QTL

2. MASTER SEMINAR
on
Presented by :
Department of Genetics &Plant breeding
Sardar Vallabhbhai Patel University of Agriculture
and Technology, Modipuram, Meerut

3. Highlights
 Introduction
 What is QTL
 QTL mapping population
 Statistical method of QTL mapping
 Role of QTL in crop improvement
 Limitations
 Merits
 Conclusion
 References

4. Introduction
 Quantitative characters have been a major area of study in genetics for
over a century, as they are a common feature of natural variation in
populations of all eukaryotes, including crop plants. For most of the
period up to 1980, the study of quantitative traits has involved
statistical techniques based on means, variances and covariances of
relatives.
 Two major developments during the 1980s changed the scenario: (i)
the discovery of extensive, yet easily visualized, variability at the
DNA level that could be used as markers; and (ii) development of
statistical packages that can help in analyzing variation in a
quantitative trait in congruence with molecular marker data generated
in a segregating population. With phenomenal improvements in
molecular marker technology in the last two decades, identification
and utilization of polymorphic DNA markers as a framework around
which the polygenes could be located, has improved multiple-fold.

5. What is a Quantitative Trait Locus?
Mapping markers linked to QTLs identifies regions of
the genome that may contain genes involved in the expression
of the quantitative trait. But what functions could these genes
be encoding. To answer this question we should consider a
trait such as yield. What types of qualitative genes could be
involved in the expression of yield? The first event required
for yield is meiosis. Therefore any gene that is involved in
gamete formation could potentially be considered a QTL.
Any of the genes involved in the protein and
carbohydrate biosynthetic pathways could also affect the
final yield of a plant and could also be considered to be QTLs.
As we saw above, the markers associated with a QTL each
account for only a portion of the genetic variance. Likewise
each of these genes of known function may only account for a
portion of the final yield.

6.  Beavis et al. (1991) analyzed four populations of maize
and found molecular markers linked to plant height. No marker was
consistently associated as a QTL with plant height in all four
populations. Each of the ten maize chromosomes contained a marker
linked to a QTL for at least one of the four populations. The authors
further were able to demonstrate that a number of the QTLs identified
by the molecular markers mapped to regions containing genes known
to have a qualitative effect on plant height.
 The statistical analysis of quantitative traits provided
valuable information for the plant breeder. Molecular analysis of
quantitative traits now provides new tools, not only as selection tools
for plant breeding, but as starting points for the cloning of these genes.
These objectives could not have been realized without molecular
markers.

7. Principle of QTL mapping
• Correlate phenotypic data with marker data i.e.
trait value with chromosome map.
• To calculate the strength of the association
between genotype and phenotype
• It is not difficult in populations of most crop
plants to identify and map a good number of
segregating (10 to 50) per chromosome.
• If , QTL and marker is closely linked, chance
of recombination will be less.

8. Objectives of QTL Mapping
• The vast majority of molecular marker research in quantitative traits
has been devoted to mapping QTL.
• These experiments basically have the following major objectives:
 To identify the regions of the genome that affect the trait of interest
 To analyze the effect of the QTL on the trait
 Salient Requirements for QTL Mapping
 A suitable mapping population generated from phenotypically
contrasting parents
 A saturated linkage map based on molecular markers
 Reliable phenotypic screening of mapping population.

9. Major and Minor QTLs
• A QTL is considered to be a major QTL when it
explains 10% or more of the phenotypic variation
for the concerned trait. In contrast a QTL accounting
for less than 10% of the phenotypic variation is
called minor QTL
• Major QTLs show consistent expression across
environment, while minor QTLs are expressed in
some but not in other environment.

10. Mapping of QTL
• Use of marker
• Development of mapping population (RILs,
NILs, DH and BC)
• Parental survey
• Generation of molecular data
• Construction of map
• Generation of phenotypic data
• Data analysis.

