.5Q-NC017139464.6X-NC017350265.5X-NC010877066.9Q-NC010877067.7Q-NC015270868.3Q-NC002744768.6Q-NC008177668.8 X-NC019432769.1Q-NC017389669.2 Q-NC019432769.7X-NC004840269.8 Q-NC001374572.0Q-NC003013472.3X-NC0174077Q-NC017407773.8 Q-NC001018374.0X-NC001018374.1X-NC003366475.0X-NC019947580.1 X-NC002196281.7X-NC005992581.9 Q-NC001111583.2X-NC003860485.3Q-NC007090585.5X-NC0038574X-NC0197151Q-NC019715185.8Q-NC003857485.9Q-NC017035386.2Q-NC015258289.6Q-NC004043190.3Q-NC006717391.7Q-NC005549891.8 Q-NC010674292.8Q-NC010640693.2X-NC019958893.7X-NC011314095.0Q-NC011314095.8Q-NC011234196.4Q-NC0187990Q-NC015494896.6Q-NC014365797.9Q-NC018568998.3 Q-NC011158898.8Q-NC005809799.1 Q-NC0009486100.0X-NC0199635100.7

Q-NC0109723102.7

Q-NC0008954106.4

Q-NC0109666107.9Q-NC0107333X-NC0200197108.3

Q-NC0151488110.2X-NC0199375110.9Q-NC0199163X-NC0004542111.8

Q-NC0008643115.1

Q-NC0111488119.4X-NC0008756Q-NC0008756120.3 Q-NC0196554120.7

• Utilized high resolution mapping populations• Large percentage of SNPs are in genes

RFLP →…→SSR→…→SNP→…?

Genotyping platform changes enable growth in marker assisted breeding applications

44-49816-171-000829-1

0 0.5 1 1.5 2 2.5

Key Factors AffectingVolume and Cost

'98 '99 '00 '01 '02 '03 '04 '05 '06

Calendar year

SNPsAutomationReagent cost reductionIT systems

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Susceptible Resistant

Latest

Susceptible

Resistant

Marker Assisted Selection; Replacing phenotyping with genotyping

Ranges for key parameters of a global corn breeding program.

Breeding Populations

Segregating Lines

Finished Lines

Test Crosses

Finished Hybrids

Nursery Rows

Pipeline Step

Yield Trial Plots

2,000 – 5,000

200,000 – 500,000

10,000 – 30,000

200,000 – 500,000

20,000 – 50,000

1,000,000 – 2,000,000

Number of Items

2,000,000 – 4,000,000

Parental Selection

Screen Lines at Specific Loci

Select on Marker & Phenotype

Choice of Testers

Hybrid Prediction

Marker Applications

Quality Assurance

Multiple Impacts of Molecular Markers on Plant BreedingMarker Assisted BackcrossingQuality Assurance:

– Parent-offspring tests– Genetic purity tests– Event tests

Germplasm analysis– Fingerprinting germplasm– Arrangement of diversity (clustering, PCA, etc.)– Protection of intellectual property.

Trait Analysis– Association of traits with genomic regions (“QTL-mapping”)– Understanding of relationship among traits (linkage vs. pleiotropy?)– Understanding the causes of variation (aid in gene cloning)

Breeding– Alternative or support to selection for qualitative and quantitative traits– Increase rate of genetic gain:

– Selection during off-season cycles– Selection of hybrid traits on inbred individuals– Early selection (e.g. pre-flowering)

Breeding ITPutting it all

together

Global field breeding IT system:collection of phenotypic information

All crops & all world regionsManage breeding process

– Create breeding pops– Organize and execute field trials– QC and analysis of phenotypic

Global standardization– Pedigree system– Data attributes

Centralized databaseEvery plant is uniquely identified

All genotyping labsManage genotyping lab

– Marker inventory– Marker selection– Sample tracking– QC and analysis of genotypic data

Global standardization– Methodologies– Nomenclature

Centralized databaseEvery sample is uniquely identified

Global laboratory IT systems: collection of genotypic information

Field System: Phenotypic data collection, and analysis

Genotyping System: Genotypic data collection, and analysis

Analysis System: Integrate and analyze phenotypic and genotypic data

DataData

Decisions

Created an integrated data analysis and decision making system

NewTechnology

LINEDEVELOPMENTBREEDING

BREEDINGTECHNOLOGY

COMMERCIAL BREEDING

Marker LabsStatisticsPathologyWinter NurseriesDiscovery BreedingTrait Integration

F1 to National TestingTechnology Optimization

Monsanto designed the organization to support the breeding strategy

Product CharacterizationChannel Tech SupportTestingQA/QC

CHANNEL DEPLOYMENT

Partitioned the process into components to enable optimal execution

Data mining:The environment

Being alert for, understanding and exploring patterns in the environment at multiple granularities

