qtl statics

8/12/2019 QTL statics

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Quantitative Trait LociMAPPING

Non Credit Seminar

NISHAT

2009BS122M


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CONTENT

Advantages and limitation of QTL mapping

Methods of QTL mapping and processing of this

Data using computer Software

Mapping of QTL and different Approaches

QTL & its Trait identification

Future Aspects: a wish from current research


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QTLs are genes whose phenotypic effects show a continuousrange of variation in a population and is more or less normally

distributed

A quantitative trait locus/loci (QTL) is the locationof individual

locus or multiple loci in the genome that affects a trait that is

measured on a quantitative (linear) scale.

Examples of quantitative traits are:

- Plant height (measured on a ruler)

- Grain yield (measured on a balance)

Definition of QTL


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Trait effects

In this example genome have 3 loci, one associated with

decreased yield, and one associated with higher yield. The

phenotype, depending on the size of the effect of each QTL

and how they work together, may be low yield.

A variety may have some QTL that increase a trait (for

example, increase yield) and others that decrease thetrait. These work together to create the phenotype of

the plant.


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Finding good QTLSo the key is identifying the good QTL those that affect

the trait in the direction you want, and then separatingthose from the negative ones. This is where QTL

identification techniques are important. e.g.

Positive QTL: Grain Yield, Disease resistance, Oil content,

Protein or Mineral linked.

Negative QTL: Plant Height, Environment effected traits.

Note that these techniques are simply statisticalcorrelations, just like genetic mapping and any marker-

trait correlations; however, because we are looking for

many markers that correlate with a single trait, it is

somewhat more complex statistically.


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QTL Mapping

QTL mapping is the statistical study of the alleles that occur ina locus and the phenotypes (physical forms or traits) that they

produce.

The process of constructing linkage maps and conducting QTLanalysisto identify genomic regions associated with traitsis

known as QTL mapping (McCouch & Doerge, 1995)


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QTL mapping is based on the segregation of genes andmarkers via chromosome recombination (called

crossing-over) during meiosis (i.e. sexual reproduction),

thus allowing their analysis in the progeny (Paterson,

1996).

Partition of mapping population into different genotypic

classes based on genotype Marker and apply correlative

statistics to determine the significant difference of onegenotype with another with respect to trait being

measured.

Principles of QTL Mapping


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Prerequisites for QTL

mapping Availability of a good linkage map (this can be done at

the same time the QTL mapping)

A segregating population derived from parents that

differ for the trait(s) of interest, and which allow for

replication of each segregant, so that phenotype can

be measured with precision (such as RILs or DHs)

A good assay for the trait(s) of interest

Software available for analyses


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The purpose of the phenotyping experiment is to assign a trait

value to each mapping population member. This value is then

combined with the allele score at the set of marker loci distributed

throughout the genome. A data file is then created which includesall the trait data and all the marker data for the entire population.

various software applications can be applied to this data file to

identify statistical associations between the presence of

alternative alleles and the trait value.

Overview of QTL mapping


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Approaches to mapping

1. Experimental crosses (Segregating Population)

- Backcrosses

- F2 intercrosses

- Recombinant inbred (RI) lines

- Double Haploids

2. Pedigree analysis

3. Association studies (Linkage disequilibrium, LD mapping)

- With candidate genes (direct approach)

- Localized association studies (chromosomal region)

- Whole-genome association studies


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Need of Segregating Population

o In natural population consistent association between QTLand Marker genotype will not usually exists (Except where

marker is completely linked to QTL, which is very rare). So

to study the recombination b/w QTL and marker

segregating population is useful.


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Types and Size of population

A segregating population using parental lines that are highlycontrasting phenotypic ally for the trait.

The parent lines should be genetically divergent.

The size of mapping population depend upon type of

mapping population employed for analysis, genetic nature

of target trait.

Note: The choice of mapping population could vary based

upon the objectives of experiment and timeframe

as well as resources available for undertaking QTL

analysis.


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Backcross (BC)

Advantages: It is easier to identify QTL as there

are less epistatic and linkage drag effects;

especially useful for crosses with wild species.

