QTL MAPPING & ANALYSIS

Submitted To:
Dr. K.B.Eshwari
Associate Professor
Dept.of GPBR
Submitted By:
MANJUNATH S KENCHARAHUT
RAM/16-49
M.Sc(Ag)
Dept.of GPBR
Quantitative trait loci(QTL)
mapping in genetic analysis

QTL MAPPING
• Many agriculturally important traits such as yield, quality
and some forms of disease resistance are controlled by
many genes and are known as “quantitative traits or
polygenic or multifactorial or complex traits”.
 These traits show continuous variation in a population.
 These traits do not fall into discrete classes.
 They are measurable.

Quantitative Trait Loci
The loci controlling quantitative traits are called quantitative trait loci or QTL.
Term first coined by Gelderman in 1975.
It is the region of the genome that is associated with an effect on a
quantitative trait.
It can be a single gene or cluster of linked genes that affect the trait.
QTLs have the following characteristics
These traits are controlled by multiple genes, each segregating according to
Mendel's laws.
These traits can also be affected by the environment to varying degrees.
Many genes control any given trait and Allelic variations are fully functional.
Individual gene effects is small &The genes involved can be dominant,or co-
dominant.
The genes involved can be subject to epistasis or pleiotrophic effect.

QTL Mapping
The process of constructing linkage maps and conducting QTL analysis i.e.
to identify genomic regions associated with traits is known as QTL mapping.
Identification and location of polygenes or QTL by use of DNA markers.
It involves testing DNA markers throughout the genome for the likelihood
that they are associated with a QTL.
Principles of QTL mapping
Genes and markers segregate via chromosome recombination during meiosis,
thus allowing their analysis in the progeny.
The detection of association between phenotype and genotype of markers.
QTL analysis depends on the linkage disequilibrium.
QTL analysis is usually undertaken in segregating mapping populations.

Objectives of QTL Mapping
The basic objective is to detect QTL, while minimizing the occurrence of
false positives (Type I errors, that is declaring an association between a marker
and QTL when in fact one does not exist).
 To identify the regions of the genome that affects the trait of interest.
 To analyze the effect of the QTL on the trait.
 How much of the variation for the trait is caused by a specific region?
 What is the gene action associated with the QTL – additive effect? Dominant
effect?
 Which allele is associated with the favorable effect?

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
 Molecular Markers
 Sophisticated Laboratory

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 species
that are highly heterozygous and outcrossing.
Use: best when inbred lines are available
TYPE OF MAPPING POPULATIONS

Advantage: Fast and easy to construct
Disadvantage: F3 families are still very heterozygous,
so the precision of the estimates can be low
(because of the high standard error); can’t be
replicated
F2 populations
Jampatong et al. (2002)

 True breeding or homozygous
 Immortal collection
 Replicate experiments in different
environments
 Molecular Marker database can be
updated
Recombinant inbred (RI) lines
Advantages: fixed lines so can be replicated
across many locations and/or years; can
eliminate problem of background
heterozygosity
Disadvantages: Can take a long time to
produce. (Some species are not amenable).
He P et al.(2001)

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)

Advantage: Very precise and
statistically strong, as background is
constant; especially useful for
validation experiments
Disadvantage: Can take time to
construct; only useful for specific
target QTL
Near Isogenic Lines (NILs)
Szalma SJ et al.

Steps involved in QTL Mapping:
 Selection of parental lines
 Sufficient polymorphism
 Parental lines are highly contrasting phenotypically
 Genetically divergent
 Selection of molecular markers (dominant/codominant)
 Making crosses
 Creation of mapping population

 Phenotyping of the progenies
 Genotyping of the progenies
 Construction of linkage map
 Screening the mapping population using polymorphic molecular
markers
 Segregation patterns
 Data is then analyzed using a statistical package such as MAPMAKER
or JOINMAP
 Assigning them to their linkage groups on the basis of recombination
values
 For practical purposes, in general recombination events considered to
be less than 10 recombinations per 100 meiosis, or a map distance of
less than 10 centiMorgans(cM).

Summary of QTL analysis
Recombinant Inbred Lines
(RILs,F2,F3,Doubled Haploid Lines)
Genotype with
molecular markers
Analyse trait data for each
line
Link trait data with marker data
- Mapping software
Trait QTL mapped at bottom of
small chromosome
Parent 1 Parent 2
QTL
Create a
Linkage
map with
molecularm
arkers

 QTL analysis
 It is based on the principle of detecting an association between
phenotype and the genotype of the markers.
 Markers are used to partition the mapping population into different
genotypic groups based on the presence or absence of a particular
marker locus and to determine whether significant differences exist
between groups with respect to the trait being measured.
 A significant difference between phenotypic means of the groups,
depending on the marker system and type of mapping population

 It is not easy to do this analysis manually and so with the help of a
computer and a software it is done.
 The segregation data of both the phenotype and the genotype are
collected and arranged in an excel sheet for QTL analysis using the
appropriate software.