Detecting plot level anomalies and interactions

Exploring patterns of variability and interaction in a target population of environments

Leveraging commonalities and accounting for differences at a global scale

Understanding of what happens in the field• Cause: Anhydrous ammonia applied diagonally, malfunction for one pass through the field, reduced the grain yields in that strip

Stuck TW bucket door, grain dribbling through bucket. The two column pattern shows this

problem with one side of the combine

From planting time …

…to harvest

Some systematic field variation can be accounted for

Yield Trial Residual Maps

Residuals With Field Trend

Adjusted Data

Corn testing site map

Leveraging environmental data

Source: Loffler, CM, &al. & Cooper, M. 2005. Classification of Maize Environment Using Crop Simulation and Geographic Information Systems. Crop Science 46:1708-1716

Environment-standardized GGE biplot of grain yield of 18 maize hybrids (H1-H18) grown in 266 environments over three years, stratified by environment class

Geographic distribution and variability of five abiotic environment classes (EC) in the United States for CRM 110 maize in 1952–2002

Published Pioneer Hi-Bred work, using GIS data (mostly precipitation, temperature and day length) to classify environments and better understand underlying reasons for of observed genotype- by-environment interactions.

Subtropical

Tropical

Subtropical

Temperate

l Tropicall Subtropicall Temperatel High Lands

Temperate

Global corn germplasm

Traits under selection vary based on environmentTrait Temperate Sub-Tropical TropicalYield 1 1 1Standability 1 2 2Dry down 1 2 3Maturity 1 2 3Stability 2 1 1Tolerance to abiotic stress 3 1 1Disease Tolerance 3 1 1Grain Quality 3 2 1

Key traits under selection in a NA corn breeding programsYield, Yield, YieldAgronomic traits based on breeding target market.

– Tolerance to root lodging, stalk lodging, dry down, early vigor, etc.Disease Traits

– Rusts, stalk rots, ear rots, etc.Consistency over diverse growing conditions.Production characteristics (COGs).

-10.00

-5.00 0.00 5.00 10.00 15.00 20.00

U.S. effect

-24/35 loci signif. in both continents-8 U.S. locations and 3 E.U. locations

Selection index score (heavily weighted by yield)

Parent A favorable

Parent B favorable

Identify “global QTLs”Directional consistency of QTLs between US and Europe

Data mining:The base

germplasm

Mining information from base germplasm

Compilations from the literature

Linkage disequilibrium

Association tests

Source: Mikel, MA and Dudley, JW. 2006. Evolution of North American Dent Corn from Public to Proprietary Germplasm . Crop Science 46:1193-1205

Lineage of elite Lancaster, Oh07-Midland, and Oh43 corn inbred lines

Data Mining is not always about applying of sophisticated algorithms.

Carefully compiling available information can lead to interesting insights.

Refereed papers are one source, not the only one.

Example of other sources: - PVP certificates, - Patent applications, - Public disclosure to regulatory agencies.

Linkage disequilibrium in haploids extracted from old open-pollinated maize varieties and synthetics

Key originating germplasm• Reid• Krug• Minnesota 13• Lancaster • Leaming• Midland

Synthetics (from inbreds)• Corn Borer Synthetic (BCBS)• Stiff Stalk Synthetic (BSSS)• Pioneer Long Ear• Two Ear Synthetic• Long Ear Synthetic

Cultivars of local importance• Alph• Golden Glow• Goldmine• Hays• Jellicorse• Johnson County White• Longfellow• Murdock• Osterland

GOALS:• Assess amount of linkage disequilibrium within Old OP material?• Use haploid plants to avoid linkage phase estimation.• Compare doubling efficiency for different germplasm types.

0 20 40 60 80 100

Pairwise Distance (cM)

Group 1 Pop's

Group 2 Pop's

Group 3 Pop's

Standard F2

Population name

Group for R2

analysis

Alph 1Pion Long Ear 1Two Ear Synth 1

Osterland 1BSSS 1Hays 1

Johnson County White 1Long Ear Synth 1

BCBS 2Longfellow 2

Murdock 2Goldmine 2

Golden Glow 2Jellicorse 2Midland 3

Krug 3Leaming 3Minn 13 3

Lancaster 3Reid Yellow dent 3

LD of different Group of OP germplasm

Table 1.Segment Genotyped

Chr. SegmentBegin pos.

End pos.

