Post on 30-Dec-2015
description
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
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 39:1560-1570
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)
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
R R R R S S S S
good correlation phenotype: genotype
Phenotype
Genotype
poor correlation phenotype: genotype
putative QTL hit
segregating progeny
8
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
9
Population 1
Parent1 (Resistant) x Parent2 (susceptible)
‘Major QTL’
‘Minor QTL’
Disease QTL detected within a specific population
P1
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
11
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?
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.
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
13
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)
14
Attempts to Map Yield QTLin 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 ina 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
aaAA
aaAA
aa AA AA aa
AAaa
AAAA
aa
aa aa
aaAA 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
bbBB
bb
bbBB
BB BB
BB
bb
bb
bbBB
24
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
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 44:436-442. Smalley, Fehr, Cianzio, Han, Sebastian, Streit
2006: Maydica 51: 293-300 Feng, Sebastian, Smith, Cooper.
Multiple lines of evidence
Very powerful tool
31
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
34
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
35
A1
A2
B1
Regions of Breeding Bias
36
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
37
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
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)
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
Thank You!