Qualitative and Quantitative traits Qualitative traits: Phenotypes with discrete and easy to measure...
-
Upload
peyton-urwin -
Category
Documents
-
view
224 -
download
1
Transcript of Qualitative and Quantitative traits Qualitative traits: Phenotypes with discrete and easy to measure...
Qualitative and Quantitative traits• Qualitative 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
Plant Height (in)
% o
f pla
nts
20 30 40
Inheritance of Quantitative traitsThe 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
×
P1 P2
Plant Height (in)
% o
f pla
nts
20 30 40
Plant Height (in)
Plant Height (in)
% o
f pla
nts
20 30 40
PARENT 1: • pure line, completely homozygote• 40 inches
PARENT 2: • pure line, completely homozygote• 20 inches
F1: range of height distribution but no type of segregation
F2: wider range of height distribution but no type of segregation
F1
F2
Inheritance of Quantitative traitsIn 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 .
From a seed lot he measured and classified the beans by weight and obtained the range of distribution for that variety.
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
He found that:The weight of the 5,494 beans he obtained followed a normal distributionAll 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
Weight (gr)
% o
f pla
nts
250 400 550
Inheritance of Quantitative traitsThe Experiment of Johannsen
Weight (gr)
% o
f pla
nts
250 400 550
Weight (gr)
% o
f pla
nts
250 400 550
Weight (gr)
% o
f pla
nts
250 400 550
Weight (gr)
% o
f pla
nts
250 400 550
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
Inheritance of Quantitative traitsJohannsen 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 wheatHe 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 whiteb) 15 red : 1 Whitec) 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 whiteb) 15 red : 1 Whitec) 63 red : 1 white
AAbbcc X aabbccP1 (red) P2 (white)
AabbccF1(red)
F2(only one locus
segregating)
¼ AAbbcc : ½ Aabbcc : ¼ aabbcc
(red) (red) (white)
Segregation 3 red : 1 white
For case a), allelic variation between the two parents was present only at one locus
Inheritance of Quantitative traits
a) 3 red : 1 whiteb) 15 red : 1 Whitec) 63 red : 1 white
AABBcc X aabbccP1 (red) P2 (white)
AaBbccF1(red)
F2(two loci
segregating)
1/16 AABBcc (red)2/16 AABbcc (red)1/16 AAbbcc (red)2/16 AaBBcc (red)4/16 AaBbcc (red)2/16 Aabbcc (red)1/16 aaBBcc (red)2/16 aaBbcc (red)1/16 aabbcc (white)
Segregation 15 red : 1 white
Inheritance of Quantitative traits
a) 3 red : 1 whiteb) 15 red : 1 Whitec) 63 red : 1 white
AABBCC X aabbccP1 (red) P2 (white)
AaBbCcF1(red)F2(three loci
segregating)
1/64 AABBCC (red)2/64 AABbCC (red)1/64 AabbCC (red)2/64AaBBCC (red)4/64 AaBbCC (red)2/64 AabbCC (red)1/64 aaBBCC (red)2/64 aaBbCC (red)1/64 aabbCC (red)
Segregation 63 red : 1 white
2/64 AABBCc (red)4/64 AABbCc (red)2/64 AabbCc (red)4/64 AaBBCc (red)8/64 AaBbCc (red)4/64 AabbCc (red)2/64 aaBBCc (red)4/64 aaBbCc (red)2/64 aabbCc (red)
1/64 AABBcc (red)2/64 AABbcc (red)1/64 Aabbcc (red)2/64 AaBBcc (red)4/64 AaBbcc (red)2/64 Aabbcc (red)1/64 aaBBcc (red)2/64 aaBbcc (red)1/64 aabbcc (white)
Inheritance of Quantitative traitsHowever, 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
AABB X aabb
P1 (purple, very dark red)
AaBbF1(red)
1/16 AABB (Purple)2/16 AABb (dark-red)1/16 AAbb (red)2/16 AaBB (dark-red)4/16 AaBb (red)2/16 Aabb (light-red)1/16 aaBB (red)2/16 aaBb (light-red)1/16 aabb (white)
1/16 : purple 4/16: dark-red 6/16: red 4/16: light-red 1/16: white
P2 (white)P1 (purple, Xvery dark red)
