Accelerated Yield TechnologyTM
Context-Specific MAS for Grain Yield
Dr. Scott Sebastian, Research Fellow, Pioneer Hi-Bred International
11-2-09 Plant Breeding Seminar at University of California Davis
Pioneer Soybean Breeding
Yield: Genetic Gain vs. Precision
USA Soybean Yield Trends (1972-2003)
55
50
Seed Yield (bu/ac)
45
40
*courtesy of James Specht:
Crop Science 39:1560-1570
35
30
25
USA Trend: y = +0.412x - 785 R2 = 0.678
20
15
1970
1975
1980
1985
1990
Production Year
1995
Mean yield gain per year:
2000
2005
~ 1%
Precision in our best trials: +/- 5%
3
Soybean Yield Map (one inbred)
typical yield range: 30 to 70 bu/a
depending on position in the field
4
Corn Yield Map (one hybrid)
yield range: 109 to 243 bu/a
depending on position in the field
5
Outline
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
6
Simple Trait Mapping
e.g. SCN Resistance in Soybean
Resistant Parent
Susceptible Parent
x
segregating progeny
Phenotype
R
R
R
R
S
good correlation
phenotype: genotype
S
S
S
putative
QTL hit
Genotype
poor correlation
phenotype: genotype
7
QTL detected in Population 1
0.0
3.5
14.7
23.0
27.7
28.0
28.1
29.0
30.9
31.1
32.7
46.5
64.7
71.4
74.9
93.2
94.2
95.2
95.5
97.8
101.6
102.3
1.9
3.0
3.4
3.6
4.0
5.4
15.3
20.6
50.2
70.6
71.4
72.5
PR
73.0
74.3
77.7
78.1
85.3
91.9
102.1
117.6
119.2
124.6
130.6
135.1
151.0
0.0
2.1
5.3
9.1
28.4
35.0
51.5
100.1
105.2
108.8
109.8
110.9
115.9
116.6
116.7
119.6
125.4
128.4
128.9
129.9
145.6
154.1
162.0
165.7
0.0
0.0
6.0
11.9
17.8
22.0
28.3
32.5
33.0
36.5
46.4
57.9
69.8
73.8
78.1
80.9
81.9
82.9
84.2
85.9
89.7
95.1
96.4
102.6
0.0
65.1
73.3
74.2
74.4
75.5
76.2 P1
80.6
84.8
85.4
90.1
6.6
12.2
12.7
23.1
23.9
27.5
43.8
48.9
49.9
50.5
52.9
53.4
56.0
56.5
62.2
68.8
69.9
80.4
87.1
94.4
96.6
100.0
102.8
107.1
116.8
0.0
6.7
82.2
112.2
113.4
115.5
117.8
121.3
122.0
126.2
128.2
135.6
0.0
3.2
16.8
26.6
37.2
40.0
43.9
46.6
50.2
55.0
56.4
58.3
58.4
61.9
63.5
64.3
65.2
65.7
69.8
70.7
71.8
73.8
82.5
56.5
120.1
123.8
121.0
59.6
72.6
74.8
74.9
75.7
76.1
87.2
100.9
116.4
120.9
140.0
39.3
53.9
79.2
80.2
84.6
85.7
87.9
88.0
89.2
89.8
105.5
113.6
115.0
124.3
129.0
133.9
151.9
157.9
5.0
P1
0.0
11.2
12.0
26.6
30.5
38.0
44.7
34.9
51.5
55.2
57.0
65.6
67.7
71.7
72.1
72.5
72.9
73.2
78.8
87.6
91.1
97.9
125.7
132.2
0.0
9.0
0.0
3.7
12.9
18.2
19.3
30.3
32.1
32.3
34.2
35.8
41.7
43.1
43.6
44.9
45.1
45.4
47.5
56.3
56.7
64.2
71.3
0.0
0.6
8.5
27.6
38.9
46.9
58.9
68.5
69.1
72.2
85.8
86.5
91.1
93.7
0.0
20.3
28.0
31.5
31.9
34.0
35.3
50.1
65.6
77.8
82.8
99.8
112.7
113.4
124.0
0.0
12.3
15.7
24.1
25.5
26.1
27.8
29.7
32.1
36.7
37.8
38.2
39.8
41.2
42.5
43.1
52.7
71.9
78.8
89.8
91.0
0.0
14.4
21.7
30.3
41.5
42.7
43.3
44.0
46.2
46.4
49.5
49.6
50.9
52.9
78.6
