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QTL mappingPP 601: Functional genomics and genes associated with a few

physiological processes (2+0)

Presented byEkatpure Sachin Chandrakant

PhD research scholar

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Introduction• Many agriculturally important traits such as yield, quality and some

forms of disease resistance are controlled by many genes

• Those are known as quantitative traits (also ‘polygenic,’ ‘multifactorial’ or ‘complex’ traits)

• Eg: yield, seed weight, fruit length, some forms of disease resistance etc

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DefinitionThe regions in the genomes that contain genes related to a particular

quantitative trait are known as Quantitative Trait Loci/ Locus (QTLs)

Term first coined by Gelderman in 1975

Identification of QTLs based only on conventional phenotypic evaluation is not possible

In 1980 discovery of molecular markers tremendously increase the identification of QTLs

The use of DNA markers in plant breeding has opened a new field in agriculture called molecular breeding

Also DNA markers were used in the construction of linkage maps Linkage maps can be utilised for identifying chromosomal regions that contain

genes controlling simple traits (controlled by a single gene) and quantitative traits using QTL

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QTL mapping• The process of constructing linkage maps and conducting QTL analysis

to identify genomic regions associated with traits with the help of molecular markers is known as QTL mapping

• Also called as genetic, gene or genome mapping

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Section I: Genetic markers

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What is mean genetic markers? Genetic markers represent genetic differences between individual organisms or

species

They do not represent the target genes themselves but act as ‘signs’ or ‘flags’

Genetic markers that are located in close proximity to genes may be referred to as ‘gene tags’

All genetic markers occupy specific genomic positions within chromosomes called ‘loci’

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Major types of genetic markersMorphological/classical /visible markers:

Which themselves are phenotypic traits or characters

Eg: flower colour, seed shape, growth habits or pigmentation

Biochemical markers: Which include allelic variants of enzymes

called isozymes Detected by electrophoresis and specific

staining

DNA or Molecular markers: Which reveal sites of variation in DNA

Main disadvantage is they are limited in number and are influenced by environmental factors or the developmentalstage of the plant

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DNA markers Most widely used type of marker predominantly due to their abundance

Selectively neutral because they are non-coding sequences

Not affected by environmental factors and/or the developmental stage of the plant

Numerous applications in plant breeding such as assessing the level of genetic diversity within germplasm and cultivar identity

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CLASSIFICATION OF DNA MARKERSBased on the method of their detection broadly divided into Hybridization-based PCR based DNA sequence-based

Visualises the genetic differences by gel electrophoresisStaining with chemicals (ethidium bromide or silver)Detection with radioactive or colourimetric probes

Polymorphic DNA markers : helps in revealing the differences between individuals of the same or different species.

Monomorphic DNA markers: Markers that do not differentiate between genotypes are called monomorphic markers

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Polymorphic vs. Monomorphic

Monomorphic

Polymorphic

Monomorphic

Polymorphic

Polymorphic

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Types of polymorphic markers

Polymorphic markers

The different forms of a DNA marker (e.g. different sized bands on gels) are called marker ‘alleles’

Codominant Dominant

Differentiate in homozygous or heterozygous Do not differentiate the homo and heterozygous

Indicate differences in size On the basis of present or absent

Have many different alleles Only two alleles

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Codominant and DominantComparison between

(a) Codominant (b) dominant markers

• Codominant markers can clearly discriminate between homozygotes and heterozygotes

• Whereas dominant markers do not

Dominant Codominant

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Section II: Construction of linkage maps

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What are linkage maps? It is the ‘road map’ of the chromosomes derived from two different

parents

Indicate the position and relative genetic distances between markers along chromosomes (signs or landmarks along a highway)

Helps to identify chromosomal locations containing genes and QTLs associated with traits of interest; such maps may then be referred to as ‘QTL’ /or ‘genetic’ maps

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Mapping principle Genes and markers segregate via chromosome recombination (called crossing-over) during meiosis

Genes or markers that are close together or tightly-linked will be transmitted together from parent to progeny more frequently than genes or markers that are located further apart

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Contd…. In a segregating population, there is a mixture of parental and recombinant

genotypes

The frequency of recombinant genotypes : infer the genetic distance between markers

The lower the frequency of recombination between two markers, the closer they are situated on a chromosome and the higher the frequency of recombination between two markers, the further away they are situated on a chromosome).

