QTL Mapping
The objectives of this section are:
• To learn basic concepts related with Quantitative
Trait Loci (QTL) analysis,
• To learn how to use QTL analysis software,
• To interpret results, and to get acquainted with
the various QTL analysis programs and QTL
databases.
• The focus will be on QTL analysis of selfpollinated plants. However, most of what is
covered can be easily extended to cross-pollinated
plants, animals, and humans.
Topics
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Inheritance of quantitative traits
Identifying trait-linked markers
Single-marker analysis
Interval mapping
Composite interval mapping
Issues in QTL detection
Association mapping
Genomic Selection
Qualitative and Quantitative traits
• Phenotypes with discrete and easy to
measure values.
• Individuals can be correctly classified
according to phenotype.
• Show mendelian inheritance (monogene)
• Little environmental effect
• Molecular markers are qualitative traits
• Examples:
• Quantitative traits:
• Individuals cannot be classified by discrete
values
• Quantitative trait distribution show a
continuous range of variation and phenotypes
can take any value
• Complex mode of inheritance (polygene)
• Moderate to great environmental effect)
• Examples: Plant height, yield, disease severity,
grain weight, etc
% of plants
• Qualitative traits:
20
30
Plant Height (in)
40
Inheritance of Quantitative traits
The study of quantitative trait inheritance followed the same steps as for Mendelian traits.
At the beginning they were thought to not follow Mendel’s laws. But it is not true
F1
% of plants
×
PARENT 2:
• pure line, completely homozygote
• 20 inches
F1: range of height distribution
but no type of segregation
P2
P1
PARENT 1:
• pure line, completely homozygote
• 40 inches
20
30
40
Plant Height (in)
Plant Height (in)
F2
% of plants
F2: wider range of height
distribution but no type of
segregation
20
30
Plant Height (in)
40
One gene controlling
the trait
P1
P2
AA X
aa
Two genes with additive effect
controlling the trait
P1
AABB X aabb
F1 AaBb
Frequency
Frequency
F1 Aa
50%
25%
0
2
1
No. of favorable alleles
1/4 : AA
1/2: Aa + aA
1/4: aa
P2
50%
25%
0 1 2 3 4
No. of favorable alleles
1/16 : AABB
4/16: AaBB + AA Bb
6/16: AaBb + AAbb + aaBB
4/16: aaBb + Aabb
1/16: aabb
Three genes with additive
effect controlling the trait
P1
P2
AABBCC X aabbcc
Frequency
F1 AaBb
50%
25%
0 1 2 3 4 5 6
No. of favorable alleles
Inheritance of Quantitative traits
P1 (purple, X
very dark red)
aabb
F1(red) AaBb
Going one step further, He saw that within
each of the groups there was also some
variation
Frequency
AABB
P2 (white)
- white
+ purple
Color intensity
1/16 : purple AABB
4/16: dark-red AaBB + AABb
6/16: red AAbb + AaBb + aaBB
4/16: light-red aaBb + Aabb
1/16: white aabb
Inheritance of Quantitative traits
Phenotype=Genotype+Environment
Frequency
Then, the distribution of a quantitative trait would follow a normal distribution
4
+ purple
3
1
2
- white
Color intensity
Analysis of quantitative traits is therefore complicated:
Same genotype: 1 and 2 show different phenotype
Same phenotype: 1, 3 and 4 is the result of three different genotypes
Inheritance of Quantitative traits
Frequency
The inheritance of quantitative traits also explains the phenomenon of transgressive segregation: In
the progeny of a cross we can get phenotypes out of the range of the parents
P2
P1
0
Cold tolerance
10
Let’s assume 5 loci with additive effects control the trait
P1
P2
aabbccddEE X AABBCCDDee
F1
F2
AaBbCcDdEe
All possible combinations of alleles at 5 loci.
Between them: AABBCCDDEE (all favorable alleles)
aabbccddee (all unfavorable alleles)
Inheritance of Quantitative traits
Quantitative traits are usually controlled by several genes with small additive
effects and influenced by the environment
Heritability h2 measures the proportion of phenotypic variation (variance) that
is due to genetic causes
P = G + E;
VP = VG + VE
h2 
VG
VP
A heritability of 40% for cold tolerance means that within that population,
genetic differences among individuals are responsible of 40% of the variation.
Therefore, 60% is due to environmental causes.
