• The loci controlling quantitative traits are called quantitative trait loci or QTL. • Term first coined by Gelderman in 1975. 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. Key steps for the QTL mapping • Collection of parental strains that differ for traits of interest • Selection of molecular markers such as RFLP, SSR and SNP that distinguish between the two parents • Development of a mapping population • Genotyping and phenotyping of the mapping population • Detection of QTL using a suitable statistical method • 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 centi Morgans(cM).

1. Identification of QTL by Bi-parental
Mapping Approach
Submitted by :-
P.TEJASREE
Ph.D 1st Year
Dept of GPBR
Course No :- PP-603
Course Title :- Molecular approaches for improving physiological traits through traits introgression
Assignment On :- Identification of QTL through Bi-parental Mapping Approach
Concepts of developing trait specific mapping population and
identification of contrasting parental lines through phenotyping
Submitted to :-
Dr. A.R. Nirmal Kumar
Assistant Professor
Dept of CPHY
1

3. Phenotyping

4. The regions within genomes that
contain genes associated with a
particular quantitative trait are known
as quantitative trait loci (QTL)
Quantitative Trait Loci (QTL)

6. • Markers that are linked to a gene or QTL controlling a particular trait (e g plant height) will indicate significant
differences when the mapping population is partitioned according to the genotype of the marker
• Based on the results in this diagram, Marker E is linked to a QTL because there is a significant difference
between means
• Marker H is unlinked to a QTL because there is no significant difference between means The closer the marker is
to the QTL of interest, the lower the chance for recombination between marker and QTL

10. Based on the genetic distance, the markers are grouped into linkage groups, and their
order in the linkage group is depicted as the linkage map.
A linkage map is a schematic representation of the relative locations of various genetic
markers present in the chromosomes of an organism as determined from the frequency of
recombination between pairs of markers.
It may be noted that it is difficult to detect linkage between
markers showing >25 % recombination.
Confirmation can be done by developing another mapping population from the same
cross and evaluating this population for the earlier observed linkage.
Validation involves evaluation of a fairly large number of unrelated germplasm showing
variation for the concerned trait for the observed marker-trait linkage. A marker that shows
linkage with the target trait in diverse unrelated materials would be useful for marker-
assisted selection (MAS) for the trait.
Linkage map

11. With out interference With interference
This is because the genetic distance estimates depend on the frequency of double crossing over, which
will always be lower in the Kosambi function than in the Haldane function for obvious reason.
There are several methods, called mapping functions, for converting recombination frequency into genetic
distance, but the two most commonly used methods are those proposed by Haldane (1919) and Kosambi (1944).
Haldane (1919) Kosambi (1944).

12. Therefore, once markers linked to the target gene are identified, very large populations and a
sufficiently large number of markers are used for mapping to identify markers located very close to
this gene; this is referred to as fine mapping or high-resolution mapping.
In order to find a marker at a distance of 0.1 cM or less from the target gene, one has to screen a
backcross population of more than 3,000 individuals for 0.95 probability of detecting at least one
recombination event. Similarly, in a species with the total genetic length of 2,000 cM, a minimum
of 20,000 markers have to be evaluated to achieve, on an average, a marker density of 10
markers/cM in the hope of finding a marker at 0.1 cM from the desired gene.
Selective mapping divides a large mapping population into several small samples.
These samples may be used for fine mapping of the desired locus that has already been mapped to a genomic region.

13. Linkage map construction
The raw data, including information about the
type of cross, number of markers, number of
progeny scored, etc., are first organized as per
the file format of the MapMaker program
using the “prepare data” command, and the
data are saved as a text file.
A pairwise analysis of data may be done to
detect linkage by giving the “sequence”
command and specifying the sequence of
the loci.
The maximum likelihood distance
between each locus pair and the
corresponding LOD score are calculated.
Now the “group” command is used to divide the
loci into linkage groups
The “compare” command is used to determine
the most likely order of loci within a linkage
group.
The “map” command is then given to
display the map of the linkage group
When a large number of loci are being mapped,
the “assign” command is used in place of the
“group” command. The “assign” command
evaluates each new locus for linkage with the
loci, called anchor loci

15. Single marker analysis (also ‘single point analysis’)
The significance of differences between the means of the marker classes can be tested by the statistical methods
used for single marker analysis include
1.T tests,
2.Maximum likelihood approach
3.Analysis of variance
4.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
The major drawback of such linear model-based (e.g. ANOVA, regression) single marker approaches is that they do
not indicate which side of the marker the QTL is located nor how far it is from the marker.
Association of a marker
with a putative QTL
(Source: Liu, 1998)

16. A significant treatment effect implies linkage to a segregating QTL.
ANOVA

17. For t-test, individuals in the population are classified
according to the genotype at a marker locus, and the
significance of difference between the trait means for the two
marker genotype groups is tested.
A significant difference indicates the marker to be linked to a
QTL affecting the trait.
This procedure is repeated for every marker locus evaluated
in the mapping population. The magnitude of difference
between the phenotypic means of the marker genotype
classes provides an estimate of the effect produced by the
substitution of a single allele at the QTL locus.
t-test

18. The regression coefficient for Y on X is
Regression
This method uses one marker at a time to test whether this marker is significantly associated with
the quantitative trait under investigation, using the statistic

19. The hypothesis of no linkage can be tested by the likelihood ratio statistic
Likelihood ratio
Therefore, for a given magnitude of QTL effect, the larger is the value of r, the smaller will
be the difference in phenotypic means of the two marker genotype groups and, at the same
time, the smaller will be the likelihood of this difference being significant.