78.7
104.8
117.0
0.0
8.0
11.1
0.0
5.0
7.8
27.9
30.6
30.9
33.7
36.1
38.2
56.1
59.5
64.7
66.5
70.2
18.6
106.4
107.2
112.3
115.1
33.5
35.9
56.3
59.9
62.1
67.0
73.9
75.6
76.4
77.2
87.1
95.4
107.7
111.1
112.8
125.2
133.8
140.7
142.2
0.0
26.1
27.1
29.4
31.8
34.5
34.6
36.9
37.4
38.0
38.1
40.8
53.2
70.6
72.6
75.9
76.5
84.6
92.6
116.7
0.0
5.4
9.5
17.3
20.4
39.8
42.3
43.6
49.7
52.1
53.7
54.2
55.1
55.8
56.3
56.9
57.0
68.4
71.1
82.1
P2
93.4
95.4
100.4
106.0
118.1
119.5
135.1
146.4
8
Disease QTL detected within a specific population
Population 1
Parent1 (Resistant)
x
Parent2 (susceptible)
P1
‘Major QTL’
P1
P2
‘Minor QTL’
9
‘Validation’ of QTL Across Populations
Major ‘additive’ gene
Population 1
Population 2
RES x SUS
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 ?
10
A validated SCN resistance gene ‘Rhg1’
Chromosome G
Map Position
0
Rhg1
.
20
.
40
.
60
.
80
.
100
.
120
.
But what is the effect of Rhg1 on yield?
11
Effect of a Rhg1 on Yield
Trait
gene
IBD
Effect of
Rhg1 on
disease
Effect on
Yield
(bu/a)
Population
Parent 1 x Parent 2
Statistical
Signif
Rhg1
Rhg1
Rhg1
Rhg1
Rhg1
Rhg1
Rhg1
93B86
93B86
93B86
93B86
93B15
93B15
93B15
YB32K01
EX36Y01
92B52
XB23Y02
92B74
ST2870
ST3630
R
R
R
R
R
R
R
+4.0
+1.9
+1.2
0.0
-0.2
-1.9
-6.3
**
*
ns
ns
ns
*
**
Rhg1
across all
across all
R
-0.2
ns
Global conclusion: Rhg1 does not affect yield.
Reality: the effect of Rhg1 on yield can be positive, neutral, or negative
depending on the population.
12
Why do yield effects of a QTL differ across populations?
Chromosome G
Rhg1
Yield Effect
0
.
Yield effects are not
distinguishable as single genes.
20
.
40
.
60
At best, a yield QTL can be
assumed as the net effect of an
entire region within a given
population.
.
80
.
100
.
Direction and magnitude of
effect can change dramatically
with both population and
environment (the context)
120
.
13
Attempts to Map Yield QTL
in the old paradigm
14
Attempts to ‘validate’ Yield QTL
Many QTL found, NONE have validated across all populations.
Population1
Population2
Population3
15
Do we need a different
paradigm for mapping Yield?
16
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
17
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
18
Implications for MAS in
a breeding program
19
Development of One Product
(before AYT)
Year0
Hundreds of Crosses
(Parent1 x Parent2)
inbreeding
Year1
MAS for simple traits
Yield Testing
Year2
20,000 lines x 1 rep
Year3 R1
5,000 lines x 2 reps
Year4 R2
500 lines x 6 reps
Year5 R3
20 lines x 25 reps
Year6 R4
4 lines x 50 reps
Many choices but terrible precision
error is ~ +/- 30% (15 bu/a)
Few choices but better precision
error ~ +/- 5% (2 to 3 bu/a)
Year7 R5
1 product
(better than parents?)