Methods to detect QTLs
 Single-marker analysis,
 Simple interval mapping and
 Composite interval mapping
 Multiple Interval Mapping
 Bayesian Interval Mapping
Single-Marker Analysis (SMA)
Also known as single- point analysis. It is the simplest method for
detecting QTLs associated with single markers.

 This method does not require a complete linkage map and can be
performed with basic statistical software programs.
 The statistical methods used for single-marker analysis include
t-tests, analysis of variance (ANOVA) and linear regression.
 Linear regression is most commonly used because the coefficient of
determination ( R2) from the marker explains the phenotypic variation
arising from the QTL linked to the marker.
Limitations
 Likelihood of QTL detection significantly decreases as the distance
between the marker and QTL increases
 It cannot determine whether the markers are associated with one or more
markers QTLs

 The effects of QTL are likely to be underestimated because they are
confounded with recombination frequencies.
To overcome these limitations the use of large number of segregating
DNA markers covering the entire genome may minimize these problems. QGene
and MapManager QTX are commonly used computer programs to perform single-
marker analysis.
Simple Interval Mapping (SIM)
 It was first proposed by Lander and Bolstein.
 It takes full advantages of the linkage map.
 This method evaluates the target association between the trait values and
the genotype of a hypothetical QTL (target QTL) at multiple analysis points
between pair of adjacent marker loci (target interval).

 Presence of a putative QTL is estimated if the log of odds ratio exceeds a
critical threshold.
 The use of linked markers for analysis compensates for recombination
between the markers and the QTL, and is considered statistically more
powerful compared to single-point analysis.
 MapMaker/QTL and QGene are used to conduct SIM.
 The principle behind interval mapping is to test a model for the presence
of a QTL at many positions between two mapped loci.

Statistical methods used for SIM
Maximum Likelihood Approach
It is assumed that a QTL is located between two markers, the two loci
marker genotypes ( i.e. AABB, AAbb, aaBB, aabb for DH progeny) each contain
mixtures of QTL genotypes.
 Maximum likelihood involves searching for QTL parameters that give the
best approximation for quantitative trait distribution that are observed for
each marker class.
 Models are evaluated by comparing the likelihood of the observed
distributions with and without finding QTL effect
 The map position of a QTL is determined as the maximum likelihood from
the distribution of likelihood values.

Logarithm of the odds ratio (LOD score):
 Linkage between markers is usually calculated using odds ratio.
 This ratio is more conveniently expressed as the logarithm of the ratio, and
is called a logarithm of odds (LOD) value or LOD score.
 LOD values of >3 are typically used to construct linkage maps.
 LOD of 2 means that it is 100 times more likely that a QTL exists in the
interval than that there is no QTL.

 LOD of 3 between two markers indicates that linkage is 1000 times more
likely (i.e. 1000:1) than no linkage.
 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 values.
The LOD score is a measure of the strength of evidence for the
presence of a QTL at a particular location.

Hypothetical output showing a LOD profile for chromosome 4. The
dotted line represents the significance threshold determined by permutation
tests. The output indicates that the most likely position for the QTL is near marker
Q (indicated by an arrow). The best flanking markers for this QTL would be Q and
R.
Interval Mapping by Regression
 It is essentially the same as the method of basic QTL analysis( regression
on coded marker genotypes) except that phenotypes are regressed on QTL
genotypes.
 Since QTL genotypes are unknown they are replaced by probabilities
estimated from the nearest flanking markers.
 Softwares used: PLABQTL,QTL Cartographer, MapQTL

Composite Interval Mapping (CIM)
 Developed by Jansen and Stam in 1994
 It combines interval mapping for a single QTL in a given interval
with multiple regression analysis on marker associated with
other QTL.
 It is more precise and effective when linked QTLs are involved.
 It considers marker interval plus a few other well chosen single
markers in each analysis, so that n-1 tests for interval - QTL
associations are performed on a chromosome with n markers.

Advantages:
 Mapping of multiple QTLs can be accomplished by the search in one
dimension.
 By using linked markers as cofactors, the test is not affected by QTL outside
the region, thereby increasing the precision of QTL mapping.
 By eliminating much of the genetic variance by other QTL, the residual
variance is reduced, thereby increasing the power of detection of QTL.
Problems
 The effects of additional QTL will contribute to sampling variation.
 If two QTL are linked their combined effects will cause biased estimates.

Multiple Interval Mapping (MIM)
 It is also a modification of simple interval mapping.
 It utilizes multiple marker intervals simultaneously to fit
multiple putative QTL directly in the model for mapping QTL.
 It provides information about number and position of QTL in
the genome.
 It also determines interaction of significant QTLs and their
contribution to the genetic variance.
 It is based on Cockerham’s model for interpreting genetic
parameters.

