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


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



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)




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)


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


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


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).


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


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


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


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



136

1H


ABG00817


63


2H

BCD9070



Act8C121ABG499124GBM1043125

scsnp23255151ABG004155

scind02281166MWG883172

DsT-24181

HVM62190

DsT-40199

ABC172scssr25538212

DsT-35218

3H



50



4H




scssr05939111

RSB001A120




5H





MWG514162MWG798A163DsT-71167

6H



89

scsnp00703MWG203197


ABG461A167WG380A171GBM1065178


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



136

1H


ABG00817


63


2H

BCD9070



Act8C121ABG499124GBM1043125

scsnp23255151ABG004155

scind02281166MWG883172

DsT-24181

HVM62190

DsT-40199

ABC172scssr25538212

DsT-35218

3H



50



4H




scssr05939111

RSB001A120




5H





MWG514162MWG798A163DsT-71167

6H



89

scsnp00703MWG203197


ABG461A167WG380A171GBM1065178


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


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



136

1H


ABG00817


63


2H

BCD9070



Act8C121ABG499124GBM1043125

scsnp23255151ABG004155

scind02281166MWG883172

DsT-24181

HVM62190

DsT-40199

ABC172scssr25538212

DsT-35218

3H



50



4H




scssr05939111

RSB001A120




5H





MWG514162MWG798A163DsT-71167

6H



89

scsnp00703MWG203197


ABG461A167WG380A171GBM1065178


ThA1199

7H

We identify the location of the QTL, the molecular markers flanking them, their effect and their interactions


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.


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


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

qualitative and quantitative traits qualitative traits: phenotypes with discrete and easy to measure...

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