QTL

1. MOLECULAR MARKERD-AIDED
BREEDING
(QTL)
By,
Pavithra B (II.M.sc.Biotechnology)
1

2. Quantitative Trait Loci
2
 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.

3. QTLs have the following
characteristics
3
 These traits are controlled by multiple genes, each
segregating according to Mendel's laws.
 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.

4. QTL Mapping
4
 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.

5. Principles of QTL mapping
5
 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.
 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

6. Objectives of QTL Mapping
6
 The basic objective is to detect QTL, while minimizing the
occurrence of false positives.
 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?

7. Prerequisites for QTL mapping
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 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

8. Steps involved in QTL Mapping:
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 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

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 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

10. Methods to detect QTLs
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 Single-marker analysis,
 Simple interval mapping and
 Composite interval mapping

11. Single-Marker Analysis (SMA)
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 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.

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 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
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.

13. Simple Interval Mapping (SIM)
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 It was first proposed by Lander and Bolstein.
 It takes full advantages 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.
 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.

14. Composite Interval Mapping
(CIM)
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 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.
 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

15. Logarithm of the odds ratio (LOD
score):
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 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.

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

17. 17
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

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19. Merits of QTL Mapping
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 Identification of novel genes
 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
 Natural variation studies provide insight into the
origins of plant evolution

20. LIMITATIONS
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 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

21. CASE STUDY
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MAPPING QTLS FOR SALT TOLERANCE IN
RICE (ORYZA SATIVA) BY BULKED
SEGREGANT ANALYSIS OF
RECOMBINANT INBRED LINES (RIL’S)
Sushma Tiwari, et al
JOURNAL:PLOS GENETICS
http://journals.plos.org/plosone/article?id=10.1371/jo
urnal.pone.0153610

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 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 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.

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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.

24. SALT STESS SUSCEPTIBILITY INDEX

25. 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

26. RESULTS AND DISCUSSION
 In this study out of 34 QTLs of CSR27/MI48 population five QTLs were reported
earlier 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.