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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
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Pioneer Soybean Breeding
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Yield: Genetic Gain vs. Precision
Mean yield gain per year: ~ 1%
Precision in our best trials: +/- 5%
*courtesy of James Specht:
Crop Science 39:1560-1570
USA Soybean Yield Trends (1972-2003)
USA Trend: y = +0.412x - 785 R2 = 0.678
15
20
25
30
35
40
45
50
55
1970 1975 1980 1985 1990 1995 2000 2005Production Year
See
d Y
ield
(b
u/a
c)
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Soybean Yield Map (one inbred) typical yield range: 30 to 70 bu/a
depending on position in the field
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Corn Yield Map (one hybrid) yield range: 109 to 243 bu/a
depending on position in the field
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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
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Simple Trait Mappinge.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
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0.03.5
14.723.027.728.028.129.030.931.132.746.564.771.474.993.294.295.295.597.8
101.6102.3
0.02.15.39.1
28.435.0
51.5
100.1105.2108.8109.8110.9115.9116.6116.7119.6125.4128.4128.9129.9145.6154.1162.0165.7
0.0
22.028.332.533.036.546.457.969.873.878.180.981.982.984.285.989.795.196.4
102.6
125.7132.2
0.06.0
11.917.8
34.951.555.257.065.667.771.772.172.572.973.278.887.691.197.9
121.0
0.0
9.0
65.173.374.274.475.576.280.684.885.490.1
120.1123.8
135.6
0.0
26.630.538.044.7
56.5
82.2
112.2113.4115.5117.8121.3122.0126.2128.2
151.9157.9
0.0
11.212.0
50.255.056.458.358.461.963.564.365.265.769.870.771.873.882.5
120.9
0.06.7
26.637.240.043.946.6
59.672.674.874.975.776.187.2
100.9
116.4
140.0
0.03.2
16.8
39.3
53.9
79.280.284.685.787.988.089.289.8
105.5113.6115.0124.3129.0133.9
0.03.7
12.918.219.330.332.132.334.235.841.743.143.644.945.145.447.556.356.764.271.3
1.93.03.43.64.05.4
15.320.6
50.2
70.671.472.573.074.377.778.185.391.9
102.1117.6119.2124.6130.6135.1
151.0
5.0
6.612.212.723.123.927.543.848.949.950.552.953.456.056.562.268.869.980.487.194.496.6
100.0102.8107.1116.8
0.00.68.5
27.6
38.9
46.9
58.968.569.172.285.886.591.193.7
124.0
0.0
20.328.031.531.934.035.3
50.1
65.6
77.882.8
99.8
112.7113.4
125.2
0.012.315.724.125.526.127.829.732.136.737.838.239.841.242.543.152.771.978.889.891.0
0.014.421.730.341.542.743.344.0
46.2
46.449.549.650.952.978.678.7
104.8
117.0
0.08.0
11.1
27.930.630.933.736.138.256.159.564.766.570.2
106.4107.2112.3115.1
0.05.07.8
18.6
33.535.9
56.359.962.167.073.975.676.477.287.195.4
107.7111.1112.8
133.8140.7142.2
0.0
26.127.129.431.834.534.636.937.438.038.140.853.270.672.675.976.584.692.6
116.7
0.05.49.5
17.320.4
39.842.343.649.752.153.754.255.155.856.356.957.068.471.182.193.495.4
100.4106.0118.1119.5135.1146.4
QTL detected in Population 1
PR
P1
P1
P2
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Population 1
Parent1 (Resistant) x Parent2 (susceptible)
‘Major QTL’
‘Minor QTL’
Disease QTL detected within a specific population
P1
P1 P2
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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
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0
.
20
.
40
.
60
.
80
.
100
.
120
.
Map PositionChromosome G
A validated SCN resistance gene ‘Rhg1’
Rhg1
But what is the effect of Rhg1 on yield?
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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.
Trait gene IBD
Effect of Rhg1 on disease
Effect on Yield (bu/a)
Statistical Signif
Rhg1 93B86 YB32K01 R +4.0 **
Rhg1 93B86 EX36Y01 R +1.9 *
Rhg1 93B86 92B52 R +1.2 ns
Rhg1 93B86 XB23Y02 R 0.0 ns
Rhg1 93B15 92B74 R -0.2 ns
Rhg1 93B15 ST2870 R -1.9 *
Rhg1 93B15 ST3630 R -6.3 **
Rhg1 across all across all R -0.2 ns
Population Parent 1 x Parent 2
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0
.
