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
Inheritance of Quantitative traits
In 1903 the Danish botanist Wilhelm Johannsen measured the weight of seeds in the
Princess variety of bean. This variety is a pure line since beans are self-fertilizing .
Then he selected 19 beans of different weights
and self-pollinated them several generations
Doing this he got 19 pure lines (completely homozygous)
in case they were not at the beginning of the experiment
% of plants
From a seed lot he measured and classified the beans by weight and obtained the range of
distribution for that variety.
250
400
550
Weight (gr)
He found that:
The weight of the 5,494 beans he obtained followed a normal distribution
All lines within each of the 19 groups were genetically identical but showed also a
range of variation in weights.
The average and distribution of weight in each pure line were similar to those of
the original population
Inheritance of Quantitative traits
% of plants
The Experiment of Johannsen
250
400
550
250
400
Weight (gr)
550
% of plants
% of plants
% of plants
Weight (gr)
250
400
Weight (gr)
550
250
400
Weight (gr)
Conclusions:
•There is a genetic control that keeps the same average weight and distribution
•However not all genetically identical seeds have the same weight.
•The phenotype of each individual must be determined by the genotype and the
environmental conditions
•Without genetic variability, genetic improvement is not possible
550
Inheritance of Quantitative traits
Johannsen showed that quantitative traits are determined by genes. However he did not
find any type of mendelian segregation.
This was studied in 1909 by Swedish Herman Nilsson-Ehle who studied kernel color in wheat
He had several pure lines of red and white colored kernels. When crossing red x white he
got always red F1, but different proportions of red and white kernels depending on the
cross:
a)
3 red : 1 white
b)
15 red : 1 White
c)
63 red : 1 white
He deduced that the color was controlled by three loci. Only individuals with recessive
homozygous alleles at the three loci showed the white phenotype. When a single dominant
allele (A, B or C) is present at any of the three loci the red phenotype shows up.
Inheritance of Quantitative traits
a) 3 red : 1 white
b) 15 red : 1 White
c) 63 red : 1 white
For case a), allelic variation between the
two parents was present only at one locus
P1 (red) P2 (white)
AAbbcc X aabbcc
F1(red)
Aabbcc
¼ AAbbcc : ½ Aabbcc : ¼ aabbcc
F2
(only one locus
(red)
(red)
(white)
segregating)
Segregation 3 red : 1 white
Inheritance of Quantitative traits
a) 3 red : 1 white
b) 15 red : 1 White
c) 63 red : 1 white
P1 (red) P2 (white)
AABBcc X aabbcc
F1(red)
F2
(two loci
segregating)
AaBbcc
1/16 AABBcc
2/16 AABbcc
1/16 AAbbcc
2/16 AaBBcc
4/16 AaBbcc
2/16 Aabbcc
1/16 aaBBcc
2/16 aaBbcc
1/16 aabbcc
(red)
(red)
(red)
(red)
(red)
(red)
(red)
(red)
(white)
Segregation 15 red : 1 white
Inheritance of Quantitative traits
a) 3 red : 1 white
b) 15 red : 1 White
c) 63 red : 1 white
P1 (red) P2 (white)
AABBCC X aabbcc
F2
(three loci
segregating)
1/64 AABBCC
2/64 AABbCC
1/64 AabbCC
2/64AaBBCC
4/64 AaBbCC
2/64 AabbCC
1/64 aaBBCC
2/64 aaBbCC
1/64 aabbCC
F1(red)
AaBbCc
Segregation 63 red : 1 white
(red)
(red)
(red)
(red)
(red)
(red)
(red)
(red)
(red)
2/64 AABBCc
4/64 AABbCc
2/64 AabbCc
4/64 AaBBCc
8/64 AaBbCc
4/64 AabbCc
2/64 aaBBCc
4/64 aaBbCc
2/64 aabbCc
(red)
(red)
(red)
(red)
(red)
(red)
(red)
(red)
(red)
1/64 AABBcc
2/64 AABbcc
1/64 Aabbcc
2/64 AaBBcc
4/64 AaBbcc
2/64 Aabbcc
1/64 aaBBcc
2/64 aaBbcc
1/64 aabbcc
(red)
(red)
(red)
(red)
(red)
(red)
(red)
(red)
(white)
Inheritance of Quantitative traits
However, Nilsson-Ehle not only classified the seeds by color. He also classified them by color
intensity and saw that color intensity also had a defined segregation pattern
P1 (purple, X
very dark red)
P2 (white)
F1(red)
1/16 : purple
4/16: dark-red
6/16: red
4/16: light-red
1/16: white
He proposed that for this cross,
color intensity was determined
by two loci with two alleles each:
one that produced red pigment
(A and B) and other with no
pigment (a and b).