However, that does not mean that the cold tolerance of a certain individual
is due 40% to genetic causes and 60% to environmental causes.
h2 is a property of the population and not of individuals
Inheritance of Quantitative traits
Heritability h2 measures the proportion of phenotypic variation (variance) that
is due to genetic causes
P = G + E;
VP = VG + VE
VG
h 
VP
2
h2 ranges between 0 and 1
If h2 is 0 means :
a) The trait is not genetically controlled. All the variation we see is due to
environmental factors, or
b) The trait is genetically controlled but all individuals have the same
genotype
h2 is very useful because it allows us to predict the response to artificial selection
Inheritance of Quantitative traits
Heritability h2 measures the proportion of phenotypic variation (variance) that
is due to genetic causes
P = G + E;
VP = VG + VE
h2 
VG
VP
h2 is very useful because it allows us to predict the response to artificial selection
In plant breeding, the starting point is a segregating population (with genetic variability).
The best individuals are selected to be the progenitors of the next generation
Frequency
μ0
Selection differential (S) = μS – μ0
μS
0
Grain yield
Response to selection (R) = μR – μ0
6000
(lb/A)
Frequency
μ0 μ R
0
Grain yield
(lb/A)
6000
Realized heritability:
h2 
R
S
Is the ratio of the single-generation progress of
selection to the selection differential of the
parents. The higher h2, the higher the progress
of selection in each generation
Analysis of Quantitative traits
The analysis of quantitative traits is based on the identification of the individual
loci (QTL) controlling the trait, their location, effects and interactions
A quantitative trait locus/loci (QTL) is the location of individual locus or multiple
loci that affects a trait that is measured on a quantitative (linear) scale.
These traits are typically affected by more than one gene, and also by the
environment.
Thus, mapping QTL is not as simple as mapping a single gene that affects a
qualitative trait (such as flower color).
Analysis of Quantitative traits
There are two main approaches for QTL analysis:
a)
QTL analysis in mapping populations
b)
Association mapping
Both approaches share a set of common elements:
a) A population (array of individuals) that show variability for the trait of study
b) Phenotypic information: We need to design an experiment to estimate the
phenotypic value of each individual
c) Genotypic information: A set of molecular markers that have been run in all
the individuals of the population
d) A statistical method to estimate QTL position, effects and interactions
Analysis of Quantitative traits
QTL analysis in mapping populations
We need to develop a population from a single cross between two individuals that
show contrasting phenotypes for the trait of study.
For example, if we want to study quantitative resistance to Barley Stripe Rust
(Puccinia striiformis f. sp. Hordei) we will develop a population from the cross
between a susceptible line and a resistant line.
The offspring of that cross will show recombination between the two parents and
therefore, some individuals will be resistant and other will be susceptible
Different types of mapping populations can be used:
Doubled haploids (DH), Recombinant inbred lines (RIL), F2, Back cross (BC), etc.
Always all individuals trace back to a single cross
Analysis of Quantitative traits
QTL analysis in mapping populations
The first step is getting genotypic information for all the individuals of the
population: molecular markers
P2
Back Cross population
P1
P2
SNP
Parent 1
Parent 2
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High Throughput genotyping platform (SNP)
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G
T
T
A
A
T
T
T
G
G
A
T
T
G
G
G
T
G
G
G
A
T
G
C
A
A
G
C
T
G
A
A
T
T
T
T
C
T
A
T
T
G
C
G
T
C
G
G
T
T
T
C
A
A
G
G