20
First Yield Screen: Progeny Row Yield Test
~ 85% of plot-to-plot variation is not heritable
21
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
22
AA
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
23
AYT analysis can be simple: AA vs. aa
QTL
location
Favorable Alleles
P1 alleles
Magnitude
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
Region E:
EE
=
2 bu/a
ee
0
… or more sophisticated
Yield (predicted) = Mean + 2xAA + 4xbb + 2xDD + …. + epistasis …
24
Select winners by
Target Genotype
AA bb DD …
25
Product Development (before AYT)
Hundreds of Crosses
Year0
F1
F2
F3
Forward selection for simple traits
Year1
Yield Testing
Resources
Year2
20,000 lines x 1 rep
20,000 micro plots
Year3
5,000 lines x 2 reps
10,000 small plots
Year4
500 lines x 6 reps
3,000 med plots
Year5
20 lines x 25 reps
500 large plots
Year6
4 lines x 50 reps
200 large plots
1 product
34,000 plots + 6 years
26
Product Development with AYT
Only the Best Crosses
Year0
F1
F2
F3
Forward Selection for (simple traits)
Year1
Year2
Context-Specific MAS for Yield
Year3
Year4
Much better selection precision
Advance only the most promising genotypes
Fewer lines = better characterization in fewer years
Better Products, Faster to Market
27
What about the cost of genotyping?
28
Genotyping Efficiency
Are some genomic regions yield hotspots?
Can this reduce genotyping costs?
Can this improve QTL detection rate?
29
‘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
30
History of Soybean
60+ years of
recurrent selection for
Yield
Ancestral
Population
Elite
Population
31
Yield-associated region
Marker: genetic hitchhiker
32
Loci with evidence of selection
60+ years of
recurrent selection for
Yield
Ancestral
Population
Elite
Population
change in allele frequency
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!
33
All Markers on First 3 Chromosomes
A2
A1
5.1
5.7
14.6
17.0
18.0
19.1
27.1
28.5
48.2
69.9
75.3
83.2
86.4
87.3
96.4
0.0
2.0
5.0
8.6
19.3
20.0
23.3
33.2
50.0
73.5
78.3
89.9
93.7
96.2
B1
22.5
26.7
34.9
39.0
45.0
56.6
68.1
71.6
73.3
74.1
74.8
76.4
80.0
85.0
91.9
92.1
108.7
119.6
123.4
132.4
135.1
136.0
138.2
117.3
120.0
154.7
161.8
173.5
175.2
184.0
34
Regions of Breeding Bias
A2
A1
B1
35
Breeding Bias hotspots across the entire genome
A1
0.0
3.5
14.7
23.0
27.7
28.0
28.1
29.0
30.9
31.1
32.7
46.5
64.7
71.4
74.9
93.2
94.2
95.2
95.5
97.8
101.6
102.3
A2
0.0
2.1
5.3
9.1