He determined that the effects of
the alleles were additive and
contributed equally to the
phenotype, which depended on
the number of alleles for
pigment present
P1 (purple,
very dark red)
P2 (white)
AABB X aabb
F1(red) AaBb
1/16 AABB
2/16 AABb
1/16 AAbb
2/16 AaBB
4/16 AaBb
2/16 Aabb
1/16 aaBB
2/16 aaBb
1/16 aabb
(Purple)
(dark-red)
(red)
(dark-red)
(red)
(light-red)
(red)
(light-red)
(white)
Inheritance of Quantitative traits
P1 (purple, X
very dark red)
P2 (white)
Frequency
F1(red)
Going one step further, He saw that within
each of the groups there was also some
variation
- white
+ purple
Color intensity
1/16 : purple
4/16: dark-red
6/16: red
4/16: light-red
1/16: white
Inheritance of Quantitative traits
He deduced that many loci were involved (not only two) in the trait and taking
into account Johanssen’s findings:
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;
h
2
VP = VG + VE

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;
h
2
VP = VG + VE

VG
VP
h2 ranges between 0 and 1
If h2 is 0 means :
a)
b)
The trait is not genetically controlled. All the variation we
see is due to environmental factors, or
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
VG
2
h

P = G + E;
VP = VG + VE
VP
h2 is very useful because it allows us to predict the response to artificial selection
Frequency
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
μ0
Selection differential (S) = μS – μ0
μS
Response to selection (R) = μR – μ0
0
Grain yield
6000
(lb/A)
h
Frequency
μ0 μR
0
Grain yield
(lb/A)
Realized heritability:
6000
2

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)
b)
c)
d)
A population (array of individuals) that show variability for the trait
of study
Phenotypic information: We need to design an experiment to
estimate the phenotypic value of each individual
Genotypic information: A set of molecular markers that have been
run in all the individuals of the population
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
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
Line30
Line31
Line32
Line33
Line29
Line30
Line30
Line31
Line32
Line33
Line34
Line35
Line36
Line37
Line38
Line39
Line40
Line41
Line42
Line43
Line44
Line45
Line46
Line47
Line48
Line49
Line50