P2 (white)
F1(red)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
Inheritance of Quantitative traits
1/16 : purple 4/16: dark-red 6/16: red 4/16: light-red 1/16: white
P1 (purple, Xvery dark red)
P2 (white)
F1(red)
Going one step further, He saw that within each of the groups there was also some variation
Color intensity- white+ purple
Freq
uenc
y
Inheritance of Quantitative traits
Color intensity- white+ purple
Freq
uenc
y
He deduced that many loci were involved (not only two) in the trait and taking into account Johanssen’s findings:
Phenotype=Genotype+Environment
Then, the distribution of a quantitative trait would follow a normal distribution
Analysis of quantitative traits is therefore complicated:Same genotype: 1 and 2 show different phenotypeSame phenotype: 1, 3 and 4 is the result of three different genotypes
1 23
4
Inheritance of Quantitative traitsThe 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
Freq
uenc
yCold tolerance
P1 P2
0 10
Let’s assume 5 loci with additive effects control the trait
aabbccddEE X AABBCCDDeeP1
AaBbCcDdEeF1
P2
All possible combinations of alleles at 5 loci.Between them: AABBCCDDEE (all favorable alleles)
aabbccddee (all unfavorable alleles)
F2
Inheritance of Quantitative traitsQuantitative 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
P
G
V
Vh 2
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 traitsHeritability h2 measures the proportion of phenotypic variation (variance) that is due to genetic causes
P = G + E; VP = VG + VE
P
G
V
Vh 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 traitsHeritability h2 measures the proportion of phenotypic variation (variance) that is due to genetic causes
P = G + E; VP = VG + VEP
G
V
Vh 2
h2 is very useful because it allows us to predict the response to artificial selection
6000
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
Freq
uenc
y
Grain yield(lb/A)
0
μ0
μS
Selection differential (S) = μS – μ0
Response to selection (R) = μR – μ0
Realized heritability:
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
Freq
uenc
y
Grain yield(lb/A)
0
μ0 μRS
Rh 2
6000
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 individualc) Genotypic information: A set of molecular markers that have been
run in all the individuals of the populationd) 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 traitsQTL analysis in mapping populations
The first step is getting genotypic information for all the individuals of the population: molecular markers
Back Cross populationP1 P2
SNP Pare
nt 1
Pare
nt 2
Line
1Li
ne2
Line
3Li
ne4
Line
5Li
ne6
Line
7Li
ne8
Line
9Li
ne10
Line
11Li
ne12
Line
13Li
ne14
Line
15Li
ne16
Line
17Li
ne18
Line
19Li
ne20
Line
21Li
ne22
Line
23Li
ne24
Line
25Li
ne26
Line
27Li
ne28
Line
29Li
ne30
Line
30Li
ne31
Line
32Li
ne33
Line
29Li
ne30
Line
30Li
ne31
Line
32Li
ne33
Line
34Li
ne35
Line
36Li
ne37
Line
38Li
ne39
Line
40Li
ne41
Line
42Li
ne43
Line
44Li
ne45
Line
46Li
ne47
Line
48Li
ne49
Line
50Li
ne51
Line
52Li
ne53
Line
54Li
ne55
Line
56Li
ne57
Line
58Li
ne59