Markers that have a recombination frequency of 50% are described as ‘unlinked’ and assumed to be located far apart on the same chromosome or on different chromosomes

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E F

E Fe f

G H g h

g h

G H

G h

G H

g H

g h

E F

E f

e F

e f

GametesP

R

R

P

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R

R

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Frequency45%

5%

5%

45%

30%

20%

20%

30%

Recombination events between homologous chromosomes

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How linkage map constructed?The three main steps of linkage map construction are (1) Production of a mapping population (2) Identification of polymorphism (3) Linkage analysis of markers

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1. Production of a mapping populationRequires a segregating plant population (i.e. a population derived from sexual reproduction)

The parents selected will differ for one or more traits of interest

Population sizes: generally range from 50 to 250 individuals

For high-resolution mapping larger populations are required

If map is used for QTL studies then the mapping population must be phenotypically evaluated (i.e. trait data must be collected) before subsequent QTL mapping

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mapping population contd……

In self pollinated species, parents that are both highly homozygous (inbred)

In cross pollinating species, the situation is more complicated being heterozygous

The following populations can be used

Backcross (BC) populations (F1 X one of the parents)

F2 population (selfing of F1 hybrids) and RI (Recombinant Inbreds)

Double Haploids

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mapping population contd…… The major advantage of BC and F2 is that it can be produced in short period of time

RI obtained from F2 inbreeding consist of a series of homozygous lines

Each containing a unique combination of chromosomal segments from the original parents

But it takes 6-8 generations for development

Doubled haploid (DH) populations may be produced by induction of chromosome doubling from pollen grains

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2. Identification of polymorphism• Select an appropriate marker that shows differences between parents (polymorphic markers)

• It is critical that sufficient polymorphism exists between parents

• In Cross pollinating species, higher DNA polymorphism exits compared to inbreeding species

• So in inbreeding species, parents selected should be distantly related

• Then marker should be screened across the entire mapping population, including the parents : marker genotyping

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Expected segregation ratios for markers in different population

typesPopulation Codominant markers Dominant markers

F2 1:2:1 (AA:Aa:aa) 3:1 (B:bb)

BC 1:1 (Cc:cc) 1:1 (Dd:dd)

RI/DH 1:1 (EE:ee) 1:1 (FF:ff)

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Identification of polymorphism contd..

Generally, markers will segregate in a Mendelian fashion although distorted segregation ratios may be encountered

Significant deviations from expected ratios can be analysed using chi- square tests

In polyploid species, identifying polymorphic markers is more complicated

So mapping of diploid relatives of polyploid species is done

However, diploid relatives do not exist for all polyploid species

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3. Linkage analysis of markers • Code data for each DNA marker on each individual of a population and conduct linkage analysis

using computer programs

• Linkage between markers is usually calculated using

• Odds ratios: the ratio of linkage versus no linkage• LOD value/ LOD score: Logarithm of the odd ratio

• An LOD value of 3 between 2 markers: linkage is 1000 times more likely than no linkage (1000:1)

• Manual analysis is not feasible, so computer programmes needed• Commonly used software programs • Mapmaker/ EXP, MapManager QTX, JoinMap etc

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Linkage map

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Linkage map

• Linked markers are grouped together into ‘linkage groups,’ which represent chromosomal segments or entire chromosomes

• Polymorphic markers detected are not necessarily evenly distributed over the chromosome, but clustered in some regions and absent in others

• The accuracy of measuring the genetic distance and determining marker order is directly related to the number of individuals studied in the mapping population, min 50 individuals

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Genetic distance and mapping functions • Greater the distance between markers, the greater the chance of recombination occurring

during meiosis

• Distance along a linkage map is measured in terms of the frequency of recombination between genetic markers

• Mapping functions are required to convert recombination fractions into centiMorgans (cM)

• When map distances are small (<10 cM), the map distance equals the recombination frequency

• However, this relationship does not apply for map distances that are greater than 10 cM

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Mapping Function

• Kosambi mapping function, which assumes that recombination events influence the occurrence of adjacent recombination events

• Haldane mapping function, which assumes no interference between crossover events

• There are recombination ‘hot spots’ and ‘cold spots,’ which are chromosomal regions in which recombination occurs more frequently or less frequently, respectively

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Section III: QTL analysis

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Principle of QTL analysis

• Based on the presence or absence of a particular marker loci, the mapping population is partitioned into different genotypic groups and these groups are analyzed for significant differences with respect to the trait

• A significant difference between phenotypic means of the groups indicates that the particular marker locus is linked to a QTL controlling the trait

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Principle of QTL analysis contd…

• If, QTL & marker is closely linked, chance of recombination will be less

• So QTL and marker will be inherited together and mean of the group will have significant difference

• If loosely linked or unlinked, there is independent segregation of the marker and QTL

• There will be no significant difference between means of the genotype groups

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Methods to detect QTLs Single-Marker analysis

Simple Interval mapping

Composite Interval mapping

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1. Single-marker analysis / single point analysis

Simplest method for detecting QTLs associated with single markers.