Disadvantages: Difficult or impossible in speciesthat are highly heterozygous and outcrossing.

Use:best when inbred lines are available

Huang et al.( 2003)


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Advantage: Fast and easy to construct

Disadvantage: F3 families are still veryheterozygous, so the precision of the estimates

can be low (because of the high standard error);

cant be replicated

F2 populations

Jampatong et al.(2002)


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AA, Aa, aa

AA x aa

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F2design is more powerful in

cases of partial dominance,

since heterozygous effects can

be identified

Backcross design is more efficient

in cases of complete dominance.

Comparison ..


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True breeding or homozygous

Immortal collection

Replicate experiments in different

environments

Molecular Marker database can beupdated

Recombinant inbred (RI) lines

Advantages: fixed lines so can be replicated

across many locations and/or years; caneliminate problem of background

heterozygosity

Disadvantages: Can take a long time to

produce. (Some species are not amenable).He P et al.(2001)


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Recombinant inbred (RI) linesAA

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Basic method

Start with inbred parentallines, homozygous at every

locus

Make F1s, heterozygous atevery locus

Inbreed different F1 lines

These recombinant inbredlines are homozygous at

each locus


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Advantages: 1)Spontaneous chromosome doubling of

Haploid microspores in in vitro culture

2)Homozygosity achieved in a single step Plants.

Disadvantages: Less recombination between linked markers

Not all systems are amenable to in vitro

culture

Doubled haploid Lines(DHL)


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Advantage: Very precise and

statistically strong, as background

is constant; especially useful for

validation experiments

Disadvantage: Can take time toconstruct; only useful for specific

target QTL

Near Isogenic Lines (NILs)

Szalma SJet

M th d t d t t QTL


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Single Marker approach The simplest, and probably most common method.

Distribution of trait values is examined separately for each markerlocus.

Each marker-trait association test is performed independent of

information from all other markers, so that a chromosome with n

markers offers n separate single marker tests.

Uses: This technique is good choice when the goal is simple detection of

a QTL linked to a marker, rather than estimation of its position

and effects.

Limitations: This method can not Determine Whether the markerare associated with one or more QTLs.

The effect of QTL are likely to be under estimated because

they are confound with recombination frequencies.

Methods to detect QTLs

WITH INTERVAL MAPPING


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This technique use the two Flanking marker. A separate analysis is performed for each pair of adjacent marker

loci.

The use of such two-locus marker genotypes results in n-1separate tests of marker-trait associations for a chromosome

with n markers (one for each marker interval).

Both single-marker and interval mapping approaches are biased

when multiple QTLs are linked to the marker/interval being

considered. Methods simultaneously using three or more

marker loci attempt to reduce or remove such bias.

Advantages: Interval mapping offers increased of power ofdetection and more precise estimates of QTL effects and

position.

WITHINTERVALMAPPING

Lander and Botstein 1989

Flanking marker analysis

Si l I t l M i (SIM)


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It evaluates the association between the trait values and the expected

contribution of a QTL (the target QTL) at multiple analysis points between

each pair of adjacent marker loci (the target interval).

The expected QTL genotype is estimated from the genotypes of flanking

marker loci and their distance from the QTL. Since there is usually uncertaintyin the QTL genotype, the like- lihood is a sum of terms, one for each possible

QTL genotype, weighted by the probability of that genotype given the

genotypes of the flanking markers. The analysis point that yields the most

significant association may be taken as the location of a putative QTL.

Simple Interval Mapping (SIM)

compos te nter a mapp ng


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CIM evaluates the possibility of a target QTL at multiple analysis points across

each inter-marker interval (same as SIM). However at each point it alsoincludes the effect of one or more background markers.

compos te nterva mapp ng

Background markers: That have been shown to be associated with the trait

and therefore lie close to other QTLs (background QTLs) affecting the

trait.

Multiple QTL mapping (Jansen 1993)

The inclusion of a background marker in the analysis helps in one of two

ways, Based upon the linkage of Background marker and the target

interval

1) If they arelinked,inclusion of the background marker may help to

separate the target QTL from other linked QTLs.