Line51
Line52
Line53
Line54
Line55
Line56
Line57
Line58
Line59
P1
1_0002
1_0004
1_0011
1_0014
1_0020
1_0023
1_0024
1_0026
1_0031
1_0036
1_0041
1_0047
1_0048
1_0050
1_0051
1_0052
1_0053
1_0055
1_0061
1_0063
1_0064
1_0065
1_0071
1_0073
1_0080
1_0081
1_0083
1_0084
G
T
A
G
C
A
T
G
G
G
G
T
T
A
T
A
A
G
T
T
T
T
G
G
T
T
G
C
A
A
T
T
G
T
A
C
C
T
T
A
A
T
A
T
T
C
G
A
C
G
C
C
G
A
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
A
T
C
T
A
G
C
T
T
T
A
A
T
T
T
C
G
A
C
T
G
C
T
T
C
G
G
T
T
T
C
A
T
C
G
G
T
A
T
A
A
A
A
G
T
T
T
T
C
G
T
A
G
C
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
G
A
A
T
G
A
T
G
C
T
T
T
A
T
T
A
A
C
T
A
T
G
G
C
G
T
C
C
G
A
T
T
C
A
T
C
C
T
T
A
A
T
A
T
T
C
G
A
T
G
C
C
G
A
C
C
G
T
A
G
G
T
T
C
G
T
G
A
T
T
T
A
A
G
G
T
C
G
G
G
T
A
G
G
A
A
T
T
C
T
A
C
G
G
T
A
T
T
A
A
A
C
G
T
C
T
C
C
G
A
C
G
G
T
T
T
G
A
A
C
G
G
T
A
T
T
T
T
T
G
T
T
T
G
G
G
T
A
G
G
G
T
A
G
G
T
A
G
G
G
T
T
T
T
T
A
A
G
T
T
C
T
G
G
T
T
G
G
A
T
T
T
C
T
T
G
C
T
T
A
A
T
A
A
A
C
G
A
C
T
C
C
T
T
C
C
A
T
T
T
C
T
T
C
C
G
G
T
A
A
T
T
T
G
G
A
C
G
G
G
T
A
G
G
G
A
T
G
C
T
T
C
G
T
T
A
T
A
A
T
T
G
T
T
C
T
C
G
G
A
G
G
A
T
T
G
C
T
T
C
C
T
T
A
A
T
T
T
T
G
T
A
C
G
G
G
T
A
G
G
G
T
T
T
C
A
A
G
G
G
G
A
T
T
T
T
T
C
T
T
T
G
G
C
T
T
C
C
A
A
A
T
G
A
A
G
C
G
T
T
A
T
T
T
T
C
T
A
T
G
G
C
G
T
C
C
G
A
A
T
G
A
T
G
C
T
T
T
A
T
T
A
A
C
T
A
T
G
G
C
G
T
C
C
G
T
T
G
C
A
T
C
C
G
G
A
A
T
A
T
T
C
T
A
T
G
C
C
T
A
C
G
A
A
A
T
C
T
A
G
G
T
T
A
T
T
A
A
A
G
G
T
C
T
C
G
G
T
G
C
G
A
A
G
C
A
T
G
C
T
G
T
A
A
A
A
A
C
T
A
T
G
C
C
G
T
C
C
G
T
A
T
G
A
T
G
C
G
T
T
A
A
T
T
T
G
T
A
T
G
G
G
T
T
G
G
G
A
A
G
G
T
A
G
C
G
G
A
A
T
T
T
T
C
G
A
C
T
G
C
G
T
C
G
A
T
A
G
G
T
T
G
G
G
T
T
T
A
A
T
T
G
G
T
C
T
C
G
T
T
G
C
A
A
A
T
G
T
A
C
G
G
T
T
A
A
A
T
T
G
G
A
C
T
C
G
G
T
G
G
A
A
A
T
G
T
A
G
G
G
T
A
A
A
A
T
T
G
G
A
C
G
C
G
G
T
G
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
G
A
T
G
C
T
T
C
C
G
T
A
T
A
A
A
A
G
T
T
C
T
C
G
G
A
G
G
A
T
T
T
C
T
A
C
G
T
T
T
T
T
A
A
A
C
T
T
C
G
C
C
T
A
C
G
A
T
T
G
G
A
T
G
G
G
T
T
T
A
A
A
A
G
G
T
T
T
C
G
T
A
G
G
A
A
T
T
C
A
A
C
G
T
T
A
T
T
T
A
A
C
G
T
T
G
G
C
G
A
C
G
G
A
A
T
G
T
T
G
C
G
T
T
A
A
A
T
T
G
T
A
C
T
C
G
G
T
G
C
A
A
A
T
C
T
A
G
C
T
T
T
A
A
A
T
T
G
G
A
C
G
C
G
G
T
G
C
A
A
T
G
G
A
T
G
C
G
T
A
A
T
A
T
T
C
T
A
T
G
C
C
G
T
C
C