G
G
A
T
A
A
A
A
G
T
T
T
G
C
G
T
T
G
G
A
T
T
T
C
A
T
G
C
G
G
A
A
A
T
T
T
G
G
A
T
T
G
G
T
T
G
G
Analysis of Quantitative traits
QTL analysis in mapping populations
If molecular markers are polymorphic, we can construct a linkage map based on
recombination frequencies:
1H
0
7
12
18
22
25
26
29
30
36
48
54
58
61
68
73
86
87
96
101
111
119
121
122
130
133
136
2H
BCD1434
DsT-66
Act8A
RbgMD
MWG837B
scind00046
ABC165C
Bmac0399
GBM1007
BCD098
GBM1042
BG367013
Bmag0211
BG369940
GBM1051
ABC160
JS10C
Bmac0144A
MWG706A
KFP170
Blp
ABC261
MWG2028
KFP257B
WMC1E8
MWG912
ABG387A
scssr04163
scssr08238
3H
0
5
7
17
DsT-1
ABG058
scind02622
ABG008
36
39
42
45
56
scssr10226
scssr07759
GBM1066
Pox
scssr03381
scssr12344
scssr02236
Ebmac0684
BCD1434.2
ABG356
GBM1023
scsnp03343
vrs1
Bmag0125
DsT-41
MWG503
GBM1062
KFP203
MWG882A
ABG1032
ABG072
Ebmc0415
cnx1
Zeo1
GBM1019
Aglu5F3R2
MWG720
GBM1012
wst7
scssr08447
MWG949A
63
65
68
71
83
88
94
97
102
103
104
108
117
124
137
139
149
161
163
165
170
173
179
180
0
4H
BCD907
26
30
33
36
39
42
58
61
66
69
73
ABC171A
GBM1074
scssr10559
MWG798B
Dst-27
BCD706
DsT-39
alm
Bmac0209
ABC325
DsT-67
87
89
98
scssr25691
ABG377
Bmag0225
121
124
125
Act8C
ABG499
GBM1043
151
155
scsnp23255
ABG004
166
172
scind02281
MWG883
181
DsT-24
190
HVM62
199
DsT-40
212
218
ABC172
scssr25538
DsT-35
0
21
24
29
30
31
35
39
41
44
49
50
52
60
62
67
74
80
83
92
94
95
101
111
112
116
124
5H
MWG634
MWG077
HVM40
DsT-29
CDO542
CDO122
hvknox3
Dhn6
ABC303
scssr20569
CDO795
HVM3
DST-46
scind03751
scssr18005
Tef2
GBM1020
Bmag0353
scind10455
DsT-79
scssr14079
ABG472
GBM1059
KFP221
Ebmac0701
MWG652B
GBM1048
Hsh
HVM67
KFP241.1
ABG601
6H
0
6
8
11
12
scssr02306
MWG618
DsT-6
ABC483
ABG610
37
44
45
53
55
56
58
ABG395
scssr02503
scssr18076
Bmac0096
NRG045A
scsnp04260
Ale
79
82
85
90
100
ABC302
scind16991
scssr15334
scsnp06144
srh
111
scssr05939
120
128
134
141
RSB001A
scsnp00177
0SU-STS1
ABG003B
157
166
169
170
179
193
197
198
205
207
215
223
224
225
scssr10148
Tef3
MWG877
BE456118A
ABG496
scsnp02109
E10757A
ABG391
JS10B
ABC622
DsT-33
Bmag0113C
MWG602A
scssr03907
scssr03906
0
4
31
35
42
45
51
61
65
68
70
71
81
88
92
99
101
122
123
126
132
135
143
145
146
152
159
160
162
163
167
7H
MWG620
Bmac0316
scssr09398
MWG652A
MWG602B
scind60002
JS10A
GBM1021
GBM1068
BG299297
HVM31
rob
Bmag0009
scssr02093
ABG474
Bmac0218C
ABG388
scsnp21226
MWG820
GBM1008
scssr05599
MWG934
scind04312b
scssr00103
GBM1022
Bmac0040
DsT-18
DsT-32B
DsT-22
DsT-28
scind60001
DsT-74
MWG514
MWG798A
DsT-71
0
14
20
29
36
38
44
57
66
68
69
73
82
86
97
98
103
115
117
125
126
127
137
139
ABG704
Bmag0007
scind00694
AW982580
MWG089
CDO475
ABG380
BE602073
scssr07970
scsnp00460
ABC255
ABC165D
HvVRT2
scssr15864
GBM1030
scsnp22290
MWG808
DAK642
scind00149
scsnp00703
MWG2031
RSB001C
nud
lks2
ABC1024
Bmag0120
DsT-30
WG380B
ABC310B
Ris44
167
171
178
ABG461A
WG380A
GBM1065
196
197
199
HVM5
scssr04056
KFP255
ThA1
89
Maps have different levels of resolution
Maps: Different levels of resolution
Main factors: marker density and population size
Analysis of Quantitative traits
QTL analysis in mapping populations
The basic QTL analysis method consists in walking trough the chromosomes performing
statistical test at the positions of the markers in order to test whether
there is a marker-trait association or not
g = genotypic effect
- Classify progeny by marker genotype
- Compare phenotypic mean between classes (t-test or ANOVA)
- Significance = marker linked to QTL
- Difference between means = estimate of QTL effect
g = (µ1 - µ2)/2
µ1 = trait mean for
genotypic class AA
µ2 = trait mean for
genotypic class aa
y
βo
0
-1
aa
AA
Genotypic classes
x
Marker and QTL unlinked
F1
A
a
Q
q
F2 population
QTL genotype
Marker genotype
(x)
QQ
Qq
qq
Mean trait score
(y)
AA
1/4
1/2
1/4
Intermediate
Aa
1/4
1/2
1/4
Intermediate
aa
1/4
1/2
1/4
Intermediate
Difference between trait scores of AA and aa is zero.
