Applying Molecular Marker information and Data Mining to a Commercial Breeding Pipeline

Applying Molecular Marker information and Data Mining to a Commercial Breeding Pipeline David V. Butruille, Ph.D. Data Mining Lead - Breeding Technology Monsanto Company Ankeny, IA ISU Plant Breeding Lecture Series May 19, 2006 1

Overview Background Key processes and tools Examples of data mining and marker applications 2

A company focused on agriculture Headquartered in St. Louis, Missouri CEO Hugh Grant Monsanto 2005 Net Sales More than 14,000 employees More than 320 locations worldwide in four primary regions Europe/Africa, Asia Pacific, Latin America and North America Seeds & Genomics Ag Productivity $6.3 billion in annual sales NYSE Symbol: MON www.monsanto.com A leader in the field of plant breeding, agricultural biotechnology and genomics. 3

Our technology platforms 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 This talk s focus 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 Conventional breeding is the process of cross-pollinating plants with desirable qualities to develop improved plants in successive generations MOLECULAR BREEDING 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 Crop analytics involves the application of advanced analytical methods and technologies to identify the composition of food and feed traits. BIOTECHNOLOGY 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. 4

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 5

Technologies applied to breeding evolves over time Impact of Technology on Plant Breeding by Decade Technology 1950 1960 1970 1980 1990 2000 2010 Statistics, Experimental Design Low Mod Mod High High High High Computer Technologies None None Low Mod Mod High High Quantitative Genetics Mod Mod High High High High High Plot Mechanization None None Low Low Mod High High Winter Nurseries None None Low Mod High High High Analytic Technologies Low Low Low Low Mod High High Doubled Haploids Low Mod Mod Low Mod High High Molecular Breeding None None None Low Mod High High Genomics/Proteomics None None None None None Low High Bioinformatics None None None None None Low High Transgenic Traits None None None None Mod High High # Highs 0 0 1 2 3 9 11 Statistics and Quantitative Genetics have been important for a long time, and that will not change any time soon 6

Traditional breeding process Year 1 Choose parents based on data, judgment X Recombine genes Years 2-6 Select best based on phenotype Advance Inbreeding Advance Inbreeding Advance Inbreeding Advance Inbreeding Make hybrids, Yield test Select, Make Hybrids Yield test Select, Make Hybrids Yield test 6- to 8-year process Advance Inbreeding Select, Make Hybrids Year 7 Success rate <.01% Advanced Trials, Commercialize 7

Ranges for key parameters of a global corn breeding program. Pipeline Step Breeding Populations Segregating Lines Finished Lines Test Crosses Finished Hybrids Nursery Rows Yield Trial Plots Number of Items 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 2,000,000 4,000,000 8

Some of the key components: -Phenotype -Cycle time -Genotype -Information technology 9

Phenotypic data Quality, accuracy, and throughput 10

State-of-the-art field plot equipment mimics best farming practices. Increase in plot testing capacity High quality phenotypic data 11

Properly identify and screen for performance reducers Stewarts Wilt Gray Leaf Spot Green Snap Diplodia Ear Rot Northern Leaf Blight FAW Etc.. ECB Common Rust Southern Leaf Blight BCW CRW Southern Rust Anthracnose Leaf Blight Anthracnose Stalk Rot 12

Quality Traits: Phenotype is not measured in the field only 13

MSP Multi Season Program 14

MSP dramatically accelerates the product development Breeding Corn Soybean Sunflower Sorghum Winter Breeding Corn Soybean Sunflower Sorghum Historically breeding programs used MSP Sites to grow a second generation during the winter season decreasing development time by 50% 15

Major MSP sites are now focal points for many areas and conduct research year round Breeding Southern Hemisphere Several Crops Biotechnology Yieldguard CB Yieldguard CRW Roundup Ready Etc. Multi Gen Single Gen Winter Spring Year Round Nurseries Genomics Summer Multi Gen Trait Integration Multi Gen/Yrs Markers Multi Gen Fall Single Gen Breeding Northern Hemisphere, Several Crops 16