11. Factors affecting the Power of QTL Mapping
• Number of genes controlling the target trait(s) and their
genome positions
• Distribution of genetic effects and existence of genetic
interactions
• Heritability of the trait
• Number of genes segregating in a mapping population
•Type and size of mapping population
• Density and coverage of markers in the linkage map
• Statistical methodology employed and significance level
used for QTL mapping

12. MAPPING POPULATION
The breeding population which is used for
identification, tagging and mapping of gene/QTL is
known as mapping population. The mapping
population may be F2, BC, RILs, NILs, DH Lines.
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RILs is also called F2- derived lines, are homozygous lines
derived from individual F2 plants from a suitable cross. RILs
provides an excellent mapping strategy and a permanent mapping
source. RILs are created by single-seed descent (SSD) from F2
plants through at least five or more generations of continued
selfing. This process yield a set of lines each of which contain a
different combination of linkage blocks from the original parents.
RILs is ideal for QTL mapping.
RILs (Recombinant inbred lines)

13. Fig: Production of RILs
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14. NILs (Near isogenic lines)
Near-isogenic lines are those lines that are identical in
their genotype, except for one gene. In this programme, a
donor parent (DP) which is homozygous for the allele of
interest, is crossed with a recurrent parent (RP) which is
homozygous for the wild type or standard allele of this locus.
The resultant F1 individual backcrossed (BC) to the RP to
obtain BC generation. In each BC generation, only those BC
individuals that have the introgressed allele and which are
most similar to the RP in phenotype are selectively crossed
with the RP. The NILs have been developed in many crop sps.
by the method of backcrossing.
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15. RR X rr
Resistant Susceptible
(Donor parent) (Recurrent parent)
F1 Rr X rr
(Resistant) (RP)
(Backcross)
BC1 rr X Rr, rr
(RP) (Rejected)
BC
BC6 Rr, rr
(Rejected)
Selfed
RR, Rr, rr
(Rejected) Selfed
Resistant Susceptible
Progenies progenies
(Near isogenic lines)
Fig: Production of NILsSaturday, December 7, 2019 15

16. Fig: Production of DH Lines
P1 X P2
F1
By anther culture/microspore culture
Haploid plant
Double haploid
Colchicine
Endomitosis
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Double haploid or DH Lines :
The term haploid refer to those plant which possess a gametic
chromosome number. DH is used for the production of
homozygous plant having DNA sequence of the donor parent.
DH is used as mapping population for mapping and tagging of
the gene/QTLs.

17. The gene mapping is a technique which is used to identify genes
responsible for expression of specific traits. Quantitative trait locus is refer
to a chromosomal region that is control quantitative trait. The QTL mapping
refers to locating of genes that control expression of quantitative traits or
used to identify where QTLs are located on the chromosome. The
important mapping technique are fallows:
I. Single Marker analysis,
II. Interval mapping,
III. Composite interval mapping,
IV. Multiple interval mapping, and
V. Bayesian interval mapping -(Satagopan et al. 1996).
Limitation of QTL mapping:
– Poor resolution of agronomic QTLs.
– Small effects
– Interaction with environment and genetic background.
– Expense of genotyping.
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17
MAPPING OF GENE/QTL

18. Single-Marker Analysis (SMA)
Also known as single-point analysis. It is the
simplest method for detecting QTLs
associated with single markers.

19.  Thismethod does not requirea completelinkage map andcan 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.

20.  The effects of QTL are likely to be under estimated 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).

21.  Presence of a putative QTL is estimated if the log of odds
ratio exceeds a critical threshold.
 The use of linked markers for analysis for recombination
between the markers and the QTL, and is considered
statistically more powerful compared to single-point
analysis.
 Map Maker/QTL and Q Gene 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.

22. 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.

23. 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.

24. Multiple Interval Mapping (MIM)
It is also a modification of simple interval mapping.
It utilizesmultiple 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 forinterpreting genetic
parameters.

25. • 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.

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

27. Over view of map construction
• Mapping population
• DNA extraction and PCR
• Gel electrophoresis
• Scoring and coding (computer programme)
• Linkage map

28. Chromosome map
• Determines which marker are linked together
• Convert recombination frequency into map
units called centimorgans (cm) using mapping
functions.

29. Role of QTL in crop improvement
The theory of QTL mapping was first described in
1923 by Sax; it was noted that seed size in bean (a
complex traits) was associated with seed coat colour (a
monogenic trait). This concept was further elaborated by
Thoday in 1961, who suggested that if the segregation of
simply inherited oligogenes could be used to detect linked
QTLs, it should eventually be possible to map and
characterize all the QTL involved in the control of complex
traits. With the development of comprehensive DNA marker
maps, it has now become possible to search for QTLs
throughout the gnomes of most crop species. When QTLs
and singe genes are adequately mapped, and tightly linked
markers are available, the genes may be isolated using
map-based cloning strategies.