20. Simple interval mapping (SIM)
• It was first proposed by Lander and Botstein
• The simple interval mapping ( method makes use of linkage maps
and analyses intervals between adjacent pairs of linked markers
along chromosomes simultaneously instead of analyzing single
markers
• 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
• Presence of a putative QTL is estimated if the log of odds ratio
exceeds a critical threshold
• It takes full advantage of the linkage map
• The principle behind interval mapping is to test a model for the
presence of a QTL at many positions between two mapped loci

21. Empirically, a QTL is claimed when the LOD is larger
than a critical value predetermined (for example, 2 or 3)
The two-LOD support interval determined by
the range of the highest LOD minus two LOD
provides an empirical confidence interval for
the range of QTL location
Instead the LOD compares the likelihood of the QTL being at the
position characterized by recombination fractions rMQ, rQN against
the likelihood that it is at some position unlinked to the interval.
In contrast,
LOD score detects linkage as well as
provides an estimate of the most likely frequency
of recombination between the two genes.

23. To test marker additive effect, the test statistic is
To test marker dominance effect, the test statistic is

24. • Measure of statistical significance P value This value indicates the probability of obtaining results if the
marker was not associated with variation for the trait
• For example, a P value of 0.01 indicates a 1 probability that these results would have been obtained in the
absence of a marker trait association
• The lower the P value, the higher the probability that a QTL truly exists in the region of the marker
• (i) Suggestive QTL expected to occur once at random in a QTL mapping study (in other words, there is a
warning regarding the reliability of suggestive QTLs)
• (ii) Significant QTL LOD - 3 (p value 0.001)
• (iii) Confirmed QTL If the QTLs from the same cluster came from at least three independent studies, they
were regarded as being confirmed in this study

25. Phenotypic variance explained (R2): This value indicates the relative importance of a QTL in influencing a trait
• It is the percent of the total phenotypic variance for the trait that is accounted for by a marker
• (R2) is obtained by multiplying the (R2) value provided in the ANOVA results by 100
• In QTL mapping, threshold LOD value for detection of significant QTL is calculated by 1000 permutation test
• QTLs with LOD more than threshold are declared as significant QTL
• Among significant QTLs, the QTL having more (R2) may be called as major QTL
• Major QTLs will account for a relatively large amount of (R2) (e.g 10)
• Minor QTLs will usually account for (R2) (e.g < 10)
• Sometimes, major QTLs may refer to QTLs that are stable across environments whereas minor QTLs may refer
to QTLs that may be environmentally sensitive, especially for QTLs that are associated with disease resistance

26. Regression based method that
simultaneously considers all the markers
on a single chromosome for locating
multiple linked QTL.
Composite interval mapping (CIM)
To test for a QTL on an interval between
adjacent marker Mi and Mi+1 in particular
The number of cofactors should not exceed
2 𝑛 where n is the number of individuals
in the analysis

27. • In the computer program designed for CIM, QTL CARTOGRAPHER, a
two-step procedure for practical data analysis was
implemented.
• In the first step, np markers that are significantly associated
with the trait are selected by (forward or backward)
stepwise regression.
• In the second step (mapping step), for each testing
interval, except of the markers for the putative QTL, two
markers that are at least Ws cM away from the test interval
(one for each direction) are first picked up to fit in the
model to define a testing window for blocking other
possible linked QTL effects on the test.
• Then, those selected np markers that are outside of the
testing window are also fitted into the model to reduce the
residual variance.

28. MIM is a multiple-QTL oriented
method combining QTL mapping
analysis with the analysis of genetic
architecture of quantitative traits
through a search algorithm to search for
number, positions, effects and
interaction of significant QTL.
Multiple interval mapping

29. 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

30. Comparison of Methods of QTL Mapping
Particulars Simple Interval Mapping
Composite Interval
Mapping
Multiple
Interval
Mapping
Bayesian
Interval
Mapping
Markers used Two markers
Markers used as
cofactors
Multiple markers Two markers
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
Designated as SIM CIM MIM BIM
Precision High Very High Very High Very High

32. A genetic linkage map
of rice showing
detected QTLs on
different chromosomes.
Chromosome number is
at the top, the map
distances in centi
Morgan (cM) at the left
side and SSR markers
names at the right side
of each linkage group.

34. Mapping Software SOFTWARE FEATURES REFERENCES
MAPMAKER Interval mapping (IM
Lincoln et
al,1992
MAP
MANAGER
Single Marker Analysis (SMA), IM and multiple-trait
analysis
Manly and
elliott,1991
QGENE
Single Marker Analysis (SMA), IM and multiple-trait
analysis
Nelson 1997
MAPQTL
IM, Composite Interval Mapping (CIM), non-
parametric mapping with the kruskal-Wallis rank sum
test per marker (for non-normally distributed data),
permutation tests, etc.
Van and
Mallipard,
1996
QTL
CARTOGRAPH
ER
SMA, SIM, CIM, Bayesian Interval Mapping (BIM),
Multiple Interval Mapping (MIM), multiple trait
analysis, permutation tests, etc
Basten et al,
1994
MQTL
SIM, CIM, also analysis for main effect, QE
interactions, and can perform permutation tests
Tinker and
Mather 1995
Reference:
Marker Assisted Plant
Breeding: Principles
and Practices by B.D
Singh and A.K Singh