20
.
40
.
60
.
80
.
100
.
120
.
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)
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Attempts to Map Yield QTLin the old paradigm
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Population1 Population2
Population3
Attempts to ‘validate’ Yield QTL
Many QTL found, NONE have validated across all populations.
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Do we need a different
paradigm for mapping Yield?
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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
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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
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Implications for MAS ina breeding program
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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)
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First Yield Screen: Progeny Row Yield Test
~ 85% of plot-to-plot variation is not heritable
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AA
aaAA
aaAA
aa AA AA aa
AAaa
AAAA
aa
aa aa
aaAA AA
AA
aa
AYT: markers as ‘heritable covariates’
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bb
BB
bb
BB
bb
BB
More marker coverage = more power to detect yield QTL
Large populations, multiple environments = more power
bb bb BB
bbBB
bb
bbBB
BB BB
BB
bb
bb
bbBB
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AYT analysis can be simple: AA vs. aa
… or more sophisticated
Yield (predicted) = Mean + 2xAA + 4xbb + 2xDD + …. + epistasis …
QTL Favorable Alleles Magnitude location P1 alleles P2 alleles
Region A: AA > aa 2 bu/a
Region B: BB < bb 4 bu/a
Region C: CC = cc 0
Region D: DD > dd 2 bu/a
Region E: EE = ee 0
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Select winners by Target Genotype
AA bb DD …
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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
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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
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What about the cost of genotyping?
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Genotyping Efficiency
Are some genomic regions yield hotspots?
Can this reduce genotyping costs?
Can this improve QTL detection rate?
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‘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
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Ancestral Population
Elite Population
60+ years of recurrent selection for
Yield
History of Soybean
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Yield-associated region
Marker: genetic hitchhiker
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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
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5.1 5.714.617.018.0
19.1
27.128.548.2
69.975.383.286.4
87.3
96.4
A1
0.0 2.0 5.0 8.619.320.023.333.2
50.0
73.578.3
89.993.796.2
108.7
119.6123.4132.4135.1136.0138.2
154.7
161.8
173.5175.2
184.0
A2
22.526.7
34.939.045.056.668.171.673.374.174.876.480.085.091.992.1
117.3120.0
B1
All Markers on First 3 Chromosomes
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A1
A2
B1
Regions of Breeding Bias
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Breeding Bias hotspots across the entire genome
0.03.5
14.723.027.728.028.129.030.931.132.746.564.771.474.993.294.295.295.597.8
101.6102.3
A1
0.02.15.39.1
28.435.0
51.5
100.1105.2108.8109.8110.9115.9116.6116.7119.6125.4128.4128.9129.9145.6154.1162.0165.7
A2
0.0
22.028.332.533.036.546.457.969.873.878.180.981.982.984.285.989.795.196.4
102.6
125.7132.2
B1
0.06.0
11.917.8
34.951.555.257.065.667.771.772.172.572.973.278.887.691.197.9
121.0
B2
0.0
9.0
65.173.374.274.475.576.280.684.885.490.1
120.1123.8
135.6
C1
0.0
26.630.538.044.7
