1. MARKER ASSISTED SELECTION Individuals carrying the trait of interest are selected based on a marker which is linked to the trait and not on the trait itself. Indirect selection. Useful when the trait is difficult to measure, and/or is evident only at late developmental stages.

2. WinterFacult.Spring Kompolti Nure OSU11 Strider Igri Dicktoo 88Ab536 Tremois Morex Harrington ZCCT-H HvSnf2 Triumph

3. BARLEY STRIPE RUST www.barleyworld.org

4. BARLEY STRIPE RUST Caused by fungus Puccinia striiformis f. sp. hordei Common in Mexico and South America but usually not in Oregon. It first appeared in the US in 1991 When infection is severe, losses of 50% are common

5. BARLEY STRIPE RUST Resistant cultivars are available It is necessary to develop resistant cultivars adapted to different barley growing areas Resistance to the disease is a quantitative trait (non-Mendelian inheritance) Three Quantitative Trait Loci (QTL) for stripe rust resistance have been mapped and their effects validated. There are molecular markers located in the target regions of the three QTL

6. Cali-sib x BowmanShyri x GalenaCI10587 x Galena BSR-45D1-72D3-6 HarringtonBaronesse Orca BCDDB BCD47BCD12D3-6/B23D3-6/B61 AJOBUOPS

7. iBISON and BISON

9. OWB Chr. 4H ORO EBmac701 baal29j18 SSR markers used for MAS *

10. Flanking markers in each target region Three resistance genes in chromosome 1H, 4H and 5H

11. THE PROBLEM KURTFORD 6 –rowed hooded feed barley Well adapted to California conditions Short height Stripe Rust Susceptible

12. iBISON 1H+4H+5H Resistance alleles for the three genes 2–rowed awned barley; short height

13. I Bison 1H 4H 5H Kurtford 1H 4H 5H X F1 heterozygote at 3 BSR resistance loci X Kurtford 1H 4H 5H BC1F1 12.5% of plants expected to be heterozygote at 3 BSR resistance loci. Year 1 RR RR RR SS SS SS RS RS RS SS SS SS Segregation in each locus: ½ RS ½ SS SOLUTION: The Kurtford Conversion

14. We have 589 BC1F1 seeds and we expect to find around 74 target heterozygotes at the 3 BSR resistance loci.We need: -Confirm choice of markers -DNA extraction for 589 plants -Genotyping for the 3 BSR resistance regions -Selection of targeted genotypes Bmag399 EST4473 EST4535 Baal29j18. Bags 4e Bmag337 Bmag223 Bmag812 1H 4H 5H Summer – Fall 2007 MAS1

15. Kurtford iBison 1H+4H+5H I I I K I K I I K

17. 1.Selection of heterozygotes (~74) 2.10 seeds per selected plant ~ 740 BC1F2 plants. 3.Grow out and genotype We expect 25% of the BC1F2 to be homozygous for AT LEAST the 4H region (~ 185). Of these: ~ 104 homozygote only for 4H ~ 35 homozygote for 4H and 1H ~ 35 homozygote for 4H and 5H ~ 11 homozygote for 1H, 4H and 5H 25/64 (~ 38%) of the resistant plants will be homozygous hooded Winter 2008 MAS2 BC1F2 plants segregating in each locus: ¼ RR ½ RS ¼ SS

18. Winter 2008/2009 – seed increase Summer 2009 – validate resistance ~ 70 homozygous hooded, BSR resistant plants Field test

19. Genomic Selection (GS) A method to estimate breeding values for individuals based on many markers distributed across the genome. Breeding values are derived from marker effects that are estimated from a training population for which both marker and phenotypic data exist. The primary benefits of GS are that selection can be imposed for quantitative traits very early in the breeding process, thus substantially reducing breeding cycle time.

20. In a Breeding Population individuals are genotyped but not phenotyped A genomic estimated breeding value (GEBV) for each individual is obtained by summing the marker effects for that genotype Prediction model can be used to impose multiple generations of selection A Training Population is genotyped with a large number of markers and phenotyped for important traits Genome-wide markers are considered to be random effects and all marker effects on the phenotype are estimated simultaneously in a single model One or more markers are assumed to be in LD with each QTL affecting trait Prediction model attempts to captures the total additive genetic variance to estimate breeding value of individuals based on sum of all marker effects Genomic Selection Methodology

21. Training population (Breeding lines ) Genotyping Phenotyping Development of Prediction Model / Cross-Validation Application of prediction model Genotyping Genomic estimation of breeding values (GEBV) Selection Intermate and next cycle of GS GENOMIC SELECTION

22. BLUPs for each marker Genomic Estimated Breeding Value (GEBV):

23. GEBV True Value 3,000 SNPs Ridge Regression Results of Cross Validation