1 / 23

MARKER ASSISTED SELECTION

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. Winter.

alicia
Download Presentation

MARKER ASSISTED SELECTION

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  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 Bowman Shyri x Galena CI10587 x Galena BSR-45 D1-72 D3-6 Harrington Baronesse Orca BCD DB BCD47 BCD12 D3-6/B23 D3-6/B61 AJO BU OPS

  7. iBISON and BISON

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

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

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

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

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

  13. Summer – Fall 2007 • 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 MAS1 1H 4H 5H Bmag399 EST4473 EST4535 Baal29j18 . Bags 4e Bmag337 Bmag223 Bmag812

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

  15. Winter 2008 • Selection of heterozygotes (~74) • 10 seeds per selected plant ~ 740 BC1F2 plants. • Grow out and genotype BC1F2 plants segregating in each locus: ¼ RR ½ RS ¼ SS MAS2 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

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

  17. Genomic Selection Methodology • 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 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

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

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

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

More Related