DNA Markers Getting to the Genotype 17

18 We have identified and developed assays for thousands of corn SNPs X-NC0199888 Q-NC0111829 0.3 X-NC0199863Q-NC0110465 1.1 Q-NC0173704 1.8 Q-NC0173743X-NC0173743 1.9 Q-NC0015697 2.3 X-NC0048301 3.1 X-NC0077353 3.6 Q-NC0021713 3.9 Q-NC0148452 5.4 X-NC0044003 6.5 Q-NC0019086 7.2 X-NC0019524 7.4 X-NC0027557 8.1 X-NC0173642Q-NC0147181Q-NC0147202 8.3 Q-NC0111443 11.6 X-NC0035022 12.6 X-NC0043992X-NC0043994Q-NC0043992 14.9 Q-NC0154927 19.3 Q-NC0055177 19.6 X-NC0199850 20.8 X-NC0199955 21.1 Q-NC0148707 21.2 X-NC0070876X-NC0039304 21.5 Q-NC0066743 22.0 Q-NC0036199 22.4 Q-NC0043185 24.5 X-NC0173555 24.7 Q-NC0173555 24.8 Q-NC0068027 25.0 Q-NC0199143 26.2 Q-NC0189320 26.4 Q-NC0001369Q-NC0078736 26.6 Q-NC0110473 27.0 X-NC0189320 27.5 X-NC0036632 27.6 Q-NC0025418 28.9 X-NC0083894Q-NC0083894 29.6 X-NC0199703 3 Q-NC0147302 31.1 Q-NC0188229 31.2 Q-NC0028164 33.4 Q-NC0105051Q-NC0111393Q-NC0192724 Q-NC0191881 34.8 Q-NC0108303 35.2 X-NC0173400Q-NC0173400 35.6 X-NC0173332 36.7 Q-NC0107227 37.7 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Q-NC0155598 37.4 Q-NC0194854Q-NC0005406Q-NC0029123 Q-NC0174842Q-NC0172743Q-NC0000531 37.6 Q-NC0111212 38.2 X-NC0199295 38.5 Q-NC0143762 39.0 X-NC0199611X-NC0053602Q-NC0053602 39.8 Q-NC0147718Q-NC0173760Q-NC0147411 X-NC0148322X-NC0173760X-NC0147411 39.9 X-NC0157951 40.2 Q-NC0051974X-NC0199810Q-NC0196717 40.8 Q-NC0193974 41.6 X-NC0109648X-NC0173834X-NC0199686 Q-NC0109648Q-NC0173834Q-NC0058389 Q-NC0106501Q-NC0107157Q-NC0107289 Q-NC0108123Q-NC0109058Q-NC0109866 Q-NC0112238Q-NC0143408X-NC0107157 41.7 X-NC0199370Q-NC0143754 41.8 Q-NC0189931Q-NC0199262 43.3 Q-NC0174462 43.6 Q-NC0005140 43.7 Q-NC0012984 43.9 X-NC0173446Q-NC0195025Q-NC0173446 44.1 X-NC0199366X-NC0199679 44.2 Q-NC0105175Q-NC0143388 44.3 Q-NC0147444X-NC0147444Q-NC0198126 44.6 Q-NC0008840Q-NC0194289Q-NC0199106 44.8 X-NC0194289 44.9 Q-NC0188981 45.0 Q-NC0193325 45.3 X-NC0199655 45.5 Q-NC0005020 45.8 Q-NC0111673X-NC0021894Q-NC0009350 Q-NC0035274X-NC0199545Q-NC0043776 Q-NC0109795X-NC0109795 45.9 Q-NC0009755X-NC0040011 47.0 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Q-NC0111004Q-NC0195959X-NC0158122 28.3 Q-NC0194279 30.1 Q-NC0154801 31.8 Q-NC0195530 32.5 Q-NC0008956 32.6 Q-NC0185717 32.7 Q-NC0171018 33.8 X-NC0171018 34.3 Q-NC0193554 34.5 X-NC0200080 34.6 Q-NC0016045Q-NC0028604X-NC0016041 X-NC0146010 35.4 X-NC0199939X-NC0199890 35.8 X-NC0106501X-NC0200193 36.7 X-NC0173415 37.0 X-NC0173336Q-NC0005255X-NC0190754 X-NC0199333Q-NC0173336Q-NC0190754 Q-NC0173415X-NC0143408 37.1 X-NC0029121X-NC0172743X-NC0015355 Q-NC0004887X-NC0199574 37.3 Q-NC0155598 37.4 