G
T
A
G
C
T
A
G
G
G
G
T
A
A
T
T
T
G
G
A
C
G
G
G
T
T
G
G
A
A
A
G
G
A
A
G
G
G
G
T
T
A
T
T
T
G
T
T
T
G
G
G
G
T
G
C
A
A
T
G
C
T
A
G
C
G
G
A
A
T
A
T
T
C
G
A
C
G
C
C
G
T
C
C
G
A
A
G
G
A
A
G
G
A
A
G
G
A
A
G
G
A
A
G
G
A
A
G
G
A
A
G
A
T
T
G
C
A
T
G
A
T
T
G
C
A
T
G
A
T
T
G
C
A
T
G
A
T
T
G
A
A
T
T
C
A
T
C
A
A
T
T
C
A
T
C
A
A
T
T
C
A
T
C
A
A
T
T
G
A
A
T
C
T
A
G
G
A
A
T
C
T
A
G
G
A
A
T
C
T
A
G
G
A
A
T
G
A
A
G
G
A
A
G
G
G
G
T
T
T
T
A
A
G
G
T
T
T
G
G
G
T
C
C
A
T
T
G
C
A
T
G
C
G
T
A
A
T
T
A
A
G
T
A
T
T
G
G
T
T
G
G
A
A
T
T
C
A
T
C
C
G
T
T
A
T
A
T
T
C
T
A
T
T
C
C
G
A
C
G
G
A
A
T
C
T
A
G
G
G
G
T
T
T
A
T
T
G
T
T
C
G
C
G
G
T
G
C
A
A
A
T
G
A
T
G
G
T
G
T
T
A
T
T
T
G
T
T
T
G
G
G
G
T
G
C
G
A
T
T
G
T
A
C
C
T
G
A
A
T
A
T
T
C
T
A
C
G
C
C
G
A
C
C
G
A
T
G
G
A
A
C
G
T
T
A
T
T
A
A
A
C
T
T
T
G
C
C
G
A
C
C
G
A
T
T
G
T
T
C
C
T
T
A
A
T
A
T
T
G
G
A
C
G
C
G
G
A
G
C
High Throughput genotyping platform (SNP)
G
T
A
G
G
T
T
C
C
G
T
T
A
T
T
T
T
C
G
A
T
G
G
C
T
A
C
C
A
T
T
G
C
T
T
C
C
T
T
A
A
T
T
T
T
G
T
A
C
G
G
G
T
A
G
G
A
A
A
G
G
T
A
G
C
G
T
T
T
A
T
A
A
G
G
T
C
G
G
G
G
T
G
C
A
A
A
G
G
A
A
G
C
T
T
T
A
A
A
A
A
G
T
A
T
G
C
G
G
T
G
G
G
T
A
G
C
T
A
G
G
G
G
T
A
A
T
T
T
G
G
A
C
G
G
G
T
T
G
G
G
A
A
T
G
A
T
C
C
G
T
T
A
T
T
T
T
C
T
A
T
T
G
C
G
T
C
C
A
T
T
T
C
T
A
C
C
G
T
A
A
T
T
T
T
C
T
A
C
G
G
C
T
A
C
G
G
A
A
T
C
A
A
G
C
G
G
A
A
A
T
A
A
G
T
A
T
T
G
G
G
T
G
G
A
T
A
G
C
A
T
G
G
T
T
T
T
T
A
A
A
C
T
T
T
G
C
C
T
T
C
C
G
A
T
G
C
T
T
C
G
T
T
A
T
A
T
A
A
G
G
T
C
G
G
G
G
A
G
C
G
A
A
G
C
A
A
G
G
G
T
A
T
T
A
A
A
C
T
T
T
G
C
C
G
T
C
G
A
T
A
T
G
A
A
C
C
G
G
A
A
T
T
T
T
C
T
A
T
T
G
C
T
A
C
G
A
A
T
T
C
A
A
C
G
T
T
A
T
T
T
A
A
C
G
T
T
G
G
C
G
A
C
G
A
A
A
T
G
T
A
G
C
T
T
T
T
T
T
A
A
C
G
A
C
G
G
C
G
T
C
C
G
A
A
T
C
A
T
G
C
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
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
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
Significance trheshold
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
Probability
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
79
82
85
90
100
37
44
45
53
55
56
58
0
6
8
11
12
ABC302
scind16991
scssr15334
scsnp06144
srh
ABG395
scssr02503
scssr18076
Bmac0096
NRG045A
scsnp04260
Ale
scssr02306
MWG618
DsT-6
ABC483
ABG610
RSB001A
scsnp00177
0SU-STS1