# loci

01 1 72.4 77.8 701 2 97.8 99.9 301 3 103.1 105.1 801 4 107.7 111 401 5 113.3 114.8 502 6 94.9 98.3 603 7 82.5 83.6 903 8 93.9 97.6 703 9 109.3 110.6 704 10 69.3 69.5 304 11 73.5 74.8 604 12 76 76.3 504 13 77.7 78.9 604 14 80.5 82.5 804 15 93.6 95.8 604 16 104.1 104.7 704 17 136.7 136.7 604 18 142 146.9 604 29 147.5 147.9 505 20 106.2 106.6 906 21 38.3 38.4 606 22 144.3 145.4 607 23 57.1 60.4 707 24 61.8 65.9 1207 25 99.5 102 608 26 68.2 72.9 708 27 78.9 80.3 608 28 111.7 118.6 709 29 65.1 67.5 709 30 75.2 77.5 709 31 83.3 84.6 710 32 45 47.3 910 33 51.6 54.2 5

Population Type Population name

# Haploid plants # Loci

# Polym. loci

% seg. loci

Avg 2p(1-p) across

polym. lociMain OP Krug 108 171 135 78.9% 40.0%Main OP Lancaster 128 182 148 81.3% 35.9%Main OP Leaming 56 187 146 78.1% 36.1%Main OP Midland 179 171 133 77.8% 35.2%Main OP Minn 13 105 180 146 81.1% 37.8%Main OP Reid Yellow dent 131 185 153 82.7% 37.0%2ary OP Alph 85 186 111 59.7% 29.3%2ary OP Golden Glow 33 187 147 78.6% 37.2%2ary OP Goldmine 88 183 140 76.5% 35.3%2ary OP Hays 63 178 125 70.2% 35.6%2ary OP Jellicorse 74 177 134 75.7% 38.4%2ary OP Johnson County White 63 180 138 76.7% 36.7%2ary OP Longfellow 30 163 125 76.7% 33.6%2ary OP Murdock 31 181 144 79.6% 36.8%2ary OP Osterland 51 167 119 71.3% 34.8%

Synthetic BCBS 62 192 151 78.6% 37.9%Synthetic BSSS 66 187 155 82.9% 38.0%Synthetic Long Ear Synth 54 169 134 79.3% 35.4%Synthetic Pion Long Ear 90 175 120 68.6% 31.4%Synthetic Two Ear Synth 96 173 132 76.3% 32.8%

If we define overall efficiency as:Ear with putative di-haploid seed obtained / Total number of kernel screened.The ratio was [1:4:24] for [OP varieties : Synthetics : Process checks].

A1 A2 B1 C1 C2 D1a D2 E F I J K L MD1bB2 ONH

Finding marker-trait associations in the base germplasm

Marker association at p-value < 1 x 10-9

47 associations on 16 chromosomes658 soybean lines genotyped (either Roundup Ready® or conventional)Phenotypic data was tolerance to Roundup® herbicide (h2 = 1)

A1 A2 B1 C1 C2 D1a D2 E F I J K L MD1bB2 ONH

Finding marker-trait associations in the base germplasm

Marker association at p-value < 1 x 10-9

47 associations on 16 chromosomes

Identified only the true association when analysis accounted for population structure

658 soybean lines genotyped (either Roundup Ready® or conventional)Phenotypic data was tolerance to Roundup® herbicide (h2 = 1)

Data mining:Segregating populations

Segregating populations

How much of the variation do we truly explain with QTL?

Watch out: we see more than what we get!Schön et al. (2004)

Very large population (975 F5 testcrosses in 19 environments) and simulated populationsQTL analyses (PLABQTL)Obtained proportion of phenotypic variance explained by QTLs ( )Derived proportion of genotypic variance explained by all detected QTLs:

Data subdivided to verify impact of number of progenies and number of environments.Used resampling techniques to estimate the amount of bias in detecting QTL (comparing R2 and P in estimation and test data sets (ES vs. TS).

22 ˆˆˆ hRp adj=

Source: Schön, C.C. et al. 2004.Quantitative Trait Locus Mapping Based on Resampling in a Vast Maize Testcross Experiment and Its Relevance to Quantitative Genetics for Complex Traits. Genetics 167: 485-498

Figure 1. Proportion of the genotypic variance explained by detected QTL in estimation sets averaged over all data sets (ES) for 12 combinations of experimental data PED (N, E), using fivefold standard cross-validation and two significance levels for grain yield, grain moisture, and plant height. Individual columns are partitioned into the genotypic variance explained in test sets ( TS, solid bottom) and the bias calculated as the difference ES – TS (shaded top).

Figure 2. Mean (–), median (o), and 12.5 and 87.5% quantiles of the proportion of the genotypic variance explained in test sets calculated for individual data sets for 12 combinations of experimental data PED (N, E) using fivefold standard cross-validation and LOD 2.5 for grain yield, grain moisture, and plant height.

Figure 3-left. Mean (–), median (o), and 12.5 and 87.5% quantiles of the bias (ES – TS) calculated for individual data sets for 12 combinations of experimental data PED (N, E), using fivefold standard cross-validation for grain yield, grain moisture, and plant height.