56.5
82.2
112.2113.4115.5117.8121.3122.0126.2128.2
151.9157.9
C2
0.0
11.212.0
50.255.056.458.358.461.963.564.365.265.769.870.771.873.882.5
120.9
D1a
0.06.7
26.637.240.043.946.6
59.672.674.874.975.776.187.2
100.9
116.4
140.0
D1b
0.03.2
16.8
39.3
53.9
79.280.284.685.787.988.089.289.8
105.5113.6115.0124.3129.0133.9
D20.03.7
12.918.219.330.332.132.334.235.841.743.143.644.945.145.447.556.356.764.271.3
E
0.01.93.03.43.64.05.4
15.320.6
50.2
70.671.472.573.074.377.778.185.391.9
102.1117.6119.2124.6130.6135.1
151.0
F0.03.3
5.0
6.612.212.723.123.927.543.848.949.950.552.953.456.056.562.268.869.980.487.194.496.6
100.0102.8107.1116.8
G
0.00.68.5
27.6
38.9
46.9
58.968.569.172.285.886.591.193.7
124.0
H
0.0
20.328.031.531.934.035.3
50.1
65.6
77.882.8
99.8
112.7113.4
125.2
I
0.012.315.724.125.526.127.829.732.136.737.838.239.841.242.543.152.771.978.889.891.0
J
0.014.421.730.341.542.743.344.0
46.2
46.449.549.650.952.978.678.7
104.8
117.0
K
0.08.0
11.1
27.930.630.933.736.138.256.159.564.766.570.2
106.4107.2112.3115.1
L
0.05.07.8
18.6
33.535.9
56.359.962.167.073.975.676.477.287.195.4
107.7111.1112.8
133.8140.7142.2
M
0.0
26.127.129.431.834.534.636.937.438.038.140.853.270.672.675.976.584.692.6
116.7
N
0.05.49.5
17.320.4
39.842.343.649.752.153.754.255.155.856.356.957.068.471.182.193.495.4
100.4106.0118.1119.5135.1146.4
O
= Yield Loci= SCN Loci= BSR Loci= Rps Loci
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Hotspots segregating in a given cross
0.03.5
14.723.027.728.028.129.030.931.132.746.564.771.474.993.294.295.295.597.8
101.6102.3
A1
0.02.15.39.1
28.435.0
51.5
100.1105.2108.8109.8110.9115.9116.6116.7119.6125.4128.4128.9129.9145.6154.1162.0165.7
A2
0.0
22.028.332.533.036.546.457.969.873.878.180.981.982.984.285.989.795.196.4
102.6
125.7132.2
B1
0.06.0
11.917.8
34.951.555.257.065.667.771.772.172.572.973.278.887.691.197.9
121.0
B2
0.0
9.0
65.173.374.274.475.576.280.684.885.490.1
120.1123.8
135.6
C1
0.0
26.630.538.044.7
56.5
82.2
112.2113.4115.5117.8121.3122.0126.2128.2
151.9157.9
C2
0.0
11.212.0
50.255.056.458.358.461.963.564.365.265.769.870.771.873.882.5
120.9
D1a
0.06.7
26.637.240.043.946.6
59.672.674.874.975.776.187.2
100.9
116.4
140.0
D1b
0.03.2
16.8
39.3
53.9
79.280.284.685.787.988.089.289.8
105.5113.6115.0124.3129.0133.9
D20.03.7
12.918.219.330.332.132.334.235.841.743.143.644.945.145.447.556.356.764.271.3
E
0.01.93.03.43.64.05.4
15.320.6
50.2
70.671.472.573.074.377.778.185.391.9
102.1117.6119.2124.6130.6135.1
151.0
F0.03.3
5.0
6.612.212.723.123.927.543.848.949.950.552.953.456.056.562.268.869.980.487.194.496.6
100.0102.8107.1116.8
G
0.00.68.5
27.6
38.9
46.9
58.968.569.172.285.886.591.193.7
124.0
H
0.0
20.328.031.531.934.035.3
50.1
65.6
77.882.8
99.8
112.7113.4
125.2
I
0.012.315.724.125.526.127.829.732.136.737.838.239.841.242.543.152.771.978.889.891.0
J
0.014.421.730.341.542.743.344.0
46.2
46.449.549.650.952.978.678.7
104.8
117.0
K
0.08.0
11.1
27.930.630.933.736.138.256.159.564.766.570.2
106.4107.2112.3115.1
L
0.05.07.8
18.6
33.535.9
56.359.962.167.073.975.676.477.287.195.4
107.7111.1112.8
133.8140.7142.2
M
0.0
26.127.129.431.834.534.636.937.438.038.140.853.270.672.675.976.584.692.6
116.7
N
0.05.49.5
17.320.4
39.842.343.649.752.153.754.255.155.856.356.957.068.471.182.193.495.4
100.4106.0118.1119.5135.1146.4
O
A a
B b
C c
D d
E e
F f
G g
J jH h
I iK k
L l
R r
T t
S sV v
U u W wM m
N nO o
P p
Q q
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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
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39
USA Soybean Yield Trends (1972-2003)
USA Trend: y = +0.412x - 785 R2 = 0.678
15
20
25
30
35
40
45
50
55
1970 1975 1980 1985 1990 1995 2000 2005Production Year
See
d Y
ield
(b
u/a
c)
Our Goal: Double the Rate of Genetic Gain
*courtesy of James Specht:
Crop Science 39:1560-1570
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Thank You!