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Dr. Scott Sebastian, Research Fellow, Pioneer Hi-Bred International

Dr. Scott Sebastian, Research Fellow, Pioneer Hi-Bred International 11-2-09 Plant Breeding Seminar at University of California Davis. Accelerated Yield Technology TM Context-Specific MAS for Grain Yield. Pioneer Soybean Breeding. USA Soybean Yield Trends (1972-2003). 55. 50. 45. 40.

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Dr. Scott Sebastian, Research Fellow, Pioneer Hi-Bred International

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  1. Dr. Scott Sebastian, Research Fellow, Pioneer Hi-Bred International 11-2-09 Plant Breeding Seminar at University of California Davis Accelerated Yield TechnologyTM Context-Specific MAS for Grain Yield

  2. Pioneer Soybean Breeding

  3. USA Soybean Yield Trends (1972-2003) 55 50 45 40 35 Seed Yield (bu/ac) 30 25 2 USA Trend: y = +0.412 x - 785 R = 0.678 20 15 1970 1975 1980 1985 1990 1995 2000 2005 Production Year Yield: Genetic Gain vs. Precision *courtesy of James Specht: Crop Science 39:1560-1570 Mean yield gain per year: ~ 1% Precision in our best trials: +/- 5%

  4. Soybean Yield Map (one inbred) typical yield range: 30 to 70 bu/a depending on position in the field

  5. Corn Yield Map (one hybrid) yield range: 109 to 243 bu/a depending on position in the field

  6. The paradigm for mapping additive traits Mapping yield QTL as an additive trait Do we need a new paradigm for yield? Context-Specific Mapping Breeding Bias and genomic hotspots AYT: a combination of many tools Outline

  7. Simple Trait Mappinge.g. SCN Resistance in Soybean Resistant Parent x Susceptible Parent segregating progeny Phenotype R R R R S S S S putative QTL hit good correlation phenotype: genotype Genotype poor correlation phenotype: genotype

  8. QTL detected in Population 1 0.0 3.7 12.9 0.0 0.0 18.2 3.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.1 19.3 14.7 3.2 6.0 6.7 5.3 30.3 11.2 23.0 9.0 22.0 11.9 9.1 32.1 12.0 27.7 16.8 28.3 17.8 32.3 28.0 32.5 34.2 26.6 28.1 26.6 50.2 34.9 28.4 33.0 35.8 30.5 29.0 55.0 51.5 37.2 36.5 35.0 41.7 30.9 38.0 56.4 55.2 40.0 39.3 46.4 43.1 31.1 58.3 57.0 43.9 44.7 57.9 43.6 32.7 58.4 65.6 46.6 69.8 51.5 65.1 44.9 46.5 53.9 61.9 67.7 56.5 73.8 73.3 45.1 64.7 59.6 63.5 71.7 78.1 74.2 45.4 71.4 72.6 64.3 72.1 80.9 74.4 47.5 79.2 74.9 74.8 65.2 72.5 P1 81.9 75.5 56.3 80.2 93.2 74.9 65.7 72.9 82.9 76.2 56.7 84.6 94.2 82.2 75.7 69.8 73.2 84.2 80.6 64.2 85.7 95.2 100.1 76.1 70.7 78.8 85.9 84.8 71.3 87.9 95.5 105.2 87.2 71.8 87.6 89.7 85.4 88.0 97.8 108.8 73.8 91.1 112.2 100.9 95.1 90.1 89.2 101.6 109.8 82.5 97.9 113.4 96.4 89.8 102.3 110.9 115.5 102.6 105.5 115.9 116.4 117.8 120.1 113.6 120.9 121.0 116.6 121.3 123.8 125.7 115.0 116.7 122.0 124.3 132.2 119.6 126.2 135.6 129.0 125.4 128.2 140.0 133.9 128.4 128.9 151.9 129.9 157.9 145.6 154.1 162.0 165.7 P1 0.0 1.9 5.0 0.0 12.3 3.0 0.0 0.0 0.0 14.4 0.0 0.0 0.0 15.7 6.6 3.4 5.0 0.6 5.4 21.7 24.1 8.0 12.2 3.6 7.8 8.5 9.5 30.3 26.1 25.5 11.1 12.7 20.3 4.0 17.3 41.5 27.1 26.1 18.6 23.1 28.0 5.4 27.9 20.4 42.7 29.4 27.8 23.9 31.5 15.3 30.6 27.6 43.3 31.8 29.7 27.5 31.9 39.8 20.6 33.5 30.9 44.0 34.5 32.1 43.8 34.0 42.3 35.9 33.7 38.9 34.6 36.7 48.9 35.3 46.2 43.6 36.1 36.9 37.8 46.9 49.9 49.7 38.2 50.2 50.1 46.4 56.3 37.4 38.2 50.5 52.1 56.1 49.5 59.9 58.9 38.0 39.8 52.9 53.7 59.5 49.6 62.1 68.5 38.1 70.6 41.2 53.4 54.2 65.6 64.7 50.9 67.0 69.1 40.8 42.5 71.4 56.0 55.1 66.5 PR 52.9 73.9 72.2 53.2 43.1 72.5 56.5 55.8 70.2 77.8 78.6 75.6 85.8 70.6 52.7 73.0 62.2 56.3 82.8 78.7 76.4 86.5 72.6 71.9 74.3 68.8 56.9 77.2 91.1 75.9 77.7 78.8 69.9 57.0 87.1 93.7 76.5 89.8 78.1 80.4 68.4 99.8 95.4 84.6 91.0 85.3 106.4 87.1 71.1 104.8 107.7 92.6 91.9 107.2 P2 94.4 82.1 112.7 111.1 102.1 112.3 96.6 93.4 113.4 116.7 117.0 112.8 117.6 115.1 100.0 95.4 124.0 119.2 125.2 102.8 100.4 124.6 107.1 133.8 106.0 130.6 116.8 140.7 118.1 135.1 142.2 119.5 135.1 151.0 146.4