ABG003B
scssr05939
120
128
134
141
scssr10148
Tef3
MWG877
BE456118A
ABG496
scsnp02109
E10757A
ABG391
JS10B
ABC622
DsT-33
Bmag0113C
MWG602A
scssr03907
scssr03906
111
157
166
169
170
179
193
197
198
205
207
215
223
224
225
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
Most likely position of the QTL
CD907
BC171A
BM1074
ssr10559
WG798B
st-27
CD706
sT-39
m
mac0209
BC325
sT-67
BC172
ssr25538
sT-35
sT-40
VM62
sT-24
ind02281
WG883
snp23255
BG004
ct8C
BG499
BM1043
ssr25691
BG377
mag0225
Analysis of Quantitative traits
QTL analysis in mapping populations
Analysis of Quantitative traits
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
We identify the location of the QTL, the molecular markers
flanking them, their effect and their interactions
Analysis of Quantitative traits
Association mapping
Also called Linkage Disequilibrium mapping
No need to develop populations from a single cross. Analysis is performed
on arrays of related or unrelated individuals.
Individuals of different origin, pedigree or degree of kinship may create
population structure that can lead to false positives in the analysis.
Association between markers and QTL in mapping populations are based
only on linkage. However, in Association mapping these association can be
due to multiple factors: linkage, selection, mutation, genetic drift, kinship,
population structure, etc.
Unlike mapping populations, where only alleles from the two parents are
studied, multiple alleles may be present at any single locus.
Analysis of Quantitative traits
The analysis is based on the same principles as QTL analysis in mapping
populations.
Linkage maps are not needed
SNP
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
Line30
Line31
Line32
Line33
Line29
Line30
Line30
Line31
Line32
Line33
Line34
Line35
Line36
Line37
Line38
Line39
Line40
Line41
Line42
Line43
Line44
Line45
A higher density of markers is required
1_0002
1_0004
1_0011
1_0014
1_0020
1_0023
1_0024
1_0026
1_0031
1_0036
1_0041
1_0047
1_0048
1_0050
1_0051
1_0052
1_0053
1_0055
1_0061
1_0063
1_0064
1_0065
1_0071
1_0073
1_0080
1_0081
1_0083
1_0084
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
A
T
C
T
A
G
C
T
T
T
A
A
T
T
T
C
G
A
C
T
G
C
T
T
C
G
G
T
T
T