Main conclusions of Schön et al.Adding more genotypes is more efficient than replicating the same genotypes (provided that a minimum number of environments are sampled)Knowledge about the power of detection of these experiments (previous discussion) + low values of unbiased estimates of genotypic variance explained: confirm large number of genes with small effects model.The use of this tool may have to be trait specific

What power a large breeding program can afford us (under very simplistic assumptions)?

0.001%

0.010%

0.100%

1.000%

10.000%

100.000%1 10 100 1,000 10,000 100,000 1,000,000 10,000,000

Number of Lines

Power calculations taken to a different scaleUsing the formula that relates sample size Nneeded, at a α and β error level, to detect an effect explaining a given proportion r2 of the phenotypic variance:

Fixing α=0.01 and β=0.05With 200 linesWith 1,000 lines With 10,000 linesWith 100,000 lines

Source: Walsh, B. and Lynch, M. 1998. Genetics and Analysis of Quantitative Traits.

( ) [ ]( )( )

⎟⎟⎠

⎞⎜⎜⎝

−= −

r2 = 8.6%r2 = 1.8%r2 = 0.18%r2 = 0.018%

Entire company

PhD Breedingprogram

(for r2 small N~1/r2)

Segregating populations

Leveraging previously acquired knowledge on

marker-trait associations

We continue to expand our knowledge

Continue to add to this information each year

Over 40 Fold Increase

1999 2000 2001 2002 2003 2004 2005*

Continue to add to this information each year

Over 40 Fold Increase

*estimate

Selection based on predictive associations Proof of concept

Selection for grain moisture at harvest

Parent 2 bestParent 1 best

1 2 5 6 7 8 9 103 4

12 QTL identified in a breeding population

2.5% Change 3.9% Change-2.5

Hybrid Inbred

FavorableUnfavorable

New “data views” combined with predictive breeding methodologies create new opportunities to drive commercial success

Multiple data cuts on phenotypic informationMultiple data cuts on phenotypic information

Genome wide trait QTL knowledgeGenome wide trait QTL knowledge

Create new ‘designer’germplasmCreate new ‘designer’germplasm

New breedingparadigm

Explore genetic relationshipof germplasm

Segregating populations“Epistatic decay”

Using the data availablewithout doing the “right experiment”

Rationale

(this is a simplistic generation means analysis)

One can expect that elite breeding lines accumulate favorable effects (additive or dominant) that act additively across loci. If these are the only ones then:

In a Breeding Cross: Mean(Parent x Tester)=Mean(Progeny x Tester)

Can we expect that these lines also accumulate a greater amount of positive epistatic interactions than negative ones?

In a Breeding Cross: Mean(Parent x Tester)>Mean(Progeny x Tester)

If a positive epistatic balance is detected can we explore this further?

Possible experiment:

Several parents (total: n)

Randomly paired

Derived two random progenies per cross (total: n)

Create a 2n-entry replicated yield trial, at multiple locations and over multiple years, and common tester(s), RCB or Split-Plot.

Analyze.

Alternative approach through data mining

Query company data base.

Find first generation yield trials with parents included in trial, assuming:

– No intra-plot competition issues.

– No negative selection in nursery.

Compile difference between mid-parent and mid-progeny on a breeding cross basis.

Average difference over crosses.

Analyze (overall mean and multiple data cuts).

The DataData used:

479 projects 2000 – 2005

⎪⎩

⎪⎨⎧

BCforPRP

FforPPparentMidMP

lmeanCC

MPCEpn

Analysis:

Factor considered:– Year– Heterotic Group– Cross Type– Similarity between parents

Year Count2000 62001 562002 352003 1002004 1742005 108Total 479

CrossHeterotic

Group CountBC1 F 50

M 48F2 F 191

Results I

Proc glm data=test;class hg year cross_type;model p_yld =year hg cross_type simil/ss1;quit;

R-Square Coeff Var Root MSE P_YLD Mean0.075 -332.21 5.63 -1.69

Source DF Type I SS Mean Square F Value Pr > Fyear 5 450.13 90.03 2.84 0.0154HG 1 40.72 40.72 1.28 0.2576cross_type 1 169.61 169.61 5.35 0.0212Simil 1 551.98 551.98 17.41 <.0001

Results II

Cross Type Year C0-P (Bu/acre)BC1 2001 -1.10

2002 -3.702003 0.162004 -0.082005 -2.26

BC1 Total -0.63F2 2000 -2.21

2001 -4.632002 -2.542003 -0.402004 -2.382005 -1.39

F2 Total -1.97Grand Total -1.69

Summary points

Oriented towards the end product

Increase and sustain rate of genetic gain

Discovery versus production analyses

Very large datasets to leverage

Scale sometimes matter

AcknowledgementsIt takes a lot of people to makes this successful

Plant breeders and their staff

Multi-season programs

Data analysis teams

Genotyping labs

Management

IT teams

THANK YOU

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