Q-NC0194854Q-NC0005406Q-NC0029123 Q-NC0174842Q-NC0172743Q-NC0000531 37.6 Q-NC0111212 38.2 X-NC0199295 38.5 Q-NC0143762 39.0 X-NC0199611X-NC0053602Q-NC0053602 39.8 Q-NC0147718Q-NC0173760Q-NC0147411 X-NC0148322X-NC0173760X-NC0147411 39.9 X-NC0157951 40.2 Q-NC0051974X-NC0199810Q-NC0196717 40.8 Q-NC0193974 41.6 X-NC0109648X-NC0173834X-NC0199686 Q-NC0109648Q-NC0173834Q-NC0058389 Q-NC0106501Q-NC0107157Q-NC0107289 Q-NC0108123Q-NC0109058Q-NC0109866 Q-NC0112238Q-NC0143408X-NC0107157 41.7 X-NC0199370Q-NC0143754 41.8 Q-NC0189931Q-NC0199262 43.3 Q-NC0174462 43.6 Q-NC0005140 43.7 Q-NC0012984 43.9 X-NC0173446Q-NC0195025Q-NC0173446 44.1 X-NC0199366X-NC0199679 44.2 Q-NC0105175Q-NC0143388 44.3 Q-NC0147444X-NC0147444Q-NC0198126 44.6 Q-NC0008840Q-NC0194289Q-NC0199106 44.8 X-NC0194289 44.9 Q-NC0188981 45.0 Q-NC0193325 45.3 X-NC0199655 45.5 Q-NC0005020 45.8 Q-NC0111673X-NC0021894Q-NC0009350 Q-NC0035274X-NC0199545Q-NC0043776 Q-NC0109795X-NC0109795 45.9 Q-NC0009755X-NC0040011 47.0 X-NC0199617 47.5 Q-NC0002285Q-NC0039275 48.2 X-NC0173817Q-NC0173817X-NC0199859 48.6 Q-NC0054661 49.9 Q-NC0104512 Q-NC0028604X-NC0016041 X-NC0146010 35.4 X-NC0199939X-NC0199890 35.8 X-NC0106501X-NC0200193 36.7 X-NC0173415 37.0 X-NC0173336Q-NC0005255X-NC0190754 X-NC0199333Q-NC0173336Q-NC0190754 Q-NC0173415X-NC0143408 37.1 X-NC0029121X-NC0172743X-NC0015355 Q-NC0004887X-NC0199574 37.3 Q-NC0155598 37.4 Q-NC0194854Q-NC0005406Q-NC0029123 Q-NC0174842Q-NC0172743Q-NC0000531 37.6 Q-NC0111212 38.2 X-NC0199295 38.5 Q-NC0143762 39.0 X-NC0199611X-NC0053602Q-NC0053602 39.8 Q-NC0147718Q-NC0173760Q-NC0147411 X-NC0148322X-NC0173760X-NC0147411 39.9 X-NC0157951 40.2 Q-NC0051974X-NC0199810Q-NC0196717 40.8 Q-NC0193974 41.6 X-NC0109648X-NC0173834X-NC0199686 Q-NC0109648Q-NC0173834Q-NC0058389 Q-NC0106501Q-NC0107157Q-NC0107289 Q-NC0108123Q-NC0109058Q-NC0109866 Q-NC0112238Q-NC0143408X-NC0107157 41.7 X-NC0199370Q-NC0143754 41.8 Q-NC0189931Q-NC0199262 43.3 Q-NC0174462 43.6 Q-NC0005140 43.7 Q-NC0012984 43.9 X-NC0173446Q-NC0195025Q-NC0173446 44.1 X-NC0199366X-NC0199679 44.2 Q-NC0105175Q-NC0143388 44.3 Q-NC0147444X-NC0147444Q-NC0198126 44.6 Q-NC0008840Q-NC0194289Q-NC0199106 44.8 X-NC0194289 44.9 Q-NC0188981 45.0 Q-NC0193325 45.3 X-NC0199655 45.5 Q-NC0005020 45.8 Q-NC0111673X-NC0021894Q-NC0009350 Q-NC0035274X-NC0199545Q-NC0043776 Q-NC0109795X-NC0109795 45.9 Q-NC0009755X-NC0040011 47.0 X-NC0199617 47.5 Q-NC0002285Q-NC0039275 48.2 X-NC0173817Q-NC0173817X-NC0199859 48.6 Q-NC0054661 49.9 Q-NC0104512 50.3 Q-NC0003041 50.5 Q-NC0022717 50.6 Q-NC0003206 51.4 X-NC0173941 51.5 Q-NC0003640 51.8 X-NC0199514X-NC0196069X-NC0145256 X-NC0145704 52.3 Q-NC0172480X-NC0172480 53.2 