28.4
35.0
51.5
100.1
105.2
108.8
109.8
110.9
115.9
116.6
116.7
119.6
125.4
128.4
128.9
129.9
145.6
154.1
162.0
165.7
F
0.0
1.9
3.0
3.4
3.6
4.0
5.4
15.3
20.6
50.2
70.6
71.4
72.5
73.0
74.3
77.7
78.1
85.3
91.9
102.1
117.6
119.2
124.6
130.6
135.1
B1
0.0
B2
0.0
6.0
11.9
17.8
22.0
28.3
32.5
33.0
36.5
46.4
57.9
69.8
73.8
78.1
80.9
81.9
82.9
84.2
85.9
89.7
95.1
96.4
102.6
D1a
0.0
0.0
26.6
30.5
38.0
44.7
65.1
73.3
74.2
74.4
75.5
76.2
80.6
84.8
85.4
90.1
0.0
6.7
82.2
112.2
113.4
115.5
117.8
121.3
122.0
126.2
128.2
135.6
D2
E
0.0
3.7
12.9
18.2
19.3
30.3
32.1
32.3
34.2
35.8
41.7
43.1
43.6
44.9
45.1
45.4
47.5
56.3
56.7
64.2
71.3
0.0
3.2
16.8
26.6
37.2
40.0
43.9
46.6
50.2
55.0
56.4
58.3
58.4
61.9
63.5
64.3
65.2
65.7
69.8
70.7
71.8
73.8
82.5
56.5
120.1
123.8
121.0
D1b
11.2
12.0
9.0
34.9
51.5
55.2
57.0
65.6
67.7
71.7
72.1
72.5
72.9
73.2
78.8
87.6
91.1
97.9
125.7
132.2
C2
0.0
59.6
72.6
74.8
74.9
75.7
76.1
87.2
100.9
116.4
120.9
140.0
39.3
53.9
79.2
80.2
84.6
85.7
87.9
88.0
89.2
89.8
105.5
113.6
115.0
124.3
129.0
133.9
151.9
157.9
G
H
I
0.0
3.3
5.0
6.6
12.2
12.7
23.1
23.9
27.5
43.8
48.9
49.9
50.5
52.9
53.4
56.0
56.5
62.2
68.8
69.9
80.4
87.1
94.4
96.6
100.0
102.8
107.1
116.8
C1
0.0
0.6
8.5
27.6
38.9
46.9
58.9
68.5
69.1
72.2
85.8
86.5
91.1
93.7
0.0
20.3
28.0
31.5
31.9
34.0
35.3
50.1
65.6
77.8
82.8
99.8
112.7
113.4
124.0
J
0.0
12.3
15.7
24.1
25.5
26.1
27.8
29.7
32.1
36.7
37.8
38.2
39.8
41.2
42.5
43.1
52.7
71.9
78.8
89.8
91.0
K
0.0
14.4
21.7
30.3
41.5
42.7
43.3
44.0
46.2
46.4
49.5
49.6
50.9
52.9
78.6
78.7
104.8
117.0
L
M
0.0
8.0
11.1
0.0
5.0
7.8
27.9
30.6
30.9
33.7
36.1
38.2
56.1
59.5
64.7
66.5
70.2
18.6
106.4
107.2
112.3
115.1
33.5
35.9
56.3
59.9
62.1
67.0
73.9
75.6
76.4
77.2
87.1
95.4
107.7
111.1
112.8
125.2
133.8
140.7
142.2
151.0
= Rps Loci
= BSR Loci
= SCN Loci
= Yield Loci
N
0.0
26.1
27.1
29.4
31.8
34.5
34.6
36.9
37.4
38.0
38.1
40.8
53.2
70.6
72.6
75.9
76.5
84.6
92.6
116.7
O
0.0
5.4
9.5
17.3
20.4
39.8
42.3
43.6
49.7
52.1
53.7
54.2
55.1
55.8
56.3
56.9
57.0
68.4
71.1
82.1
93.4
95.4
100.4
106.0
118.1
119.5
135.1
146.4
36
Hotspots segregating in a given cross
A1
A
0.0
3.5
14.7
23.0
27.7
28.0
28.1
29.0
30.9
31.1
32.7
46.5
64.7
71.4
74.9
93.2
94.2
95.2
95.5
97.8
101.6
102.3
A2
0.0
2.1
5.3
9.1
a
F
D
d
E
M
l
0.0
26.6
30.5
38.0
44.7
f
82.2
112.2
113.4
115.5
117.8
121.3
122.0
126.2
128.2
e
120.1
123.8
H
G
H
I
J
m
0.0
20.3
28.0
31.5
31.9
34.0
35.3
27.6
38.9
46.9
58.9
68.5
