QTL Mapping

QTL Mapping

The objectives of this section are:• To learn basic concepts related with Quantitative

Trait Loci (QTL) analysis,• To learn how to use QTL analysis software,• To interpret results, and to get acquainted with

the various QTL analysis programs and QTL databases.

• The focus will be on QTL analysis of self-pollinated plants. However, most of what is covered can be easily extended to cross-pollinated plants, animals, and humans.

Topics

Inheritance of quantitative traits Identifying trait-linked markers Single-marker analysis Interval mapping Composite interval mapping Issues in QTL detection Association mapping Genomic Selection

Qualitative and Quantitative traits• 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

AABB X aabb

P1

AaBbF1

1/16 : AABB 4/16: AaBB + AA Bb 6/16: AaBb + AAbb + aaBB 4/16: aaBb + Aabb 1/16: aabb

P2AA X aa

P1

AaF1

1/4 : AA 1/2: Aa + aA 1/4: aa

P2

Freq

uenc

y

25%

50%

No. of favorable alleles210

Freq

uenc

y

25%

50%

No. of favorable alleles410 32

One gene controlling the trait

Two genes with additive effect controlling the trait

AABBCC X aabbcc

P1

AaBbF1

P2

Freq

uenc

y

25%

50%

No. of favorable alleles410 32

Three genes with additive effect controlling the trait

65

Inheritance of Quantitative traits

1/16 : purple AABB 4/16: dark-red AaBB + AABb 6/16: red AAbb + AaBb + aaBB 4/16: light-red aaBb + Aabb 1/16: white aabb

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

AABB aabb

AaBb

Inheritance of Quantitative traits

Color intensity- white+ purple

Freq

uenc

y

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

VVh 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

VVh 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

VVh 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 μRSRh 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 studyb) 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-10ABG0585scind026227ABG00817

scssr1022636scssr0775939GBM106642Pox45scssr0338156scssr12344scssr02236Ebmac0684

63

BCD1434.265ABG35668GBM102371scsnp0334383vrs188Bmag012594DsT-4197MWG503102GBM1062103KFP203104MWG882A108ABG1032117ABG072124Ebmc0415137cnx1139Zeo1149GBM1019161Aglu5F3R2163MWG720165GBM1012170wst7173scssr08447179MWG949A180

2H

BCD9070

ABC171A26GBM107430scssr1055933MWG798B36Dst-2739BCD70642DsT-3958alm61Bmac020966ABC32569DsT-6773scssr2569187ABG37789Bmag022598

Act8C121ABG499124GBM1043125

scsnp23255151ABG004155

scind02281166MWG883172

DsT-24181

HVM62190

DsT-40199

ABC172scssr25538212DsT-35218

3H

MWG6340MWG07721HVM4024DsT-2929CDO54230CDO12231hvknox335Dhn639ABC303scssr2056941CDO79544HVM349DST-46scind03751scssr18005

50

Tef252GBM102060Bmag035362scind1045567DsT-7974scssr14079ABG47280GBM105983KFP22192Ebmac070194MWG652B95GBM1048101Hsh111HVM67112KFP241.1116ABG601124

4H

scssr023060MWG6186DsT-68ABC48311ABG61012

ABG39537scssr0250344scssr1807645Bmac009653NRG045A55scsnp0426056Ale58

ABC30279scind1699182scssr1533485scsnp0614490srh100

scssr05939111

RSB001A120scsnp001771280SU-STS1134ABG003B141

scssr10148157Tef3166MWG877169BE456118A170ABG496179scsnp02109E10757A193ABG391197JS10B198ABC622205DsT-33207Bmag0113C215MWG602A223scssr03907224scssr03906225

5H

MWG6200Bmac0316scssr093984MWG652A31MWG602B35scind6000242JS10A45GBM102151GBM106861BG29929765HVM3168rob70Bmag0009scssr0209371ABG47481Bmac0218C88ABG38892scsnp2122699MWG820101GBM1008122scssr05599123MWG934126scind04312b132scssr00103GBM1022135Bmac0040143DsT-18145DsT-32B146DsT-22152DsT-28159scind60001DsT-74160MWG514162MWG798A163DsT-71167

6H

ABG7040Bmag000714scind0069420AW98258029MWG089CDO47536ABG38038BE60207344scssr0797057scsnp0046066ABC25568ABC165D69HvVRT273scssr1586482GBM103086scsnp22290MWG808DAK642scind00149

89

scsnp00703MWG203197RSB001C98nud103lks2115ABC1024117Bmag0120125DsT-30126WG380B127ABC310B137Ris44139

ABG461A167WG380A171GBM1065178

HVM5196scssr04056KFP255197ThA1199

7H

Maps have different levels of resolution

Maps: Different levels of resolutionMain factors: marker density and population size


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

- Classify progeny by marker genotype - Compare phenotypic mean between classes (t-test or ANOVA) - Significance = marker linked to QTL - Difference between means = estimate of QTL effect

g = (µ1 - µ2)/2

g = genotypic effect

µ1 = trait mean for genotypic class AA

µ2 = trait mean for genotypic class aa

0aa AAGenotypic classes

βo

-1 x

y

QTL genotype

Marker genotype(x)

QQ Qq qq Mean trait score (y)

AA 1/4 1/2 1/4 Intermediate

Aa 1/4 1/2 1/4 Intermediate

aa 1/4 1/2 1/4 Intermediate

Aa

Qq

F1

Marker and QTL unlinked

Difference between trait scores of AA and aa is zero.

Conclusion: No relationship between trait score (y) and marker genotype (x)

F2 population

QTL genotype

Marker genotype(x)

QQ Qq qq Mean trait score (y)

AA Most Few Rare High

Aa Few Most Few Intermediate

aa Rare Few Most Low

Aa

Qq

F1

Marker and QTL linked

Difference between trait scores of AA and aa is large.

Conclusion: Strong relationship between trait score (y) and marker genotype (x)

F2 population



136

1H



63


2H

BCD9070


Act8C121ABG499124GBM1043125

scsnp23255151ABG004155

scind02281166MWG883172

DsT-24181

HVM62190

DsT-40199

ABC172scssr25538212DsT-35218

3H


50


4H




scssr05939111



5H


6H


89


ABG461A167WG380A171GBM1065178


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



63


2H

BCD9070


Act8C121ABG499124GBM1043125

scsnp23255151ABG004155

scind02281166MWG883172

DsT-24181

HVM62190

DsT-40199

ABC172scssr25538212DsT-35218

3H


50


4H




scssr05939111



5H


6H


89


ABG461A167WG380A171GBM1065178


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

QTL Mapping

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