Conclusion: No relationship between trait score (y) and marker genotype
(x)
Marker and QTL linked
F1
A
a
Q
q
F2 population
QTL genotype
Marker genotype
(x)
QQ
Qq
qq
Mean trait score
(y)
AA
Most
Few
Rare
High
Aa
Few
Most
Few
Intermediate
aa
Rare
Few
Most
Low
Difference between trait scores of AA and aa is large.
Conclusion: Strong relationship between trait score (y) and marker
genotype (x)
Disease
severity (%)
Parent 1(Resistant)
Parent 2 (Susceptible)
Line1
Line2
Line3
Line4
Line5
Line6
Line7
Line8
Line9
Line10
Line11
Line12
Line13
Line14
Line15
Line16
Line17
Line18
Line19
Line20
Line21
Line22
Line23
Line24
Line25
Line26
Line27
Line28
Line29
Line30
5
90
56
30
59
95
31
42
94
42
15
3
84
82
30
60
26
57
12
68
53
69
43
42
67
64
46
28
41
50
91
25
DsT-66
A
B
B
A
A
A
A
A
A
A
B
B
B
B
B
A
B
B
A
A
B
B
B
A
B
B
A
A
B
B
B
B
Analysis of
1H
Quantitative traits
Average Disease severy of
plants with allele “A” (Inherited
from Resistant parent) = 49.8
Average Disease severity of
plants with allele “B” (Inherited
from Susceptible parent) = 50.3
49.8 and 50.3 are not statistically
different. Therefore, marker DsT-66
is not associated with
resitance/susceptibility to the
disease
0
7
12
18
22
25
26
29
30
36
48
54
58
61
68
73
86
87
96
101
111
119
121
122
130
133
136
BCD1434
DsT-66
Act8A
RbgMD
MWG837B
scind00046
ABC165C
Bmac0399
GBM1007
BCD098
GBM1042
BG367013
Bmag0211
BG369940
GBM1051
ABC160
JS10C
Bmac0144A
MWG706A
KFP170
Blp
ABC261
MWG2028
KFP257B
WMC1E8
MWG912
ABG387A
scssr04163
scssr08238
Disease
severity (%)
Parent 1(Resistant)
Parent 2 (Susceptible)
Line1
Line2
Line3
Line4
Line5
Line6
Line7
Line8
Line9
Line10
Line11
Line12
Line13
Line14
Line15
Line16
Line17
Line18
Line19
Line20
Line21
Line22
Line23
Line24
Line25
Line26
Line27
Line28
Line29
Line30
5
90
56
30
59
95
31
42
94
42
15
3
84
82
30
60
26
57
12
68
53
69
43
42
67
64
46
28
41
50
91
25
ABC261
A
B
B
A
B
B
A
A
B
A
A
A
B
B
A
B
A
B
A
B
B
B
A
A
B
B
A
A
A
B
B
A
Analysis of
1H
Quantitative traits
Average Disease severy of
plants with allele “A” (Inherited
from Resistant parent) = 30.4
Average Disease severity of
plants with allele “B” (Inherited
from Susceptible parent) = 69.8
30.4 and 69.8 are statistically
different. Therefore, marker
ABC261 is linked with a
resitance/susceptibility QTL.
The additive effect of the QTL is:
a = (69.8-30.4)/2 = 14.7
0
7
12
18
22
25
26
29
30
36
48
54
58
61
68
73
86
87
96
101
111
119
121
122
130
133
136
BCD1434
DsT-66
Act8A
RbgMD
MWG837B
scind00046
ABC165C
Bmac0399
GBM1007
BCD098
GBM1042
BG367013
Bmag0211
BG369940
GBM1051
ABC160
JS10C
Bmac0144A
MWG706A
KFP170
Blp
ABC261
MWG2028
KFP257B
WMC1E8
MWG912
ABG387A
scssr04163
scssr08238