1_0002 G A G A G G G G G A G G A A G A G A G G A G G G A A A G G A A A G A A G A A G A A G G A A G A G G G G A A A G G A G A G G A A A G G G A1_0004 T A T T T T A A T A T T T T A T T A A T A A T A T A A T A T T A A A A T A A A T A A A T A A A A A A T T A A T A T A T A A T A A A A T T1_0011 A T T A T T A T A T T A T T T T T A A T A A A A A A A T T T T T A A T A A T A T T A A T T A A T T T A T A A A A T A A T A A T A A T T T1_0014 G T T T T T T T G T T G T T G G T T T G T G T G G T T T G T G T T T G G G G G G T T G G T T T T G T G G G G G T T T G G G T T T T G T T1_0020 C G C C C C G C G C G G C C C C C G G C C C G G G G G C C C G C G C G C G C G C C C G C C C G G G G G C G G C G C C C C C G C G C C C C1_0023 A T A T A A A A T T A T T T T T A A A A T A A T T T T A T T A A T T A T A T A A A T A A A T A T A T T T T A T A T A A T A A A T A A A A1_0024 T A A A T A T T T A A A T T T T A A T T A T T A T A A A T A T A T A T A A A A T T A A T T A T A A T T T A A A T A A T T A A A A T A A T1_0026 G C G G C G G C C C C G G C C C G G G C G G G G G C G G C C G C G G G G G G G G C G G G C G G C C C C C G G G C C G G C G C C G G G G G1_0031 G C G C G G C C G G G G C C G C G C C C G C C C G G G G C G G G C C C G G C G A A G G C C G G C G C C C C C G C C C G G G C G C C C G C1_0036 G T G T G G T T T G G G T G T T G G T G T T G G G G G G G T G T G T G G G G A T A A G G G G T T T T G T G T G G G G T T G G T T G T G G1_0041 G T G T T G T T G T T T T G T T G T T G T G T G T T T G T T T T T T T G G G A T T A G T T G G G T T T T T T G T T G T T T G T T T G G G1_0047 T A A T A A T A A A A T A T A A A T T A A T T A T T A A A T T A T T A T T A G G T T T A T T T A A A T A T T T T A A T A A A A T T A A A1_0048 T A T A T T A A T T T T A A T A T A A A T A A A T A A T T T T T A A A A T A G C C C T A A T T A T A A A T A A A A A T T T A T T A A T A1_0050 A T A A A A T T T T T T T A A T T T T T T A A T A A A A A T A T A A T A A T A A A T T T T T A T T T T T A A A T T A T A T T T T A T A A1_0051 T A A T A A T A T A T T A T A T T T T A A A T T A A A A A A A T A A A T T A A T T A T T A A T A A A T T T A T T T T A T A T T T T T A T1_0052 A T A T A A A T A A T A A T T T T T A T A A T T T T T A A A A A T T T T T T G G C G A A T T T T A T T T A A T T T A A A A T A A T T A T1_0053 A T A T A A A T A A T A A T T T T T A T A A T T T T T A A A A A T T T T T T G A A G A A T T T T A T T T A A T T T A A A A T A A T T A T1_0055 G C G C G G C C G C G G C G G G C C C C G C G C G G G G G C G C G G C G G C A T A A G G C G G C C G C G G G G C C G C G C C C C G C G G1_0061 T G T G T T T G G G T T G G T T T T T T G T T G G G G T T T G G T G T G T G A T T A G T T T T T T G G T G T G T T T T G T T G G G T T G1_0063 T A T A T T A A T T T T A A T A T A A A T A A A T A A T T T T T A A A A T A G G T T T A A T T A T A A A T A A A A A T T T A T A A A T A1_0064 T C T C T T T T C C T C C C C C T T T T C T T C C C C T C C T T C C T C T C G C C C T T T C T C T C T C C T C T C T T C T T T C T T T T1_0065 T G G T T G G G G T G T T G T G G G G G T G G T T T G G T G T G T G G G G G A A A T T T T G G G G G G G G G G T G T G G G T G G T T G T1_0071 G C C G C C G C G C G G C G C G G G G C C C G G C C C C C C C G C C C G G C A T T A G G C C G C C C G G G C G G G G C G C G G G G G C G1_0073 G C G C G G C C G C G G C G G G C C C C G C G C G G G G G C G C G G C G G C G G C G G G C G G C C G C G G G G C C G C G C C C C G C G G1_0080 T G T T T T G G T G T T T T G T T G G T G G T G T G G T G T T G G G G T G G G A A G G T G G G G G G T T G G T G T G T G G T G G G G T T1_0081 T A T T A T T A A A A T T A A A T T T A T T T T T T T T A A A A T T T T T T A T A A T T A T T A A A A A T T T T A T T A T A A T T T T T1_0083 G C G C G G C C G C G G C G G G C C C C G C G C G G G G G C G C G G C G G C A T T A C G C G G C C G C G G G G C C G C G C C C C G C G G1_0084 C G G G C G C C G G G G C G G G C C C G C C G G C G G G G G G G C C C G C C G G T T C G G C C C C C C G C G G C G G C C G G G C G G G G
High Throughput genotyping platform (SNP)
P1 P2