C
A
T
C
G
G
T
A
T
A
A
A
A
G
T
T
T
T
C
G
T
A
G
C
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
G
A
A
T
G
A
T
G
C
T
T
T
A
T
T
A
A
C
T
A
T
G
G
C
G
T
C
C
G
A
T
T
C
A
T
C
C
T
T
A
A
T
A
T
T
C
G
A
T
G
C
C
G
A
C
C
G
T
A
G
G
T
T
C
G
T
G
A
T
T
T
A
A
G
G
T
C
G
G
G
T
A
G
G
A
A
T
T
C
T
A
C
G
G
T
A
T
T
A
A
A
C
G
T
C
T
C
C
G
A
C
G
G
T
T
T
G
A
A
C
G
G
T
A
T
T
T
T
T
G
T
T
T
G
G
G
T
A
G
G
G
T
A
G
G
T
A
G
G
G
T
T
T
T
T
A
A
G
T
T
C
T
G
G
T
T
G
G
A
T
T
T
C
T
T
G
C
T
T
A
A
T
A
A
A
C
G
A
C
T
C
C
T
T
C
C
A
T
T
T
C
T
T
C
C
G
G
T
A
A
T
T
T
G
G
A
C
G
G
G
T
A
G
G
G
A
T
G
C
T
T
C
G
T
T
A
T
A
A
T
T
G
T
T
C
T
C
G
G
A
G
G
A
T
T
G
C
T
T
C
C
T
T
A
A
T
T
T
T
G
T
A
C
G
G
G
T
A
G
G
G
T
T
T
C
A
A
G
G
G
G
A
T
T
T
T
T
C
T
T
T
G
G
C
T
T
C
C
A
A
A
T
G
A
A
G
C
G
T
T
A
T
T
T
T
C
T
A
T
G
G
C
G
T
C
C
G
A
A
T
G
A
T
G
C
T
T
T
A
T
T
A
A
C
T
A
T
G
G
C
G
T
C
C
G
T
T
G
C
A
T
C
C
G
G
A
A
T
A
T
T
C
T
A
T
G
C
C
T
A
C
G
A
A
A
T
C
T
A
G
G
T
T
A
T
T
A
A
A
G
G
T
C
T
C
G
G
T
G
C
G
A
A
G
C
A
T
G
C
T
G
T
A
A
A
A
A
C
T
A
T
G
C
C
G
T
C
C
G
T
A
T
G
A
T
G
C
G
T
T
A
A
T
T
T
G
T
A
T
G
G
G
T
T
G
G
G
A
A
G
G
T
A
G
C
G
G
A
A
T
T
T
T
C
G
A
C
T
G
C
G
T
C
G
A
T
A
G
G
T
T
G
G
G
T
T
T
A
A
T
T
G
G
T
C
T
C
G
T
T
G
C
A
A
A
T
G
T
A
C
G
G
T
T
A
A
A
T
T
G
G
A
C
T
C
G
G
T
G
G
A
A
A
T
G
T
A
G
G
G
T
A
A
A
A
T
T
G
G
A
C
G
C
G
G
T
G
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
G
A
T
G
C
T
T
C
C
G
T
A
T
A
A
A
A
G
T
T
C
T
C
G
G
A
G
G
A
T
T
T
C
T
A
C
G
T
T
T
T
T
A
A
A
C
T
T
C
G
C
C
T
A
C
G
A
T
T
G
G
A
T
G
G
G
T
T
T
A
A
A
A
G
G
T
T
T
C
G
T
A
G
G
A
A
T
T
C
A
A
C
G
T
T
A
T
T
T
A
A
C
G
T
T
G
G
C
G
A
C
G
G
A
A
T
G
T
T
G
C
G
T
T
A
A
A
T
T
G
T
A
C
T
C
G
G
T
G
C
A
A
A
T
C
T
A
G
C
T
T
T
A
A
A
T
T
G
G
A
C
G
C
G
G
T
G
C
A
A
T
G
G
A
T
G
C
G
T
A
A
T
A
T
T
C
T
A
T
G
C
C
G
T
C
C
G
T
A
G
C
T
A
G
G
G
G
T
A
A
T
T
T
G
G
A
C
G
G
G
T
T
G
G
A
A
A
G
G
A
A
G
G
G
G
T
T
A
T
T
T
G
T
T
T
G
G
G
G
T
G
C
A
A
T
G
C
T
A
G
C
G
G
A
A
T
A
T
T
C
G
A
C
G
C
C
G
T
C
C
G
A
A
G
G
A
A
G
G
A
A
G
G
A
A
G
G
A
A
G
G
A
A
G
G
A
A
G
A
T
T
G
C
A
T
G
A
T
T
G
C
A
T
G
A
T
T
G
C
A
T
G
A
T
T
G
A
A
T
T
C
A
T
C
A
A
T
T
C
A
T
C
A
A
T
T
C
A
T
C
A
A
T
T
G
A
A
T
C
T
A
G
G
A
A
T
C
T
A
G
G
A
A
T
C
T
A
G
G
A
A
T
G
A
A
G
G
A
A
G
G
G
G
T
T
T
T
A
A
G
G
T
T
T
G
G
G
T
C
C
A
T
T
G
C
A
T
G
C
G
T
A
A
T
T
A
A
G
T