2) If they are not linked, inclusion of the background marker makes the

analysis more sensitive to the presence of a QTL in the target interval.

(Zeng 1993, 1994).


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INTERVALMAPPING

Advantages Takes proper account of

missing data

Interpolate positionsbetween markers

Provide a support interval

Provide more accurate

estimate of QTL effect

Disadvantages

Intense computation

Rely on a genetic map withgood quality

Difficult to incorporate

covariate


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The power of a QTL-detection experiment, defined as the probability

of detecting a QTL at a given level of statistical significance,

depends upon the strength of the QTL and the number of progenyin the population. If we consider the strength of the QTL in terms

of the fraction of the total trait variance that it explains, we can

define three categories of QTLs. Those which explain over 20% of

the variance are strong QTLs; traits controlled by such QTLs can

be considered almost Mendelian. Strong QTLs can be detected

with a power greater than 80% even with the AXB/BXA set of

recombinant inbred strains. At the other extreme, weak QTLs,

which explain 1% or less of the trait variance, require at least a

thousand progeny to detect them with high power. Detection of

such QTLs is not routinely feasible.

The number of progeny required to detect a QTL is, roughly

speaking, proportional to the variance of the nongenetic

(environmental) contributions and inversely proportional to the

square of the strength of the QTL.


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Name of Software URL of the site

Mapmaker/QTL ftp://genome.wi.mit.edu/pub/mapmaker3

QTL Cartographer http://statgen.ncsu.edu/qtlcart/

Map Manager QT http://mcbio.med.buffalo.edu/mapmgr.html

QGene [email protected]

MapQTL http://www.cpro.dlo.nl/cbw/

PLABQTL http://www.unihohenheim.de/~ipspwww/soft.html

MQTL ftp://gnome.agrenv.mcgill.ca/pub/genetics/software/M

QTL/

Multimapper http://www.RNI.Helsinki.FI/~mjs/The QTL Cafe http://sun1.bham.ac.uk/g.g.seaton/

Epistat http://www.larklab.4biz.net/epistat.htm

Manly et al. (1999)


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MULTIPLEREGRESSIONANALYSIS

To obtain the final estimates of the effects of the QTLdetected with CIM

proportion of phenotypic variation accounted for by an

individual QTL


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The mapping approaches described so far require crosses.

Mapping with experimental crosses requires organisms that

can be manipulated and that have sufficiently short generation

times to be tractable.

Humans cannot be crossed for ethical reasons, and many other

organisms (perhaps most other organisms?) cannot be studied

this way for practical reasons (e.g. elephants, whales, giant

sequoias, many insects, many rodents,.....).

Pedigree Analysis

M i i h di


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The basic idea in pedigree mapping is to follow affected

individuals and markers in related families. Markers that are

co-inherited with disease status are linked to the causative

gene.

Mapping with pedigrees

Two unrelated pedigrees

Haplotypes in each pedigree are

identified by numbers (differentfor each family), and

determined from five linked

markers

Dark blue - affected

White - unaffected

Light blue - status uncertain

In each case one haplotype, in

red, co-segregates with the

disease


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Linkage Disequilibrium (LD)

Non random association of alleles (nucleotides) at different genes

(sites)

The non-independence of alleles at different loci. This occurs

when certain combinations of alleles across loci occur more often

than expected by chance alone, based on their respective allelefrequencies in the population.


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1 2

COMPLETELINKAGEDISEQUILIBRIUM

Adapted from Rafalski (2002) Curr

Opin Plant Biol 5:94-100.

D=1

r2=1

6

6

Locus 1

Locus

2

Same mutational history

and no recombination.

No resolution


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1 2

LINKAGEDISEQUILIBRIUM

D=1

r2=0.33

3

6

Locus 1

Locus

2

Different mutational history

and no recombination.

Some resolution

3


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1 2

LINKAGEEQUILIBRIUM

D=0

r2=0

3

3

Locus 1

Locus

2

Same mutational history

with recombination.