Analysis of Quantitative traitsQTL analysis in mapping populations
If molecular markers are polymorphic, we can construct a linkage map based on recombination frequencies:
BCD14340DsT-667Act8A12RbgMD18MWG837B22scind0004625ABC165C26Bmac039929GBM100730BCD09836GBM104248BG36701354Bmag021158BG36994061GBM105168ABC16073JS10C86Bmac0144A87MWG706A96KFP170101Blp111ABC261119MWG2028121KFP257B122WMC1E8130MWG912133ABG387Ascssr04163scssr08238
136
1H
DsT-10ABG0585scind026227
ABG00817
scssr1022636scssr0775939GBM106642Pox45scssr0338156scssr12344scssr02236Ebmac0684
63
BCD1434.265ABG35668GBM102371scsnp0334383vrs188Bmag012594DsT-4197MWG503102GBM1062103KFP203104MWG882A108ABG1032117ABG072124Ebmc0415137cnx1139Zeo1149GBM1019161Aglu5F3R2163MWG720165GBM1012170wst7173scssr08447179MWG949A180
2H
BCD9070
ABC171A26GBM107430scssr1055933MWG798B36Dst-2739BCD70642DsT-3958alm61Bmac020966ABC32569DsT-6773
scssr2569187ABG37789Bmag022598
Act8C121ABG499124GBM1043125
scsnp23255151ABG004155
scind02281166MWG883172
DsT-24181
HVM62190
DsT-40199
ABC172scssr25538212
DsT-35218
3H
MWG6340MWG07721HVM4024DsT-2929CDO54230CDO12231hvknox335Dhn639ABC303scssr2056941
CDO79544HVM349DST-46scind03751scssr18005
50
Tef252GBM102060Bmag035362scind1045567DsT-7974scssr14079ABG47280
GBM105983KFP22192Ebmac070194MWG652B95GBM1048101Hsh111HVM67112KFP241.1116ABG601124
4H
scssr023060MWG6186DsT-68ABC48311ABG61012
ABG39537scssr0250344scssr1807645Bmac009653NRG045A55scsnp0426056Ale58
ABC30279scind1699182scssr1533485scsnp0614490srh100
scssr05939111
RSB001A120
scsnp001771280SU-STS1134ABG003B141
scssr10148157Tef3166MWG877169BE456118A170ABG496179scsnp02109E10757A193
ABG391197JS10B198ABC622205DsT-33207Bmag0113C215MWG602A223scssr03907224scssr03906225
5H
MWG6200Bmac0316scssr093984
MWG652A31MWG602B35scind6000242JS10A45GBM102151GBM106861BG29929765HVM3168rob70Bmag0009scssr0209371
ABG47481Bmac0218C88ABG38892scsnp2122699MWG820101GBM1008122scssr05599123MWG934126scind04312b132scssr00103GBM1022135
Bmac0040143DsT-18145DsT-32B146DsT-22152DsT-28159scind60001DsT-74160
MWG514162MWG798A163DsT-71167
6H
ABG7040Bmag000714scind0069420AW98258029MWG089CDO47536
ABG38038BE60207344scssr0797057scsnp0046066ABC25568ABC165D69HvVRT273scssr1586482GBM103086scsnp22290MWG808DAK642scind00149
89
scsnp00703MWG203197
RSB001C98nud103lks2115ABC1024117Bmag0120125DsT-30126WG380B127ABC310B137Ris44139
ABG461A167WG380A171GBM1065178
HVM5196scssr04056KFP255197
ThA1199
7H
Analysis of Quantitative traitsQTL 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 whetherthere is a marker-trait association or not
Analysis of Quantitative traits
BCD14340DsT-667Act8A12RbgMD18MWG837B22scind0004625ABC165C26Bmac039929GBM100730BCD09836GBM104248BG36701354Bmag021158BG36994061GBM105168ABC16073JS10C86Bmac0144A87MWG706A96KFP170101Blp111ABC261119MWG2028121KFP257B122WMC1E8130MWG912133ABG387Ascssr04163scssr08238
136
1H
DsT-10ABG0585scind026227
ABG00817
scssr1022636scssr0775939GBM106642Pox45scssr0338156scssr12344scssr02236Ebmac0684
63
BCD1434.265ABG35668GBM102371scsnp0334383vrs188Bmag012594DsT-4197MWG503102GBM1062103KFP203104MWG882A108ABG1032117ABG072124Ebmc0415137cnx1139Zeo1149GBM1019161Aglu5F3R2163MWG720165GBM1012170wst7173scssr08447179MWG949A180
2H
BCD9070
ABC171A26GBM107430scssr1055933MWG798B36Dst-2739BCD70642DsT-3958alm61Bmac020966ABC32569DsT-6773
scssr2569187ABG37789Bmag022598
Act8C121ABG499124GBM1043125
scsnp23255151ABG004155
scind02281166MWG883172
DsT-24181
HVM62190
DsT-40199
ABC172scssr25538212
DsT-35218
3H
MWG6340MWG07721HVM4024DsT-2929CDO54230CDO12231hvknox335Dhn639ABC303scssr2056941
CDO79544HVM349DST-46scind03751scssr18005
50