Q-NC0153304Q-NC0188632Q-NC0199117 Q-NC0199142 53.9 X-NC0053108 54.0 Q-NC0111360X-NC0170312X-NC0171068 Q-NC0170312Q-NC0009295Q-NC0171068 54.1 Q-NC0109090 54.3 X-NC0173432Q-NC0173432 54.6 X-NC0199394 54.8 Q-NC0084196 56.5 Q-NC0016730 56.6 Q-NC0031358 57.0 Q-NC0107941 57.2 Q-NC0112090 57.7 Q-NC0173818X-NC0173818 58.2 X-NC0011002 59.5 X-NC0199358 64.4 Q-NC0018392 64.5 Q-NC0171394 64.6 X-NC0173502 65.5 X-NC0108770 66.9 Q-NC0108770 67.7 Q-NC0152708 68.3 Q-NC0027447 68.6 Q-NC0081776 68.8 X-NC0194327 69.1 Q-NC0173896 69.2 Q-NC0194327 69.7 X-NC0048402 69.8 Q-NC0013745 72.0 Q-NC0030134 72.3 X-NC0174077 Q-NC0174077 73.8 Q-NC0010183 74.0 X-NC0010183 74.1 X-NC0033664 75.0 X-NC0199475 80.1 X-NC0021962 81.7 X-NC0059925 81.9 Q-NC0011115 83.2 X-NC0038604 85.3 Q-NC0070905 85.5 X-NC0038574X-NC0197151Q-NC0197151 85.8 Q-NC0038574 85.9 Q-NC0170353 86.2 Q-NC0152582 89.6 Q-NC0040431 90.3 Q-NC0067173 91.7 Q-NC0055498 91.8 Q-NC0106742 92.8 Q-NC0106406 93.2 X-NC0199588 93.7 X-NC0113140 95.0 Q-NC0113140 95.8 Q-NC0112341 96.4 50.3 Q-NC0003041 50.5 Q-NC0022717 50.6 Q-NC0003206 51.4 X-NC0173941 51.5 Q-NC0003640 51.8 X-NC0199514X-NC0196069X-NC0145256 X-NC0145704 52.3 Q-NC0172480X-NC0172480 53.2 Q-NC0153304Q-NC0188632Q-NC0199117 Q-NC0199142 53.9 X-NC0053108 54.0 Q-NC0111360X-NC0170312X-NC0171068 Q-NC0170312Q-NC0009295Q-NC0171068 54.1 Q-NC0109090 54.3 X-NC0173432Q-NC0173432 54.6 X-NC0199394 54.8 Q-NC0084196 56.5 Q-NC0016730 56.6 Q-NC0031358 57.0 Q-NC0107941 57.2 Q-NC0112090 57.7 Q-NC0173818X-NC0173818 58.2 X-NC0011002 59.5 X-NC0199358 64.4 Q-NC0018392 64.5 Q-NC0171394 64.6 X-NC0173502 65.5 X-NC0108770 66.9 Q-NC0108770 67.7 Q-NC0152708 68.3 Q-NC0027447 68.6 Q-NC0081776 68.8 X-NC0194327 69.1 Q-NC0173896 69.2 Q-NC0194327 69.7 X-NC0048402 69.8 Q-NC0013745 72.0 Q-NC0030134 72.3 X-NC0174077 Q-NC0174077 73.8 Q-NC0010183 74.0 X-NC0010183 74.1 X-NC0033664 75.0 X-NC0199475 80.1 X-NC0021962 81.7 X-NC0059925 81.9 Q-NC0011115 83.2 X-NC0038604 85.3 Q-NC0070905 85.5 X-NC0038574X-NC0197151Q-NC0197151 85.8 Q-NC0038574 85.9 Q-NC0170353 86.2 Q-NC0152582 89.6 Q-NC0040431 90.3 Q-NC0067173 91.7 Q-NC0055498 91.8 Q-NC0106742 92.8 Q-NC0106406 93.2 X-NC0199588 93.7 X-NC0113140 95.0 Q-NC0113140 95.8 Q-NC0112341 96.4 Q-NC0187990Q-NC0154948 96.6 Q-NC0143657 97.9 Q-NC0185689 98.3 Q-NC0111588 98.8 Q-NC0058097 99.1 Q-NC0009486 10 X-NC0199635 100.7 Q-NC0109723 102.7 Q-NC0008954 106.4 Q-NC0109666 107.9 Q-NC0107333X-NC0200197 108.3 Q-NC0151488 110.2 X-NC0199375 110.9 Q-NC0199163X-NC0004542 111.8 Q-NC0008643 115.1 Q-NC0111488 119.4 X-NC0008756Q-NC0008756 120.3 Q-NC0196554 120.7 Utilized high resolution mapping populations Large percentage of SNPs are in genes

