Outline of Barley CAP Approach

1. Breeding lines from 10 breeding programs are phenotyped (over 40 traits) in collaborative trials and individual breeder trials PHENOTYPING & GENOTYPING BREEDING LINES Breeding lines from 10 breeding programs are genotyped at the Cereal Crops Research (USDA-ARS) with two Oligo Pool Assays (OPAs) consisting of 3,072 SNPs. THE HORDEUM TOOLBOX All genotype and phenotype data gathered by Barley CAP breeders is uploaded to The Hordeum Toolbox (THT) webportal. THT consolidates information from ten breeding programs into one and uses it to map genes controlling yield, malt and food quality, and disease tolerance. 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 Oregon Wolfe Barley Genetic Map The Oregon Wolfe Barley (OWB) population was genotyped for SNPs using pilot OPA1, 2 and 3 (4,608 SNPs) and the data integrated with DArT and other markers, i.e., NEPs, RFLPs and SSRs, to create an integrated 2383-locus map. An integrated SNP map of 2,943 SNP markers has also been developed from three barley mapping populations (Steptoe x Morex; OWB; and Morex x Barke). HarvEST (http://harvest.ucr.edu/) includes the integrated barley SNP map and rice synteny viewer. We would like to thank Drs. Robbie Waugh (Scottish Crop Research Institute) and Nils Stein (IPK) for their assistance in the development of these SNP maps. U.S. BARLEY COORDINATED AGRICULTURAL PROJECT Barley CAP Consortium Outline of Barley CAP Approach Overall approach: 96 lines from each of 10 breeding programs from each year of the project are used for SNP genotyping (3,072 SNPs) and phenotyping for over 40 traits. Association mapping is used to detect QTL. MAS strategies will be developed from the QTL information. Visualization of Haplotype Structure 10 BREEDING PROGRAMS COLORADO Blake Cooper(Busch Agricultural Resources, Inc) USDA-ARS, IDAHO Donald Obert UNIVERSITY OF MINNESOTA Kevin Smith MONTANA STATE UNIVERSITY Tom Blake NORTH DAKOTA STATE UNIVERSITY Richard Horsley OREGON STATE UNIVERSITY Patrick Hayes UTAH STATE UNIVERSITY Dave Hole VIRGINIA TECH Carl Griffey WASHINGTON STATE UNIVERSITY Steven Ullrich *Denotes breeding programs Ten breeding programs each contribute 96 lines per year to the project. Breeding programs are in rows; populations identified by structure are in columns. Bar lengths proportional to # lines from a program within each population. A. No Admixture model, k = 10; lines assigned according to posterior probability of belonging to each population.Ten subpopulations line up fairly well with 10 breeding programs. So subpopulations are not explicitly named and number designations remained. k = # optimal subpopulations identified by program structure. B. Admixture model, k = 7; lines assigned by genome fraction of line originating from each population. Gray bars = genome fraction of line originating from population is < 0.80. Column 8 (“Admix”) contains lines for which no population contributed > 0.50. X-axis = abbreviation for 7 subpopulations are (i) 2- or 6-row, (ii) spring (sp) or winter (wi), (iii) 2-letter abbreviation for breeding program that seemed most dominant. Y-axis: 2-letter abbreviation of breeding program submitting lines. k = # optimal subpopulations identified by program structure. Data identifying subpopulations across 1816 breeder lines in years 1 & 2 show: • Breeding programs create separate populations within a crop. For example, N2 and N6 programs are primarily subpopulations unto themselves (Panel B). • Different breeding programs align on basis of shared targets and germplasm exchange. For example, commonalities between MT, BA and AB in first column (Panel B). • Programs exchange