Dr. Scott Sebastian, Research Fellow, Pioneer Hi-Bred International Plant Breeding Seminar at University of California Davis Accelerated Yield Technology TM Context-Specific MAS for Grain Yield

Pioneer Soybean Breeding

3 Yield: Genetic Gain vs. Precision Mean yield gain per year: ~ 1% Precision in our best trials: +/- 5% *courtesy of James Specht: Crop Science 39:

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

QTL detected in Population 1 PR P1 P2

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

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

Map PositionChromosome G A validated SCN resistance gene ‘Rhg1’ 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.

Chromosome G Why do yield effects of a QTL differ across populations? Rhg1 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)

14 Attempts to Map Yield QTL in the old paradigm

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

16 Do we need a different paradigm for mapping Yield?

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

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

19 Implications for MAS in a breeding program

20 Development of One Product (before AYT) 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?) Year0 Year1 Year2 Year3 R1 Year4 R2 Year5 R3 Year6 R4 Year7 R5 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 AA aa AA aa AA aa AA aa AA aa AA aa AA aa AYT: markers as ‘heritable covariates’

23 bb BB bb BB bb BB More marker coverage = more power to detect yield QTL Large populations, multiple environments = more power 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 Genotyping Efficiency Are some genomic regions yield hotspots? Can this reduce genotyping costs? Can this improve QTL detection rate?

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: Smalley, Fehr, Cianzio, Han, Sebastian, Streit 2006: Maydica 51: Feng, Sebastian, Smith, Cooper. Multiple lines of evidence Very powerful tool

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

32 Yield-associated region Marker: genetic hitchhiker

33 Ancestral Population Elite Population 60+ years of recurrent selection for Yield Loci with evidence of selection Reliable measure of: 1)which genomic regions were most important over time 2)response to the ‘average environment’ implicitly leverages a century of breeding progress! change in allele frequency

A A B1 All Markers on First 3 Chromosomes

35 A1 A2 B1 Regions of Breeding Bias

36 Breeding Bias hotspots across the entire genome A A B B C C D1a D1b D E F G H I J K L M N O = Yield Loci = SCN Loci = BSR Loci = Rps Loci

37 Hotspots segregating in a given cross A A B B C C D1a D1b D E F G H I J K L M N O A a B b C c D d E e F f G g J j H h I i K k L l R r T t S s V v U u W w M m N n O o P p Q q

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

39 Our Goal: Double the Rate of Genetic Gain *courtesy of James Specht: Crop Science 39:

Thank You!