RFLP SSR SNP? Key Factors Affecting Volume and Cost SNPs Automation Reagent cost reduction IT systems Genotyping platform changes enable growth in marker assisted breeding applications Data points (M) '98 '99 '00 '01 '02 '03 '04 '05 '06 Calendar year Cost per DP ($) VIC 2 1.5 1 0.5 44-49816-171-000829-1 0 0 0.5 1 1.5 2 2.5 FAM 19

Marker Assisted Selection; Replacing phenotyping with genotyping Ol d Susceptible Resistant New 1.8 1.6 1.4 Latest 1.2 Susceptible Resistant VIC 1 0.8 0.6 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 FAM 20

Ranges for key parameters of a global corn breeding program. Pipeline Step Breeding Populations Segregating Lines Finished Lines Test Crosses Finished Hybrids Nursery Rows Yield Trial Plots Number of Items 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 2,000,000 4,000,000 Marker Applications Parental Selection Screen Lines at Specific Loci Select on Marker & Phenotype Choice of Testers Hybrid Prediction Quality Assurance 21

Multiple Impacts of Molecular Markers on Plant Breeding Marker Assisted Backcrossing Quality 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) 22

Breeding IT Putting it all together 23

Global field breeding IT system: collection of phenotypic information All crops & all world regions Manage breeding process Create breeding pops Organize and execute field trials QC and analysis of phenotypic data Global standardization Pedigree system Data attributes Centralized database Every plant is uniquely identified Global laboratory IT systems: collection of genotypic information All genotyping labs Manage genotyping lab Marker inventory Marker selection Sample tracking QC and analysis of genotypic data Global standardization Methodologies Nomenclature Centralized database Every sample is uniquely identified 24

Created an integrated data analysis and decision making system Field System: Phenotypic data collection, and analysis Genotyping System: Genotypic data collection, and analysis Data Decisions Analysis System: Integrate and analyze phenotypic and genotypic data Data 25

Monsanto designed the organization to support the breeding strategy New Technology BREEDING TECHNOLOGY LINE DEVELOPMENT BREEDING COMMERCIAL BREEDING CHANNEL DEPLOYMENT Marker Labs Statistics Pathology Winter Nurseries Discovery Breeding Trait Integration F1 to National Testing Technology Optimization Product Characterization Channel Tech Support Testing QA/QC PRODUCT MANAGEMENT Partitioned the process into components to enable optimal execution 26

Data mining: The environment 27

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 28

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 to harvest From planting time Stuck TW bucket door, grain dribbling through bucket. The two column pattern shows this problem with one side of the combine 29

Some systematic field variation can be accounted for Yield Trial Residual Maps Residuals With Field Trend BAD Adjusted Data GOOD 30

Corn testing site map 31

Leveraging environmental data 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. 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 Source: Loffler, CM, &al. & Cooper, M. 2005. Classification of Maize Environment Using Crop Simulation and Geographic Information Systems. Crop Science 46:1708-1716 32

