QTL for Kernel Composition Mapping Populations Derived from Illinois Long Term Selection Strains Oil

1
QTL for Kernel CompositionMapping Populations
Derived from Illinois Long Term Selection
Strains Oil Protein - Starch Fatty Acids -
Tocopherols Carotenoids
2
(No Transcript)
3
Quantitative Variation
4
Many Kernels from Selected Ears Used for Next
Cycle Bulk
5
IHP
Low Starch
RLP
RHP
High Starch
ILP
6
Using the High Powered Comptometers
Strains Cycle 26 or so
Alice and Daisy Average Cycle 29
7
Higher Oil More Energy Feeding Monogastric
Animals
Me
8
IHP
IRLP
ILP
IRHP

• Selection for grain Protein alters endosperm
  development
• Illinois High Protein Seeds almost all Horny
  endosperm
• Illinois Low Protein Seeds almost all Starchy
  endosperm
• Higher Protein - Feeding Advantages Ruminants
• Higher Starch Wet Milling and Ethanol Advantages

9
RationaleAlter Concentrations for Added Value
Higher Oil Feeding AdvantagesFatty Acids
Quality of Meat Tocopherols Meat Shelf Life
Human Health
10
Concept Based on Genetic Kernel Composition
Modification ConsiderationsAlter A Number of
Kernel Components Small Amount Collective
Effect Enhanced Feed Value1. Monogastric 2.
Ruminants
11
Concept Combine Whole Genome Selection Efforts
withSelected Transgenic EffortsMany QTL for a
Precursor Compound One Transgene ConvertsAlso
Provides NonTransgenic Option
12
QTL in IHO x ILO
X
13
Have Mapped A Lot of QTL for Oil and Protein,
Starch in Different Genetic Backgrounds
High x Low F23 Per Se TestcrossBackcross
Populations with B73Oil Strains Protein
Strains IHO ILP
14

There is Not Simple Way to Fairly
Comprehensively Graphically Represent This
Collective Data in Seminar
2.0
5.01
9.01
2.02

04
8.05
3.06
5.05
10.05
4.08
5.07
10.07
2.09
X
VI
VII
VIII
II
III
IV
V
IX
I
15
We Have Extensive DataSets and Trying to Develop
a Small DataBase. We Can Discuss QTL Results
Individually and In Groups. I Brought Some
Articles
16
Performed Evaluation of Random Mated Populations
High x Low F2 RM 4, 6, 10 IHO x
ILOIHP x ILPEnabled More Precise Mapping of QTL
17
Today, Highlight Two Oil QTL Studies(IHO x
B73)B73 S1sBejing High Oil x B73F2s and
F23sMoving High Oil QTL into Standard Dent
Background
18

(IHO x B73)B73 S1s
2.0
5.01
9.01
2.02

04
8.05
3.06
5.05
10.05
gt
4.08
5.07
10.07
2.09
gt
QTL for Oil Concentration
X
VI
VII
VIII
II
III
IV
V
IX
I
PLABQTL Final Regression Model 46-54 of Variance
19

Bejing High Oil x B73 F2 Seeds and/or F23 Seeds
2.0
5.01
9.01

2.02




04

8.05
3.06
5.05
10.05
4.08
5.07
10.07
2.09
QTL for Oil Concentration

Common with IHO/B73
X
VI
VII
VIII
II
III
IV
V
IX
I
Analysis by QTL Cartographer
20
Bejing HO vs. Illinois HOSome QTL in
CommonSome QTL DifferentSome Common QTL May
Have Different Alleles, Coupling PhaseLargest
QTL in IHO/B73 on 6Largest QTL in BHO/B73 on 1

21
Macro Components Starch, Protein, Oil
F23s Per Se TestcrossBC1s Per Se
TestcrossNear Isogenic Lines for Selected Oil
QTL, Notably Bin 6.04 DAPs, Suitable for RNA
Profiling
22
Fatty Acids (Oleic Acid)Tocopherols (Vitamin E)
Carotenoids (Pro-Vitamin A)Appeal
Nutritional ValueHealth, AntioxidantsBiosynthet
ic Pathways Provide Opportunity for Basic QTL
StudyCandidate Genes
23