  9. Disease QTL detected within a specific population Population 1 Parent1 (Resistant) x Parent2 (susceptible) P1 ‘Major QTL’ P1 P2 ‘Minor QTL’

  10. ‘Validation’ of QTL Across Populations Major ‘additive’ gene Population 2 RES x SUS Population 1 RES x SUS Chromosome G position 3 Population 3 RES x SUS These QTL did not ‘validate’ across populations. Does that mean they are not real ?

  11. A validated SCN resistance gene ‘Rhg1’ Chromosome G Map Position 0 . 20 . 40 . 60 . 80 . 100 . 120 . Rhg1 But what is the effect of Rhg1 on yield?

  12. Effect of a Rhg1 on Yield Global conclusion: Rhg1 does not affect yield. Reality: the effect of Rhg1 on yield can be positive, neutral, or negative depending on the population.

  13. Why do yield effects of a QTL differ across populations? Chromosome G Yield Effect Yield effects are not distinguishable as single genes. At best, a yield QTL can be assumed as the net effect of an entire region within a given population. Direction and magnitude of effect can change dramatically with both population and environment (the context) Rhg1 0 . 20 . 40 . 60 . 80 . 100 . 120 .

  14. Attempts to Map Yield QTLin the old paradigm

  15. Attempts to ‘validate’ Yield QTL Many QTL found, NONE have validated across all populations. Population1 Population2 Population3

  16. Do we need a different paradigm for mapping Yield?

  17. What if ? Population1 Population2 These QTL are valid for Population 2 These QTL are valid for Population 1 Population3 These QTL are valid for Population 3

  18. Context-Specific Mapping How valid are the Yield QTL within a given context? Population1 QTL are only as valid as the data used to detect them ! More progeny + more environments = more confidence

  19. Implications for MAS ina breeding program

  20. Development of One Product(before AYT) Year0 Year1 Year2 Year3 R1 Year4 R2 Year5 R3 Year6 R4 Year7 R5 Hundreds of Crosses (Parent1 x Parent2) MAS for simple traits Yield Testing 20,000 lines x 1 rep 5,000 lines x 2 reps 500 lines x 6 reps 20 lines x 25 reps 4 lines x 50 reps 1 product (better than parents?) inbreeding Many choices but terrible precision error is ~ +/- 30% (15 bu/a) Few choices but better precision error ~ +/- 5% (2 to 3 bu/a)