30. A list of RFLP markers linked to QTLs
Crop QTLs Per cent
variance
Located on
chromos
ome
Marker
Wheat
(T. aestivum)
Powdery
mildew
(Erysiphe
graminis)
7B Xglk 750
Xgwml la
Xpsr547
Xpsr129
Grain yield
Qgyld.un1.3A.2 28.1
(phenotypic)
Xbarc67
Kernel per
square
meter
QPksm.
Unl.3A.2
19.1
(phenotypic)
Xbed1555XE

31. Rice
(O. sativa)
Embryo length
qEML-2 25.7
(phenotypic)
2 C132(107)
Complete
resistant
Pi5(t) 25.4
(phenotypic)
5 RG788
Pi7(t) 19.1
(phenotypic)
11 RG16
Oat Crown rust
(Paccinia
coronota cda.
F.sp. avenae)
9.6-14
(phenotypic)
Linkage
group 29
bcd1280a
Sweet corn Common rust
(Paccinia sorghi)
20
(phenotypic)
rp3 locus
3:04
Umc50
Rapeseed Erucic acid
eru1 64 Linkage
group 8
pN168Elb/pC2E
2 (12)
eru2 Linkage
group 13
pO87E4(26)

32. Importance of QTLs mapping
Quantitative traits locus (QTL) mapping is a highly
effective approach for studying genetically complex
forms of plant disease resistance.
With QTL mapping, the roles of specific resistance
loci can be described, race-specificity of partial
resistance genes can be assessed, and interactions
between resistance genes, plant development, and
the environment can be analyzed.
Outstanding examples include: quantitative
resistance to the rice blast fungus, late blight of
potato, gray leaf spot of maize, bacterial wilt of
tomato, and the soybean cyst nematode.

33. One of the first published reports of QTL mapping with DNA
markers involved fruit size, fruit pH and soluble solids in tomato
by Paterson et al., (1988).
A total of 237 backcross progenies from a cross between
cultivated tomato (Lycopersicon esculentum Mill) and a wild
relative, L. chinielewskii, were analysed with 70 RFLP loci. This
analysis uncovered 6 QTLs for fruit size, 5 for fruit pH, and 4 for
soluble solids. Subsequently, the tomato fruit QTL system was
analysed in further detain to discern trends across species,
generations, and environments (Paterson, 1991).
An F2 population of 350 individuals from a cross between L.
esculentuin x L. chmielewskii along with the corresponding F2—
derived F3 families was grown at three locations.
Basic Example of QTLs

34. A total of 29 putative QTLs for fruit size, pH, and
soluble solids were identified; out of these, only
four loci were detected in all the 3 locations.
This result suggests that the QTL studies
performed in a single location may tend to
underestimate the total number of relevant QTLs.
The identified QTLs for fruit size accounted for
76% of the total variation in the trait.
The QTLs for soluble solids contributed 44% of
total variation in this trait, while 34% of total
variation in fruit pH was due to the QTLs affecting
this trait.

35. Stuber et al. (1982) developed a high-yielding maize
population by selecting over ten cycles for increased yield.
They next determined the allelic frequencies for eight
isozyme loci that had been shown to be associated with yield.
These frequencies gave them a base-line from which a new
population could be constructed. The new population had
essentially the same allelic frequencies as the high yielding
population developed by selection. Next the yield and
ears/plant were measured in the base population, the high-
yielding population developed via selection, and the
population constructed based on isozyme frequencies.
This utility is also higher if crosses are envisioned
only among the best parents rather than among all parents.
Never the less, we show that among crosses the variance of
progeny means is generally much greater than the variance of
progeny standard deviations, restricting the utility of
estimates of progeny standard deviations to a relatively small
parameter space.
Development of high yielding Maize variety

36. Frary et al. (2000) find one QTL fw2.2 which was responsible
for increasing fruit size in tomato.
They reported that domestication of many plants has correlated
with dramatic increases in fruit size.
In tomato, one quantitative trait locus (QTL), fw2.2, was
responsible for a large step in this process. When transformed
into large-fruited cultivars, a cosmid derived from the fw2.2
region of a small-fruited wild species reduced fruit size by the
predicted amount and had the gene action expected for fw2.2.
 The cause of the QTL effect is a single gene, ORFX, that is
expressed early in floral development, controls carpel cell
number, and has a sequence suggesting structural similarity to the
human oncogene c-H-ras p21.
 Alterations in fruit size, imparted by fw2.2 alleles, are most
likely due to changes in regulation rather than in the sequence
and structure of the encoded protein
Identification of QTL for fruit size