Global corn germplasm l l l l Tropical Subtropical Temperate High Lands Temperate Subtropical Tropical Subtropical Temperate 33

Traits under selection vary based on environment Trait Temperate Sub-Tropical Tropical Yield 1 1 1 Standability 1 2 2 Dry down 1 2 3 Maturity 1 2 3 Stability 2 1 1 Tolerance to abiotic stress 3 1 1 Disease Tolerance 3 1 1 Grain Quality 3 2 1 Key traits under selection in a NA corn breeding programs Yield, Yield, Yield Agronomic 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). 34

Identify global QTLs Directional consistency of QTLs between US and Europe 20 Europe effect 15.00 Parent A favorable 10 5.00 0-5.00 0 5.00 10 15.00 20-5.00 Selection index score (heavily weighted by yield) -10 Parent B favorable U.S. effect -24/35 loci signif. in both continents -8 U.S. locations and 3 E.U. locations 35

Data mining: The base germplasm 36

Mining information from base germplasm Compilations from the literature Linkage disequilibrium Association tests 37

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. Source: Mikel, MA and Dudley, JW. 2006. Evolution of North American Dent Corn from Public to Proprietary Germplasm. Crop Science 46:1193-1205 38

Linkage disequilibrium in haploids extracted from old open-pollinated maize varieties and synthetics 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. Key originating germplasm Reid Krug Minnesota 13 Lancaster Leaming Midland Cultivars of local importance Alph Golden Glow Goldmine Hays Jellicorse Johnson County White Longfellow Murdock Osterland Synthetics (from inbreds) Corn Borer Synthetic (BCBS) Stiff Stalk Synthetic (BSSS) Pioneer Long Ear Two Ear Synthetic Long Ear Synthetic 39

LD of different Group of OP germplasm R-squared (200 obs moving avg) 0.4 0.35 0.3 0.25 0.2 0.15 0.1 5 0 Group 1 Pop's Group 3 Pop's Standard F2 Group 2 Pop's 0 20 40 60 80 100 Pairwise Distance (cm) Population name Group for R2 analysis Alph 1 Pion Long Ear 1 Two Ear Synth 1 Osterland 1 BSSS 1 Hays 1 Johnson County White 1 Long Ear Synth 1 BCBS 2 Longfellow 2 Murdock 2 Goldmine 2 Golden Glow 2 Jellicorse 2 Midland 3 Krug 3 Leaming 3 Minn 13 3 Lancaster 3 Reid Yellow dent 3 40

Table 1.Segment Genotyped Chr. Segment Begin pos. End pos. # loci 01 1 72.4 77.8 7 01 2 97.8 99.9 3 01 3 103.1 105.1 8 01 4 107.7 111 4 01 5 113.3 114.8 5 02 6 94.9 98.3 6 03 7 82.5 83.6 9 03 8 93.9 97.6 7 03 9 109.3 110.6 7 04 10 69.3 69.5 3 04 11 73.5 74.8 6 04 12 76 76.3 5 04 13 77.7 78.9 6 04 14 80.5 82.5 8 04 15 93.6 95.8 6 04 16 104.1 104.7 7 04 17 136.7 136.7 6 04 18 142 146.9 6 04 29 147.5 147.9 5 05 20 106.2 106.6 9 06 21 38.3 38.4 6 06 22 144.3 145.4 6 07 23 57.1 60.4 7 07 24 61.8 65.9 12 07 25 99.5 102 6 08 26 68.2 72.9 7 08 27 78.9 80.3 6 08 28 111.7 118.6 7 09 29 65.1 67.5 7 09 30 75.2 77.5 7 09 31 83.3 84.6 7 10 32 45 47.3 9 10 33 51.6 54.2 5 Population Type Population name # Haploid plants # Loci # Polym. loci % seg. loci Avg 2p(1-p) across polym. loci Main OP Krug 108 171 135 78.9% 4% 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]. 41

Finding marker-trait associations in the base germplasm A1 A2 B1 B2 C1 C2 D1a D1b D2 E F G H I J K L M N O 10 cm 658 soybean lines genotyped (either Roundup Ready or conventional) Phenotypic data was tolerance to Roundup herbicide (h 2 = 1) Marker association at p-value < 1 x 10-9 47 associations on 16 chromosomes 42