Tef252GBM102060Bmag035362scind1045567DsT-7974scssr14079ABG47280
GBM105983KFP22192Ebmac070194MWG652B95GBM1048101Hsh111HVM67112KFP241.1116ABG601124
4H
scssr023060MWG6186DsT-68ABC48311ABG61012
ABG39537scssr0250344scssr1807645Bmac009653NRG045A55scsnp0426056Ale58
ABC30279scind1699182scssr1533485scsnp0614490srh100
scssr05939111
RSB001A120
scsnp001771280SU-STS1134ABG003B141
scssr10148157Tef3166MWG877169BE456118A170ABG496179scsnp02109E10757A193
ABG391197JS10B198ABC622205DsT-33207Bmag0113C215MWG602A223scssr03907224scssr03906225
5H
MWG6200Bmac0316scssr093984
MWG652A31MWG602B35scind6000242JS10A45GBM102151GBM106861BG29929765HVM3168rob70Bmag0009scssr0209371
ABG47481Bmac0218C88ABG38892scsnp2122699MWG820101GBM1008122scssr05599123MWG934126scind04312b132scssr00103GBM1022135
Bmac0040143DsT-18145DsT-32B146DsT-22152DsT-28159scind60001DsT-74160
MWG514162MWG798A163DsT-71167
6H
ABG7040Bmag000714scind0069420AW98258029MWG089CDO47536
ABG38038BE60207344scssr0797057scsnp0046066ABC25568ABC165D69HvVRT273scssr1586482GBM103086scsnp22290MWG808DAK642scind00149
89
scsnp00703MWG203197
RSB001C98nud103lks2115ABC1024117Bmag0120125DsT-30126WG380B127ABC310B137Ris44139
ABG461A167WG380A171GBM1065178
HVM5196scssr04056KFP255197
ThA1199
7H
Disease severity (%) DsT-66
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
Parent 1(Resistant) 5Parent 2 (Susceptible) 90Line1 56Line2 30Line3 59Line4 95Line5 31Line6 42Line7 94Line8 42Line9 15Line10 3Line11 84Line12 82Line13 30Line14 60Line15 26Line16 57Line17 12Line18 68Line19 53Line20 69Line21 43Line22 42Line23 67Line24 64Line25 46Line26 28Line27 41Line28 50Line29 91Line30 25
ABBAAAAAAABBBBBABBAABBBABBAABBBB
Analysis of Quantitative traits
BCD14340DsT-667Act8A12RbgMD18MWG837B22scind0004625ABC165C26Bmac039929GBM100730BCD09836GBM104248BG36701354Bmag021158BG36994061GBM105168ABC16073JS10C86Bmac0144A87MWG706A96KFP170101Blp111ABC261119MWG2028121KFP257B122WMC1E8130MWG912133ABG387Ascssr04163scssr08238
136
1H
DsT-10ABG0585scind026227
ABG00817
scssr1022636scssr0775939GBM106642Pox45scssr0338156scssr12344scssr02236Ebmac0684
63
BCD1434.265ABG35668GBM102371scsnp0334383vrs188Bmag012594DsT-4197MWG503102GBM1062103KFP203104MWG882A108ABG1032117ABG072124Ebmc0415137cnx1139Zeo1149GBM1019161Aglu5F3R2163MWG720165GBM1012170wst7173scssr08447179MWG949A180
2H
BCD9070
ABC171A26GBM107430scssr1055933MWG798B36Dst-2739BCD70642DsT-3958alm61Bmac020966ABC32569DsT-6773
scssr2569187ABG37789Bmag022598
Act8C121ABG499124GBM1043125
scsnp23255151ABG004155
scind02281166MWG883172
DsT-24181
HVM62190
DsT-40199
ABC172scssr25538212
DsT-35218
3H
MWG6340MWG07721HVM4024DsT-2929CDO54230CDO12231hvknox335Dhn639ABC303scssr2056941
CDO79544HVM349DST-46scind03751scssr18005
50
Tef252GBM102060Bmag035362scind1045567DsT-7974scssr14079ABG47280
GBM105983KFP22192Ebmac070194MWG652B95GBM1048101Hsh111HVM67112KFP241.1116ABG601124
4H
scssr023060MWG6186DsT-68ABC48311ABG61012
ABG39537scssr0250344scssr1807645Bmac009653NRG045A55scsnp0426056Ale58
ABC30279scind1699182scssr1533485scsnp0614490srh100
scssr05939111
RSB001A120
scsnp001771280SU-STS1134ABG003B141
scssr10148157Tef3166MWG877169BE456118A170ABG496179scsnp02109E10757A193
ABG391197JS10B198ABC622205DsT-33207Bmag0113C215MWG602A223scssr03907224scssr03906225
5H
MWG6200Bmac0316scssr093984
MWG652A31MWG602B35scind6000242JS10A45GBM102151GBM106861BG29929765HVM3168rob70Bmag0009scssr0209371
ABG47481Bmac0218C88ABG38892scsnp2122699MWG820101GBM1008122scssr05599123MWG934126scind04312b132scssr00103GBM1022135
Bmac0040143DsT-18145DsT-32B146DsT-22152DsT-28159scind60001DsT-74160
MWG514162MWG798A163DsT-71167
6H
ABG7040Bmag000714scind0069420AW98258029MWG089CDO47536