  21. First Yield Screen: Progeny Row Yield Test ~ 85% of plot-to-plot variation is not heritable

  22. AYT: markers as ‘heritable covariates’ AA aa AA aa AA AA aa AA AA aa aa AA aa AA aa aa aa AA AA aa AA

  23. More marker coverage = more power to detect yield QTL Large populations, multiple environments = more power BB bb BB bb bb bb BB BB bb BB BB bb BB BB bb bb BB bb BB bb bb

  24. AYT analysis can be simple: AA vs. aa … or more sophisticated Yield (predicted) = Mean + 2xAA + 4xbb + 2xDD + …. + epistasis …

  25. Select winners by Target Genotype AA bb DD …

  26. Product Development (before AYT) Hundreds of Crosses F1 F2 F3 Forward selection for simple traits Yield Testing 20,000 lines x 1 rep 5,000 lines x 2 reps 500 lines x 6 reps 20 lines x 25 reps 4 lines x 50 reps 1 product Year0 Year1 Year2 Year3 Year4 Year5 Year6 Resources 20,000 micro plots 10,000 small plots 3,000 med plots 500 large plots 200 large plots 34,000 plots + 6 years

  27. Product Development with AYT Only the Best Crosses F1 F2 F3 Forward Selection for (simple traits) Context-Specific MAS for Yield Much better selection precision Advance only the most promising genotypes Fewer lines = better characterization in fewer years Better Products, Faster to Market Year0 Year1 Year2 Year3 Year4

  28. What about the cost of genotyping?

  29. Are some genomic regions yield hotspots? Can this reduce genotyping costs? Can this improve QTL detection rate? Genotyping Efficiency

  30. ‘Breeding Bias’ aka ‘Genetic Hitchhiking’ aka ‘Selection Sweep’ 1995: US Patent 5,437,69. Sebastian, Hanafey, Tingey (soy example) 1998: US Patent 5,746,023. Hanafey, Sebastian, Tingey (corn example) 2004: Crop Science 44:436-442. Smalley, Fehr, Cianzio, Han, Sebastian, Streit 2006: Maydica 51: 293-300 Feng, Sebastian, Smith, Cooper. Multiple lines of evidence Very powerful tool

  31. History of Soybean 60+ years of recurrent selection for Yield Elite Population Ancestral Population

  32. Yield-associated region Marker: genetic hitchhiker

  33. Loci with evidence of selection 60+ years of recurrent selection for Yield Elite Population Ancestral Population change in allele frequency • Reliable measure of: • which genomic regions were most important over time • response to the ‘average environment’ • implicitly leverages a century of breeding progress!

  34. All Markers on First 3 Chromosomes A2 0.0 2.0 B1 5.0 A1 8.6 19.3 22.5 20.0 5.1 26.7 23.3 5.7 34.9 33.2 14.6 39.0 17.0 18.0 45.0 56.6 50.0 19.1 68.1 71.6 27.1 73.3 28.5 74.1 48.2 74.8 73.5 78.3 76.4 80.0 85.0 89.9 69.9 91.9 93.7 75.3 92.1 96.2 83.2 86.4 108.7 87.3 117.3 119.6 96.4 120.0 123.4 132.4 135.1 136.0 138.2 154.7 161.8 173.5 175.2 184.0