Finding marker-trait associations in the base germplasm A1 A2 B1 B2 C1 C2 D1a D1b D2 E F G H I J K L M N O RR 10 cm Marker association at p-value < 1 x 10-9 658 soybean lines genotyped (either Roundup Ready or conventional) 47 associations on 16 chromosomes Phenotypic data was tolerance to Roundup herbicide (h 2 = 1) Identified only the true association when analysis accounted for population structure 43

Data mining: Segregating populations 44

Segregating populations How much of the variation do we truly explain with QTL? 45

Watch out: we see more than what we get! Schön et al. (2004) Very large population (975 F5 testcrosses in 19 environments) and simulated populations QTL analyses (PLABQTL) 2 R adj Obtained proportion of phenotypic variance explained by QTLs ( ) Derived proportion of genotypic variance explained by all detected QTLs: pˆ = Rˆ 2 hˆ adj 2 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 R 2 and P in estimation and test data sets (ES vs. TS). 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 46

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). 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 47

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. 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: 485-49848

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 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 What power a large breeding program can afford us (under very simplistic assumptions)? 49

Power calculations taken to a different scale Using the formula that relates sample size N needed, at a α and β error level, to detect an effect explaining a given proportion r 2 of the phenotypic variance: Fixing α=1 and β=5 With 200 lines With 1,000 lines With 10,000 lines With 100,000 lines r 2 = 8.6% r 2 = 1.8% r 2 = 0.18% r 2 = 18% Percent Phenotypic variance 1000% 100% 1.000% 0.100% 10% 01% ( 2 1 r ) z 1 [ α 2] Number of Lines ( ) = 2 + z 2 1 r 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 Source: Walsh, B. and Lynch, M. 1998. Genetics and Analysis of Quantitative Traits. N (for r 2 small N~1/r 2 ) r PhD Breeding program Entire company ( 1 β ) 50 2

Segregating populations Leveraging previously acquired knowledge on marker-trait associations 51

We continue to expand our knowledge Continue to add to this information each year ~No. of Marker-Phenotype Associations Over 40 Fold Increase 1999 2000 2001 2002 2003 2004 2005* *estimate 52

Selection based on predictive associations Proof of concept Selection for grain moisture at harvest 1 2 3 4 5 6 7 8 9 10 3.5 2.5 Favorable Unfavorable Change in Grain Moisture (%) 1.5 0.5-0.5 12 QTL identified in a breeding population Parent 1 best Parent 2 best -1.5-2.5 2.5% Change 3.9% Change Hybrid Inbred 53

New data views combined with predictive breeding methodologies create new opportunities to drive commercial success Multiple data cuts on phenotypic information Create new designer germplasm New breeding paradigm Explore genetic relationship of germplasm Genome wide trait QTL knowledge 54

Segregating populations Epistatic decay Using the data available without doing the right experiment 55

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? 56

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. 57

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). 58

The Data Data used: 479 projects 2000 2005 Year Count 2000 6 2001 56 2002 35 2003 100 2004 174 2005 108 Total 479 Cross Heterotic Group Count BC1 F 50 M 48 F2 F 191 M 190 Analysis: Ep = C 0 MP C 0 = C0 MP = Mid mean = n i= 1 n P1+ P 2 parent = 2 1 4 P1+ l 1 3 4 for F 2 RP + Factor considered: Year Heterotic Group Cross Type for BC Similarity between parents 59

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 Mean 75-332.21 5.63-1.69 Source DF Type I SS Mean Square F Value Pr > F year 5 450.13 93 2.84 154 HG 1 40.72 40.72 1.28 0.2576 cross_type 1 169.61 169.61 5.35 212 Simil 1 551.98 551.98 17.41 <.0001 60

Results II Cross Type Year C0-P (Bu/acre) BC1 2001-1.10 2002-3.70 2003 0.16 2004-8 2005-2.26 BC1 Total -0.63 F2 2000-2.21 2001-4.63 2002-2.54 2003-0.40 2004-2.38 2005-1.39 F2 Total -1.97 Grand Total -1.69 61