(IHO x B73)B73 S1s
QTL for Fatty Acid Concentration. Oleic,
Linoleic, Strearic, Palmitic, Linolenic
2.0
5.01
9.01
2.02

04
8.05
3.06
5.05
10.05
4.08
5.07
10.07
2.09
QTL for Oil Concentration
X
VI
VII
VIII
II
III
IV
V
IX
I
From Final Regression Models PLABQTL
24
Does Not Appear to Be a Strong Relation Between
QTL for Oil Concentration and Specific Fatty
Acid ConcentrationsWhere we had an initial
association in Bin 6.04, Oil, Oleic LinoleicFine
mapping separated peaks of the Different QTL
25
Fatty Acid Desaturase 2Converts Oleic to
Linoleic AcidFad 2 ESTs Did Not Map to Any
Oleic Acid Linoleic Acid QTLModel - The QTL
for Oleic/Linoleic Acid May be Modifiers or
Transcription Factors for Fad2Combine Bin 6.04
QTL for High Oleic with Transgenes
26
Tocopherols
Antioxidants Sequester Free Radicals
27
Tocopherols
OH
O
d-tocopherol
CH3
OH
Lots of Gamma Tocopherol in Maize
CH3
O
g-tocopherol
CH3
CH3
OH
CH3
O
a-tocopherol
CH3
28
Health Benefits

• Free Radicals - Oxidative Damage
• Linked to Degenerative Diseases
• Cancer
• Coronary Heart Disease
• Cataracts
• Antioxidant Vitamins - Increase
• Reduce risk

29
HPLC Chromatogram 290 nm
30
Chromosome 1
31
Chromosome 5
32
Tocopherols
1
5
2
Nc130
0
P200689
0
Phi083
0
Bnlg1429
13
Nc007
19
BNL1206
57
Umc5
31
sxd1
Umc14
33
Umc167
90
Phi113
69
N279
104
Umc67
108
46
Phi127
P150018
95
CO7BO2CD
100
63
Mmc0063
Cqrak57a
156
P100014
114
Cfbb.58
168
P200668
172
Umc33
180
Dupssr012
189
N298
79
Mmc0041
197
P200531
147
Phi053
209
Umc106
221
Phi048
169
Bmc1396
97
Bmc1671
237
g-tocopherol
a-tocopherol
d-tocopherol
33
Epistatic Interactions Tocopherols
1
5
7
P200689
0
Bnlg2203
0
Nc130
0
Bnlg1429
13
Phi034
15
Nc007
19
Ra1
28
BNL1206
57
Umc1015
39
Bmc1070
52
Umc167
90
Phi113
69
Phi091
60
N279
104
Umc67
108
Dupssr009
65
P150018
95
CO7BO2CD
100
Bmc1805
86
Cqrak57a
156
P100014
114
Cfbb.58
168
P200668
172
Umc33
180
Dupssr13
111
Dupssr012
189
Mmc0041
197
P200531
147
Phi053
209
Umc106
221
Bmc1671
237
N380
140
Phi048
169
g-tocopherol
a-tocopherol
34
QTL mapping summary Tocopherols
?
?
Number of QTL included in final multiple
regression models and proportion of
phenotypic and genotypic variation, respectively,
explained by the final multiple regression models
35
W64a x A632 (F3)
White Cap 1
U.S.Brazilian Inbred
Commercial Inbred
IHO x ILO (F3)
36
Carotenoid Biosynthetic Pathway
GGPP
Lycopene
PSY
y1
Phytoene
b- LCY
b- LCY e- LCY
PDS
2x
Phytofluene
vp5
z- carotene
a- carotene
b- carotene
ZDS
2x
Neurosporene
b-crytptoxanthin
vp9
Zeaxanthin
Lutein
37
HPLC Chromatogram to Quantify Carotenoids
38
Major QTL Positions for Carotenoids A632 x W64A
F23s IHOxB73 BC1S1s
Y1 Phytoene Synthase
2.0
5.01
9.01
2.02
Y1