• Bayesian Interval Mapping (BIM) (Satagopan et al. in
1996)
 It provides a model for QTL mapping
 It provides information about number and position of
QTL and their effects
 The BIM estimates should agree with MIM estimates
and should be similar to CIM estimates.
 It provides information posterior estimates of multiple
QTL in the intervals.
 It can estimate QTL effect and position separately.

Comparison of methods of QTL Mapping
Particulars Interval
mapping
Composite
Interval
Mapping
Multiple
Interval
Mapping
Bayesian
Interval
Mapping
1. Markers
used
Two markers Markers used as
cofactors
Multiple
markers
Two markers
2. Information
obtained
about
Number and
position of QTL
Number and
position of QTL
and interaction
of QTLs
Number and
position of QTL
Number and
position of QTL
and their effects
3. Designated
as
SIM SIM MIM BIM
4. Precision High Very high Very high Very high

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 and
expensive e.g circadium rhythm screens
Can identify New functional alleles of known function genes
e.g.Flowering time QTL,EDI was the CRY2 gene
Natural variation studies provide insight into the origins of
plant evolution
Identification of novel genes

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.
• Future Prospects
 Constant improvements of Molecular platforms
 New Types of genetic materials( e.g. introgression lines: small effect QTLs
can be detected)
 Advances in Bioinformatics

CASE STUDY
MAPPING QTLS FOR SALT TOLERANCE IN
RICE (ORYZASATIVAL.) BY BULKED
SEGREGANT ANALYSIS OF
RECOMBINANT INBRED LINES (RIL’S)
SushmaTiwari, et al
JOURNAL:PLOS GENETICS
NASS RATING :12.66

• ABSTRACT
• Rapid identification of QTLs for reproductive stages
tolerance using bulked segregant analysis(BSA) of bi-
parental recombinant inbredlines(RIL).
• The parents and bulks were genotype using a 50K SNP chip
to identify genomic regions showing homogeneity for
contrasting allele showing polymorphic SNPs in the two
bulks. The method was applied to ‘CSR11/MI48’ RILs
segregating for reproductive stage salt tolerance.
• The method was validated further with ‘CSR27/MI48’ RILs
used earlier for mapping salt tolerance QTLs using low
density SSR markers.
• BSA with 50K SNP chip revealed 5,021 polymorphic loci and
34 QTL regions. This not only confirmed the location of
previously mapped QTLs but also identified several new
QTLs, and provided a rapid way to scan the whole genome
for mapping QTLs for complex agronomic traits in rice.

MATERIALS AND METHODS
• A mapping population of 216 recombinant
inbred lines (RILs) was developed from across
between rice varieties CSR11 and MI48 using
single seed descent method.
• Mapping QTLs for Salt Stress in Rice by BSA
Using 50K SNP Chip.

SALT STESS SUSCEPTIBILITY INDEX

MODERATE
SODICITY
HIGH
SODICITY

Correlation coefficients of yield and yield component traits with SSI for grain yield
under normal, moderate and high sodicity regimes in CSR11/MI48 RILs.

Analysis of heterogeneous loci in different RIL pool sizes and mixture of parental DNA
samples using 50K SNP chip
Genomic DNA from 10 to 50 individual RILs of CSR11/MI48 mapping population were pooled
in equal amounts, with higher pools including all the RILs of lower pools for the analysis of
allele heterogeneity (Redline). Computational expectations on Bootstrap analysis of the pools
of 10 to 50 lines, showing successive increase in heterogeneity upto 94% (Greenline).
Observed heterogeneity with mixing of genomic DNA from the two parental lines in the

QTL positions identified in CSR27/MI48 population by BSA using 50k SNP chip
Physical map position of QTLs with green color showing tolerant allele coming from tolerant
parent CSR27 (11loci), red color showing tolerant allele coming from sensitive parent MI48
(23loci). Blue and violet bars represent earlier identified QTLs by (Ammar et al and
Panditeta) ,respectively

RESULTS AND DISCUSSION
• study out of 34 QTLs of CSR27/MI48 population five QTLs
were reported earlier in the and found 29 novel QTL
regions on rice chromosomes 1,2,3,5,6,9,11 and 12 due to
dense SNP map of polymorphic locus covering all regions of
the genome.
• Earlier highest 41 QTLs have been reported by Ghomi et al,
on all the 12 rice chromosomes for salinity tolerance at
seedling stage in rice.
• There are several reports on QTL mapping for salt stress by
SSR genotyping on whole population in rice but no one has
done QTL mapping by BSA approach for salt stress in rice.It
gives clear picture that QTL mapping effective in
identification of tolerant alleles.

QTL MAPPING & ANALYSIS

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QTL MAPPING & ANALYSIS