ABG38038BE60207344scssr0797057scsnp0046066ABC25568ABC165D69HvVRT273scssr1586482GBM103086scsnp22290MWG808DAK642scind00149
89
scsnp00703MWG203197
RSB001C98nud103lks2115ABC1024117Bmag0120125DsT-30126WG380B127ABC310B137Ris44139
ABG461A167WG380A171GBM1065178
HVM5196scssr04056KFP255197
ThA1199
7H
Disease severity (%) ABC261
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
Parent 1(Resistant) 5Parent 2 (Susceptible) 90Line1 56Line2 30Line3 59Line4 95Line5 31Line6 42Line7 94Line8 42Line9 15Line10 3Line11 84Line12 82Line13 30Line14 60Line15 26Line16 57Line17 12Line18 68Line19 53Line20 69Line21 43Line22 42Line23 67Line24 64Line25 46Line26 28Line27 41Line28 50Line29 91Line30 25
ABBABBAABAAABBABABABBBAABBAAABBA
Analysis of Quantitative traitsQTL analysis in mapping populations
BCD14340
DsT-667
Act8A12
RbgMD18
MWG837B22
scind0004625
ABC165C26
Bmac039929
GBM100730
BCD09836
GBM104248
BG36701354
Bmag021158
BG36994061
GBM105168
ABC16073
JS10C86
Bmac0144A87
MWG706A96
KFP170101
Blp111
ABC261119
MWG2028121
KFP257B122
WMC1E8130
MWG912133
ABG387Ascssr04163scssr08238
136
1HDsT-1
0ABG058
5scind02622
7ABG008
17scssr10226
36scssr07759
39GBM1066
42Pox
45scssr03381
56scssr12344scssr02236Ebmac0684
63BCD1434.2
65ABG356
68GBM1023
71scsnp03343
83vrs1
88Bmag0125
94DsT-41
97MWG503
102GBM1062
103KFP203
104MWG882A
108ABG1032
117ABG072
124Ebmc0415
137cnx1
139Zeo1
149GBM1019
161Aglu5F3R2
163MWG720
165GBM1012
170wst7
173scssr08447
179MWG949A
180
2HBCD907
0ABC171A
26GBM1074
30scssr10559
33MWG798B
36Dst-27
39BCD706
42DsT-39
58alm
61Bmac0209
66ABC325
69DsT-67
73scssr25691
87ABG377
89Bmag0225
98Act8C
121ABG499
124GBM1043
125scsnp23255
151ABG004
155scind02281
166MWG883
172DsT-24
181HVM62
190DsT-40
199ABC172scssr25538
212DsT-35
218
3HMWG634
0MWG077
21HVM40
24DsT-29
29CDO542
30CDO122
31hvknox3
35Dhn6
39ABC303scssr20569
41CDO795
44HVM3
49DST-46scind03751scssr18005
50Tef2
52GBM1020
60Bmag0353
62scind10455
67DsT-79
74scssr14079ABG472
80GBM1059
83KFP221
92Ebmac0701
94MWG652B
95GBM1048
101Hsh
111HVM67
112KFP241.1
116ABG601
124
4Hscssr02306
0MWG618
6DsT-6
8ABC483
11ABG610
12ABG395
37scssr02503
44scssr18076
45Bmac0096
53NRG045A
55scsnp04260
56Ale
58ABC302
79scind16991
82scssr15334
85scsnp06144
90srh
100scssr05939
111RSB001A
120scsnp00177
1280SU-STS1
134ABG003B
141scssr10148
157Tef3
166MWG877
169BE456118A
170ABG496
179scsnp02109E10757A
193ABG391
197JS10B
198ABC622
205DsT-33
207Bmag0113C
215MWG602A
223scssr03907
224scssr03906
225
5HMWG620
0Bmac0316scssr09398
4MWG652A
31MWG602B
35scind60002
42JS10A
45GBM1021
51GBM1068
61BG299297
65HVM31
68rob
70Bmag0009scssr02093
71ABG474
81Bmac0218C
88ABG388
92scsnp21226
99MWG820
101GBM1008
122scssr05599
123MWG934
126scind04312b
132scssr00103GBM1022
135Bmac0040
143DsT-18
145DsT-32B
146DsT-22
152DsT-28
159scind60001DsT-74
160MWG514
162MWG798A
163DsT-71
167
6HABG704
0Bmag0007
14scind00694
20AW982580
29MWG089CDO475
36ABG380
38BE602073
44scssr07970
57scsnp00460
66ABC255
68ABC165D
69HvVRT2
73scssr15864
82GBM1030
86scsnp22290MWG808DAK642scind00149
89scsnp00703MWG2031
97RSB001C
98nud
103lks2
115ABC1024
117Bmag0120
125DsT-30
126WG380B
127ABC310B
137Ris44
139ABG461A
167WG380A
171GBM1065
178HVM5
196scssr04056KFP255
197ThA1
199
7HPr
obab
ility
Significance trheshold
Most likely position of the QTL
Analysis of Quantitative traits
BCD14340DsT-667Act8A12RbgMD18MWG837B22scind0004625ABC165C26Bmac039929GBM100730BCD09836GBM104248BG36701354Bmag021158BG36994061GBM105168ABC16073JS10C86Bmac0144A87MWG706A96KFP170101Blp111ABC261119MWG2028121KFP257B122WMC1E8130MWG912133ABG387Ascssr04163scssr08238