Resolution

3

3


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Markers are used to partition the mapping population into

different genotypic groups and to determine whethersignificant differences exist between groups with respect to the

trait being measured.

A significant difference between phenotypic means and markersystem in mapping population indicates that the marker locus

being used to partition the mapping population is linked to a

QTL controlling the trait.

Therefore, the QTL and marker will be usually be inheritedtogether in the progeny, and the mean of the group with the

tightly-linked marker will be significantly different (P


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A difference in mean phenotype indicates that linkage disequilibrium

present in the population and marker is linked to a QTL. But this does

not mean that every marker that is linked to a QTL is expected to show

a mean difference in phenotype. This vary with the extent of LD

Linkage between markers is usually calculated using odds ratios

logarithm of odds:- the ratio of linkage versus no linkage

This ratio is generally called LOD score. LOD values of >3 are typically

used to construct linkage maps. A LOD value of 3 between genes and

marker indicates that linkage is 1000 times more likely (i.e. 1000:1)than no linkage (null hypothesis). LOD values may be lowered in order

to detect a greater level of linkage or to place additional markers within

maps constructed at higher LOD value.

Extent of LD

Risch et al.1992


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o Mutation

o Drift

o Population structure (admixture)

o Gene flow

o

Population contraction

o Selection (hitchhiking or epistasis)

What causes LD?


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o Recombination

o Gene conversion

o Recurrent mutation

What reduces LD?


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The amount and extent of LD that exists in the populations

that are used for genetic improvement is the net result of allthe forces that create and break-down LD and is therefore,

the result of the breeding and selection history of each

population, along with random sampling.

On this basis, populations that have been closed for many

generations are expected to be in linkage equilibrium, except

for closely linked loci. Thus, in those populations, only

markers that happen to be tightly linked to QTL may show an

association with phenotype, and even then there is no

guarantee because of the chance effects of random

sampling.

Implications of the extent of LD

With candidate genes


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With candidate genes Candidate genes is the genes that are associated with the trait

of interest.

This technique evaluate markers that are inside the genes or

close to genes that are thought to be associated with the trait of

interest.

The candidate gene approach utilizes knowledge from speciesthat are rich in genome information (e.g. human, mouse),

effects of mutations in other species, previously identified QTL

regions, and/or knowledge of the physiological basis of traits to

identify genes that are thought to play a role in the physiology

of the trait.

Following mapping and identification of polymorphisms within

the gene in the plants, the association of genotype at the

candidate gene with phenotype can be estimated in a closed

breeding population. child et al.(2003)

Whole Genome scan


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Whole Genome scan

The whole genome scan approach to QTL detection uses

random genetic markers spread over the genome toidentify genomic regions that harbor QTL. The QTL

regions are detected by following the co-segregation of

markers with phenotype in structured populations using

interval mapping .

(Haley et al. 1994)

Summary of QTL analysis


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Summary of QTL analysis

Recombinant Inbred Lines

(RILs,F2,F3,Doubled Haploid Lines)

Genotype with

molecular markers Analyse trait data for eachline

Link trait data with marker data

- Mapping software

Trait QTL mapped at bottom of

small chromosome

Parent1 Parent 2

QTL

Create a

Linkage

map withmolecularm

arkers


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Merits of QTL Mapping

Where mutant approaches fail to detect genes with phenotypic

functions , QTL mapping can help

Good alternative when mutant screening is laborious andexpensive e.g circadium rhythm screens

Can identify New functional alleles of known function genes

e.g.Flowering time QTL,EDI was the CRY2 geneNatural variation studies provide insight into the origins of

plant evolution

Identification of novel genes


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Limitations....

Mainly identifies loci with large effects

Less strong ones can be hard to pursue

No. of QTLs detected, their position and effects

are subjected to statistical error

Small additive effects / epistatic loci are not detected

and may require further analyses

Cloning can be challenging but not impossible


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FUTUREPROSPECTS

Constant improvements of Molecular platforms

New Types of genetic materials( e.g. introgression lines:

small effect QTLs can be detected)

Advances in Bioinformatics

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