A
T
T
G
G
T
T
G
G
A
A
T
T
C
A
T
C
C
G
T
T
A
T
A
T
T
C
T
A
T
T
C
C
G
A
C
G
G
A
A
T
C
T
A
G
G
G
G
T
T
T
A
T
T
G
T
T
C
G
C
G
G
T
G
C
A
A
A
T
G
A
T
G
G
T
G
T
T
A
T
T
T
G
T
T
T
G
G
G
G
T
G
C
G
A
T
T
G
T
A
C
C
T
G
A
A
T
A
T
T
C
T
A
C
G
C
C
G
A
C
C
G
A
T
G
G
A
A
C
G
T
T
A
T
T
A
A
A
C
T
T
T
G
C
C
G
A
C
C
G
A
T
T
G
T
T
C
C
T
T
A
A
T
A
T
T
G
G
A
C
G
C
G
G
A
G
C
G
T
A
G
G
T
T
C
C
G
T
T
A
T
T
T
T
C
G
A
T
G
G
C
T
A
C
C
A
T
T
G
C
T
T
C
C
T
T
A
A
T
T
T
T
G
T
A
C
G
G
G
T
A
G
G
A
A
A
G
G
T
A
G
C
G
T
T
T
A
T
A
A
G
G
T
C
G
G
G
G
T
G
C
A
A
A
G
G
A
A
G
C
T
T
T
A
A
A
A
A
G
T
A
T
G
C
G
G
T
G
G
4
1H-0-3_0969
1H-27.35-3_1276
1H-49.7-1_0159
1H-51.23-1_1484
1H-55.49-2_0798
1H-61.53-1_0798
1H-73.94-2_1126
1H-95.42-2_1373
1H-121.12-2_0908
1H-137.83-2_0138
2H-27.29-2_1015
2H-45.55-3_0363
2H-63.53-1_0191
2H-81.33-1_0859
2H-90.1-1_0969
2H-113.48-3_1402
2H-127.64-3_0310
2H-139.65-1_0551
3H-2.9-2_0159
3H-41-3_0953
3H-51.73-1_1313
3H-54.4-3_1008
3H-56.4-2_1062
3H-59.89-1_0373
3H-69.6-3_1242
3H-76.98-3_1346
3H-91.25-2_0659
3H-109.14-2_1513
3H-130.19-1_0280
3H-142.32-3_0137
3H-168.4-2_1267
4H-18.01-3_0150
4H-28.4-2_1374
4H-48.5-1_0577
4H-52.75-1_0946
4H-65.05-2_0906
4H-68.21-3_1536
4H-93.13-3_0142
4H-113.92-1_1066
5H-2.09-2_0226
5H-37.11-3_0410
5H-50.27-2_1308
5H-51-2_1011
5H-51.6-2_1260
5H-59.4-2_0961
5H-60.74-3_1280
5H-84.51-2_0096
5H-103.92-2_0327
5H-117.47-1_1200
5H-132.63-2_0259
5H-142.2-3_1366
5H-159.09-1_0820
5H-179.06-1_0254
6H-1.34-2_0881
6H-24.36-1_0868
6H-42.36-3_0783
6H-49.4-2_0291
6H-54.6-1_0962
6H-55.94-1_0513
6H-60.23-1_0270
6H-65.03-1_1261
6H-74.55-3_1088
6H-90.15-1_0202
6H-112.32-1_0239
6H-126.18-3_1498
7H-14.96-1_0841
7H-37.55-2_0126
7H-54.37-1_0772
7H-68.46-3_0639
7H-77.85-2_0879
7H-79.6-1_0370
7H-79.6-3_0835
7H-87.97-1_0143
7H-110.99-2_0385
7H-133.79-2_1104
7H-144.45-1_0843
Analysis of Quantitative traits
6
5
Significance threshold
3
2
1
0
Statistical test are performed at the position of each marker.
The average phenotype of individuals with one genotypic class (with a
certain allele) is tested against the average phenotype of individuals with
other genotypic class (other allele)
If differences between genotypic classes are statistically different,
then there is marker-QTL association