136
1H
DsT-10ABG0585scind026227
ABG00817
scssr1022636scssr0775939GBM106642Pox45scssr0338156scssr12344scssr02236Ebmac0684
63
BCD1434.265ABG35668GBM102371scsnp0334383vrs188Bmag012594DsT-4197MWG503102GBM1062103KFP203104MWG882A108ABG1032117ABG072124Ebmc0415137cnx1139Zeo1149GBM1019161Aglu5F3R2163MWG720165GBM1012170wst7173scssr08447179MWG949A180
2H
BCD9070
ABC171A26GBM107430scssr1055933MWG798B36Dst-2739BCD70642DsT-3958alm61Bmac020966ABC32569DsT-6773
scssr2569187ABG37789Bmag022598
Act8C121ABG499124GBM1043125
scsnp23255151ABG004155
scind02281166MWG883172
DsT-24181
HVM62190
DsT-40199
ABC172scssr25538212
DsT-35218
3H
MWG6340MWG07721HVM4024DsT-2929CDO54230CDO12231hvknox335Dhn639ABC303scssr2056941
CDO79544HVM349DST-46scind03751scssr18005
50
Tef252GBM102060Bmag035362scind1045567DsT-7974scssr14079ABG47280
GBM105983KFP22192Ebmac070194MWG652B95GBM1048101Hsh111HVM67112KFP241.1116ABG601124
4H
scssr023060MWG6186DsT-68ABC48311ABG61012
ABG39537scssr0250344scssr1807645Bmac009653NRG045A55scsnp0426056Ale58
ABC30279scind1699182scssr1533485scsnp0614490srh100
scssr05939111
RSB001A120
scsnp001771280SU-STS1134ABG003B141
scssr10148157Tef3166MWG877169BE456118A170ABG496179scsnp02109E10757A193
ABG391197JS10B198ABC622205DsT-33207Bmag0113C215MWG602A223scssr03907224scssr03906225
5H
MWG6200Bmac0316scssr093984
MWG652A31MWG602B35scind6000242JS10A45GBM102151GBM106861BG29929765HVM3168rob70Bmag0009scssr0209371
ABG47481Bmac0218C88ABG38892scsnp2122699MWG820101GBM1008122scssr05599123MWG934126scind04312b132scssr00103GBM1022135
Bmac0040143DsT-18145DsT-32B146DsT-22152DsT-28159scind60001DsT-74160
MWG514162MWG798A163DsT-71167
6H
ABG7040Bmag000714scind0069420AW98258029MWG089CDO47536
ABG38038BE60207344scssr0797057scsnp0046066ABC25568ABC165D69HvVRT273scssr1586482GBM103086scsnp22290MWG808DAK642scind00149
89
scsnp00703MWG203197
RSB001C98nud103lks2115ABC1024117Bmag0120125DsT-30126WG380B127ABC310B137Ris44139
ABG461A167WG380A171GBM1065178
HVM5196scssr04056KFP255197
ThA1199
7H
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
A higher density of markers is required
SNP Line
1Li
ne2
Line
3Li
ne4
Line
5Li
ne6
Line
7Li
ne8
Line
9Li
ne10
Line
11Li
ne12
Line
13Li
ne14
Line
15Li
ne16
Line
17Li
ne18
Line
19Li
ne20
Line
21Li
ne22
Line
23Li
ne24
Line
25Li
ne26
Line
27Li
ne28
Line
29Li
ne30
Line
30Li
ne31
Line
32Li
ne33
Line
29Li
ne30
Line
30Li
ne31
Line
32Li
ne33
Line
34Li
ne35
Line
36Li
ne37
Line
38Li
ne39
Line
40Li
ne41
Line
42Li
ne43
Line
44Li
ne45
1_0002 G A G G G G G A G G A A G A G A G G A G G G A A A G G A A A G A A G A A G A A G G A A G A G G G G A A A1_0004 T T T T A A T A T T T T A T T A A T A A T A T A A T A T T A A A A T A A A T A A A T A A A A A A T T A A1_0011 T A T T A T A T T A T T T T T A A T A A A A A A A T T T T T A A T A A T A T T A A T T A A T T T A T A A1_0014 T T T T T T G T T G T T G G T T T G T G T G G T T T G T G T T T G G G G G G T T G G T T T T G T G G G G1_0020 C C C C G C G C G G C C C C C G G C C C G G G G G C C C G C G C G C G C G C C C G C C C G G G G G C G G1_0023 A T A A A A T T A T T T T T A A A A T A A T T T T A T T A A T T A T A T A A A T A A A T A T A T T T T A1_0024 A A T A T T T A A A T T T T A A T T A T T A T A A A T A T A T A T A A A A T T A A T T A T A A T T T A A1_0026 G G C G G C C C C G G C C C G G G C G G G G G C G G C C G C G G G G G G G G C G G G C G G C C C C C G G1_0031 G C G G C C G G G G C C G C G C C C G C C C G G G G C G G G C C C G G C G A A G G C C G G C G C C C C C1_0036 G T G G T T T G G G T G T T G G T G T T G G G G G G G T G T G T G G G G A T A A G G G G T T T T G T G T1_0041 