37. Limitations
• Poor resolution of agronomic QTLs
• Small effects
• Interaction with environment and genetic
background
• Expense of genotyping

38. Merits of QTL Mapping
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,EDI was the CRY2 gene
Natural variation studies provide insight into the origins of
plant evolution

39. Conclusion
This one is the newly developed genetic tool
which can accelerate the breeding procedure. By
this technique we may compress the breeding
cycle. One can use the linked markers to evaluate
the nature of gene action between QTLs such as
epistasis. It is also possible to learn about the
nature of variation of a character and to transform
the data to normal distribution.
The programme provides several useful genetic
information besides the putative chromosomal
locations of the gene clusters. It provides the
degree of likelihood of presence of a QTLs and
used in MAS and MAB.

40. References
Baldet, P.; Hernould, M.; Laporte, F.; Mounet, F.; Just, D.; Mouras, A.; Chevalier, C. and C.
Rothan. (2006). Expression of cell proliferation-related genes in early developing
flowers is affected by a fruit load reduction in tomato plants. J. Exp. Bot., 57: 961-970.
Beavis et al. (1991). Analyzed four populations of maize and found molecular markers
linked to plant height. Theo. Appl. Genet., 83:141-145.
Dwivedi et al. (2005). pyramided, 18 drought tolerant QTLs in the genetic background of
IR64, from the two donor, STYH (Chinese) and BR24 (Bangladesh).
E. van der Knaap; Sanyal, A.; Jackson, S. A. and Tanksley, S. D. (2004). High-Resolution Fine
Mapping and Fluorescence in Situ Hybridization Analysis of sun, a Locus Controlling
Tomato Fruit Shape, Reveals a Region of the Tomato Genome Prone to DNA
Rearrangements. Genetics, 168: 2127-2140.
F. Z. Zhang, C. Wagstaff, A. M. Rae, A. K. Sihota, C. W. Keevil, S. D. Rothwell, G. J. J.
Clarkson, R. W. Michelmore, M. J. Truco, M. S. Dixon. (2007). QTLs for shelf life in
lettuce co-locate with those for leaf biophysical properties but not with those for leaf
developmental traits. J. Exp. Bot., 58: 1433-1449
Frary et al. (2000). QTL fw2.2 which was responsible for increasing fruit size in tomato.
Science. 289: 85-88.
Jeroen, S. Werij; Kloosterman, B.; Celis-Gamboa, C.; Ric de Vos, C.H.; America, T.; Visser,
R.G.F.; Bachem, C.W.B. (2007). Unravelling enzymatic discoloration in potato through a
combined approach of candidate genes, QTL, and expression analysis. Theo. Appl.
Genet., 115:245-252.

41. LI et al. 2004 Plant molecular Breeding they have reported 36 QTLs for drought
originated from seven different donors.
M. T. Brewer, J. B. Moyseenko, A. J. Monforte, and E. van der Knaap. (2007).
Morphological variation in tomato: a comprehensive study of quantitative
trait loci controlling fruit shape and development.
O’Toole, J.C. (1982). Adoption of rice to drought-prone environments. In
Drought Resistance in Crops with Emphasis on Rice. Los Banos (Philippines):
International rice Research Institute. pp. 195-216.
Stevens, R.; Buret, M.; Duffe, P.; Garchery, C.; Baldet, P.; Rothan, C. and Causse,
M. (2007). Candidate Genes and Quantitative Trait Loci Affecting Fruit
Ascorbic Acid Content in Three Tomato Populations. Plant Physiology, 143:
1943-1953.
Stuber et al. (1982). High-yielding maize population by selecting over ten cycles
for increased yield. Crop Sci., 22:737-740.
Yun, S. J.; Gyenis, L.; Bossolini, E.; Hayes, P. M.; Matus, I.; Smith, K. P.; Steffenson,
B. J.; Tuberosa, R. and Muehlbauer, G. J. (2006). Validation of Quantitative
Trait Loci for Multiple Disease Resistance in Barley Using Advanced Backcross
Lines Developed with a Wild Barley. Crop Sci., 46: 1179-1186.