  35. Regions of Breeding Bias A2 B1 A1

  36. Breeding Bias hotspots across the entire genome A1 A2 B1 B2 C1 C2 D1a D1b D2 E 0.0 3.7 12.9 0.0 0.0 18.2 3.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.1 19.3 14.7 3.2 6.0 6.7 5.3 30.3 11.2 23.0 9.0 22.0 11.9 9.1 32.1 12.0 27.7 16.8 28.3 17.8 32.3 28.0 32.5 34.2 26.6 28.1 26.6 50.2 34.9 28.4 33.0 35.8 30.5 29.0 55.0 51.5 37.2 36.5 35.0 41.7 30.9 38.0 56.4 55.2 40.0 39.3 46.4 43.1 31.1 58.3 57.0 43.9 44.7 57.9 43.6 32.7 58.4 65.6 46.6 69.8 51.5 65.1 44.9 46.5 53.9 61.9 67.7 56.5 73.8 73.3 45.1 64.7 59.6 63.5 71.7 78.1 74.2 45.4 71.4 72.6 64.3 72.1 80.9 74.4 47.5 79.2 74.9 74.8 65.2 72.5 81.9 75.5 56.3 80.2 93.2 74.9 65.7 72.9 82.9 76.2 56.7 84.6 94.2 82.2 75.7 69.8 73.2 84.2 80.6 64.2 85.7 95.2 100.1 76.1 70.7 78.8 85.9 84.8 71.3 87.9 95.5 105.2 87.2 71.8 87.6 89.7 85.4 88.0 97.8 108.8 73.8 91.1 112.2 100.9 95.1 90.1 89.2 101.6 109.8 82.5 97.9 113.4 96.4 89.8 102.3 110.9 115.5 102.6 105.5 115.9 116.4 117.8 120.1 113.6 120.9 121.0 116.6 121.3 123.8 125.7 115.0 116.7 122.0 124.3 132.2 119.6 126.2 135.6 129.0 125.4 128.2 140.0 133.9 128.4 128.9 151.9 129.9 157.9 145.6 154.1 162.0 165.7 F G H I J K L M N O 0.0 3.3 0.0 0.0 1.9 5.0 0.0 12.3 3.0 0.0 0.0 0.0 14.4 0.0 0.0 0.0 15.7 6.6 3.4 5.0 0.6 5.4 21.7 24.1 8.0 12.2 3.6 7.8 8.5 9.5 30.3 26.1 25.5 11.1 12.7 20.3 4.0 17.3 41.5 27.1 26.1 18.6 23.1 28.0 5.4 27.9 20.4 42.7 29.4 27.8 23.9 31.5 15.3 30.6 27.6 43.3 31.8 29.7 27.5 31.9 39.8 20.6 33.5 30.9 44.0 34.5 32.1 43.8 34.0 42.3 35.9 33.7 38.9 34.6 36.7 48.9 35.3 46.2 43.6 36.1 36.9 37.8 46.9 49.9 49.7 38.2 50.2 50.1 46.4 56.3 37.4 38.2 50.5 52.1 56.1 49.5 59.9 58.9 38.0 39.8 52.9 53.7 59.5 49.6 62.1 68.5 38.1 70.6 41.2 53.4 54.2 65.6 64.7 50.9 67.0 69.1 40.8 42.5 71.4 56.0 55.1 66.5 52.9 73.9 72.2 53.2 43.1 72.5 56.5 55.8 70.2 77.8 78.6 75.6 85.8 70.6 52.7 73.0 62.2 56.3 82.8 78.7 76.4 86.5 72.6 71.9 74.3 68.8 56.9 77.2 91.1 75.9 77.7 78.8 69.9 57.0 87.1 93.7 76.5 89.8 78.1 80.4 68.4 99.8 95.4 84.6 91.0 85.3 106.4 87.1 71.1 104.8 107.7 92.6 91.9 107.2 94.4 82.1 112.7 111.1 102.1 112.3 96.6 93.4 113.4 116.7 117.0 112.8 117.6 115.1 100.0 95.4 124.0 119.2 125.2 102.8 100.4 124.6 107.1 133.8 106.0 130.6 116.8 140.7 118.1 135.1 142.2 119.5 135.1 151.0 146.4 = Rps Loci = BSR Loci = SCN Loci = Yield Loci