69.1
72.2
85.8
86.5
91.1
93.7
N
50.1
n
O
99.8
112.7
113.4
124.0
P
65.6
77.8
82.8
125.2
0.0
12.3
15.7
24.1
25.5
26.1
27.8
29.7
32.1
36.7
37.8
38.2
39.8
41.2
42.5
43.1
52.7
71.9
78.8
89.8
91.0
o
Q
46.2
p
46.4
49.5
49.6
50.9
52.9
78.6
78.7
104.8
117.0
i
26.6
37.2
40.0
43.9
46.6
h
79.2
80.2
84.6
85.7
87.9
88.0
89.2
89.8
105.5
113.6
115.0
124.3
129.0
133.9
J
116.4
L
q
0.0
5.0
7.8
27.9
30.6
30.9
33.7
36.1
38.2
56.1
59.5
64.7
66.5
70.2
18.6
106.4
107.2
112.3
115.1
r
N
S
T
133.8
140.7
142.2
O
0.0
5.4
9.5
17.3
20.4
0.0
U
33.5
35.9
56.3
59.9
62.1
67.0
73.9
75.6
76.4
77.2
87.1
95.4
107.7
111.1
112.8
k
j
M
0.0
8.0
11.1
R
K
53.9
59.6
72.6
74.8
74.9
75.7
76.1
87.2
100.9
120.9
16.8
39.3
g
K
0.0
14.4
21.7
30.3
41.5
42.7
43.3
44.0
I
E
0.0
3.7
12.9
18.2
19.3
30.3
32.1
32.3
34.2
35.8
41.7
43.1
43.6
44.9
45.1
45.4
47.5
56.3
56.7
64.2
71.3
0.0
3.2
140.0
151.9
157.9
D2
0.0
6.7
50.2
55.0
56.4
58.3
58.4
61.9
63.5
64.3
65.2
65.7
69.8
70.7
71.8
73.8
82.5
56.5
135.6
b
D1b
11.2
12.0
65.1
73.3
74.2
74.4
75.5
76.2
80.6
84.8
85.4
90.1
5.0
6.6
12.2
12.7
23.1
23.9
27.5
43.8
48.9
49.9
50.5
52.9
53.4
56.0
56.5
62.2
68.8
69.9
80.4
87.1
94.4
96.6
100.0
102.8
107.1
116.8
0.0
F
34.9
51.5
55.2
57.0
65.6
67.7
71.7
72.1
72.5
72.9
73.2
78.8
87.6
91.1
97.9
0.0
0.6
8.5
D1a
9.0
121.0
125.7
132.2
G
50.2
151.0
c
C2
0.0
0.0
3.3
0.0
1.9
3.0
3.4
3.6
4.0
5.4
15.3
20.6
L
C1
0.0
6.0
11.9
17.8
C
51.5
100.1
105.2
108.8
109.8
110.9
115.9
116.6
116.7
119.6
125.4
128.4
128.9
129.9
145.6
154.1
162.0
165.7
B2
0.0
22.0
28.3
32.5
33.0
36.5
46.4
57.9
69.8
73.8
78.1
80.9
81.9
82.9
84.2
85.9
89.7
95.1
96.4
102.6
28.4
35.0
B
70.6
71.4
72.5
73.0
74.3
77.7
78.1
85.3
91.9
102.1
117.6
119.2
124.6
130.6
135.1
B1
s
V
26.1
27.1
29.4
31.8
34.5
34.6
36.9
37.4
38.0
38.1
40.8
53.2
70.6
72.6
75.9
76.5
84.6
92.6
116.7
t
u W
v
w
39.8
42.3
43.6
49.7
52.1
53.7
54.2
55.1
55.8
56.3
56.9
57.0
68.4
71.1
82.1
93.4
95.4
100.4
106.0
118.1
119.5
135.1
146.4
37
Accelerated Yield TechnologyTM
a combination of many tools
MAS for simple traits across populations
Breeding Bias & other tools to find hotspots
Context-Specific MAS for yield within each pop
38
Our Goal: Double the Rate of Genetic Gain
USA Soybean Yield Trends (1972-2003)
55
50
Seed Yield (bu/ac)
45
40
35
*courtesy of James Specht:
30
Crop Science 39:1560-1570
25
USA Trend: y = +0.412x - 785 R2 = 0.678
20
15
1970
1975
1980
1985
1990
Production Year
1995
2000
2005
39
Thank You!