G T T G T T G T T T T G T T G T T G T G T G T T T G T T T T T T T G G G A T T A G T T G G G T T T T T T1_0047 A T A A T A A A A T A T A A A T T A A T T A T T A A A T T A T T A T T A G G T T T A T T T A A A T A T T1_0048 T A T T A A T T T T A A T A T A A A T A A A T A A T T T T T A A A A T A G C C C T A A T T A T A A A T A1_0050 A A A A T T T T T T T A A T T T T T T A A T A A A A A T A T A A T A A T A A A T T T T T A T T T T T A A1_0051 A T A A T A T A T T A T A T T T T A A A T T A A A A A A A T A A A T T A A T T A T T A A T A A A T T T A1_0052 A T A A A T A A T A A T T T T T A T A A T T T T T A A A A A T T T T T T G G C G A A T T T T A T T T A A1_0053 A T A A A T A A T A A T T T T T A T A A T T T T T A A A A A T T T T T T G A A G A A T T T T A T T T A A1_0055 G C G G C C G C G G C G G G C C C C G C G C G G G G G C G C G G C G G C A T A A G G C G G C C G C G G G1_0061 T G T T T G G G T T G G T T T T T T G T T G G G G T T T G G T G T G T G A T T A G T T T T T T G G T G T1_0063 T A T T A A T T T T A A T A T A A A T A A A T A A T T T T T A A A A T A G G T T T A A T T A T A A A T A1_0064 T C T T T T C C T C C C C C T T T T C T T C C C C T C C T T C C T C T C G C C C T T T C T C T C T C C T1_0065 G T T G G G G T G T T G T G G G G G T G G T T T G G T G T G T G G G G G A A A T T T T G G G G G G G G G1_0071 C G C C G C G C G G C G C G G G G C C C G G C C C C C C C G C C C G G C A T T A G G C C G C C C G G G C1_0073 G C G G C C G C G G C G G G C C C C G C G C G G G G G C G C G G C G G C G G C G G G C G G C C G C G G G1_0080 T T T T G G T G T T T T G T T G G T G G T G T G G T G T T G G G G T G G G A A G G T G G G G G G T T G G1_0081 T T A T T A A A A T T A A A T T T A T T T T T T T T A A A A T T T T T T A T A A T T A T T A A A A A T T1_0083 G C G G C C G C G G C G G G C C C C G C G C G G G G G C G C G G C G G C A T T A C G C G G C C G C G G G1_0084 G G C G C C G G G G C G G G C C C G C C G G C G G G G G G G C C C G C C G G T T C G G C C C C C C G C G
Analysis of Quantitative traits
0
1
2
3
4
5
6
1H-0
-3_0
969
1H-2
7.35
-3_1
276
1H-4
9.7-1
_015
91H
-51.2
3-1
_148
41H
-55.4
9-2
_079
81H
-61.5
3-1
_079
81H
-73.9
4-2
_112
61H
-95.4
2-2
_137
31H
-121
.12-2
_090
81H
-137
.83-2
_013
82H
-27.2
9-2
_101
52H
-45.5
5-3
_036
32H
-63.5
3-1
_019
12H
-81.3
3-1
_085
92H
-90.1
-1_0
969
2H-1
13.48
-3_1
402
2H-1
27.64
-3_0
310
2H-1
39.65
-1_0
551
3H-2
.9-2
_015
93H
-41
-3_0
953
3H-5
1.73
-1_1
313
3H-5
4.4-3
_100
83H
-56.4
-2_1
062
3H-5
9.89
-1_0
373
3H-6
9.6-3
_124
23H
-76.9
8-3
_134
63H
-91.2
5-2
_065
93H
-109
.14-2
_151
33H
-130
.19-1
_028
03H
-142
.32-3
_013
73H
-168
.4-2
_126
74H
-18.0
1-3
_015
04H
-28.4
-2_1
374
4H-4
8.5-1
_057
74H
-52.7
5-1
_094
64H
-65.0
5-2
_090
64H
-68.2
1-3
_153
64H
-93.1
3-3
_014
24H
-113
.92-1
_106
65H
-2.09
-2_0
226
5H-3
7.11
-3_0
410
5H-5
0.27
-2_1
308
5H-5
1-2
_101
15H
-51.6
-2_1
260
5H-5
9.4-2
_096
15H
-60.7
4-3
_128
05H
-84.5
1-2
_009
65H
-103
.92-2
_032
75H
-117
.47-1
_120
05H
-132
.63-2
_025
95H
-142
.2-3
_136
65H
-159
.09-1
_082
05H
-179
.06-1
_025
46H
-1.34
-2_0
881
6H-2
4.36
-1_0
868
6H-4
2.36
-3_0
783
6H-4
9.4-2
_029
16H
-54.6
-1_0
962
6H-5
5.94
-1_0
513
6H-6
0.23
-1_0
270
6H-6
5.03
-1_1
261
6H-7
4.55
-3_1
088
6H-9
0.15
-1_0
202
6H-1
12.32
-1_0
239
6H-1
26.18
-3_1
498
7H-1
4.96
-1_0
841
7H-3
7.55
-2_0
126
7H-5
4.37
-1_0
772
7H-6
8.46
-3_0
639
7H-7
7.85
-2_0
879
7H-7
9.6-1
_037
07H
-79.6
-3_0
835
7H-8
7.97
-1_0
143
7H-1
10.99
-2_0
385
7H-1
33.79
-2_1
104
7H-1
44.45
-1_0
843
7H-1
66.56
-3_0
826
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
Significance threshold