04
ZDS
?
8.05
3.06
5.05
10.05
4.08
5.07
10.07
2.09
ZDS Zeta Carotene Desaturase
X
VI
VII
VIII
II
III
IV
V
IX
I
Y1 and ZDS are in Carotenoid Biosynthetic Pathway
39
Epistatic Interactions Carotenoids
6
9
8
Bnlg238
0
Bmc1724
0
N260
0
Phi126
3
Y1ssr
19
Bmc2235
27
Phi017
22
Umc1006
33
Umc103
38
Umc1741
53
Umc65
50
Umc1983
55
Bnlg244
43
Bmc1834
60
Phi027
51
Phi081
70
Umc81
57
Nc009
68
ACCase
59
Bnlg666
86
Umc114
62
Umc21
76
Bnlg162
87
Umc76
65
Bmc1922
79
Bmc1599
91
Phi129
86
Umc1130
100
P200554
81
Umc20
97
Bmc1056
145
N291
119
Dupssr15
147
Umc1069
166
Bnlg619
129
Bmc1131
177
Umc62
164
Agrr21
Bnlg128
139
186
Lutein
ß-carotene
ß-cryptoxanthin
40
Epistatic Interactions Total Carotenoids
6
9
Bnlg238
0
y1Y1 vs Y1Y1
Bmc1724
0
Phi126
3
Y1ssr
19
Phi017
22
Umc1006
33
Umc65
50
Bnlg244
43
Phi027
51
Umc81
57
Nc009
68
ACCase
59
Umc114
62
Umc21
76
Umc76
65
Bmc1922
79
Phi129
86
P200554
81
Umc20
97
N291
119
Dupssr15
147
Bnlg619
129
Umc62
164
Bnlg128
139
Use of Mutant with Dosage Effect to Better Reveal
Other QTL?
41
Breeding for Higher Levels Beta-CaroteneSurveyin
g Diverse Germplasm Materials Developed at
IllinoisMaking Genetic CrossesCreating
Synthetics
42
(No Transcript)
43
Means and Ranges from 2003 Survey New and Wider
Set of Lines
44
Chemical Structure of Vitamin A and Pro-vitamin A
45
Vitamin A Equivalents from CI.7, DE3, Mo17 and B73
46
Phytoene Synthase -Y1 Candidate Gene for QTL
Determined Association of Y1SSR Marker -
Carotenoids Sequencing Fragments for
Associative Analysis-Nucleotide
47
Carotenoid Biosynthetic Pathway
GGPP
Lycopene
PSY
y1
Phytoene
b- LCY
b- LCY e- LCY
PDS
2x
Phytofluene
vp5
z- carotene
a- carotene
b- carotene
ZDS
2x
Neurosporene
b-crytptoxanthin
vp9
Zeaxanthin
Lutein
48
Gel image of Y1ssr marker
227bp-
-224bp
216bp-
210bp-
-204bp
49
Means of Groups using the fragment sizes of Y1ssr
marker
groups include only yellow and orange lines, no
whites
50
Multiple Regression of Means Y1ssr Marker Groups
51
Single Factor Analysis among three groups and
their marker means
52
Pre-screening with TLC

• RAPID
• INEXPENSIVE

Identification of the Top Lines for more detailed
HPLC analysis
53

• The number of functional (-OH) groups
• is the basis of carotenoid separation
• The RATIO of the two upper bands (pro-vitamin
  A)
• relative to the last band are visually
  estimated
• Qualitative, can we use to prescreen and
  eliminate 60 70 80?, then run HPLC

54
SummaryDetect Sets of QTL that Effect Oil and
Can be Transferred to Another BackgroundThere
are Lots of Oil QTL in Different Genetic
Backgrounds and Their Likely are More Favorable
Alleles in Diverse GermplasmQTL for Oil, Fatty
Acids Tocopherols Do Not Correspond
WellCandidate Genes for Carotenoids Being
Pursued
55
AcknowledgmentsIllinois Missouri
Biotechnology Alliance (USDA)BARD US Agency
for International DevelopmentCFARJeff Wong,
Sultana Islam, Jeremy Johnson, Joe King, Jim
Wassom