germplasm. For example, row for MN program has a gray bar, indicating introgression from N6. This means MN has used N6 lines as breeding parents. QTL MINER (LINKAGE DISEQUILIBRIUM MAPPING) MAS, USDA GENOTYPING LAB Marker-Trait Associations for Malting Quality, and Resistance to Fusarium Head Blight & DON USDA regional genotyping centers service small grain breeding programs by increasing efficiency of generating marker data and by eliminating the need for individual researchers to do genotyping. QTL Miner software is used to identify SNP markers associated with loci controlling traits of interest. Traditional mapping uses populations of progenies from two parents, whereas QTL miner uses trait and genotype data from breeding programs. 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 0 CHROMOSOME 1H cM Position 12.234 22.188 35.428 77.502 77.502 88 112.049 Number 1.1. 1.2. 1.3. 1.4. 1.5. 1.6. 1.7. Trait Affected alpha-amylase activity Grain protein content Kernel plumpness beta glucan (barley) Kernel plumpness Deoxynivalenol Test weight 1.1. 1.2. 1.3. 1.4. 1.5. 1.6. 1.7. CHROMOSOME 2H cM Position 4.405 15.397 46.469 50-56 66.181 72.752 125-132 173.642 Number 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 2.7. 2.8. Trait Affected beta glucan (barley) Malt extract Grain protein content Fusarium head blight beta glucan (barley) Diastatic Power Deoxynivalenol Grain protein content 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 2.7. 2.8. CHROMOSOME 3H cM Position 35.124 52-65 112.809 154.837 166.117 Number 3.1. 3.2. 3.3. 3.4. 3.5. Trait Affected beta glucan (malt) Deoxynivalenol alpha-amylase activity Diastatic Power Soluble/Total protein 3.2. 3.3. 3.4. 3.5. 3.1. cM Position 3 6.775 23.743 25.941 21-36 24-36 43.399 43.399 46.734 54.441 40-61 96.363 Number 4.1. 4.2. 4.3. 4.4. 4.5. 4.6. 4.7. 4.8. 4.9. 4.10. 4.11. 4.12. Trait Affected Deoxynivalenol beta glucan (malt) Diastatic Power alpha-amylase activity Deoxynivalenol Deoxynivalenol Grain protein content Grain protein content Kernel plumpness beta glucanase activity Deoxynivalenol Grain protein content CHROMOSOME 4H 4.3. 4.5. 4.7. 4.8. 4.2. 4.4. 4.9. 4.1. 4.12. 4.10. 4.11. 4.6. cM Position 0 14.506 44.847 57.263 122.722 157.379 176.129 190-192 239.081 Number 5.1. 5.2. 5.3. 5.4. 5.5. 5.6. 5.7. 5.8. 5.9. Trait Affected Grain protein content Malt extract alpha-amylase activity Diastatic Power alpha-amylase activity beta glucan (malt) Grain protein content Deoxynivalenol alpha-amylase activity CHROMOSOME 5H 5.1. 5.2. 5.3. 5.4. 5.5. 5.6. 5.7. 5.8. 5.9. CHROMOSOME 6H cM Position 30.074 30.074 42-61 42-67 67.419 86.393 124-127 Number 6.1. 6.2. 6.3. 6.4. 6.5. 6.6. 6.7. Trait Affected Fine coarse difference Test weight Fusarium head blight Deoxynivalenol alpha-amylase activity Diastatic Power Fusarium head blight 6.3. 6.7. 6.1. 6.2. 6.4. 6.5. 6.6. CHROMOSOME 7H cM Position 31.837 60.879 61.966 74.094 122.249 163.886 Number 7.1. 7.2. 7.3. 7.4. 7.5. 7.6. Trait Affected Kernel Weight alpha-amylase activity beta glucan (malt) Extract viscosity Grain protein content alpha-amylase activity 7.1. 7.2. 7.3. 7.4. 7.5. 7.6. Summary • 2,943 SNPs were placed on the barley genetic map. • Ten breeding programs contributed 96 lines over two years (1,920 lines) that were phenotyped for 40 traits and genotyped with 3,072 SNP markers. • Analysis of haplotype structure showed that breeding programs create separate populations but commonalities exist because of shared targets and germplasm exchange. • Association mapping led to QTL identification for malting quality, winter hardiness, and Fusarium head blight and deoxynivalenol resistance – information that is being used for MAS approaches.