  37. Hotspots segregating in a given cross A1 A2 B1 B2 C1 C2 D1a D1b D2 E 0.0 3.7 12.9 0.0 0.0 18.2 3.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.1 19.3 14.7 3.2 6.0 6.7 5.3 I i 30.3 11.2 23.0 9.0 22.0 11.9 9.1 32.1 12.0 27.7 16.8 28.3 17.8 32.3 C c F f 28.0 32.5 34.2 K k 26.6 28.1 26.6 50.2 34.9 28.4 33.0 35.8 30.5 29.0 55.0 51.5 37.2 36.5 35.0 41.7 30.9 38.0 56.4 55.2 40.0 39.3 46.4 43.1 31.1 58.3 57.0 43.9 44.7 57.9 43.6 32.7 58.4 65.6 46.6 69.8 51.5 65.1 44.9 46.5 53.9 61.9 67.7 A a 56.5 73.8 73.3 45.1 64.7 59.6 63.5 71.7 78.1 74.2 45.4 71.4 72.6 64.3 72.1 80.9 74.4 47.5 79.2 74.9 74.8 65.2 72.5 81.9 75.5 56.3 80.2 93.2 74.9 65.7 72.9 82.9 D d 76.2 56.7 84.6 94.2 82.2 75.7 69.8 73.2 84.2 80.6 64.2 85.7 95.2 100.1 76.1 70.7 78.8 85.9 84.8 71.3 87.9 95.5 105.2 87.2 71.8 87.6 89.7 85.4 88.0 97.8 108.8 73.8 91.1 112.2 100.9 95.1 90.1 J j 89.2 101.6 109.8 82.5 97.9 113.4 96.4 E e H h 89.8 102.3 110.9 115.5 102.6 105.5 115.9 116.4 117.8 120.1 113.6 120.9 121.0 116.6 121.3 123.8 125.7 115.0 116.7 122.0 124.3 132.2 119.6 B b 126.2 135.6 129.0 125.4 G g 128.2 140.0 133.9 128.4 128.9 151.9 129.9 157.9 145.6 154.1 162.0 165.7 F G H I J K L M N O 0.0 3.3 0.0 0.0 1.9 5.0 M m 0.0 12.3 3.0 0.0 0.0 0.0 14.4 0.0 0.0 0.0 Q q W w 15.7 6.6 3.4 5.0 0.6 5.4 21.7 24.1 U u 8.0 12.2 3.6 7.8 8.5 9.5 30.3 26.1 25.5 11.1 12.7 20.3 4.0 17.3 41.5 27.1 26.1 R r 18.6 23.1 28.0 5.4 27.9 20.4 42.7 29.4 27.8 23.9 31.5 15.3 30.6 27.6 43.3 31.8 29.7 27.5 31.9 39.8 20.6 33.5 30.9 44.0 34.5 32.1 43.8 34.0 42.3 35.9 33.7 38.9 34.6 36.7 48.9 35.3 46.2 43.6 36.1 36.9 37.8 46.9 49.9 49.7 38.2 50.2 50.1 46.4 56.3 37.4 38.2 50.5 52.1 N n P p 56.1 49.5 59.9 58.9 38.0 39.8 52.9 53.7 59.5 49.6 62.1 68.5 38.1 70.6 41.2 53.4 54.2 65.6 64.7 S s 50.9 67.0 69.1 40.8 42.5 71.4 56.0 55.1 66.5 O o 52.9 73.9 72.2 53.2 43.1 72.5 56.5 55.8 70.2 77.8 78.6 75.6 85.8 70.6 52.7 73.0 62.2 56.3 82.8 V v 78.7 76.4 86.5 72.6 71.9 74.3 68.8 56.9 77.2 91.1 75.9 77.7 78.8 69.9 57.0 87.1 93.7 76.5 89.8 78.1 80.4 68.4 99.8 95.4 84.6 91.0 85.3 106.4 87.1 71.1 104.8 107.7 92.6 91.9 107.2 L l 94.4 82.1 112.7 111.1 102.1 112.3 96.6 93.4 113.4 116.7 117.0 112.8 117.6 115.1 T t 100.0 95.4 124.0 119.2 125.2 102.8 100.4 124.6 107.1 133.8 106.0 130.6 116.8 140.7 118.1 135.1 142.2 119.5 135.1 151.0 146.4

  38. MAS for simple traits across populations Breeding Bias & other tools to find hotspots Context-Specific MAS for yield within each pop Accelerated Yield TechnologyTM a combination of many tools

  39. USA Soybean Yield Trends (1972-2003) 55 50 45 40 35 Seed Yield (bu/ac) 30 25 2 USA Trend: y = +0.412 x - 785 R = 0.678 20 15 1970 1975 1980 1985 1990 1995 2000 2005 Production Year Our Goal: Double the Rate of Genetic Gain *courtesy of James Specht: Crop Science 39:1560-1570

  40. Thank You!

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