The statistical methods used : t-tests, analysis of variance (ANOVA) and linear regression

Linear regression is most commonly used: Coefficient of determination (R2) : explains the phenotypic variation arising from the QTL linked to the marker

Does not require a complete linkage map and can be performed with basic statistical software programs

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Single-marker analysis• QGene and MapManager QTX are commonly used computer programs to perform single-

marker analysis

Disadvantage

• A QTL is from a marker, the less likely it will be detected because recombination may occur between them

• Large no of segregating DNA markers may be used for overcoming this issue

Marker linkage group P value R2E 2 <0.0001 91

F 2 0.0001 58

G 2 0.0230 26

H 2 0.5701 2

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2. Simple Interval Mapping

• Instead of analyzing single markers, the intervals between adjacent pairs of linked markers along chromosomes is analysed

• This compensates for recombination between the markers and the QTL, and is considered statistically more powerful

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3. Composite interval mapping

• Combines interval mapping with linear regression and includes additional genetic markers in the statistical model in addition to an adjacent pair of linked markers for interval mapping

• More precise and effective at mapping QTL

• QTL Cartographer, MapManager QTX and PLABQTL are commonly used softwares

Understanding interval mapping results

• SIM and CIM produce a profile of the likely sites for a QTL between adjacent linked markers

• The statistical results are typically presented using a logarithmic of odds (LOD) score or likelihood ratio statistic (LRS)

• LRS = 4.6 × LOD

• Position for a QTL: position where the highest• LOD value is obtained

Understanding interval mapping results contd…

• Real QTL: The peak must also exceed a specified significance and is determined using permutation tests

• The phenotypic values of the population are ‘shuffled’ whilst the marker genotypic values are held constant and QTL analysis is performed some 500-1000 times to assess the level of false positive marker-trait associations and significant levels are determined

• Previously, LOD score of between 2.0 to 3.0 (most commonly 3.0) was usually chosen as the significance threshold

Reporting and describing QTLs from Interval mapping

• Based on most closely linked markers on linkage maps

• The chromosomal regions represented by rectangles are usually the region that exceeds the significance threshold

• “flanking” markers: Pair of markers that are most tightly-linked markers on each side of a QTL and this selection is more reliable than selection based on a single marker

• Reason: There is a much lower chance of recombination between two markers and QTL compared to the chance of recombination between a single marker and QTL

• QTLs can only be detected for traits that segregate between the parents

• Several criteria may be used for phenotypic evaluation of a single trait

• QTLs that are detected in common regions (based on different criteria of a single trait) are likely to be important OTL for controlling that trait

•Mapping populations may also be constructed based on parents that segregate for multiple traits.

• This is advantageous because QTLs controlling the different traits can be located on a single map

•May be difficult with completely or semi-destructive bioassays (e.g. screening for resistance to necrotrophic fungal pathogens)

QTL can be major or minor based on the proportion of the phenotypic variation i.e.. R2 value

Major QTLs: >10%), stable QTL

Minor QTLs: <10%. environmentally sensitive especially (disease resistance)

(1) Suggestive; (2) Significant; (3) Highly significant to “avoid a flood of false positive claims” and to ensure that “true hints of

linkage” were not missed

Significant and highly-significant: significance levels of 5 and 0.1%, respectively

Suggestive: Expected to occur once at random in a QTL mapping study

MapManager QTX reports QTL mapping results with this classification

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Molecular DiversityScreening for molecular polymorphism

Parental Selection Phenotypic DifferenceGood contrast between parent for

target trait (e.g. Resistance vs. Susceptibility

Segregating PopulationBack cross, DH, RIL, NIL

GenotypingAssay of mapping population using

polymorphic molecular markers

Linkage MapMAPMAKER, JOINMAP, LINKAGE

Phenotypic EvolutionMeasurements of target traits in

field/ green house replicated trials

QTL analysisMAPMAKER-QTL, QTL-cartographer, QGene

QTL validationRepetition of experiment in crosses Involving different

base germplasm

Breeding ApplicationsMarker Assisted Selection, Recurrent genome selection, QTL introgression, Differential QTL selection,

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Thank You